Social inequalities in self-reported SARS-CoV-2 infection in Brazilian adults: PNAD COVID-19

Mateus Andrade Rocha; Cândido Norberto Bronzoni de Mattos; Marcos Pascoal Pattussi

doi:10.1590/1980-549720240042

. 2024 Aug 30;27:e240042. doi: 10.1590/1980-549720240042

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Social inequalities in self-reported SARS-CoV-2 infection in Brazilian adults: PNAD COVID-19

Mateus Andrade Rocha ^I,^II, Cândido Norberto Bronzoni de Mattos ^I, Marcos Pascoal Pattussi ^I

PMCID: PMC11383518 PMID: 39230100

ABSTRACT

Objective:

To investigate inequalities related to race/ethnicity and socioeconomic status in self-reported positive diagnosis for COVID-19 in Brazilian adults.

Methods:

Data available from the National Household Sample Survey COVID-19 (PNAD COVID 19) (July/September/November, 2020) were used in this retrospective investigation. The analyses considered the sampling design, primary sampling units, strata and sample weights. Poisson regression with robust variance was used to estimate prevalence ratio (PR) and the 95% confidence interval (95%CI) of the associations.

Results:

In July, September and November 2020, with regard to the rapid test, indigenous people were 2.45 (95%CI 1.48–4.08), 2.53 (95%CI 1.74–4.41) and 1.23 (95%CI 1.11–1.86) times more likely to report a positive history of SARS-CoV-2 infection, respectively. With regard to the RT-PCR test in November, indigenous people were more likely to test positive for COVID-19 (PR: 1.90; 95%CI 1.07–3.38). It was observed that the indigenous group was 1.86 (95%CI 1.05–3.29) and 2.11 (95%CI 1.12–3.59) times more likely to test positive for COVID-19 in September and November (2020). Income was associated with testing positive for COVID-19: in November, individuals whose income ranged from R$0.00–R$1.044 were more likely (PR: 1.69; 95%CI 1.16–23.06) to test positive using the RT-PCR test; participants whose income was in this range were also more likely to be diagnosed with COVID-19 using blood tests (PR: 1.72; 95%CI 1.43–2.07).

Conclusion:

The data presented show an association between race/ethnicity and economic status with a positive diagnosis of COVID-19.

Keywords: Public health, COVID-19, Social inequity, Adults

INTRODUCTION

In the first trimester of 2020, a new highly transmissible and pathogenic type of COVID-19 coronavirus was responsible for infecting a large number of individuals globally, triggering the COVID-19 pandemic¹. The COVID-19 virus affects the respiratory system, causing mild symptoms in many people, but it can lead to critical conditions in a percentage of cases, with massive alveolar damage and respiratory failure, which can contribute with death^2,3. Brazil emerged as the pandemic epicenter of the coronavirus disease. Only during the first wave of the pandemic (from March to November, 2020), more than 7.9 million cases and more than 100 thousand deaths caused by the disease were registered in the country⁴. The infection diagnosis can be carried out through a variety of tests, with oral and blood fluids. However, the gold-standard diagnostic method for COVID-19 is based on a reverse transcription polymerase chain reaction (RT-PCR) molecular test, aiming at detecting viral RNA in respiratory samples, such as nasopharyngeal swabs or bronchial aspirate⁵.

Evidence shows that being male, at a more advanced age, with unhealthy habits (for example, smoking), obesity and diagnosis of chronic diseases (for example, hypertension, diabetes and respiratory and cardiovascular diseases) present higher risk of infection and evolution to a critical or mortal disease status^6–9. Besides the factors related with individual organic conditions, the risks of infection and its severe course are distributed unequally in society⁴. International literature, especially in developed countries, reports higher impact of the disease in people at low socioeconomic levels, and in minority racial/ethnic groups^10,11.

Recently, a population-base Brazilian study observed that indigenous populations, large families and families with low socioeconomic status had higher prevalence of antibodies for SARS-CoV-2 in comparison to the white population, small families and with high socioeconomic status¹². Cross-sectional findings also showed that lower schooling and income and higher number of individuals in the household were strongly associated with higher mortality rates caused by COVID-19¹³. In this sense, the objective of this study was to investigate race and monthly income inequalities in the self-reported infection by SARS-CoV-2 in adults during the first wave of the pandemic in Brazil.

METHODS

This repeated cross-sectional study was performed with the data made available by the National Household Sample Survey COVID-19 (PNAD). The objective was to estimate the number of people with symptoms related to flu-like syndrome and to monitor the impacts of the COVID-19 pandemic in the Brazilian labor market¹⁴. This study's report was performed according to the orientations from the Strengthening the Reporting of Observational Studies in Epidemiology (Strobe) Statement¹⁵. The use of public secondary data in this study exempts the need for approval of the use of this information by the Research Ethics Committee.

The data were collected by approximately two thousand agents from the Brazilian Institute of Geography and Statistics (IBGE), based on structured interviews via telephone in about 193.6 thousand households, distributed in 3,364 cities in all Brazilian macroregions. Data collection took place between May and November, 2020, during the COVID-19 pandemic in Brazil¹². For this study, we used data from July, September and November, 2020.

The selection and training of the research team were performed by the Coordination of Training and Improvement of the National School of Sciences and Statistics from IBGE. The training was composed of two content modules; one regarding the approach to the participant in the telephone call, and the other about the application of the research survey. The complete methodological process of PNAD COVID-19 can be accessed in previous studies^14,16,17.

Participants were invited to answer the following questions:

"Have you been diagnosed with the COVID-19 virus" (yes; no; I cannot answer that);
"Have you taken any test to know if you were infected with coronavirus" (yes; no; I cannot answer that);
"What test was performed to verify if you had COVID-19?" (swab collection from the mouth/nose RT-PCR; through fingerstick (fast test); blood collection through the vein in the arm (blood test).

For each test, there was a single question regarding the history of testing for COVID-19 (yes or no). Inconclusive responses, without results or results that were ignored in the tests, were excluded. Positive answers in the utilized tests were the study outcomes.

Characteristics related to race/ethnicity and family income of the individuals were also obtained. The monthly family income of participants was collected in absolute numbers (Brazilian Real – R$), and then classified according to the distribution per quartiles, being: R$≥R$ 2,500; R$ 2,499–R$ 1,430; R$ 1,429–R$ 1,045; R$1,044–0. Race/ethnicity of participants was identified according to the criteria of IBGE and considered white, black, yellow, mixed and indigenous participants. Other exposure variables explored sociodemographic aspects related to social distancing and owning cleaning and protection items, which were also considered as confounding factors in this study: age in complete years (categorized in age groups based on criteria from: 18–29; 30–39; 40–49; 50–59; ≥60 years of age), gender (male; female), schooling (complete/incomplete higher education; complete high school; complete/incomplete elementary school; incomplete elementary school/no schooling), morbidities (no morbidities; 1 morbidity; 2 morbidities; ≥3 morbidities), utensils alcohol (does not have alcohol; has alcohol), utensils mask (does not have masks; has masks), distancing (strictly isolated; going out only for basic needs; going out for work or essential activities; did not practice distancing). The study's questionnaire containing the variables used in this study is found in Appendix 1.

All statistical analyses were conducted using the Stata Statistical Package (Version 16.0) (Stata Corp, College Station, Texas, USA). All data supporting the findings in this study are available with the corresponding author with a previous request, according to the FAIR Data Principles (www.force11.org/group/fairgroup/fairprinciples). The variables were described through relative and absolute frequencies. Poisson Regression with robust variance was used to estimate prevalence ratio (PR) and 95% confidence interval (95%CI) in the association between outcomes and exposure variables. In the multivariate analysis, exposure variables were controlled by confounding factors associated with the outcome at a significance level lower than 10%. The analyses considered sampling design, primary sampling units, strata and sampling weights. A significance level lower than 5% was chosen to consider associations between exposure and outcome as being significant.

RESULTS

Generally, the performance of any test for COVID-19 was reported by 26.673 (July); 35,587 (September); and 45,180 (November) participants. Taking a RT-PCR test for COVID- 19 in July, September and November was reported 7,026, 12,943 and 18,308 participants, respectively. In the same period, 49,407 rapid tests were performed, as follows: July: n=11,630; September: n=16,954; November: n=20,823. Also, the history of blood tests was reported by 6,886 (July), 10,668 (September) and 13,102 (November) interviewed individuals. Tables 1 e 2 present the sampling distribution of exposure variables according to the history of infection by SARS-CoV-2 per testing modalities.

Table 1. Sample description according to demographic and socioeconomic variables related to the prevalence of positive RT-PCR results in July (n=7,026), September (n=12,943) and November (n=18,308), and COVID-19 rapid test in July (n=11,630), September (n=16,954) and November (n=20,823), 2020.

Variables		July		September		November
Variables		n (%)	+COVID (95%CI)^*	n (%)	+COVID (95%CI)^*	n (%)	+COVID (95%CI)^*
RT-PCR
	Total	7,026 (100)	27.8 (26.0–29.6)	12,943 (100)	28.3 (27.8–28.7)	18,308 (100)	29.3 (28.9–29.7)
Race
	White	3,320 (47.3)	26.6 (24.3–29.0)	6,245 (48.3)	26.9 (26.4–27.3)	9,201 (50.3)	28.1 (26.2–30.0)
	Black	601 (8.6)	27.8 (23.1–33.0)	1,139 (8.8)	27.5 (27.0–28.1)	1,570 (8.6)	27.3 (23.7–30.8)
	Mixed race	3,028 (43.1)	29.4 (26.9–32.1)	5,420 (41.9)	30.7 (28.6–32.8)	7,361 (40.2)	31.7 (29.9–33.4)
	Yellow	52 (0.7)	22.7 (7.8–50.4)	98 (0.8)	14.4 (9.6–199.2)	120 (0.7)	20.0 (15.4–24.6)
	Indigenous	22 (0.3)	35.8 (13.8–66.0)	38 (0.3)	39.5 (31.6–47.4)	52 (0.3)	40.0 (34.5–46.3)
Age group (years)
	18–29	1,317 (20.3)	24.4 (21.4–27.8)	2,722 (21.0)	26.3 (22.7–29.8)	3,923 (21.4)	27.1 (25.2–28.9)
	30–39	1,711 (26.3)	29.1 (26.0–32.3)	3,126 (24.2)	29.5 (26.9–32.1)	4,355 (23.8)	31.2 (29.4–32.9)
	40–49	1,443 (22.2)	29.2 (26.1–32.6)	2,847 (22.0)	29.4 (26.3–325)	3,953 (21.6)	30.8 (28.5–33.1)
	50–59	1,065 (16.4)	32.4 (28.5–36.5)	2,173 (16.8)	29.3 (25.4–33.2)	3,018 (16.5)	31.2 (28.9–33.5)
	≥60	959 (14.8)	26.1 (22.2–30.4)	2,075 (16.0)	27.6 (23.6–31.6)	3,059 (16.7)	26.5 (24.4–28.6)
Gender
	Male	3,240 (46.1)	27.7 (25.6–30.0)	5,852 (45.2)	28.7 (25.3–31.3)	8,260 (45.1)	29.7 (27.1–32.4)
	Female	3,786 (53.9)	27.8 (25.7–29.9)	7,091 (54.8)	28.3 (26.8–29.7)	10,048 (54.9)	29.3 (28,1–30,5)
Schooling
	Higher Education	2,885 (41.1)	25.9 (23.7–28.3)	5,419 (41.9)	27.0 (23.8–30.2)	7,899 (43.1)	29.1 (27.1–31.0)
	Complete HS	1,957 (27.8)	31.6 (28.7–34.6)	3,891 (30.1)	30.6 (26.5–34.7)	5,427 (29.6)	30.9 (28.2–33.6)
	Complete ES/incomplete ES	822 (11.7)	29.9 (25.5–34.6)	1,567 (12.1)	28.9 (23.6–34.1)	2,166 (11.8)	29.8 (27.1–32.5)
	Incomplete ES/No schooling	1,362 (19.4)	25.1 (21.6–28.9)	2,066 (16.0)	27.9 (23.6–32.1)	2,816 (15.4)	27.6 (24.7–30.5)
Income
	≥R$ 2,500	1,993 (43.7)	28.1 (25.3–31.1)	3,610 (41.0)	27.3 (24.3–30.3)	5,055 (41.0)	30.0 (27.8–32.1)
	R$ 2,499–R$ 1,430	1,183 (25.9)	27.6 (24.4–31.1)	2,292 (26.1)	28.9 (25.6–32.1)	3,189 (25.9)	29.4 (26.2–32.6)
	R$ 1,429–R$ 1,045	1,081 (23.7)	27.6 (24.2–31.3)	2,212 (25.1)	29.6 (26.1–33.1)	3,103 (25.2)	30.6 (28.2–33.1)
	R$ 1,044–0	307 (6.7)	25.3 (18.7–33.3)	682 (7.8)	26.7 (20.3–33.1)	968 (7.9)	27.7 (21.9–33.4)
Rapid test
	Total	11,630 (100)	16.2 (15.1–17.3)	16,954 (100)	19.7 (18.5–20.8)	20,823 (100)	19.9 (18.7–21.8)
Race
	White	4,707 (40.5)	13.1 (11.8–14.6)	7,033 (41.5)	16.4 (13.9–18.8)	8,771 (42.1)	17.0 (14.9–19.0)
	Black	1,035 (8.9)	16.3 (13.1–20.1)	1,622 (9.6)	20.4 (17.3–23.5)	1,962 (9.4)	19.3 (16.7–21.9)
	Mixed race	5,748 (49.4)	18.9 (17.3–20.6)	8,108 (47.8)	22.3 (20.6–24.1)	9,856 (47.3)	22.2 (20.9–23.5)
	Yellow	75 (0.7)	12.7 (5.7–26.1)	86 (0.5)	18.6 (4.6–32.6)	117 (0.6)	18.8 (3.8–33.8)
	Indigenous	61 (0.5)	34.9 (20.9–52.2)	101 (0.6)	36.6 (22.8–50.4)	113 (0.5)	22.1 (11.1–33.1)
Age group (years)
	18–29	2,085 (19.5)	15.2 (13.2–17.4)	3,418 (20.2)	19.7 (17.2–22.2)	4,127 (19.8)	19.8 (17.9–21.7)
	30–39	2,676 (25.0)	15.9 (14.0–17.9)	3,977 (23.5)	19.2 (17.0–21.4)	4,716 (22.6)	20.2 (18.6–21.8)
	40–49	2,397 (22.4)	17.0 (15.1-19.1)	3,716 (21.9)	21.6 (19.7–23.5)	4,581 (22.0)	21.6 (19.9–23.3)
	50–59	1,871 (17.5)	16.0 (14.0–18.2)	2,924 (17.2)	19.6 (16.0–23.2)	3,676 (17.7)	18.6 (16.4–20.8)
	≥60	1,659 (15.5)	17.4 (14.9–20.2)	2,919 (17.2)	18.3 (14.7–21.8)	3,723 (17.9)	17.8 (15.2–20.4)
Gender
	Male	5,470 (47.0)	15.2 (13.9–16.6)	7,819 (46.1)	18.6 (16.9–20.3)	9,604 (46.1)	18.9 (17.4–20.5)
	Female	6,160 (53.0)	17.0 (15.7–18.4)	9,135 (53.9)	20.7 (19.0–22.4)	11,219 (53.9)	20.4 (18.8–-21.9)
Schooling
	Higher Education	3,817 (32.8)	12.7 (11.3–14.2)	5,729 (33.8)	17.2 (16.3–18.1)	6,961 (33.4)	17.7 (16.8–18.6)
	Complete HS	3,391 (29.2)	18.2 (16.4–20.1)	5,316 (31.4)	20.7 (19.6–21.7)	6,422 (30.8)	20.9 (19.9–21.9)
	Complete ES/incomplete ES	1,578 (13.6)	19.3 (16.9–22.0)	2,107 (12.4)	22.1 (20.4–23.7)	2,667 (12.8)	21.8 (20.3–23.3)
	Incomplete ES/No schooling	2,844 (24.4)	17.0 (15.1–19.1)	3,802 (22.4)	20.9 (19.6–22.2)	4,773 (22.9)	19.8 (18.7–20.9)
Income
	≥R$ 2,500	2,563 (36.3)	12.3 (10.6–14.2)	3,682 (34.4)	15.7 (13.8–17.5)	4,431 (34.0)	16.5 (14.4–18.6)
	R$ 2,499 – R$ 1,430	1,828 (25.8)	15.4 (13.3–17.8)	2,727 (25.5)	18.9 (17.3–20.5)	3,253 (25.0)	19.3 (17.5–21.1)
	R$ 1,429- R$ 1,045	1,993 (28.2)	16.8 (14.8–19.1)	3,076 (28.7)	20.6 (18.9–22.2)	3,764 (28.9)	20.5 (18.5–22.6)
	R$ 1,044 – 0	687 (9.7)	19.2 (15.6–23.5)	1,224 (11.4)	19.9 (16.7–23.1)	1,580 (12.1)	19.5 (15.5–23.5)

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Prevalence rates and 95% confidence intervals for positive RT-PCR tests or rapid tests for COVID-19.

CI: confidence interval; HS: high school; ES: elementary school.

Table 2. Sample description according to demographic and socioeconomic variables related to the prevalence of positive blood test for COVID-19 in July (n=6,886), September (n=10,668) and November (n=13,102), 2020.

Variables		July		September		November
Variables		n (%)	+COVID (95%CI)^*	n (%)	+COVID (95%CI)^*	n (%)	+COVID (95%CI)^*
Total		6,886 (100)	25.8 (24.2–27.5)	10,668 (100)	28.5 (27.3–29.7)	13,102 (100)	28.2 (27.4–28.9)
Race
	White	3,040 (44.2)	21.7 (19.6–23.9)	4,637 (43.5)	24.2 (22.3–26.1)	5,828 (44.5)	25.1 (23.4–26.8)
	Black	530 (7.7)	23.6 (19.2–28.6)	876 (8.2)	29.3 (27.1–31.5)	1,068 (8.2)	28.3 (26.4–30.2)
	Mixed race	3,217 (46.7)	31.3 (28.8–33.9)	5,026 (47.1)	33.0 (21.6–34.4)	6,040 (46.1)	31.8 (20.5–33.1)
	Yellow	54 (0.8)	22.2 (9.5–43.6)	75 (0.7)	12.0 (3.1–20.9)	96 (0.7)	15.8 (6.9–24.7)
	Indigenous	44 (0.6)	30.4 (14.2–53.6)	52 (0.5)	37.3 (22.3–52.3)	65 (0.5)	42.2 (27.2–57.2)
Age group (years)
	18–29	1,138 (17.8)	24.1 (21.0–27.5)	1,971 (18.5)	27.6 (25.5–29.7)	2,387 (18.2)	28.1 (26.2–30.0)
	30–39	1,581 (24.8)	26.3 (23.2–29.6)	2,465 (23.1)	28.4 (26.4–30.4)	2,911 (22.2)	29.6 (27.8–31.4)
	40–49	1,441 (22.6)	26.7 (23.6–30.0)	2,263 (21.2)	29.2 (26.9–31.5)	2,845 (21.7)	28.3 (26.2–30.4)
	50–59	1,121 (17.5)	24.5 (21.3–28.0)	1,934 (18.1)	29.7 (26.9–32.5)	2,384 (18.2)	28.5 (26.7–30.3)
	≥60	1,103 (17.3)	27.0 (23.5–30.8)	2,035 (19.1)	28.9 (26.5–31.3)	2,575 (19.7)	27.4 (25.8–29.0)
Gender
	Male	3,185 (46.2)	24.8 (22.7–27.0)	4,892 (45.9)	28.2 (26.3–30.1)	5,954 (45.4)	27.6 (25.9–30.6)
	Female	3,701 (53.8)	26.7 (24.8–28.7)	5,776 (54.1)	29.2 (27.7–30.7)	7,148 (54.6)	29.1 (27.5–30.7)
Schooling
	Higher Education	2,733 (39.7)	20.6 (18.6–22.9)	4,307 (40.4)	24.4 (22.6–26.2)	5,317 (40.6)	25.1 (23.6–26.6)
	Complete ES	1,812 (26.3)	30.6 (27.7–33.7)	3,042 (28.5)	30.7 (28.4–33.0)	3,667 (28.0)	30.0 (28.1–31.9)
	Complete ES/incomplete ES	815 (11.8)	28.1 (24.0–32.5)	1,177 (11.0)	33.4 (30.7–36.1)	1,491 (11.4)	31.4 (29.0–33.6)
	Incomplete ES/no schooling	1,526 (22.2)	29.6 (26.3–-33.1)	2,142 (20.1)	32.1 (29.5–34.7)	2,627 (20.1)	31.3 (29.5–33.3)
Income
	≥R$ 2,500	1,888 (43.9)	19.1 (16.7–21.8)	2,869 (41.3)	22.9 (20.8–25.0)	3,420 (40.5)	23.4 (21.5–25.3)
	R$ 2,499–R$ 1,430	964 (22.4)	25.6 (22.1–-29.5)	1,619 (23.3)	28.7 (25.5–31.9)	1,994 (23.6)	27.9 (24.9–30.9)
	R$ 1,429–R$ 1,045	1,088 (25.3)	25.0 (21.9–28.4)	1,772 (25.5)	28.8 (25.9–31.7)	2,162 (25.6)	28.6 (26.4–30.8)
	R$ 1,044–0	364 (8.4)	31.1 (25.0–37.8)	684 (9.9)	33.0 (27.3–38.7)	872 (10.3)	29.7 (25.5–33.9)

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Prevalence rates and 95% confidence intervals for positive RT-PCR tests or rapid tests for COVID-19.

CI: confidence interval; HS: high school; ES: elementary school.

Table 3 presents PR and 95% CI of the crude and adjusted analyses between the performance of the three different tests (RT-PCR, rapid test and blood test) during the months of July, September and November (2020) and the exposure variables. After adjusting for potential confounding factors, a significant effect of race/ethnicity on COVID-19 testing via RT-PCR was observed among Indigenous participants in November (PR: 1.90); 95%CI 1.07–3.38), considering that this group presented higher chances of infection by SARS-CoV-2 via RT-PCR in comparison do white individuals. Participants of mixed race/ethnicity showed a higher PR of positive RT-PCR tests in September (PR: 1.20; 95%CI 1.10–1.30), in comparison to white people. Also, individuals with lower income R$ 0–1) had more chances of presenting a RT-PCR test to verify the infection by COVID-19 than those with higher family income (≥R$ 2,500) in September (PR: 1.87; 95%CI 1.15–2.67) and November (PR: 1.69; 95%CI 1.16–3.06).

Table 3. Crude and adjusted analyses for positive RT-PCR, rapid test and blood test for COVID-19 in July, September and November, 2020.

Variables		July		September		November
Variables		_CRUDEPR (95%CI)	_ADJUSTEDPR (95%CI)	_CRUDEPR (95%CI)	_ADJUSTEDPR (95%CI)	_CRUDEPR (95%CI)	_ADJUSTEDPR (95%CI)
RT-PCR Test
Race
	White	1.00	1.00	1.00	1.00	1.00	1.00
	Black	1.05 (0.86–1.27)	1.09 (0.88–1.35)	1.06 (0.92–1.22)	1.05 (0.91–1.21)	0.96 (0.85–1.09)	0.97 (0.86–1.10)
	Mixed race	1.11 (0.98–1.25)	1.05 (0.92–1.21)	1.21 (1.11–1.31)	1.20 (1.10–1.30)	1.18 (1.11–1.27)	1.20 (1.12–1.29)
	Yellow	0.85 (0.34–2.16)	0.72 (0.26–1.99)	0.46 (0.26–0.81)	0.44 (0.25–0.77)	0.65 (0.41–1.02)	0.65 (0.41–1.02)
	Indigenous	1.35 (0.65–2.79)	1.39 (0.37–5.14)	1.73 (1.09–3.33)	1.63 (0.85–3.15)	1.92 (1.08–3.40)	1.90 (1.07–3.38)
Income
	≥R$ 2,500	1.00	1.00	1.00	1.00^*	1.00	1.00
	R$ 2,499–R$ 1,430	0.98 (0.84–1.15)	0.90 (0.76–1.05)	1.09 (0.97– 1.23)	1.05 (0.93–1.19)	0.98 (0.89–1.08)	0.97 (0.87–1.08)
	R$ 1,429–R$ 1,045	0.98 (0.83–1.16)	0.90 (0.75–1.07)	1.14 (1.01–1.28)	1.08 (0.94–1.24)	1.05 (0.95–1.15)	1.04 (0.93–1.16)
	R$ 1,044–0	0.90 (0.66–1.22)	0.78 (0.56–1.08)	0.99 (0.82–1.19)	1.87 (1.15–2.67)	1.31 (0.98–1.76)	1.69 (1.16–3.06)
Rapid test
Race
	White	1.00	1.00/	1.00^*	1.00	1.00	1.00^*
	Black	1.24 (0.98–1.57)	1.09 (0.84–1.42)	1.31 (1.15–1.51)	1.31 (1.02–1.32)	1.17 (1.04–1.33)	1.16 (1.03–1.32)
	Mixed race	1.44 (1.26–1.64)	1.22 (1.04–1.44)	1.47 (1.35–1.59)	1.47 (1.35–1.60)	1.42 (0.91–2.26)	1.41 (0.90–2.21)
	Yellow	0.97 (0.45–2.07)	1.04 (0.43–2.51)	1.17 (0.67–2.02)	1.07 (0.58–1.87)	1.16 (0.73–1.85)	1.15 (0.72–1.84)
	Indigenous	2.66 (1.69–4.19)	2.45 (1.48–4.08)	2.95 (1.96–4.45)	2.53 (1.74–4.41)	1.39 (1.29–1.50)	1.23 (1.11–1.86)
Income
	≥R$ 2,500	1.00	1.00	1.00^*	1.00^*	1.00	1.00^*
	R$ 2,499–R$ 1,430	1.25 (1.02–1.53)	1.14 (0.93–1.41)	1.26 (1.11–1.44)	1.27 (1.11–1.45)	1.19 (1.07–1.36)	1.22 (1.08–1.37)
	R$ 1,429–R$ 1,045	1.36 (1.13–1.65)	1.16 (0.94–1.43)	1.39 (1.22–1.58)	1.42 (1.25–1.61)	1.31 (1.17–1.47)	1.33 (1.19–1.49)
	R$ 1,044–0	1.56 (1.22–2.00)	1.20 (1.00–1.57)	1.35 (1.13–1.59)	1.38 (1.17–1.64)	1.22 (1.06–1.42)	1.25 (1.08–1.45)
Blood test
Race
	White	1.00	1.00	1.00	1.00	1.00	1.00^*
	Black	1.09 (0.87–1.36)	1.07 (0.82–1.38)	1.30 (1.11–1.53)	1.30 (1.11–1.53)	1.18 (1.02–1.37)	1.18 (1.02–1.37)
	Mixed race	1.44 (1.28–1.63)	1.30 (1.11–1.53)	1.84 (1.41–1.69)	1.54 (1.41–1.69)	1.39 (1.28–1.51)	1.39 (1.28–1.51)
	Yellow	1.02 (0.48–2.18)	0.78 (0.31–1.99)	0.43 (0.21–0.86)	0.43 (0.21–0.86)	0.56 (0.32–0.98)	0.56 (0.32–0.98)
	Indigenous	1.40 (0.74–2.68)	1.14 (0.53–2.48)	1.36 (1.02–3.29)	1.86 (1.05–3.29)	2.18 (1.32–3.59)	2.11 (1.12–3.59)
Income
	≥R$ 2,500	1.00	1.00	1.00	1.00^*	1.00	1.00^*
	R$ 2,499–R$ 1,430	1.34 (1.10–1.64)	1.28 (1.05–1.56)	1.36 (1.18–1.56)	1.38 (1.20–1.59)	1.27 (1.12–1.44)	1.28 (1.13–1.45)
	R$ 1,429–R$ 1,045	1.31 (1.09–1.57)	1.21 (1.00–1.46)	1.37 (1.19–1.57)	1.41 (1.22–1.62)	1.31 (1.16–1.48)	1.34 (1.18–1.52)
	R$ 1,044–0	1.63 (1.27–2.08)	1.39 (1.08–1.79)	1.67 (1.39–2.00)	1.72 (1.43–2.07)	1.38 (1.17–1.63)	1.43 (1.21–1.69)

Open in a new tab

Adjusted for schooling, gender and age group

Self-identified Indigenous participants had a higher likelihood of being diagnosed with COVID-19 through rapid tests compared to white participants in all months of follow-up: July (RP: 2.45; 95%CI 1.48–4.08), September (PR: 2.53; 95%CI 1.74–4.41), November (PR: 1.23; 95%CI 1.11–1.86). Monthly average income of participants was associated with the prevalence of COVID-19 rapid tests: participants with lower monthly income presented higher chances of undergoing a fast test than those who earn ≥2,500 reais per month in July, September and November (2020). It was identified that mixed race participants had higher prevalence of positive testing for COVID-19 through blood tests in all months of follow-up: July (PR: 1,30; 95%CI 1.11–1.53), September (PR: 1.54; 95%CI 1.41–1.69), November (PR: 1.39; 95%CI 1.28–1.51), compared to white individuals. Individuals self-identified as Indigenous also showed a higher prevalence ratio of positive COVID-19 testing through blood tests, but only in September (PR: 1.86; 95%CI 1.05–3.29) and November (PR: 2.11; 95%CI 1.12–3.59), 2020. The lower monthly family income was associated with the lower chances of positive results in the blood test in all months of follow-up than those whose income was ≥R$ 2,500/month (Table 3).

DISCUSSION

This study used data from PNAD COVID-19 to investigate the inequalities related to race and income in the history of COVID-19 testing according to diagnostic modalities in adults during the first wave of the pandemic in Brazil. The results indicate that Indigenous individuals and those with lower average monthly income were more likely to be diagnosed with COVID-19, regardless of the test used and the examination period.

In this study, non-white individuals, especially those of mixed race and/or Indigenous descent, exhibited higher seroprevalence for COVID-19 compared to white subjects.

This finding can be attributed to the social conditions to which minority populations are exposed, predisposing them to higher rates of infection with the disease in this group ¹⁸. This finding corroborates the historical vulnerability experienced by the Indigenous population, in particular, which has faced higher rates of infection in the past, such as during the Spanish flu, H1N1 virus infection, and SARS-CoV¹⁹.

Indeed, this population group is heavily impacted by inequities in social determinants of health. In addition to cultural and geographical barriers, Indigenous people experience higher levels of poverty, malnutrition, lower schooling, difficulty accessing health services, and precarious basic sanitation systems^19,20. Furthermore, it is important to highlight the common characteristic of geographic isolation in this population as a potential barrier to accessing healthcare measures aimed at combating the pandemic. Furthermore, immunological factors specific to Indigenous populations and the presence of chronic diseases increase susceptibility to outbreaks of infectious diseases, rendering these peoples more vulnerable ²¹. This reality is exacerbated when the exposure of this community is linked to governmental neglect in addressing the disease, as reported by the National Health Council (CNS) and the National Human Rights Council (CNDH)²².

The results of this study highlight a significant association between testing via RT-PCR and blood tests and the monthly income of participants. Individuals with lower income were more likely to test positive using these diagnostic modalities. In Brazil, the spread of the COVID-19 virus began among individuals from higher economic classes, and subsequently, the virus spread rapidly among people from less privileged economic backgrounds, as some of these individuals continued their daily activities out of necessity for subsistence^23,24. Studies have reported that unfavorable socioeconomic conditions, lower levels of education, and a higher number of household residents may predispose individuals to a higher rate of COVID-19 infection¹². The Economic Commission for Latin America and the Caribbean (ECLAC) suggests that the impoverishment of the Brazilian population in recent years may have increased the impact of COVID-19 in the country ²⁵. Disadvantaged groups tend to have less structured occupations and insufficient income for survival^26–28, often engaged in jobs that do not allow remote work, requiring the use of public transportation for commuting and thus increasing contact among people^29,30. Furthermore, insufficient testing conducted in Brazil during the period of the PNAD COVID-19, due to the limited number of tests available in the public health system (SUS) and the Brazilian government's failure to procure them, resulted in individual purchases of tests, thereby excluding disadvantaged populations from disease diagnosis^26,28,29. However, in the outcomes of the tests conducted, higher positivity rates are highlighted in the economically more vulnerable population.

The results presented in this study should be interpreted in light of its limitations. The main limitation of this investigation refers to its cross-sectional nature, which does not allow for causal inference in the identified associations. The use of self-reported data can also be considered a limitation, as subjective perception is influenced by verbal behavior, which in turn is reinforced by the individual's environment²⁵.

Similarly, it is important to highlight limitations related to test results. The RT-PCR test is recommended for symptomatic patients in the acute phase of the disease between the third and seventh day. Therefore, potential false negatives should be considered, which can occur when the amount of collected viral genome is insufficient or when the viral replication window period is missed⁵. However, this test has high sensitivity and specificity: 97.2% and 98.9%, respectively⁵.

Furthermore, COVID-19 diagnosis can be achieved based on the immune response to SARS-CoV-2 infection, using immunochromatographic serological tests for rapid detection of IgG/IgM antibodies in blood, serum, or plasma samples from individuals⁵. The main limitation of this tool is the requirement for testing from the eighth day after the onset of symptoms. Therefore, disease detection based on this detection method may occur during a period of patient recovery, posing a challenge for surveillance and transmission control services. The IgM and IgG serological tests have a sensitivity of 84.5% and specificity of 91.6%^4,5. However, immunochromatographic rapid tests showed low sensitivity, particularly in the early stages of the pandemic³¹.

It is important to note that the sensitivity of a diagnostic test refers to its ability to correctly identify positive cases of the disease, while specificity indicates its ability to correctly identify negative results^4,5.

Based on a literature review conducted by the authors, there have been no studies published so far that have sought to validate these self-reported positive COVID-19 results. Cohort studies with biochemical tests could better illustrate the disparities in disease transmission. Besides, it's important to consider that in April 2020, the most vulnerable indigenous lands were those located on the outskirts of major urban centers such as Manaus, the Rio Branco-Porto Velho axis, Fortaleza, Salvador, and capitals in the South and Southeast of Brazil. Therefore, considering the scope of the PNAD COVID-19 in major urban centers, these findings may represent a partial picture of reality, as they do not effectively reach villages and indigenous lands in isolated regions of the country. Finally, the results described here are valid and robust, contributing to a field that has been relatively underexplored in Brazilian literature.

This study identified a significant association between ethnicity/race and economic status with positive COVID-19 outcomes among Brazilian adults. This situation reflects vulnerability in these groups and underscores the need for the development and expansion of more equitable public policies that address the needs of vulnerable groups during a public health crisis.

Supplementary Material

1980-5497-rbepid-27-e240042-Suppl01.pdf^{(237KB, pdf)}

Footnotes

Funding:

This study was partly funded by the Coordination for the Improvement of Higher Education Personnel (Capes) – Brazil – Code 001/2020.

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Rev Bras Epidemiol. 2024 Aug 30;27:e240042. [Article in Portuguese] doi: 10.1590/1980-549720240042.2

Desigualdades sociais no autorrelato de infecção por SARS-CoV-2 em adultos brasileiros: Pnad COVID-19

Mateus Andrade Rocha ^I,^II, Cândido Norberto Bronzoni de Mattos ^I, Marcos Pascoal Pattussi ^I

RESUMO

Objetivo:

Investigar as desigualdades relacionadas a raça/etnia e condição socioeconômica no autorrelato de resultado positivo para COVID-19 em adultos brasileiros.

Métodos:

Os dados disponibilizados pela Pesquisa Nacional por Amostra de Domicílios (Pnad) COVID-19 (julho/setembro/novembro, 2020) foram utilizados nesta investigação retrospectiva. As análises consideraram o desenho amostral, unidades primárias de amostragem, estratos e pesos amostrais. Regressão de Poisson com variância robusta foi utilizada para estimar as razões de prevalência (RP) e o intervalo de confiança de 95% (IC95%) das associações.

Resultados:

Nos meses de julho, setembro e novembro de 2020, referente ao teste rápido, os indígenas tinham 2,45 (IC95% 1,48–4,08), 2,53 (IC95% 1,74–4,41) e 1,23 (IC95% 1,11–1,86) vezes maior probabilidade de reportar o histórico positivo de infecção por SARS-CoV-2, respectivamente. Com relação ao teste RT-PCR no mês de novembro, os indígenas apresentaram mais chance de testarem positivo para COVID-19 (RP: 1,90; IC95% 1,07–3,38). Foi observado que o grupo de indígenas apresentou 1,86 (IC95% 1,05–3,29) e 2,11 (IC95% 1,12–3,59) vezes mais chances de positivarem para COVID-19 em setembro e novembro (2020). A renda esteve associada com a testagem positiva para a COVID-19: no mês de novembro, indivíduos com renda variando entre R$ 0,00–R$ 1,044 tiveram maior probabilidade (RP: 1,69; IC95% 1,16–23,06) de testarem positivo através do teste RT-PCR; participantes com renda variando na referida faixa de valor também apresentaram maior chance de serem diagnosticados com COVID-19 através de testes sanguíneos (RP: 1,72; IC95% 1,43–2,07).

Conclusão:

Os dados apresentados evidenciam a associação entre a raça/etnia e o status econômico com o resultado positivo para COVID-19.

Palavras-chave: Saúde pública, COVID-19, Iniquidade social, Adultos

INTRODUÇÃO

No primeiro trimestre de 2020, um novo tipo de coronavírus da COVID-19 altamente transmissível e patogênico foi responsável por infectar um elevado número de indivíduos globalmente, desencadeando a pandemia de COVID-19¹. O vírus da COVID-19 afeta o sistema respiratório, causando sintomas leves em muitas pessoas, mas pode levar a condições críticas em uma porcentagem dos casos, com danos alveolares maciços e insuficiência respiratória que podem contribuir para a morte^2,3. O Brasil emergiu como epicentro pandêmico da doença do coronavírus. Somente durante a primeira onda da pandemia (de março a novembro de 2020), foram registrados mais de 7,9 milhões de casos e mais de 100 mil mortes pela doença no país⁴. O diagnóstico da infecção pode ser realizado através de uma variedade de testes com fluídos orais e sanguíneos. Contudo o método diagnóstico padrão-ouro para COVID-19 se baseia em um teste molecular da reação em cadeia de polimerase de transcrição reversa RT-PCR, com o objetivo de detectar o RNA do vírus em amostras respiratórias como cotonetes nasofaríngeos ou aspirado brônquico⁵.

Evidências demonstram que o sexo masculino, a idade mais avançada, hábitos não saudáveis (exemplo, fumar tabaco), a obesidade e o diagnóstico de doenças crônicas (exemplo, hipertensão, diabetes e doenças respiratórias e cardiovasculares) apresentam maior risco de infecção e evolução para um estado crítico ou mortal da doença^6–9. Além dos fatores relacionados às condições orgânicas individuais, os riscos de infecção e o curso grave estão distribuídos de forma desigual na sociedade⁴. A literatura internacional, especialmente em países desenvolvidos, relata um maior impacto da doença nas pessoas de baixo nível socioeconômico e em grupos minoritários raciais/étnicos^10,11.

Recentemente, estudo brasileiro de base populacional constatou que as populações indígenas, famílias numerosas e famílias com baixa condição socioeconômica possuíam uma prevalência mais elevada de anticorpos para o SARS-CoV- 2 em relação à população branca, famílias não numerosas e com alto nível socioeconômico¹². Achados transversais também evidenciaram que a menor escolaridade e renda e o maior número de indivíduos na residência familiar estavam fortemente associados com taxas mais elevadas de mortalidade por COVID-19¹³. Nesse sentido, o objetivo deste estudo foi investigar as desigualdades de raça e renda mensal no autorrelato de infecção por SARS-CoV-2 em adultos durante a primeira onda da pandemia no Brasil.

MÉTODOS

Este estudo retrospectivo foi realizado com os dados disponibilizados pela Pesquisa Nacional por Amostra de Domicílios (Pnad) COVID-19. O objetivo da pesquisa era estimar o número de pessoas com sintomas referidos associados à síndrome gripal e monitorar os impactos da pandemia de COVID-19 no mercado de trabalho brasileiro¹⁴. O reporte deste estudo foi realizado de acordo com as orientações do Strengthening the Reporting of Observational Studies in Epidemiology (Strobe) Statement ¹⁵. A utilização de dados secundários públicos neste estudo dispensa a aprovação do uso dessas informações pelo Comitê de Ética em Pesquisa.

Os dados foram coletados por cerca de 2 mil agentes do Instituto Brasileiro de Geografia e Estatística (IBGE) com base em entrevistas estruturadas via telefone em cerca de 193,6 mil domicílios, distribuídos em 3.364 municípios em todas as macroregiões do país. A coleta de dados ocorreu entre os meses de maio e novembro de 2020, no curso da pandemia da COVID-19 no Brasil¹². Para este estudo, foram utilizados os dados dos meses de julho, setembro e novembro de 2020.

A seleção e treinamento da equipe de pesquisa ocorreu por meio da Coordenação de Treinamento e Aperfeiçoamento da Escola Nacional de Ciências e Estatística do IBGE.. O treinamento foi composto por dois módulos de conteúdo, um deles referente à abordagem ao participante no contato telefônico e o outro sobre a aplicação do questionário da pesquisa. O processo metodológico completo do Pnad COVID-19 pode ser acessado em estudos prévios^14,16,17.

Os participantes foram convidados a responder às seguintes perguntas:

"Você já foi diagnosticado com o vírus da COVID-19?" (sim; não; não sei responder);
"Você já realizou algum teste para saber se estava infectado pelo coronavírus?" (sim; não; não sei responder);
"Qual foi o teste realizado para verificar se você estava com COVID-19?" (coleta com cotonete na boca e(ou) nariz RT-PCR; através de furo no dedo (teste rápido); coleta de sangue através da veia do braço (testagem sanguínea).

Respostas inconclusivas, sem resultado ou resultados ignorados nos testes foram excluídos. Os históricos de testagem positiva (sim ou não) nos testes utilizadas foram considerados os desfechos deste estudo.

Características relacionadas a raça/etnia e renda familiar dos indivíduos também foram obtidas. A renda familiar mensal dos participantes foi coletada em número absoluto (real brasileiro – R$) e posteriormente classificada de acordo com a distribuição pelos quartis, sendo: R$≥R$ 2.500; R$ 2.499–R$ 1.430; R$ 1.429–R$ 1.045; R$1.044–0. A raça/etnia dos participantes foi identificada de acordo com os critérios do IBGE e considerou participantes brancos, pretos, amarelos, pardos e indígenas. Outras variáveis de exposição exploraram os aspectos sociodemográficos, relacionados ao distanciamento social e à posse de itens de limpeza e proteção também foram considerados como fatores de confusão do presente estudo: idade em anos completos (categorizada em faixa-etária com base nos critérios do IBGE: 18–29; 30–39; 40–49; 50–59; ≥60 anos), sexo (masculino; feminino), escolaridade (ensino superior completo/incompleto; ensino médio completo; ensino fundamental completo/incompleto; ensino fundamental incompleto/sem instrução), morbidades (sem morbidades; 1 morbidade; 2 morbidades; ≥3 morbidades), utensílios álcool (não possui álcool; possui álcool), utensílios máscara (não possui máscara; possui máscara), distanciamento (rigorosamente isolado; sair apenas para necessidades básicas; sair para trabalho ou atividades essenciais; não fez distanciamento). O questionário de pesquisa contendo as variáveis utilizadas neste estudo podem ser encontradas no Apêndice 1.

Todas as análises estatísticas foram realizadas usando Stata Statistical Package (Versão 16.0) (Stata Corp, College Station, Texas, EUA). Todos os dados que suportam os achados deste estudo estão disponíveis com o autor correspondente mediante solicitação prévia, de acordo com os Princípios de Dados FAIR (www.force11.org/group/fairgroup/fairprinciples). As variáveis foram descritas através das frequências relativas e absolutas. Regressão de Poisson com variância robusta foi utilizada para estimar a razão de prevalência (RP) e o intervalo de confiança de 95% (IC95%) na associação dos desfechos e as variáveis de exposição. Na análise multivariada, as variáveis de exposição foram controladas por fatores de confusão associados ao desfecho num nível de significância menor que 10%. As análises levaram em conta o desenho amostral, unidades primárias de amostragem, estratos e pesos amostrais. Um nível de significância menor que 5% foi adotado para considerar as associações entre exposição e desfecho como significativas.

RESULTADOS

De maneira geral, a realização de algum teste para COVID-19 foi reportada por 26,673 (julho), 35,587 (setembro) e 45,180 (novembro) participantes. A realização do teste de RT-PCR para COVID-19 nos meses de julho, setembro e novembro foi reportada por 7,026, 12,943 e 18,308 participantes, respectivamente. Nesse mesmo período, 49,407 testagens rápidas foram realizadas, em que: julho: n=11,630; setembro: n=16,954; novembro: n=20,823. Ainda, o histórico de testagem sanguínea foi reportado por 6,886 (julho), 10,668 (setembro) e 13,102 (novembro) indivíduos entrevistados. As Tabelas 1 e 2 apresentam a distribuição amostral das variáveis de exposição de acordo com o histórico de infecção por SARS-CoV-2 por modalidades de testagem.

Tabela 1. Descrição da amostra conforme variáveis demográficas e socioeconômicas de acordo com a prevalência de teste RT-PCR positivo em julho (n=7.026), setembro (n=12.943) e novembro (n=18.308) e teste rápido para COVID-19 em julho (n=11.630), setembro (n=16.954) e novembro (n=20.823), 2020.

Variáveis		Julho		Setembro		Novembro
Variáveis		n (%)	+COVID (IC95%)^*	n (%)	+COVID (IC95%)^*	n (%)	+COVID (IC95%)^*
RT-PCR
	Total	7.026 (100)	27,8 (26,0–29,6)	12.943 (100)	28,3 (27,8–28,7)	18.308 (100)	29,3 (28,9–29,7)
Raça
	Branca	3.320 (47,3)	26,6 (24,3–29,0)	6.245 (48,3)	26,9 (26,4–27,3)	9.201 (50,3)	28,1 (26,2–30,0)
	Preta	601 (8,6)	27,8 (23,1–33,0)	1.139 (8,8)	27,5 (27,0–28,1)	1.570 (8,6)	27,3 (23,7–30,8)
	Parda	3.028 (43,1)	29,4 (26,9–32,1)	5.420 (41,9)	30,7 (28,6–32,8)	7.361 (40,2)	31,7 (29,9–33,4)
	Amarela	52 (0,7)	22,7 (7,8–50,4)	98 (0,8)	14,4 (9,6–199,2)	120 (0,7)	20,0 (15,4–24,6)
	Indígena	22 (0,3)	35,8 (13,8–66,0)	38 (0,3)	39,5 (31,6–47,4)	52 (0,3)	40,0 (34,5–46,3)
Faixa etária (anos)
	18–29	1.317 (20,3)	24,4 (21,4–27,8)	2.722 (21,0)	26,3 (22,7–29,8)	3.923 (21,4)	27,1 (25,2–28,9)
	30–39	1.711 (26,3)	29,1 (26,0–32,3)	3.126 (24,2)	29,5 (26,9–32,1)	4.355 (23,8)	31,2 (29,4–32,9)
	40–49	1.443 (22,2)	29,2 (26,1–32,6)	2.847 (22,0)	29,4 (26,3–325)	3.953 (21,6)	30,8 (28,5–33,1)
	50–59	1.065 (16,4)	32,4 (28,5–36,5)	2.173 (16,8)	29,3 (25,4–33,2)	3.018 (16,5	31,2 (28,9–33,5)
	≥60	959 (14,8)	26,1 (22,2–30,4)	2.075 (16,0)	27,6 (23,6–31,6)	3.059 (16,7)	26,5 (24,4–28,6)
Sexo
	Masculino	3.240 (46,1)	27,7 (25,6–30,0)	5.852 (45,2)	28,7 (25,3–31,3)	8.260 (45,1)	29,7 (27,1–32,4)
	Feminino	3.786 (53,9)	27,8 (25,7–29,9)	7.091 (54,8)	28,3 (26,8–29,7)	10.048 (54,9)	29,3 (28,1–30,5)
Escolaridade
	Superior	2.885 (41,1)	25,9 (23,7–28,3)	5.419 (41,9)	27,0 (23,8–30,2)	7.899 (43,1)	29,1 (27,1–31,0)
	EM completo	1.957 (27,8)	31,6 (28,7–34,6)	3.891 (30,1)	30,6 (26,5–34,7)	5.427 (29,6)	30,9 (28,2–33,6)
	EF completo/EM incompleto	822 (11,7)	29,9 (25,5–34,6)	1.567 (12,1)	28,9 (23,6–34,1)	2.166 (11,8)	29,8 (27,1–32,5)
	EF incompleto/sem instrução	1.362 (19,4)	25,1 (21,6–28,9)	2.066 (16,0)	27,9 (23,6–32,1)	2.816 (15,4)	27,6 (24,7–30,5)
Renda
	≥R$ 2.500	1.993 (43,7)	28,1 (25,3–31,1)	3.610 (41,0)	27,3 (24,3–30,3)	5.055 (41,0)	30,0 (27,8–32,1)
	R$ 2.499–R$ 1.430	1.183 (25,9)	27,6 (24,4–31,1)	2.292 (26,1)	28,9 (25,6–32,1)	3.189 (25,9)	29,4 (26,2–32,6)
	R$ 1.429–R$ 1.045	1.081 (23,7)	27,6 (24,2–31,3)	2.212 (25,1)	29,6 (26,1–33,1)	3.103 (25,2)	30,6 (28,2–33,1)
	R$ 1.044–0	307 (6,7)	25,3 (18,7–33,3)	682 (7,8)	26,7 (20,3–33,1)	968 (7,9)	27,7 (21,9–33,4)
Teste rápido
	Total	11.630 (100)	16,2 (15,1–17,3)	16.954 (100)	19,7 (18,5–20,8)	20.823 (100)	19,9 (18,7–21,8)
Raça
	Branca	4.707 (40,5)	13,1 (11,8–14,6)	7.033 (41,5)	16,4 (13,9–18,8)	8.771 (42,1)	17,0 (14,9–19,0)
	Preta	1.035 (8,9)	16,3 (13,1–20,1)	1.622 (9,6)	20,4 (17,3–23,5)	1.962 (9,4)	19,3 (16,7–21,9)
	Parda	5.748 (49,4)	18,9 (17,3–20,6)	8.108 (47,8)	22,3 (20,6–24,1)	9.856 (47,3)	22,2 (20,9–23,5)
	Amarela	75 (0,7)	12,7 (5,7–26,1)	86 (0,5)	18,6 (4,6–32,6)	117 (0,6)	18,8 (3,8–33,8)
	Indígena	61 (0,5)	34,9 (20,9–52,2)	101 (0,6)	36,6 (22,8–50,4)	113 (0,5)	22,1 (11,1–33,1)
Faixa etária (anos)
	18–29	2.085 (19,5)	15,2 (13,2–17,4)	3.418 (20,2)	19,7 (17,2–22,2)	4.127 (19,8)	19,8 (17,9–21,7)
	30–39	2.676 (25,0)	15,9 (14,0–17,9)	3.977 (23,5)	19,2 (17,0–21,4)	4.716 (22,6)	20,2 (18,6–21,8)
	40–49	2.397 (22,4)	17,0 (15,1v19,1)	3.716 (21,9)	21,6 (19,7–23,5)	4.581 (22,0)	21,6 (19,9–23,3)
	50–59	1.871 (17,5)	16,0 (14,0–18,2)	2.924 (17,2)	19,6 (16,0–23,2)	3.676 (17,7)	18,6 (16,4–20,8)
	≥60	1.659 (15,5)	17,4 (14,9–20,2)	2.919 (17,2)	18,3 (14,7–21,8)	3.723 (17,9)	17,8 (15,2–20,4)
Sexo
	Masculino	5.470 (47,0)	15,2 (13,9–16,6)	7.819 (46,1)	18,6 (16,9–20,3)	9.604 (46,1)	18,9 (17,4–205)
	Feminino	6.160 (53,0)	17,0 (15,7–18,4)	9.135 (53,9)	20,7 (19,0–22,4)	11.219 (53,9)	20,4 (18,8–-21,9)
Escolaridade
	Superior	3.817 (32,8)	12,7 (11,3–14,2)	5.729 (33,8)	17,2 (16,3–18,1)	6.961 (33,4)	17,7 (16,8–18,6)
	EM completo	3.391 (29,2)	18,2 (16,4–20,1)	5.316 (31,4)	20,7 (19,6–21,7)	6.422 (30,8)	20,9 (19,9–21,9)
	EF completo/EM incompleto	1.578 (13,6)	19,3 (16,9–22,0)	2.107 (12,4)	22,1 (20,4–23,7)	2.667 (12,8)	21,8 (20,3–23,3)
	EF incompleto/sem instrução	2.844 (24,4)	17,0 (15,1–19,1)	3.802 (22,4)	20,9 (19,6–22,2)	4.773 (22,9)	19,8 (18,7–20,9)
Renda
	≥R$ 2.500	2.563 (36,3)	12,3 (10,6–14,2)	3.682 (34,4)	15,7 (13,8–17,5)	4.431 (34,0)	16,5 (14,4–18,6)
	R$ 2.499 – R$ 1.430	1.828 (25,8)	15,4 (13,3–17,8)	2.727 (25,5)	18,9 (17,3–20,5)	3.253 (25,0)	19,3 (17,5–21,1)
	R$ 1.429- R$ 1.045	1.993 (28,2)	16,8 (14,8–19,1)	3.076 (28,7)	20,6 (18,9–22,2)	3.764 (28,9)	20,5 (18,5–22,6)
	R$ 1.044 – 0	687 (9,7)	19,2 (15,6–23,5)	1.224 (11,4)	19,9 (16,7–23,1)	1.580 (12,1)	19,5 (15,5–23,5)

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Prevalências e intervalos de confiança 95% para os testes RT-PCR ou teste rápido positivos para COVID-19.

IC: intervalo de confiança; EM: ensino médio; EF: ensino fundamental.

Tabela 2. Descrição da amostra conforme variáveis demográficas e socioeconômicas de acordo com a prevalência de teste de sangue positivo para COVID-19 em julho (n=6.886), setembro (n=10.668) e novembro (n=13.102), 2020.

Variáveis		Julho		Setembro		Novembro
Variáveis		n (%)	+COVID (IC95%)^*	n (%)	+COVID (IC95%)^*	n (%)	+COVID (IC95%)^*
	Total	6.886 (100)	25,8 (24,2–27,5)	10.668 (100)	28,5 (27,3–29,7)	13.102 (100)	28,2 (27,4–28,9)
Raça
	Branca	3.040 (44,2)	21,7 (19,6–23,9)	4.637 (43,5)	24,2 (22,3–26,1)	5.828 (44,5)	25,1 (23,4–26,8)
	Preta	530 (7,7)	23,6 (19,2–28,6)	876 (8,2)	29,3 (27,1–31,5)	1.068 (8,2)	28,3 (26,4–30,2)
	Parda	3.217 (46,7)	31,3 (28,8–33,9)	5.026 (47,1)	33,0 (21,6–34,4)	6.040 (46,1)	31,8 (20,5–33,1)
	Amarela	54 (0,8)	22,2 (9,5–43,6)	75 (0,7)	12,0 (3,1–20,9)	96 (0,7)	15,8 (6,9–24,7)
	Indígena	44 (0,6)	30,4 (14,2–53,6)	52 (0,5)	37,3 (22,3–52,3)	65 (0,5)	42,2 (27,2–57,2)
Faixa etária (anos)
	18–29	1.138 (17,8)	24,1 (21,0–27,5)	1.971 (18,5)	27,6 (25,5–29,7)	2.387 (18,2)	28,1 (26,2–30,0)
	30–39	1.581 (24,8)	26,3 (23,2–29,6)	2.465 (23,1)	28,4 (26,4–30,4)	2.911 (22,2)	29,6 (27,8–31,4)
	40–49	1.441 (22,6)	26,7 (23,6–30,0)	2.263 (21,2)	29,2 (26,9–31,5)	2.845 (21,7)	28,3 (26,2–30,4)
	50–59	1.121 (17,5)	24,5 (21,3–28,0)	1.934 (18,1)	29,7 (26,9–32,5)	2.384 (18,2)	28,5 (26,7–30,3)
	≥60	1.103 (17,3)	27,0 (23,5–30,8)	2.035 (19,1)	28,9 (26,5–31,3)	2.575 (19,7)	27,4 (25,8–29,0)
Sexo
	Masculino	3.185 (46,2)	24,8 (22,7–27,0)	4.892 (45,9)	28,2 (26,3–30,1)	5.954 (45,4)	27,6 (25,9–30,6)
	Feminino	3.701 (53,8)	26,7 (24,8–28,7)	5.776 (54,1)	29,2 (27,7–30,7)	7.148 (54,6)	29,1 (27,5–30,7)
Escolaridade
	Superior	2.733 (39,7)	20,6 (18,6–22,9)	4.307 (40,4)	24,4 (22,6–26,2)	5.317 (40,6)	25,1 (23,6–26,6)
	EM completo	1.812 (26,3)	30,6 (27,7–33,7)	3.042 (28,5)	30,7 (28,4–33,0)	3.667 (28,0)	30,0 (28,1–31,9)
	EF completo/EM incompleto	815 (11,8)	28,1 (24,0–32,5)	1.177 (11,0)	33,4 (30,7–36,1)	1.491 (11,4)	31,4 (29,0–33,6)
	EF incompleto/sem instrução	1.526 (22,2)	29,6 (26,3–-33,1)	2.142 (20,1)	32,1 (29,5–34,7)	2.627 (20,1)	31,3 (29,5–33,3)
Renda
	≥R$ 2,500	1.888 (43,9)	19,1 (16,7–21,8)	2.869 (41,3)	22,9 (20,8–25,0)	3.420 (40,5)	23,4 (21,5–25,3)
	R$ 2.499–R$ 1.430	964 (22,4)	25,6 (22,1–-29,5)	1.619 (23,3)	28,7 (25,5–31,9)	1.994 (23,6)	27,9 (24,9–30,9)
	R$ 1.429–R$ 1.045	1.088 (25,3)	25,0 (21,9–28,4)	1.772 (25,5)	28,8 (25,9–31,7)	2.162 (25,6)	28,6 (26,4–30,8)
	R$ 1.044–0	364 (8,4)	31,1 (25,0–37,8)	684 (9,9)	33,0 (27,3–38,7)	872 (10,3)	29,7 (25,5–33,9)

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Prevalências e intervalos de confiança 95% para os testes RT-PCR ou teste rápido positivos para COVID-19.

IC: intervalo de confiança; EM: ensino médio; EF: ensino fundamental.

A Tabela 3 apresenta as RP e o IC95% da análise bruta e ajustada entre a realização dos três diferentes testes (RT-PCR, teste rápido e testagem sanguínea) durante os meses de julho, setembro e novembro (2020) e as variáveis de exposição. Após realizados os ajustes para os potenciais fatores de confusão, foi observado um efeito significativo da raça/etnia na testagem de COVID-19 através do teste RT-PCR entre os participantes indígenas em novembro (RP: 1,90; IC95% 1,07–3,38), visto que esse grupo apresentou maior probabilidade de infecção por SARS-CoV-2 via teste RT-PCR comparado aos indivíduos brancos. Participantes de etnia parda apresentaram maior RP de testagem positiva através do RT-PCR em setembro (RP: 1,20; IC95% 1,10–1,30), comparado aos sujeitos brancos. Ainda, indivíduos com menor renda (R$ 0–1) apresentaram maior probabilidade de apresentar teste RT_PCR para verificar a ocorrência de infecção por COVID-19 do que aqueles com renda familiar mais elevada (≥R$ 2.500) em setembro (RP: 1,87; IC95%1,15–2,67) e novembro (RP: 1,69; IC95% 1,16–3,06).

Tabela 3. Análise bruta e ajustada para os testes RT-PCR, teste rápido e de sangue positivo para COVID-19 em julho, setembro e novembro, 2020.

Variáveis		Julho		Setembro		Novembro
Variáveis		RP_BRUTA (IC95%)	RP_AJUSTADA (IC95%)	RP_BRUTA (IC95%)	RP_AJUSTADA (IC95%)	RP_BRUTA (IC95%)	RP_AJUSTADA (IC95%)
Teste RT-PCR
Raça
	Branca	1,00	1,00	1,00	1,00	1,00	1,00
	Preta	1,05 (0,86–1,27)	1,09 (0,88–1,35)	1,06 (0,92–1,22)	1,05 (0,91–1,21)	0,96 (0,85–1,09)	0,97 (0,86–1,10)
	Parda	1,11 (0,98–1,25)	1,05 (0,92–1,21)	1,21 (1,11–1,31)	1,20 (1,10–1,30)	1,18 (1,11–1,27)	1,20 (1,12–1,29)
	Amarela	0,85 (0,34–2,16)	0,72 (0,26–1,99)	0,46 (0,26–0,81)	0,44 (0,25–0,77)	0,65 (0,41–1,02)	0,65 (0,41–1,02)
	Indígena	1,35 (0,65–2,79)	1,39 (0,37–5,14)	1,73 (1,09–3,33)	1,63 (0,85–3,15)	1,92 (1,08–3,40)	1,90 (1,07–3,38)
Renda
	≥R$ 2.500	1,00	1,00	1,00	1,00^*	1,00	1,00
	R$ 2.499–R$ 1.430	0,98 (0,84–1,15)	0,90 (0,76–1,05)	1,09 (0,97– 1,23)	1,05 (0,93–1,19)	0,98 (0,89–1,08)	0,97 (0,87–1,08)
	R$ 1.429–R$ 1.045	0,98 (0,83–1,16)	0,90 (0,75–1,07)	1,14 (1,01–1,28)	1,08 (0,94–1,24)	1,05 (0,95–1,15)	1,04 (0,93–1,16)
	R$ 1.044–0	0,90 (0,66–1,22)	0,78 (0,56–1,08)	0,99 (0,82–1,19)	1,87 (1,15–2,67)	1,31 (0,98–1,76)	1,69 (1,16–3,06)
Teste rápido
Raça
	Branca	1,00	1,00/	1,00^*	1,00	1,00	1,00^*
	Preta	1,24 (0,98–1,57)	1,09 (0,84–1,42)	1,31 (1,15–1,51)	1,31 (1,02–1,32)	1,17 (1,04–1,33)	1,16 (1,03–1,32)
	Parda	1,44 (1,26–1,64)	1,22 (1,04–1,44)	1,47 (1,35–1,59)	1,47 (1,35–1,60)	1,42 (0,91–2,26)	1,41 (0,90–2,21)
	Amarela	0,97 (0,45–2,07)	1,04 (0,43–2,51)	1,17 (0,67–2,02)	1,07 (0,58–1,87)	1,16 (0,73–1,85)	1,15 (0,72–1,84)
	Indígena	2,66 (1,69–4,19)	2,45 (1,48–4,08)	2,95 (1,96–4,45)	2,53 (1,74–4,41)	1,39 (1,29–1,50)	1,23 (1,11–1,86)
Renda
	≥R$ 2.500	1,00	1,00	1,00^*	1,00^*	1,00	1,00^*
	R$ 2.499–R$ 1.430	1,25 (1,02–1,53)	1,14 (0,93–1,41)	1,26 (1,11–1,44)	1,27 (1,11–1,45)	1,19 (1,07–1,36)	1,22 (1,08–1,37)
	R$ 1.429–R$ 1.045	1,36 (1,13–1,65)	1,16 (0,94–1,43)	1,39 (1,22–1,58)	1,42 (1,25–1,61)	1,31 (1,17–1,47)	1,33 (1,19–1,49)
	R$ 1.044–0	1,56 (1,22–2,00)	1,20 (1,00–1,57)	1,35 (1,13–1,59)	1,38 (1,17–1,64)	1,22 (1,06–1,42)	1,25 (1,08–1,45)
Teste sangue
Raça
	Branca	1,00	1,00	1,00	1,00	1,00	1,00^*
	Preta	1,09 (0,87–1,36)	1,07 (0,82–1,38)	1,30 (1,11–1,53)	1,30 (1,11–1,53)	1,18 (1,02–1,37)	1,18 (1,02–1,37)
	Parda	1,44 (1,28–1,63)	1,30 (1,11–1,53)	1,84 (1,41–1,69)	1,54 (1,41–1,69)	1,39 (1,28–1,51)	1,39 (1,28–1,51)
	Amarela	1,02 (0,48–2,18)	0,78 (0,31–1,99)	0,43 (0,21–0,86)	0,43 (0,21–0,86)	0,56 (0,32–0,98)	0,56 (0,32–0,98)
	Indígena	1,40 (0,74–2,68)	1,14 (0,53–2,48)	1,36 (1,02–3,29)	1,86 (1,05–3,29)	2,18 (1,32–3,59)	2,11 (1,12–3,59)
Renda
	≥R$ 2.500	1,00	1,00	1,00	1,00^*	1,00	1,00^*
	R$ 2.499–R$ 1.430	1,34 (1,10–1,64)	1,28 (1,05–1,56)	1,36 (1,18–1,56)	1,38 (1,20–1,59)	1,27 (1,12–1,44)	1,28 (1,13–1,45)
	R$ 1.429–R$ 1.045	1,31 (1,09–1,57)	1,21 (1,00–1,46)	1,37 (1,19–1,57)	1,41 (1,22–1,62)	1,31 (1,16–1,48)	1,34 (1,18–1,52)
	R$ 1.044–0	1,63 (1,27–2,08)	1,39 (1,08–1,79)	1,67 (1,39–2,00)	1,72 (1,43–2,07)	1,38 (1,17–1,63)	1,43 (1,21–1,69)

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Ajustado para escolaridade, sexo e faixa etária.

Os participantes autodeclarados indígenas apresentaram maior chance de serem diagnosticados com COVID-19 por meio de testes rápidos do que participantes de raça branca em todos os meses de acompanhamento: julho (RP: 2,45; IC95% 1,48–4,08), setembro (RP: 2,53; IC95% 1,74–4,41), novembro (RP: 1,23; IC95% 1,11–1,86). A renda média mensal dos participantes esteve associada com a prevalência de testagem rápida para COVID-19: os participantes com menor renda mensal apresentaram maior probabilidade de realizar teste rápido do que aqueles que ganham ≥2,500 reais mensais em julho, setembro e novembro (2020). Foi identificado que os participantes pardos tinham maiores prevalências de testagem positiva para COVID-19 através de exames sanguíneos em todos os meses de acompanhamento: julho (RP: 1,30; IC95% 1,11–1,53), setembro (RP: 1,54; IC95% 1,41–1,69), novembro (RP: 1,39; IC95% 1,28–1,51), comparado aos sujeitos brancos. Indivíduos autodeclarados como indígenas também apresentaram maior RP de testagem positiva para COVID-19 por testes sanguíneos, mas apenas em setembro (RP: 1,86; IC95% 1,05–3,29) e novembro (RP: 2,11; IC95% 1,12–3,59) de 2020. A menor renda familiar mensal esteve associada à menor probabilidade de resultado positivo no teste sanguíneo em todos os meses de acompanhamento do que aqueles com renda ≥R$ 2.500/mês (Tabela 3).

DISCUSSÃO

Este estudo retrospectivo utilizou os dados do Pnad COVID-19 para investigar as desigualdades relacionadas a raça e renda no histórico de testagem para COVID-19 de acordo com as modalidades diagnósticas em adultos durante a primeira onda da pandemia no Brasil. Os resultados indicam que indivíduos indígenas e com menor renda média mensal apresentaram maior probabilidade de serem diagnosticados com COVID-19, independente do teste utilizado e o período de realização do exame.

Neste estudo, indivíduos não brancos, especialmente aqueles pardos e(ou) indígenas, apresentaram maior soroprevalência para COVID-19, quando comparado com os sujeitos brancos. Esse achado pode ser justificado pelas condições sociais que as minorias populacionais estão expostas, predispondo a maior taxa de infecção pela doença nesse grupo¹⁸. Tal resultado corrobora a situação histórica de vulnerabilidade vivida pela população indígena, em particular, ao qual apresentou maiores quadros infecciosos no passado, como na gripe espanhola, infecção pelo vírus da H1N1 e SARS-CoV¹⁹.

De fato, esse grupo populacional é fortemente impactado pelas iniquidades nos determinantes sociais da saúde. Além das barreiras culturais e geográficas, os indígenas apresentam quadros mais elevados de pobreza, desnutrição, baixa escolaridade, dificuldade de acesso aos serviços de saúde e precarização dos sistemas de saneamento básico^19,20. Além disso, é importante destacar a característica de isolamento geográfico comum nessa população como potencial obstáculo ao acesso às medidas de atenção em saúde voltadas para o combate à pandemia. Ademais, fatores imunológicos característicos dos indígenas e a presença das doenças crônicas aumentam a suscetibilidade a surtos de doenças infecciosas, tornando os povos mais vulneráveis²¹. Agrava-se essa realidade quando a exposição dessa comunidade é associada a omissão governamental de anteparo diante da doença, como reportado no relatório elaborado pelo Conselho Nacional de Saúde (CNS) e pelo Conselho Nacional de Direitos Humanos (CNDH)²².

Os resultados deste estudo evidenciam uma associação significativa entre a testagem via RT-PCR e exame sanguíneo e a renda mensal dos participantes. Indivíduos com menor renda apresentaram maior chance de testagem positiva através dessas modalidades diagnósticas. No Brasil, a disseminação do vírus da COVID-19 teve início em indivíduos de classe econômica mais elevada e posteriormente o vírus se espalhou rapidamente entre as pessoas de classe econômica menos favorecida, visto que parte desses indivíduos realizaram a manutenção das suas atividades diárias por necessidade de subsistência^23,24. Estudos têm reportado que as condições socioeconômicas desfavoráveis, reduzido nível de escolaridade e o maior número de residentes no domicílio podem predispor a maior taxa de infecção pela COVID-19¹². A Comissão Econômica para a América Latina e o Caribe (Cepal) sugere que o empobrecimento da população brasileira nos últimos anos pode ter aumentado o impacto da COVID-19 no país²⁵. Grupos desfavorecidos tendem a ter ocupações menos estruturadas e com uma renda insuficiente para sua sobrevivência^26–28., geralmente inseridos em ocupações que não oferecem a possibilidade de trabalhar em casa, necessitando utilizar o transporte público para a locomoção e, assim, aumentando o contato entre as pessoas^29,30. Ademais, a insuficiente testagem realizada no Brasil durante o período da Pnad COVID-19, resultado do escasso número de testes disponíveis no SUS e omissão do governo brasileiro na aquisição, resultou na compra dos testes de forma individual, afastando as populações desfavorecidas do diagnóstico da doença^26,28,29. Todavia, nos desfechos dos testes realizados, a maior positividade é destacada na população mais sensível economicamente.

Os resultados apresentados neste estudo devem ser interpretados à luz de suas limitações. A principal limitação desta investigação se refere à natureza transversal do estudo, que não permite a inferência da causalidade nas associações identificadas. O uso de dados autorreportados também pode ser considerado uma limitação, uma vez que a percepção subjetiva é fruto do comportamento verbal, e esse, por sua vez, é reforçado pelo ambiente ao qual o indivíduo está inserido²⁵.

Do mesmo modo, cabe destacar as limitações dadas pelos resultados dos testes. O teste RT-PCR está indicado para pacientes sintomáticos na fase aguda da doença entre o terceiro e o sétimo dia, assim devem-se considerar possíveis resultados falsos negativos que ocorrem quando a quantidade de genoma viral coletado é insuficiente ou o período da janela de tempo da replicação viral é perdido⁵. Entretanto esse teste possui alta sensibilidade e especificidade, respectivamente 97,2 e 98,9%⁵.

Além disso, é possível realizar o diagnóstico de COVID-19 com base na resposta imune à infecção pelo SARS-CoV 2, através dos testes sorológicos imunocromatográficos para detecção rápida dos anticorpos IgG/IgM, em amostras de sangue, soro ou plasma dos indivíduos⁵. A principal limitação dessa ferramenta é a necessidade da realização, a partir do oitavo dia do início dos sintomas. Assim, o resultado da doença que se apoia nessa detecção possivelmente ocorrerá em um momento de recuperação do paciente, aumentando o desafio dos serviços de vigilância e controle da transmissão. Os testes sorológicos IgM e IgG possuem sensibilidade de 84,5% e especificidade de 91,6%^4,5., porém os testes rápidos imunocromatográficos apresentavam baixa sensibilidade, particularmente no início da pandemia³¹.

Importa destacar que a sensibilidade de um teste diagnóstico diz respeito à capacidade de identificar corretamente o resultado positivo da doença, e a especificidade aponta a capacidade de identificar a ausência^4,5.

Com base numa revisão de literatura realizada pelos autores, até o momento, não foram publicados estudos que procuraram validar essas questões autorreferidas de resultados positivos para COVID-19. Estudos com delineamentos de coorte e testes bioquímicos poderiam melhor ilustrar as desigualdades no contágio pela doença. Ademais, é importante considerar que, em abril de 2020, as terras indígenas mais vulneráveis eram aquelas localizadas às margens dos grandes centros urbanos, como Manaus, eixo Rio Branco-Porto Velho, Fortaleza, Salvador e capitais do Sul e Sudeste do Brasil. Assim, considerando a atuação da Pnad COVID-19 nos grandes centros urbanos, sugere-se que essas descobertas podem representar um quadro menor da realidade, uma vez que não alcança efetivamente aldeias e terras indígenas em regiões isoladas do país. Por fim, os resultados aqui descritos são válidos e robustos, somando a um campo pouco explorado na literatura brasileira.

Este estudo identificou associação significativa entre a etnia/raça e a situação econômica com resultado positivo para COVID-19 em adultos brasileiros. Esse panorama reflete uma situação de vulnerabilidade nesses grupos e demonstra a necessidade da elaboração e expansão de políticas públicas mais equitativas que contemplem grupos mais vulneráveis em contexto de crise de saúde pública.

Footnotes

FONTE DE FINANCIAMENTO:

Este estudo foi financiado em parte pela Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Capes) – Brasil – Código 001/2020.

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1980-5497-rbepid-27-e240042-Suppl01.pdf^{(237KB, pdf)}

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PERMALINK

Social inequalities in self-reported SARS-CoV-2 infection in Brazilian adults: PNAD COVID-19

Mateus Andrade Rocha

Cândido Norberto Bronzoni de Mattos

Marcos Pascoal Pattussi

Roles

ABSTRACT

Objective:

Methods:

Results:

Conclusion:

INTRODUCTION

METHODS

RESULTS

Table 1. Sample description according to demographic and socioeconomic variables related to the prevalence of positive RT-PCR results in July (n=7,026), September (n=12,943) and November (n=18,308), and COVID-19 rapid test in July (n=11,630), September (n=16,954) and November (n=20,823), 2020.

Table 2. Sample description according to demographic and socioeconomic variables related to the prevalence of positive blood test for COVID-19 in July (n=6,886), September (n=10,668) and November (n=13,102), 2020.

Table 3. Crude and adjusted analyses for positive RT-PCR, rapid test and blood test for COVID-19 in July, September and November, 2020.

DISCUSSION

Supplementary Material

Footnotes

REFERENCES

Desigualdades sociais no autorrelato de infecção por SARS-CoV-2 em adultos brasileiros: Pnad COVID-19

Mateus Andrade Rocha

Cândido Norberto Bronzoni de Mattos

Marcos Pascoal Pattussi

Roles

RESUMO

Objetivo:

Métodos:

Resultados:

Conclusão:

INTRODUÇÃO

MÉTODOS

RESULTADOS

Tabela 2. Descrição da amostra conforme variáveis demográficas e socioeconômicas de acordo com a prevalência de teste de sangue positivo para COVID-19 em julho (n=6.886), setembro (n=10.668) e novembro (n=13.102), 2020.

Tabela 3. Análise bruta e ajustada para os testes RT-PCR, teste rápido e de sangue positivo para COVID-19 em julho, setembro e novembro, 2020.

DISCUSSÃO

Footnotes

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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