Primary health care and social isolation against COVID-19 in Northeastern Brazil: Ecological time-series study.

BACKGROUND: Brazil is witnessing a massive increase of corona virus disease (COVID-19). Its peculiar primary health care (PHC) system faces a burden due to the contagion occurring in the community environment. Then, the aim is to estimate the effect of the coverage of primary health care and social isolation on the evolution of confirmed cases and deaths by COVID-19, controlling sociodemographic, economic and health system aspects.
METHODS: A time series design was designed with data on diagnosed cases of COVID-19 and their deaths as outcomes in the capital cities of the Northeast region of Brazil. Independent variables such as PHC coverage, hospital beds, social isolation, demographic density, Gini index and other indicators were analyzed. A Autoregressive Generalized Linear Model method was applied for model the relationship.
RESULTS: We identified an exponential growth of cases (y = 0.00250.71x; p-value<0,001). However, there is a high variability in the occurrence of outcomes. PHC coverage≥75% (χ2 = 9.27; p-value = 0.01) and social isolation rate (χ2 = 365.99; p-value<0.001) proved to be mitigating factors for the spread of COVID-19 and its deaths. Capitals with hospital beds ≥ 3.2 per thousand inhabitants had fewer deaths (χ2 = 9.02; p-value = 0.003), but this was influenced by PHC coverage (χ2 = 30,87; p-value<0.001).
CONCLUSIONS: PHC mitigates the occurrence of Covid-19 and its deaths in a region of social vulnerability in Brazil together with social isolation. However, it is not known until when the system will withstand the overload in view of the low adhesion to social isolation, the lack of support and appropriate direction from the government to its population.

Introduction

The new coronavirus disease (COVID-19) outbreak in the world culminated in the Public Health Emergency Declaration of International Importance by the World Health Organization [1], on January 30th, 2020. COVID-19 spread rapidly across the world and, on February 3rd, 2020, the Brazilian Ministry of Health declared a Public Health Emergency of National Importance due to human infection [2]. The situation evolves in a continuous overload in health services with the expansion of the pandemic in the country, totaling 10.517.232 cases and 254.221 confirmed deaths on 28/02/2021 [3].

COVID-19 infection may be asymptomatic or progress to Severe Acute Respiratory Syndrome (SARS-Cov-2), which requires intensive care. The main symptoms include self-reported fever, fatigue, loss of smell and taste, dry cough, myalgia and dyspnea and uncommon symptoms include sputum production, headache, hemoptysis and diarrhea [4-8].

In Brazil, primary health care (PHC) services have played an essential role in the management of COVID-19 cases. PHC main actions include measures of clinical screening, social support and reception, monitoring of home isolation until discharge from isolation. For severe cases, it includes clinical stabilization, referral and transportation to specialized centers or hospital services [9]. The PHC community-based profile allows an intimate relationship with the population under its responsibility, which can be directly related to the capacity to refrain contagious cases of COVID-19 in the Brazilian scenario from spreading.

Contagion control generally comes with quarantining of those already infected associated with other preventive measures, such as social isolation through the closing of schools, travel restrictions, use of masks, greater care in hand hygiene and objects [10-13]. Additionally, if no control measures were taken, the virus could reach 40–70% of the population, and between 5–10% of this population would need to be hospitalized [10]. As a result, social isolation, with adherence by the population, is considered a factor of great impact against the spread of the epidemic [12].

Brazil, as a country of continental dimensions, with heterogeneous social scenarios, demonstrates that the pandemic effects are also different in space and time, thus justifying analysis and different actions for the management of contagion by COVID-19 in different areas. Most of the population depends exclusively on the Unified Health System (SUS), where 72% of its locations have an insufficient amount of coverage in the number of hospital beds, mainly in intensive care [14]. In this context, the PHC role becomes even more essential, in order to guarantee the integrality of care, longitudinality of actions and coordination of care for infected cases, since it is PHC that holds the knowledge of the reality and data of each individual and population [9].

Several factors can be barriers or facilitators for a desirable outcome of COVID-19 cases in Brazil. Social, behavioral, demographic, biological and health service organization aspects can make a difference in the design and implementation of public policies to combat the pandemic [15]. Assessing these aspects in regions of social vulnerability might bring important benefits in the quality of care for the infected, in the optimization of resources and in slowing the expansion of cases and deaths.

The factors mentioned above may have even greater impact in the Northeastern Brazil. According to the report of the United Nations Development Programme [16], this region of Brazil, despite having great natural and cultural wealth, is characterized by high levels of social inequality and income concentration, which reflects in lower education levels, quality of life and access to health and sanitation services. This profile, that may be observed in other developing countries, places the Northeastern Brazil in a situation of great social vulnerability. This context makes fighting the epidemic in this area more challenging for public managers.

Given the above, this study aims to identify the effect of PHC coverage and social isolation on the dissemination of cases and deaths by COVID-19 in Northeastern Brazil, controlling socio-demographic and economic conditions as well as the organization of health services. We hypothesize that both social isolation and PHC coverage have their effects modulated by contextual conditions in mitigating the COVID-19 pandemic.

Materials and methods

This is an ecological time-series study with descriptive and analytical components, based on population, using secondary data. This design allows the establishment of a cause-effect relationship for public health interventions. The population from this study were residents of the main larger cities from the nine Federative Units of northeastern Brazil, in view of the greater concentration of cases in these cities and because they are generally a reference for economy and health services for other cities within the Federative Units.

Brazil is the largest country in Latin America, and it is geographically divided into five regions. The Northeastern region is historically marked by strong social inequality, with a population of approximately 53 million people. It is the region with the largest number of units of the Brazilian federation, nine in total, and with an area equivalent to around 18% of Brazilian territory [17]. In this study, we are analyzing the entire population of data, so it becomes unnecessary to have a sample size to represent the design.

Secondary data were collected from SUS Data Department (DATASUS), including the study outcomes: confirmed cases and deaths by COVID-19 [3] and some independent variables. Other data were collected in the database of the Brazilian Institute of Geography and Statistics (IBGE), which was compiled by the Brazilian agency of the United Nations Development Programme (UNDP) [16].

The time series data refer to the period from February 26 to May 16, 2020. The study primary outcome was COVID-19 cases diagnosed (y) and secondary outcome was deaths by COVID-19 (y’). The independent variables (x) were time, analyzed through epidemiological weeks; PHC coverage, Family Health Strategy (FHS) coverage through the proportion of ESF (Family Health Strategy) services in PHC in March 2020; evaluation score of the PHC health services quality by the Quality Monitoring and Evaluation Program (PMAQ); number of beds for hospitalization in April 2020; the Human Development Index (HDI), the Gini index, Gross Domestic Product (GDP) per capita, distance to the epicenter of the pandemic in Brazil, Demographic Density, percentage of employed persons and the percentage of social isolation in the nine Federative Units.

PHC coverage was stratified into less than 50%, from 50 to 74% and above 75% of the population to estimate nested relationships with other variables [18]. PMAQ score was also stratified below (<2) and above the average points (≧ 2) of the assessment. The number of hospital beds was transformed by the number of beds per thousand inhabitants (beds/population ratio) and stratified into less than 3.2 beds per thousand inhabitants and 3.2 or more beds, based on the world average [19]. The categorization of this variable was done through the analysis of their respective quants, considering that there is no criterion available in the literature to classify the strata. The other variables were treated as they were collected.

HDI is an important tool for assessing the development of certain localities and being measured by the geometric mean of the sum of life expectancy at birth, education index and income index. In Brazil, it is used as a key index of the United Nations Millennium Development Goals [16]. The Gini index was used as an instrument to measure the degree of income concentration in the capitals, being used as a measure of social inequality, ranging from 0 to 1, and the closer to 1 the greater the inequality in that location, their categorization was done according to the distribution of their data, considering that there is no criterion for stratification available in the literature. GDP per capita was used to assess the degree of cities development, the higher its value, the more developed the city is. The distance to the epicenter of the pandemic in Brazil is measured for kilometer to São Paulo City. Demographic density was observed to portray the spatial distribution of inhabitants per square kilometer (km2) and the percentage of employed people were considered to be persons who worked for at least one full hour with remuneration in cash, products, goods or benefits (housing, food, clothing, training, etc.), or in work without direct remuneration in support of the economic activity of a member of the household or a relative who lives in another domicile, or, still, those who had paid work from which they were temporarily away that week [16].

The social isolation index was extracted from a monitoring system (https://mapabrasileirodacovid.inloco.com.br/pt/), which calculates the percentage of the population of the cities that are following isolation recommendations. It is estimated by the triangulation of cell towers to measure the displacement greater than 200 meters from the devices for personal use as well as the concentration of these people in certain locations. To this end, they use the polygons of all regions from IBGE database in order to guarantee an accurate and true to categorization. Distance to the epicenter of the pandemic in Brazil variable was include in the model for cases COVID-19 outcome and beds/population ratio was applied in death’s model because conceptual reasons in relationship to outcomes.

The theoretical model analysis was set up to observe the association between the temporal evolution of COVID-19 cases and deaths. Such independent variables underwent crude analysis in order to estimate their relationship with the outcomes and then be included in an adjusted model to extract the main effects of each factor. Categorical variables also performed a nested analysis based on their conceptual and spatial relationship if they had a main effect on the adjusted model.

Statistical analysis was performed using the Generalized Linear Models (GLM) with a work correlation matrix Autoregressive (AR) of order 1, since it is a time-series where previous cases affect the number of subsequent cases, that is, there is data correlation. The log link function with Gamma distribution was used because the data of the independent variables did not present a direct linear relationship with the outcome and this was a count variable. The fit quality of the model was assessed by the quality information criterion (QIC), when the lowest estimate indicates the best fit, obeying the parsimony of the theoretical model.

Hypothesis tests were performed with the Wald chi-square test between the outcomes and the independent variables, selecting those with “p” values equal to or less than 0.10 to be included in the adjusted model. A significance level of 5% (α ≦ 0.05) was adopted. The measure of effect used to present the relationship magnitude was the equation coefficients of the crude and adjusted model, aided by its exponentialized version. The null hypothesis of the present study is that there is no association between the outcome and the independent variables.

Results

In the nine cities analyzed, we identified an accumulated of 43,969 cases and 2,588 deaths due to COVID-19 up to the twentieth epidemiological week. The average number of diagnosed in the cities evaluated cases was 4885.44 (± 1574.85), 287.55 (± 125.80) of deaths and an overall lethality of 4.4%.

PHC has an average population coverage of 65.62% in the sample, but with great variability (±19.83) between cities as well as its quality, which has an average of 2.12, with very different outliers (Table 1). GDP per capita (24814.72±3169.57), demographic density (3745.26±2330.05) and the beds/population ratio (3.68±0.72), also revealed a diversity of scenarios.

Table 1

Frequency distribution of the contextual characteristics of Northeastern cities in Brazil.

	Total	Aracaju	Fortaleza	João Pessoa	Maceió	Natal	Recife	Salvador	São Luís	Teresina
COVID-19 cases	43987	2032	15162	1540	2542	1288	9413	5267	5597	1146
COVID-19 deaths	2588	20	1163	72	131	36	573	185	379	29
Lethality	4.2	1.0	8.0	5.0	5.0	3.0	6.0	4.0	7.0	3.0
FHS coverage (%)	55.69 (±23.68)	70.36	49.89	86.57	26.95	37.42	56.39	36.39	37.26	100.00
PHC coverage (%)	65.62 (±19.83)	75.54	61.36	96.34	44.60	54.93	64.87	47.53	45.43	100.00
FHS-PHC ratio (%)	82.04 (±11.66)	93.14	81.30	89.85	60.42	68.12	86.92	76.56	82.01	100.00
PHC quality	2.12 (±0.54)	2.27	1.25	2.53	2.07	2.23	2.13	1.40	2.01	3.20
GDP per capta	24814.72 (±3169.57)	25185.55	23436.66	24319.82	21210.09	26497.08	31743.72	21231.48	27226.41	22481.67
Demographic Density	3745.26 (±2330.05)	3140.65	7786.44	3421.28	1854.10	4805.24	7039.64	3859.44	1215.69	584.94
Employed persons (%)	34.30 (±4.79)	35.80	32.00	36.10	26.40	36.60	43.90	28.60	33.40	35.90
GINI	0.50 (±0.01)	0.47	0.51	0.50	0.52	0.53	0.49	0.49	0.49	0.50
HDI	0.75 (±0.01)	0.77	0.75	0.76	0.72	0.76	0.77	0.759	0.76	0.75
Number of beds	5062.55 (±2762.23)	2382	8710	2922	3029	3138	9204	8852	3961	3365
Beds/population ratio	3.68 (±0.72)	3.62	3.26	3.61	2.97	3.54	5.59	3.08	3.59	3.89
Social Isolation (%)	46.32 (±4.27)	46.58	49.71	45.07	45.23	43.85	49.14	44.74	46.68	45.88

Coronavirus disease 2019 (COVID-19); Family Health Strategy (FHS); Primary Health Care (PHC); Gross Domestic Product (GDP); Human Development Index (HDI); Quality Monitoring and Evaluation Program (PMAQ).

Social isolation proves to be low in the sample studied, 46.32 (± 4.27), compared to what is recommended by the scientific and health authorities. The characterization by income can be seen in Table 1.

The epidemic evolution of COVID-19 cases in the sample reveals an exponential curve pattern, following the equation y = 529.66, which explains 89% of data variability (R2ajus = 0.89). Death events demonstrate an evolution of the exponentional curve, y = 7.90, which explains 87% of cases variability (R2ajus = 0.87). It is possible to identify “front steps” pattern in the occurrence of COVID-19 cases in the analyzed capitals, due to the magnitude of the dissemination of cases. The same is observed for cases of death (Fig 1).

Fig 1

Temporal distribution of COVID-19 cases and their deaths in the capitals of the Northeast of Brazil.

It shows the occurrence of distribution "front steps" motivated by speed and different magnitudes of contagion in the analyzed capitals.

In Table 2, crude analysis for COVID-19 new cases shows that the temporality of epidemiological weeks (χ2 = 541.76; degree freedom (df) = 1; p-value<0.001), PHC coverage strata (χ2 = 40.78; df = 2; p-value<0.001), PHC quality strata (χ2 = 48.79; df = 1; p-value<0.001), demographic density (χ2 = 5.64; df = 1; p-value = 0.01) and social isolation rate (χ2 = 7.96; df = 1; p-value = 0.005) establish an association with the viral outbreak. As for the cases of deaths due to COVID-19, the association is observed with temporality of the epidemiological weeks (χ2 = 2599.84; df = 1; p-value<0.001), PHC coverage strata (χ2 = 24.85; df = 2; p-value<0.001), PHC quality strata (χ2 = 55.42; df = 1; p-value<0.001) and social isolation rate (χ2 = 9.48; gl = 1; p-value = 0.002) as well as the demographic density (χ2 = 3.12; df = 1; p-value = 0.07), bed/population ratio (χ2 = 2.75; df = 1; p-value = 0.09) and the quantity of COVID-19 cases (χ2 = 3.10; df = 1; p-value = 0.07) diagnosed with marginal significance (p-value<0.10), Table 3.

Table 2

Crude model for association with incident COVID-19 cases in the time-series of twenty epidemiological weeks in Northeastern cities in Brazil.

Variable	B	Hypothesis test
		Wald χ2	df	p-value
Epidemiological week	0.12	541.76	1	<0.001
PHC coverage
<50%	1.12	25.99	1	<0.001
50–74%	1.80	20.29	1	<0.001
≥75%	0
FHS-PHC ratio	-0.006	0.09	1	0.76
PMAQ assessment
Below average	1.15	5.68	1	0.017
Above average	0
GDP per capta	5.264E-5	0.86	1	0.35
Demographic Density	2.28E-4	5.65	1	0.01
Employed persons (%)	0.008	0.02	1	0.86
HDI	9.42	0.85	1	0.35
GINI	-1.49	0.01	1	0.92
Social Isolation (%)	.001	7.96	1	0.005
Epicenter Distance	0.001	0.81	1	0.36

Family Health Strategy (FHS); Primary Health Care (PHC); Gross Domestic Product (GDP); Human Development Index (HDI); Quality Monitoring and Evaluation Program (PMAQ). Model regression coefficient (B); Degrees of Freedom (df).

Table 3

Crude model for association with COVID-19 deaths in the time-series of twenty epidemiological weeks in Northeastern cities in Brazil.

Variable	B	Hypothesis test
		Wald χ2	df	p-value
Epidemiological week	0.12	2599.84	1	<0.001
COVID-19 cases	2.05E-4	3.10	1	0.07
PHC coverage
<50%	1.62	13.85	1	<0.001
50–74%	2.56	21.84	1	<0.001
≥75%	0
FHS-PHC ratio	-0.006	0.068	1	0.794
PMAQ assessment	1.28	3.78	1	0.05
Below average
Above average	0
GDP per capta	7.144E-5	1.17	1	0.27
Demographic Density	2.58E-4	3.12	1	0.07
Employed persons (%)	0.01	0.05	1	0.80
GINI	8.69	0.20	1	0.65
HDI	10.00	0.57	1	0.44
Social Isolation (%)	0.001	9.48	1	0.002
Beds/population ratio
<3.2	-0.77	2.75	1	0.09
≥3.2	0

Coronavirus disease 2019 (COVID-19); Family Health Strategy (FHS); Primary Health Care (PHC); Gross Domestic Product (GDP); Human Development Index (HDI); Quality Monitoring and Evaluation Program (PMAQ). Model regression coefficient (B); Degrees of Freedom (df).

It is relevant to highlight the non-significant effect in the crude analysis of social variables, such as Gini index and HDI, as well as the economic variables, such as GDP per capita of the cities. FHS coverage also showed no association with the time-series of COVID-19 cases (Table 2) and deaths (Table 3).

After adjusting for confounding variables, there are major effects of the pandemic temporality (χ2 = 625.38; df = 1; p-value<0.001), social isolation rate (χ2 = 365.99; df = 1; p-value<0.001), demographic density (χ2 = 3.97; df = 1; p-value = 0.04) and PHC coverage (χ2 = 9.27; df = 2; p-value = 0.01) in the number of cases accumulated in the studied sample. There is a progression over time (B = 0.17; p-value<0.001) that was already predicted because it is a pandemic and that slightly increases in contexts of greater population density (B = 2.46E-4; p-value<0.001). On the other hand, social isolation rate shows a negative association with the number of cases (B = -0.007; p-value<0.001) as well as PHC coverage above 50%, where cities with coverage below 50% present on average three times more cases than those with coverage greater than 75%. This seems to be independent of the quality of PHC assistance (Table 4).

Table 4

Adjusted model for the association with incident COVID-19 cases in the time-series of twenty epidemiological weeks in Northeastern cities in Brazil.

Variable	B	Hypothesis test			RR	CI95%
		Wald χ2	df	p-value		Lower	Upper
Interception	2.59	60.15	1	<0.001	13.36	6.94	25.72
Epidemiological week	0.17	625.38	1	<0.001	1.19	1.18	1.21
Social Isolation (%)	-0.007	365.99	1	<0.001	0.993	0.992	0.994
Demographic Density	2.46E-4	3.97	1	0.04	1.0002	1.000004	1.0004
PHC coverage
<50%	1.12	9.09	1	0.003	3.08	1.48	6.42
50–74%	0.53	0.62	1	0.42	1.70	0.45	6.36
≥75%	0				1
PMAQ Assessment
Below average	0.310	0.67	1	0.41	1.36	0.65	2.85
Above average	0				1
Scale	0.72

Primary Health Care (PHC); Quality Monitoring and Evaluation Program (PMAQ). Model regression coefficient (B); Degrees of Freedom (df) Risk Relative (RR); Confidence Interval (CI).

For deaths, there are major effects of pandemic temporality (χ2 = 70.87; df = 1; p-value<0.001), social isolation rate (χ2 = 41.04; df = 1; p-value<0.001), demographic density (χ2 = 4.78; df = 1; p-value = 0.02), COVID-19 cases (χ2 = 4.94; df = 1; p-value = 0.02), PHC coverage (χ2 = 31.75; df = 2; p-value<0.001) and bed/population ratio (χ2 = 9.02; df = 1; p-value = 0.003). Pandemic temporality (B = 0.07), more slightly the demographic density (B = 3.20E-4) and the number of diagnosed COVID-19 cases (B = 1.62E-4) were related to the increase in deaths from the virus. In contrast, social isolation rate (B = -0.004) demonstrates an inversely proportional relationship, as well as the nesting of PHC coverage variables and beds/population ratio (χ2 = 30,87; df = 3 p-value<0.001), where we identified that PHC coverage strata above 50% together with the supply of hospital beds above 3.2 per thousand inhabitants has two (B = 0.85) to six times (B = 1.89) less chances of having deaths due to COVID-19 (Table 5).

Table 5

Adjusted model for the association with COVID-19 deaths in the time-series of twenty epidemiological weeks in Northeastern cities in Brazil.

Variable	B	Hypothesis test			RR	CI95%
		Wald χ2	df	p-value		Lower	Upper
Interception	0.76	3.48	1	0.06	2.14	0.96	4.77
Epidemiological week	0.07	257.64	1	<0.001	1.07	1.06	1.08
COVID-19 cases	1.62E-4	4.62	1	0.03	1.0001	1.00001	1.0003
Demographic Density	3.20E-4	4.63	1	0.03	1.0003	1.00002	1.001
Social Isolation (%)	-0.004	41.04	1	<0.001	0.996	0.995	0.997
PMAQ Assessment
Below average	-0.26	0.44	1	0.50	0.76	0.34	1.69
Above average	0				1
PHC coverage (Beds/population ratio)
<50% PHC with <3.2 beds/pop.	0.85	7.83	1	0.005	2.35	1.29	4.29
<50% PHC with ≥3.2 beds/pop.	1.89	30.55	1	<0.001	6.64	3.39	13.01
50–74% PHC with ≥3.2 beds/pop.	-0.26	0.14	1	0.70	0.76	0.19	2.98
≥75% PHC with ≥3.2 beds/pop	0				1
Scale	0.74

Coronavirus disease 2019 (COVID-19); Primary Health Care (PHC); Quality Monitoring and Evaluation Program (PMAQ). Model regression coefficient (B); Degrees of Freedom (df) Risk Relative (RR); Confidence Interval (CI).

Discussion

Seeking to test the theoretical hypothesis of the study to estimate the effect of PHC and social isolation on the time-series of COVID-19 cases and deaths, the analysis points out that the extent of PHC coverage in the cities of Northeastern Brazil together with social distancing are associated with fewer cases and deaths up to the 20th epidemiological week. These are likely to be the modifiable factors listed by this study for timely actions by health system managers in order to mitigate the spread of the epidemic and its consequences for the health of the population [20].

The cities included in this study show an exponential growth in COVID-19 cases after 50 days of monitoring by the Ministry of Health, similar to countries like China [21,22], which may influence the use of hospital services in the same pattern as it happened in Lombardy, Italy [23]. However, the pattern is not homogeneous across all capitals, which leads us to assume that there are contextual factors that may be modulating this dissemination and also deaths.

Lethality is lower than Italy with a month of outbreak and higher than China for the same period [24], but dissemination and deaths significantly vary in cities such as Fortaleza and Aracajú. This indicator is extremely influenced by the number of cases, population demographic factors and, mainly, by the health system’s capacity to cope, specifically the installed capacity of intensive care services, material resources such as mechanical respirators and trained human resources [24].

PHC and access to it are essential for the progress of a population’s health conditions, including communicable diseases. One of SUS principles is universal access, a very important step towards achieving improvements in health indicators, however not all the population is effectively covered by the PHC system in Brazil [17]. In this study, there was a significant association between low PHC coverage with cases and deaths from COVID-19, controlling the effects of social isolation, demographic density and serial evolution. It can be inferred that infection by COVID-19 is a PHC-sensitive condition for mitigate the pressure on hospital services that are limited mainly in socioeconomic vulnerable regions.

To date, no other studies have addressed these topics. Greater PHC coverage promotes a decrease in new cases, hospitalizations and deaths, as it has already been shown to be effective for other health conditions such as HIV control, infant mortality, stroke, arterial hypertension and diabetes [25,26]. The northeastern region of Brazil is the one that has the largest current coverage and the greatest temporal evolution of it [17,25]. However, we have no way of guessing how long PHC will endure this burden and also fail to provide care for the other health conditions in which it has been successful.

The likely explanation for the mitigating effect of PHC on the spread of COVID-19 is in the portfolio of its attributions, functions and micropolitics. In summary: it is community-based and linked to the population’s territory, it develops capillary actions for stratification of social and biological vulnerability, screening of health conditions, individual and collective monitoring of cases, and coordination of care within the network [27]. These attributes and functions can enhance specific actions for the moment, such as educating users about social isolation, monitoring suspected and confirmed cases, as well as serving as a link to other types of social support, such as access to emergency government aid [9].

In addition, we would also like to highlight that this study observed an inverse association between social isolation and COVID-19 cases and deaths. Isolation is one of the main measures used to control these cases. Corroborating our findings, the study by Ferguson et al. [28], also known as the Imperial College Study, showed that combining quarantine together with social isolation were effective in controlling the spread of COVID-19. A systematic review published in the Cochrane Library database [11] evaluated the effect of quarantine alone and combined with social distance, showing that associated measures seem to be more effective.

Adherence to social isolation in the studied population was considered low. Even so, we had an inverse and significant correlation between social isolation and the diagnosed cases and deaths by COVID-19. However, it is likely that greater population adherence to social isolation would lead to stronger effects in reducing contagion. Thus, for this moment, it is recommended that isolation measures be carried out or maintained combined with school closures, travel restrictions, social distance and others, from the initial stage of the outbreak. Social restriction measures, despite appearing to be more effective in epicenters of the disease, also have effects in cities with fewer cases, minimizing the spread of the epidemic [11,22,28,29].

As COVID-19 is mainly transmitted through the airways and respiratory droplets, a high population concentration in the same place would facilitate transmission. In our study, the cities with the highest demographic density, Fortaleza and Recife, had a higher number of cases and deaths. These results are reinforced by findings from other studies [7]. Furthermore, the cities Fortaleza and Recife have been in critical situation for the management of the health system due to the greater spread of the disease and high number of deaths. This reinforces the hypothesis that it is a great challenge to preserve social distancing in an area of greater concentration of people in space. Despite the demographic density being a difficult modifiable factor, other prophylaxis actions for COVID-19 are necessary, such as: PHC reinforcement and increase in number of hospital beds, public hygiene points, distribution of personal protective equipment, among others.

Along with PHC coverage and social isolation, beds/population ratio for hospitalization above the world average of 3.2 per thousand inhabitants showed an effect in mitigating the drastic events of deaths in those infected with COVID-19 in the studied sample. This is due to the virus’s ability to generate critical clinical cases in subpopulations with biological vulnerability such as diabetes, hypertension and chronic kidney disease [23]. However, the nested analysis of the beds/population ratio and PHC coverage showed the effect of the interaction between these variables, meaning that the implementation of more hospital beds for critical care without the proper support for PHC expansion may be ineffective for controlling the pandemic due to the non-stagnation of new cases. This is the example of the city of São Luís, which has a hospital beds/population ratio above the world average, but with low PHC coverage, presenting lethality similar to large cities such as Fortaleza and Recife.

In our study, social and economic indicators such as the Gini index, HDI, GDP per capita and ratio of persons employed were not significantly related to the increase in the number of cases and deaths in northeastern cities, which may indicate that transmission of COVID-19 is not influenced by these variables after the transmission started, unlike other locations in the world [30]. Thus, COVID-19 spreads in space regardless of social or economic stratification in these cities. High HDI countries, such as Spain (0.893), France (0.891), United Kingdom (0.920) and Sweden (0.937) were strongly impacted by this pandemic [16].

Despite the impactful findings for Brazilian public health, it is prudent to mention some limitations that must be analyzed. The first is that COVID-19 is underdiagnosed in Brazil as well as elsewhere in the world, which leads to underreporting of events. A second limitation is the outdating of ecological measures such as the HDI and Gini index in the Brazilian information systems, however we believe that their modification over time is minimal. The third limitation concerns the use of the social isolation rate of the federal unit as a proxy for the performed in the city and as an aggregate data of individual observation measures.

The COVID-19 pandemic brings new elements and specific characteristics that affect each region of the world in a specific way. This originality poses as a great challenge for the reorganization of health services across the planet. From the variables addressed in this study, we state that an expansion and qualification of PHC services, associated with an increase in the number of hospital beds for care, as well as maintenance policies or even expansion of social restrictions and possible relaxation of distance measures in opportune time and manner, are important for the current pandemic scenario in Northeastern Brazil. Additional COVID-19 prevention strategies, not measured in this study, are relevant tools that should be considered by public managers.

Although the Brazilian Federal Government declared Public Health Emergency of National Importance in February 2020, effective actions to prevent and combat the epidemic were taken late, contradicting the dimension of the public health problem that this condition presented to the country. Including nowadays, wrong decisions and guidelines by public authorities are part of the routine in the Brazilian Government. Well-articulated actions between public managers and political leaders, with a positioning based on scientific evidence and technical criteria, may provide time and breath for health services to adjust to the new demands produced by this pandemic. Other regions of Brazil and the world with characteristics of social vulnerability and organization of health services similar to those of the cities of Northeastern Brazil, can make use of the results found here in assisting decision making, with regard to public policies to be adopted and also to the guidelines for the population.

(XLS)

Click here for additional data file.

8 Feb 2021

PONE-D-20-19401

PRIMARY HEALTH CARE AND SOCIAL ISOLATION AGAINST COVID-19 IN NORTHEASTERN BRAZIL: ECOLOGICAL TIME-SERIES STUDY

PLOS ONE

Dear Dr. Assis,

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Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The study presents the results of an original research, important for a famous and known region as the Northeast of Brazil.

The analyzes were carried out to a high technical standard and are described in sufficient detail.

The conclusions were presented in an appropriate manner and supported by the data.

I would like to mention in the text that some observational studies checklist (STROBE?) was followed.

Reviewer #2: This is a relevant study, which shows the concern and influence of primary health care against Pandemic by COVID-19.

Minor revisions are suggested.

In the Background:

Update of epidemiological data to date, although the study has been based on a previous situation.

Add the characteristic symptoms of loss of smell and taste, in some cases, as already well evidenced in the literature.

In the discussion

The paragraphs are long and some information is repeated, which can be reduced.

Final considerations

Just one paragraph can be enough to complete the study. I suggest summarizing the last one or putting it in the previous section as suggestions.

Reviewer #3: Dear Author,

The article is interesting and contributes a lot to the performance of health services in Brazil and in countries with similar characteristics in the particular context of coronavirus disease (COVID-19), but requires some revisions. The study brings as strengths the fact that the infection by COVID-19 is a condition sensitive to PHC, which would reduce the demand for hospital services, in addition to the inclusion of social and economic variables in the analysis of the number of cases and deaths of the population studied and as a weak point the lack of access to individual data, characteristic of the type of study.

In the abstract on page 1, line 9, I suggest reviewing the information on "Brazil is the new epicenter of coronavirus disease", due to the constant changes in the pandemic scenario.

In Introduction, line 45 on page 3, I suggest updating the number of cases and deaths due to their rapid growth and the importance of this information in this context.

In the materials and methods, in line 115 on page 6, I suggest placing the start date of the time-series. Line 127 on page 6 was missing the reference to stratify the PMAQ score. Line 138 on page 7 was missing the reference to categorize the Gini index.

In the results, on lines 225 and 237, tables 4 and 5 are not present in the submission file, I suggest inserting the tables or removing this information if it does not exist.

Table 1: Authors should review the sum of the frequencies of the number of cases compared to the total value presented in the same table.

In the discussion, line 268, the authors state that greater PHC coverage reduces the number of cases of hospitalization and deaths, as it has not proved to be effective for other health conditions.

This statement seems contradictory within the context already exposed by the authors regarding PHC in the manuscript and in the finalization of the paragraph of that statement. I suggest revising the translation of this sentence or making it clearer how PHC coverage would influence the reduction of cases and deaths due to COVID-19 and not in other situations.

Line 301: I suggest making it clear what is considered a suitable environment for the transmission of COVID-19

Reviewer #4: In this manuscript, the authors evaluate the possible correlation between positive cases and deaths because of COVID-19 and various demographic indexes, among which primary health care coverage stands out. I find this work very interesting, pointing out several key points to take note of in order to face the current pandemic, as well as the possible future challenges that public health could face. I have, however, some comments that I would like the authors to address:

Main points:

1) In line 194, the authors point out that the death curve follows a cubic function, a choice that generates some doubts. The R-squared value of 0.23 does not seem to be very encouraging, but above all, I find it difficult to justify from an epidemic theory a curve of the cubic type, with a local minimum close to zero at x approximately 14. Can the authors justify this choice? In addition, it would be good to provide graphs where the mathematical curve and the real data are included.

2) Given the months that have passed since the initial submission, the authors may wish to update the results presented. I also understand that the authors might prefer to focus only on the initial phase of exponential case growth. Whatever the choice, I would suggest clarifying it in the text and reviewing comments that the authors might have made about the future (for example, line 30).

Minor points:

3) In general, the text is written in good English. Although I would check the word "contamination" (line 12), "tax" (line 23), "persons" (line 142).

4) When writing the models with the mathematical equation y(x), the authors should make it explicit in the text what represents each variable.

5) Mathematical variables (x, y, p, chi^2, etc.) should be written in italics, this should be checked throughout the manuscript. Do not use "p" (line 173). but p-value.

6) The symbol that corresponds to the Chi-square value must be the Greek symbol, not a Latin x since it is confused with the variable x in mathematical models. Please, check the entire manuscript.

7) On line 25, a parenthesis is missing and there is a double ";" when the Chi-square value and the p-value are reported. On line 144, there is an extra space after the word “clothing”.

8) I find some inconsistencies in the use of upper and lower case throughout the text. Please, check:

- line 28: covid

- lines 99 and 103: federative units of northeastern

- line 142: Km

- lines 185, 190, 198: table

9) In the paragraph on line 172, or where the authors deem appropriate, the null hypothesis of the analysis should be explicitly clarified. Although it is standard that the null hypothesis is that the variables are independent, this is an arbitrary choice, so it should be clarified in the text.

10) In lines 225 and 237, reference is made to tables 4 and 5, which do not exist.

11) I find some drawbacks with the paragraph between lines 96 and 101:

- Line 96: what is "it"? It is preferable to use "in this work / study ...".

- Line 97: what is "secondary data"?

- In the last sentence, the first half (lines 98-99) is repeated with the next paragraph (line 102).

- Review the wording of the phrase "to be commonly an economic reference and health organization for cities ...".

- In general I find this paragraph somewhat weak compared to the paragraphs that follow. Authors could evaluate a general edition of this paragraph.

12) On line 106, I suggest a period instead of a comma after “million people”.

13) I find some drawbacks in the sentence on line 110. The beginning is redundant (“were collected” is mentioned twice). I don't understand what “(3)” represents. I suggest clarifying what the "other independent variables" are.

14) Line 119. What is ESF?

15) Line 180. Is the average taken between cities? Or maybe days?

In summary, I think the authors present a good work, which could benefit from the points I make. I recommend a minor review of the work.

**********

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Reviewer #1: Yes: Marcelo Cardoso de Souza

Reviewer #2: No

Reviewer #3: Yes: Thais Sousa Rodrigues Guedes

Reviewer #4: No

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25 Mar 2021

Lidia Adriana Braunstein

Editor-in-Chief

Plos One

march 04, 2021.

Dear Lidia Adriana Braunstein,

Thank you for your email with the reviewers’ comments. We have reviewed the comments and edited the manuscript accordingly. Please, find attached our point-by-point response to the reviewers. All authors have read this protocol and agree with the Plos One policy. We hope the revised manuscript is now suitable for publication.

Sincerely. Sanderson José Costa de Assis.

Reviewer Comments:

Reviewer #1

1. The study presents the results of an original research, important for a famous and known region as the Northeast of Brazil.

The analyzes were carried out to a high technical standard and are described in sufficient detail.

The conclusions were presented in an appropriate manner and supported by the data.

I would like to mention in the text that some observational studies checklist (STROBE?) was followed.

Response: Thank you for your comments.

Reviewer #2: This is a relevant study, which shows the concern and influence of primary health care against Pandemic by COVID-19.

Background

1. Update of epidemiological data to date, although the study has been based on a previous situation.

Response: Thank you for your comments. The sentence was rewritten:

The situation evolves in a continuous overload in health services with the expansion of the pandemic in the country, totaling 10.517.232 cases and 254.221 confirmed deaths on 28/02/2021

2. Add the characteristic symptoms of loss of smell and taste, in some cases, as already well evidenced in the literature.

Response: Thank you for your comments. The sentence was rewritten:

The main symptoms include self-reported fever, fatigue, loss of smell and taste, dry cough, myalgia and dyspnea and uncommon symptoms include sputum production, headache, hemoptysis and diarrhea.

Discussion

1. The paragraphs are long and some information is repeated, which can be reduced.

Response: Thank you for your comments. Changes were made to the text

Final considerations

1. Just one paragraph can be enough to complete the study. I suggest summarizing the last one or putting it in the previous section as suggestions.

Response: Thank you for your comments. The sentence was rewritten:

Although the Brazilian Federal Government declared Public Health Emergency of National Importance in February 2020, effective actions to prevent and combat the epidemic were taken late, contradicting the dimension of the public health problem that this condition presented to the country. Including nowadays, wrong decisions and guidelines by public authorities are part of the routine in the Brazilian Government. Well-articulated actions between public managers and political leaders, with a positioning based on scientific evidence and technical criteria, may provide time and breath for health services to adjust to the new demands produced by this pandemic. Other regions of Brazil and the world with characteristics of social vulnerability and organization of health services similar to those of the cities of Northeastern Brazil, can make use of the results found here in assisting decision making, with regard to public policies to be adopted and also to the guidelines for the population.

Reviewer #3: The article is interesting and contributes a lot to the performance of health services in Brazil and in countries with similar characteristics in the particular context of coronavirus disease (COVID-19), but requires some revisions. The study brings as strengths the fact that the infection by COVID-19 is a condition sensitive to PHC, which would reduce the demand for hospital services, in addition to the inclusion of social and economic variables in the analysis of the number of cases and deaths of the population studied and as a weak point the lack of access to individual data, characteristic of the type of study.

Abstract

1. In the abstract on page 1, line 9, I suggest reviewing the information on "Brazil is the new epicenter of coronavirus disease", due to the constant changes in the pandemic scenario.

Response: Thank you for your comments. The sentence was rewritten:

Brazil is witnessing a massive increase of corona virus disease (COVID-19).

Introduction

1. In Introduction, line 45 on page 3, I suggest updating the number of cases and deaths due to their rapid growth and the importance of this information in this context.

Response: Thank you for your comments. The sentence was rewritten:

The situation evolves in a continuous overload in health services with the expansion of the pandemic in the country, totaling 10.517.232 cases and 254.221 confirmed deaths on 28/02/2021

Materials e methods

1. In the materials and methods, in line 115 on page 6, I suggest placing the start date of the time-series.

Response: Thank you for your comments. The sentence was rewritten:

The time series data refer to the period from February 26 to May 16, 2020

2. Line 127 on page 6 was missing the reference to stratify the PMAQ score.

Response: Thank you for your comments. The sentence was rewritten:

The categorization of this variable was done through the analysis of their respective quants, considering that there is no criterion available in the literature to classify the strata.

3. Line 138 on page 7 was missing the reference to categorize the Gini index.

Response: Thank you for your comments. The sentence was rewritten:

Their categorization was done according to the distribution of their data, considering that there is no criterion for stratification available in the literature.

Results

1. In the results, on lines 225 and 237, tables 4 and 5 are not present in the submission file, I suggest inserting the tables or removing this information if it does not exist.

Response: Thank you for your comments. The tables were inserted.

2. Table 1: Authors should review the sum of the frequencies of the number of cases compared to the total value presented in the same table.

Response: Thank you for your comments. Correction was performed.

Discussion

1. In the discussion, line 268, the authors state that greater PHC coverage reduces the number of cases of hospitalization and deaths, as it has not proved to be effective for other health conditions.

This statement seems contradictory within the context already exposed by the authors regarding PHC in the manuscript and in the finalization of the paragraph of that statement. I suggest revising the translation of this sentence or making it clearer how PHC coverage would influence the reduction of cases and deaths due to COVID-19 and not in other situations.

Response: Thank you for your comment. The sentence “it has not been shown to be effective” was rewritten as “it has already been shown to be effective”.

2. Line 301: I suggest making it clear what is considered a suitable environment for the transmission of COVID-19.

Response: Thank you for your comments. The sentence was rewritten:

As COVID-19 is mainly transmitted through the airways and respiratory droplets, a high population concentration in the same place would facilitate transmission.

Reviewer #4: In this manuscript, the authors evaluate the possible correlation between positive cases and deaths because of COVID-19 and various demographic indexes, among which primary health care coverage stands out. I find this work very interesting, pointing out several key points to take note of in order to face the current pandemic, as well as the possible future challenges that public health could face. I have, however, some comments that I would like the authors to address:

1. In line 194, the authors point out that the death curve follows a cubic function, a choice that generates some doubts. The R-squared value of 0.23 does not seem to be very encouraging, but above all, I find it difficult to justify from an epidemic theory a curve of the cubic type, with a local minimum close to zero at x approximately 14. Can the authors justify this choice? In addition, it would be good to provide graphs where the mathematical curve and the real data are included.

Response:

We are grateful for the relevant observation of the reviewer and found a mistake in the description of the curve equation. Both case and death distributions have an exponential curve. In addition, two graphs with case distributions are attached to the body of the results.

2. Given the months that have passed since the initial submission, the authors may wish to update the results presented. I also understand that the authors might prefer to focus only on the initial phase of exponential case growth. Whatever the choice, I would suggest clarifying it in the text and reviewing comments that the authors might have made about the future (for example, line 30).

Response: Thank you for your comments. We prefer to focus only on the initial phase of exponential case growth.

3. In general, the text is written in good English. Although I would check the word "contamination" (line 12), "tax" (line 23), "persons" (line 142).

Response: Thank you for your comment. The words were replaced by “confirmed cases”, “rate”, and “people”.

4. When writing the models with the mathematical equation y(x), the authors should make it explicit in the text what represents each variable.

Response:

We appreciate the question. The algebraic representations of the model were specified in the method.

5. Mathematical variables (x, y, p, chi^2, etc.) should be written in italics, this should be checked throughout the manuscript. Do not use "p" (line 173). but p-value.

Response: Thank you for your comments. Altration was performed in the entire manuscript.

6. The symbol that corresponds to the Chi-square value must be the Greek symbol, not a Latin x since it is confused with the variable x in mathematical models. Please, check the entire manuscript.

Response: Thank you for your comments. Altration was performed in the entire manuscript.

7. On line 25, a parenthesis is missing and there is a double ";" when the Chi-square value and the p-value are reported. On line 144, there is an extra space after the word “clothing”.

Response: Thank you for your comments. The sentences have been rewritten:

On line 25: Capitals with hospital beds ≥ 3.2 per thousand inhabitants had fewer deaths (χ²=9.02; p-value=0.003), but this was influenced by PHC coverage (χ²=30,87; p-value<0.001).

On line 144: Demographic density was observed to portray the spatial distribution of inhabitants per square kilometer (Km²) and the percentage of employed persons were considered to be persons who worked for at least one full hour with remuneration in cash, products, goods or benefits (housing, food, clothing, training, etc.), or in work without direct remuneration in support of the economic activity of a member of the household or a relative who lives in another domicile, or, still, those who had paid work from which they were temporarily away that week.

8. I find some inconsistencies in the use of upper and lower case throughout the text. Please, check:

- line 28: covid

- lines 99 and 103: federative units of northeastern

- line 142: Km

- lines 185, 190, 198: table

Response: Thank you for your comments. Adjustments were made in every manuscript.

9. In the paragraph on line 172, or where the authors deem appropriate, the null hypothesis of the analysis should be explicitly clarified. Although it is standard that the null hypothesis is that the variables are independent, this is an arbitrary choice, so it should be clarified in the text.

Response: Thank you for your comments. The following sentence has been added.

The null hypothesis of the present study is that there is no association between the outcome and the independent variables.

10. In lines 225 and 237, reference is made to tables 4 and 5, which do not exist.

Response: Thank you for your comments. The tables were inserted.

11. I find some drawbacks with the paragraph between lines 96 and 101:

- Line 96: what is "it"? It is preferable to use "in this work / study ...".

- Line 97: what is "secondary data"?

- In the last sentence, the first half (lines 98-99) is repeated with the next paragraph (line 102).

- Review the wording of the phrase "to be commonly an economic reference and health organization for cities ...".

- In general I find this paragraph somewhat weak compared to the paragraphs that follow. Authors could evaluate a general edition of this paragraph.

Response: Thank you for your comments. Please find the responses below topic by topic.

- Text was rewritten in line 96.

- Secondary data refers to data collected by other data sources different than the data directly collected for this specific study. The secondary data in this study was collected from the public national Brazilian database Datasus, IBGE and UNDP.

- Sentence from lines 98-99 were rewritten with information from line 102, that was completed changed to avoid repetition.

- The sentence was reviewed and rewritten for better understanding.

- We believe that the editions after the reviewer’s comments and suggestions have improved the paragraph.

12. On line 106, I suggest a period instead of a comma after “million people”.

Response: Thank you for your comments. The sentences have been rewritten:

The Northeastern region is historically marked by strong social inequality, with a population of approximately 53 million people. It is the region with the largest number of units of the Brazilian federation, nine in total, and with an area equivalent to around 18% of Brazilian territory

13. I find some drawbacks in the sentence on line 110. The beginning is redundant (“were collected” is mentioned twice). I don't understand what “(3)” represents. I suggest clarifying what the "other independent variables" are.

Response: Thank you for your comments. The sentences have been rewritten:

Secondary data were collected from SUS Data Department (DATASUS), among them the study outcomes: confirmed cases and deaths by COVID-193 and some independent variables. Other data were collected in the database of the Brazilian Institute of Geography and Statistics (IBGE), which was compiled by the Brazilian agency of the United Nations Development Programme (UNDP)16.

As for the other independent variables, they are described in the paragraph of line 113:

The time series data refer to the period from February 26 to May 16, 2020. The study primary outcome was COVID-19 cases diagnosed and secondary outcome was deaths by COVID-19. The independent variables were time, analyzed through epidemiological weeks; PHC coverage, Family Health Strategy (FHS) coverage through the proportion of ESF services in PHC in March 2020; evaluation score of the PHC health services quality by the Quality Monitoring and Evaluation Program (PMAQ); number of beds for hospitalization in April 2020; the Human Development Index (HDI), the Gini index, Gross Domestic Product (GDP) per capita, distance to the epicenter of the pandemic in Brazil, Demographic Density, percentage of employed persons and the percentage of social isolation in the nine Federative Units.

14. Line 119. What is ESF?

Response: Thank you for your comments. The sentence was rewritten:

The independent variables were time, analyzed through epidemiological weeks; PHC coverage, Family Health Strategy (FHS) coverage through the proportion of ESF (Family Health Strategy) services in PHC in March 2020; evaluation score of the PHC health services quality by the Quality Monitoring and Evaluation Program (PMAQ); number of beds for hospitalization in April 2020; the Human Development Index (HDI), the Gini index, Gross Domestic Product (GDP) per capita, distance to the epicenter of the pandemic in Brazil, Demographic Density, percentage of employed persons and the percentage of social isolation in the nine Federative Units.

15. Line 180. Is the average taken between cities? Or maybe days?

Response: Thank you for your comments. The sentence was rewritten:

In the nine cities analyzed, we identified an accumulated of 43,969 cases and 2,588 deaths due to COVID-19 up to the twentieth epidemiological week. The average number of diagnosed in the cities evaluated cases was 4885.44 (± 1574.85), 287.55 (± 125.80) of deaths and an overall lethality of 4.4%.

All changes made to the text are highlighted in the manuscript.

Thank you for your comment. The manuscript has been revised accordingly.

Sincerely,

Sanderson José Costa de Assis. Federal University of Rio Grande do Norte. Corresponding author. Natal, Rio Grande do Norte, Brazil.

Mobile: +5584996219425

e-mail: sanderson_assis@hotmail.com

Submitted filename: Response to Reviewers.doc

Click here for additional data file.

8 Apr 2021

PRIMARY HEALTH CARE AND SOCIAL ISOLATION AGAINST COVID-19 IN NORTHEASTERN BRAZIL: ECOLOGICAL TIME-SERIES STUDY

PONE-D-20-19401R1

Dear Dr. Assis,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Academic Editor

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Comments to the Author

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Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

Reviewer #4: All comments have been addressed

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

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Reviewer #2: (No Response)

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Reviewer #4: The authors have answered all my questions satisfactorily and I believe that the manuscript was improved enough to be published, for which my recommendation to the editor is to accept the submitted work.

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Reviewer #1: Yes: Marcelo Cardoso de Souza

Reviewer #2: No

Reviewer #3: No

Reviewer #4: No

16 Apr 2021

PONE-D-20-19401R1

Primary health care and social isolation against COVID-19 in Northeastern Brazil: Ecological time-series study

Dear Dr. Costa de Assis:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

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Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Lidia Adriana Braunstein

Academic Editor

PLOS ONE