Abstract

The aim of this article was to analyze the possible relationship between social inequality indicators and the spatial distribution of ZIKV cases in a state in Northeastern Brazil in 2015-16. This is an ecological study with the data of notified ZIKV cases and the sociodemographic indicators of Rio Grande do Norte state (RN), based on information from the State Public Health Department (SESAP-RN) and DATASUS. The data were analyzed in Terraview version 4.2.2, Geoda version 1.12 and IBM SPSS Statistics 21. Both the average incidence rate (AIR) of ZIKV cases in 2015-16 (Moran = 0.139; p= 0.03) and the AIR of violence (Moran= 0.295; p= 0.02), average household income (Moran= 0.344; p=0.01) and unemployment rate (Moran= 0.231; p=0.01) exhibited a geographic spatial distribution pattern. In multiple linear regression analysis, the variables AIR of violence and average household income explained 55% of the variation in the AIR of ZIKV in 2015-16 (adjusted R2 = 0.55). Municipalities with more notifications of violence and higher average income, such as the state capital, reported a higher number of ZIKV cases, possibly due to better organization, greater awareness of socioenvironmental problems and easier access to health services.

Key words
Zika virus; Violence; Health indicators; Microcephaly; Epidemiology

Introduction

The Zika virus (ZIKV) is a disease caused by a group of viruses ecologically known as arboviruses¹1 Lopes N, Nozawa C, Linhares REC. Características gerais e epidemiologia dos arbovírus emergentes no Brasil. Rev Pan-Amazônica Saude 2014; 5(3):55-64.. In this case, from the genus Flavivirus, transmitted by the genus Aedes, whose main vectors are Aedes aegypti and Aedes albopictus. There is also the possibility of blood and neonatal transmission, although the real role of these transmission pathways in spreading this infection remains unknown²2 Brasil. Ministério da Saúde (MS). Secretaria de Vigilância em Saúde. Coordenação-Geral de Desenvolvimento da Epidemiologia em Serviços. Guia de Vigilância em Saúde. Volume Único. Brasília: MS; 2019..

Despite being considered an acute disorder with mild and self-limited manifestations, the ZIKV has become a worldwide problem due to its impact on health, and its association with a series of neurological disturbances, such as Guillain-Barré syndrome (GBS) and congenital malformations, in which microcephaly stands out²2 Brasil. Ministério da Saúde (MS). Secretaria de Vigilância em Saúde. Coordenação-Geral de Desenvolvimento da Epidemiologia em Serviços. Guia de Vigilância em Saúde. Volume Único. Brasília: MS; 2019..

This virus emerged in Brazil in 2015 with the autochthonous confirmation in Bahia state, followed by Rio Grande do Norte (RN)³3 Zanluca C, Melo VCA, Mosimann ALP, Santos GIV, Santos CND, Luz K. First report of autochthonous transmission of Zika virus in Brazil. Memórias Instituto Oswaldo Cruz 2015; 110(4):569-572.^,44 Lima-Camara TN. Arboviroses emergentes e novos desafios para a saúde pública no Brasil. Rev Saúde Pública 2016; 50:36.. It was initially detected in RN in the municipality of Natal and later in São Gonçalo do Amarante³3 Zanluca C, Melo VCA, Mosimann ALP, Santos GIV, Santos CND, Luz K. First report of autochthonous transmission of Zika virus in Brazil. Memórias Instituto Oswaldo Cruz 2015; 110(4):569-572.^,55 Governo do Estado do Rio Grande do Norte. Secretaria de Estado da Saúde Pública (SESAP). SESAP monitora casos de Zika vírus no Estado [página na Internet]. 2015. Disponível em: http://www.parquedasdunas.rn.gov.br/Conteudo.asp?TRAN=ITEM&TARG=76613&ACT=null&PAGE=null&PARM=null&LBL=NOT%C3%8DCIA
http://www.parquedasdunas.rn.gov.br/Cont... , with 8,743 and 113 suspected and confirmed cases, respectively. Between January and November 2016, 5,971 and 205 suspected and confirmed cases, respectively, were reported⁶6 Brasil. Ministério da Saúde (MS). Secretaria de Vigilância em Saúde . Monitoramento dos casos de dengue, febre de chikungunya e febre pelo vírus Zika, até a Semana Epidemiológica 52, 2016 [documento na Internet]. Boletim Epidemiológico 2017; 48(3):1-11. Disponível em: https://portalarquivos2.saude.gov.br/images/pdf/2017/abril/06/2017-002-Monitoramento-dos-casos-de-dengue--febre-de-chikungunya-e-febre-pelo-v--rus-Zika-ate-a-Semana-Epidemiologica-52--2016.pdf
https://portalarquivos2.saude.gov.br/ima... .

One of the hypotheses to justify this dissemination was the involvement of environmental and socioeconomic factors in the area, under the premise that this knowledge is essential in planning and managing health strategies to address this disorder⁷7 Nunes J, Pimenta DN. A epidemia de Zika e os limites da saúde global. Lua Nova 2016; 98:21-46.^,88 Menezes SA, Costa YA, Costa HP, Gildo MGP, Sampaio MGV. Arboviroses: O Impacto da Febre Zika na Sociedade. Rev Expressão Católica Saude 2016; 1(1):25-32..

A number of studies suggest a correlation between the social determinants of health such as ethnicity, schooling, and income, and increased incidence of ZIKV and its consequences, such as microcephaly⁹9 Butler D. Brazil's birth-defects puzzle: Zika virus might not be only factor in reported microcephaly surge. Nature 2016; 535(7613):475-477.

10 Araújo TVB, Ximenes RAA, Miranda-Filho DB, Souza WV, Montarroyos UR, Melo APL, Valongueiro S, Albuquerque MFPM, Braga C, Filho SPB, Cordeiro MT, Vazquez E, Cruz DDCS, Henriques CMP, Bezerra LCA, Castanha PMDS, Dhalia R, Marques-Júnior ETA, Martelli CMT, Rodrigues LC, Investigators from the Microcephaly Epidemic Research Group, Brazilian Ministry of Health, Pan American Health Organization, Instituto de Medicina Integral Professor Fernando Figueira, State Health Department of Pernambuco. Association between microcephaly, Zika virus infection, and other risk factors in Brazil: final report of a case-control study. Lancet Infect Dis 2018; 18(3):328-336.^-1111 Marinho F, Araújo VEM, Porto DL, Ferreira HL, Coelho MRS, Lecca RCR, Oliveira H, Poncioni IPA, Maranhão MHN, Mendes YMMB, Fernandes RM, Lima RB, Rabello Neto DL. Microcefalia no Brasil: prevalência e caracterização dos casos a partir do Sistema de Informações sobre Nascidos Vivos (Sinasc), 2000-2015. Epidemiol Serv Saude 2016; 25(4):701-712..

Social inequalities in health are the perceived and measurable differences related to access to prevention, cure and health rehabilitation services. They are closely linked to the individuals’ social environment and are significantly influenced by social conditions and the area in which they live¹²12 Barreto ML. Desigualdades em Saúde: uma perspectiva global. Cien Saude Colet 2017; 22(7):2097-2108.^,1313 Travassos C, Oliveira EXG, Viacava F. Desigualdades geográficas e sociais no acesso aos serviços de saúde no Brasil: 1998 e 2003. Cien Saude Colet 2006; 11(4):975-986..

Assessment of social inequalities in the context of arbovirus epidemics is important given that both Aedes aegypti and Aedes albopictus, the primary arbovirus vectors in the country, have extended their geographic range to urban environments, especially cities in developing countries, where the accelerated urbanization process has been poorly planned, leading to sociospatial problems¹⁴14 Rochlin I, Ninivaggi DV, Hutchinson ML, Farajollahi A. Climate Change and Range Expansion of the Asian Tiger Mosquito (Aedes albopictus) in Northeastern USA: Implications for Public Health Practitioners. PLoS ONE 2013; 8:e60874..

An example of a sociospatial problem that impacts public health is structural inequality, involving luxurious neighborhoods with adequate infrastructure and public services on one hand and precarious neighborhoods with poorly distributed income distribution, no basic services such as a sewer system, trash collection and fresh water supply, on the other, characterizing a permanent state of physical-environmental insecurity¹⁵15 Almeida CAP, Silva RM. Análise da ocorrência dos casos de dengue e sua relação com as condições socioambientais em espaços urbanos: os casos de João Pessoa, Cabedelo e Bayeux, no estado da Paraíba-Brasil. Hygeia 2018; 14(27):56-79.^,1616 Gottschalg MFS. Segregação Sócio-Espacial Urbana e Intervenção Estatal: Uma abordagem geográfico-social. Belo Horizonte: CRESS; 2012..

The health-disease process involves complex factors related to the living conditions of individuals, known as the social determinants of health (SDH). Along with health status, these socioeconomic, cultural, ethical/racial, psychological and behavioral factors make it possible to identify where and how public health interventions should be conducted in order to provide a greater impact, thereby reducing inequities¹1 Lopes N, Nozawa C, Linhares REC. Características gerais e epidemiologia dos arbovírus emergentes no Brasil. Rev Pan-Amazônica Saude 2014; 5(3):55-64.^,1717 Sant'anna CF, Cezar-Vaz MR, Cardoso LS, Erdmann AL, Soares JFS. Determinantes Sociais de Saúde: características da comunidade e trabalho das enfermeiras na saúde da família. Rev Gaúcha Enferm2010; 31(1):92-99.^,1818 Buss PM, Pellegrini Filho A. A saúde e seus determinantes sociais. Physis 2007; 17(1):77-93..

However, despite the confirmed relationship between social determinants and disease, the technological and biomedical aspects are still neglected²2 Brasil. Ministério da Saúde (MS). Secretaria de Vigilância em Saúde. Coordenação-Geral de Desenvolvimento da Epidemiologia em Serviços. Guia de Vigilância em Saúde. Volume Único. Brasília: MS; 2019.. An example of this approach is the ZIKV epidemic, revealing how global health systems neglect the diversity of experiences and the multiple situations of inequality, thereby hindering full understanding of the disease⁷7 Nunes J, Pimenta DN. A epidemia de Zika e os limites da saúde global. Lua Nova 2016; 98:21-46..

As such, the aim of this study was to analyze the relationship between social inequality and spatial distribution of ZIKV cases in a state of Northeastern Brazil in 2015-2016.

Methodology

Study design, site and population

This is an ecological investigation whose study units are the municipalities of Rio Grande do Norte state (RN), located in Northeastern Brazil and bordered by the Atlantic Ocean to the East and North, Paraíba state to the South and Ceará state to the North. It is divided into 167 municipalities and covers an area of 52,811,110 Km², representing 3.42% of the Northeast region and 0.62% of the country.

According to the Brazilian Institute of Geography and Statistics (IBGE)¹⁹19 Brasil. Instituto Brasileiro de Geografia e Estatística (IBGE). Censo Populacional 2010 [página na Internet]. 2010. Disponível em: https://censo2010.ibge.gov.br/
https://censo2010.ibge.gov.br/... , RN is divided into four geographic mesoregions, according to the following similar social and economic criteria: the Coastal Potiguar (consisting of 25 municipalities), East Potiguar (43 municipalities), Central Potiguar (37 municipalities), and West Potiguar (62 municipalities).

Data collection and the variable studied

Based on the influence of socioenvironmental factors on the incidence of ZIVK cases, this study aimed to analyze the relationship between social inequality indicators and the spatial distribution of ZIKV cases in RN state in 2015-2016. Initially, the number of ZIKV cases in 2015-16 was obtained from the Information of Disease Notification System (SINAN), followed by adding the number of cases divided by the population during these years, obtaining a ratio per 10,000 inhabitants, denominated Average Incidence Rate (AIR) of ZIKV in 2015-2016.

The following socioenvironmental variables were tested: 1) AIR of violence in 2014: which corresponds to the incidence of violence in 2014 divided by the population of that year, obtaining a ratio per 10,000 inhabitants; 2) Average per capita household income in 2010: average household income of individuals living in a specific location, in the year under study; 3) Unemployment rate in 2010: proportion (%) of the economically active resident population aged 16 years or older unemployed during the reference period, in a specific geographic location, during the year under study; 4) Illiteracy in 2010: proportion of illiterates in the population; 5) Water supply in 2010: percent of the resident population supplied with water in 2010; 6) Sanitation facilities in 2010: percent of the resident population with sanitary facilities in 2010; 7) Gini Index in 2010: degree of inequality in the distribution of individuals according to per capita household income; 8) Gross Domestic Product (GDP) in 2013: municipal GDP. Values are presented in thousands of Brazilian reais, with no deflator or correction factor.

These socioenvironmental variables were collected from the DATASUS website under “Health Information”, restricting the search to demographic and socioeconomic information during periods near 2015, the year of the ZIKV outbreak in the country.With respect to the ethical aspects of the research, this study was not submitted to the ethics committee, since there was no possibility whatsoever of physical or moral damage to the individuals, and the principles contained in resolution 466/12 were strictly adhered to.

Data analysis

The cartographic basis used was that of the IBGE, obtained on its website. First, thematic maps of the aforementioned variables were created using TerraView 4.2.2 software, a phase that included exploratory analysis of spatial data. The quantitative legend of the AIR of ZIKV in 2015-16 was divided into five ranges, using the “same steps” option. The gray scale was used for visual comparison and an image gradient to create the legend, where the municipalities with the worst situation are represented by the darkest color. Spatial dependence was analyzed using the Global Moran index, which estimates spatial autocorrelation, varying between -1 and +1, in addition to providing statistical significance (p-value). According to the spatial dependence hypothesis, the Moran values can be positive (direct correlation; similar values tend to be located near one another) or negative (inverse correlation; high values are surrounded by low values and vice versa). The presence of clusters was analyzed using the local Moran’s index (LISA), which determines the dependence of data in relation to their neighbors. This indicator makes it possible to identify the spatial association that characterizes the occurrence of clusters between the polygons under study. These data were presented in a box map (showing the presence of clusters without considering significance), and Moran map (showing the clusters with statistical significance).GeoDa software, version 1.12 (Spatial Analysis Laboratory, University of Illinois, Urbana-Champaign, USA) was used in LISA bivariate analysis to assess the spatial correlation between the outcome variable (AIR of ZIKV in 2015-16) and the independent variables, which exhibited spatial distribution based on the Global Moran Index and met the criteria established for bivariate analysis. Thus, there is a linear association between a variable xk at location i, xk I and the spatial lag corresponding to the other variable Wyi 1 (1k1 = xk ‘Wyi/n). To that end, thematic maps were created with each pair of variables and their statistical significance was determined.

Spatial dependence can be direct or reverse according to the Moran value obtained. There are two types of relationships between neighbors in the clusters, as follows: High-High (high AIR of ZIKV in 2015-2016 and high rates of the independent variable), Low-Low (low AIR of ZIKV in 2015-2016 and low rates of the independent variable), High-Low (high AIR of ZIKV in 2015-2016 and low rates of the independent variable), Low-High (low AIR of ZIKV in 2015-2016 and high rates of the independent variable).

The independent variables of this study that exhibited autocorrelation in spatial analysis were tested using the multivariate spatial regression model in the GeoDa version 1.12 program and the model that best represented the relationship of the variables was the classic linear regression model. The IBM SPSS Statistics 21 program was used in regression analysis. The independent variables that best predicted the average incidence rate of the ZIKV in 2105-2016 remained in the final model.

Results

In regard to the existence of spatial autocorrelation for the AIR of ZIKV, Moran’s Global Coefficient was 0.172 and significant for 2015 (p = 0.04), and 0.075 and non-significant for 2016 (p = 0.07). However, the AIR of 2015-16 showed autocorrelation in the distribution of these cases, with a Global Moran Coefficient of 0.139 and p-value = 0.03 (Figure 1).

With respect to analysis of spatial dependence, no spatial autocorrelation was found for the variables water supply (Moran = 0.001, p = 0.46), illiteracy (Moran = 0.039, p = 0.18), Gini index (Moran = 0.049, p = 0.22), sanitary facilities (Moran = 0.001, p = 0.46) and gross domestic product (GDP) (Moran = 0.007, p= 0.42). These indicators did not meet the criteria established for bivariate analysis and were therefore not included in subsequent analyses. In this respect, the AIR of violence, average household income and unemployment rate will be considered.

The map of RN shows that the largest number of municipalities with a high average incidence rate of ZIKV are in the Coastal Potiguar mesoregion, where the state capital is located (Figure 1c).

In relation to the box map, which shows the autocorrelation of the variables, the Coastal Potiguar mesoregion exhibited the largest number of high-high clusters in the AIR of ZIKV in 2015-2016 (Figure 2a) and AIR of violence (Figure 2b). The latter rate also displayed high-high clusters in part of the East Potiguar mesoregion (Figure 2b). This type of cluster was found in the Central Potiguar mesoregion for average household income (Figure 2c). For unemployment rate, the high-high cluster was observed in part of the Central and West Potiguar regions (Figure 2d).

The Moran Map shows all the polygons with a significant spatial autocorrelation index, where this study demonstrates that the municipalities of the East Potiguar mesoregion, such as Natal, and others belonging to the metropolitan region of the capital, including Extremoz and Parnamirim, displayed significant high-high clusters in the AIR of ZIKV in 2015-16 and the AIR of violence and average household income, but not the unemployment rate (Figure 3). For average household income, a high-high cluster was observed for municipalities in the Central Potiguar mesoregion, specifically the East Serido microregion (Figure 3c).

With respect to spatial bivariate analysis (LISA), the municipalities of Extremoz, Natal and São Gonçalo exhibited clusters with high AIR of ZIKV in 2015-16 and high AIR of violence (Figure 4a). Extremoz and Natal also obtained a high AIR of ZIKV in 2015-16 and high average household income. The last finding was also recorded for the municipality of Parnamirim (Figure 4b).

Moreover, clusters of high AIR of ZIKV were found in 2015-16 and a low unemployment rate in the municipalities of Serrinha, Caicó and Timbaúba dos Batistas (Figure 4c).

In multiple linear regression, to determine whether the AIR of violence, average household income and unemployment rate would be able to predict the AIR of ZIKV, the AIR of violence and average household income explained 55% of the variation in the AIR of ZIKV (adjusted R² = 0.55). This analysis resulted in a statistically significant model [F (1.164) = 102,37; p = 0.001; adjusted R² = 0.550], demonstrating that the AIR of violence (β = 0.609, t = 9.402, p= 0.001) and average household income (β = 0.200, t = 3.093, p = 0.002) are predictors of the AIR of ZIKV (Table 1).

Discussion

The present study showed that the AIR of ZIKV in 2015-2016 exhibited spatial distribution in RN, with the formation of high-high clusters for AIR of violence and household income, primarily in the metropolitan region of Natal. The formation of unemployment rate clusters was diffuse in the state, which was confirmed once again in multiple regression analysis, where the variable was not a predictor for AIR of ZIKV.

These findings corroborate the importance of social inequality indicators in understanding the health-disease process of a population⁷7 Nunes J, Pimenta DN. A epidemia de Zika e os limites da saúde global. Lua Nova 2016; 98:21-46.^,99 Butler D. Brazil's birth-defects puzzle: Zika virus might not be only factor in reported microcephaly surge. Nature 2016; 535(7613):475-477.^,2020 Garcia LP. Epidemia do vírus Zika e microcefalia no Brasil: Emergência, evolução e enfrentamento. Brasília: IPEA; 2018. (Texto para Discussão 2368).^,2121 Fontoura FC, Cardoso MVLML. Association between congenital malformation and neonatal and maternal variables in neonatal units of a Northeast Brazilian city. Texto Contexto Enferm 2014; 23(4):907-914.. Studies have shown a correlation between the social determinants of health, such as ethnicity, schooling, income and ZIKV, and their consequences, including microcephaly⁹9 Butler D. Brazil's birth-defects puzzle: Zika virus might not be only factor in reported microcephaly surge. Nature 2016; 535(7613):475-477.

10 Araújo TVB, Ximenes RAA, Miranda-Filho DB, Souza WV, Montarroyos UR, Melo APL, Valongueiro S, Albuquerque MFPM, Braga C, Filho SPB, Cordeiro MT, Vazquez E, Cruz DDCS, Henriques CMP, Bezerra LCA, Castanha PMDS, Dhalia R, Marques-Júnior ETA, Martelli CMT, Rodrigues LC, Investigators from the Microcephaly Epidemic Research Group, Brazilian Ministry of Health, Pan American Health Organization, Instituto de Medicina Integral Professor Fernando Figueira, State Health Department of Pernambuco. Association between microcephaly, Zika virus infection, and other risk factors in Brazil: final report of a case-control study. Lancet Infect Dis 2018; 18(3):328-336.^-1111 Marinho F, Araújo VEM, Porto DL, Ferreira HL, Coelho MRS, Lecca RCR, Oliveira H, Poncioni IPA, Maranhão MHN, Mendes YMMB, Fernandes RM, Lima RB, Rabello Neto DL. Microcefalia no Brasil: prevalência e caracterização dos casos a partir do Sistema de Informações sobre Nascidos Vivos (Sinasc), 2000-2015. Epidemiol Serv Saude 2016; 25(4):701-712..

It is important to underscore that less than one year after its introduction in Brazil, ZIKV is unequally distributed in all regions, with a higher number of cases in the Northeast and Southeast, and subsequent continuous transmission throughout most of the country⁹9 Butler D. Brazil's birth-defects puzzle: Zika virus might not be only factor in reported microcephaly surge. Nature 2016; 535(7613):475-477.^,1111 Marinho F, Araújo VEM, Porto DL, Ferreira HL, Coelho MRS, Lecca RCR, Oliveira H, Poncioni IPA, Maranhão MHN, Mendes YMMB, Fernandes RM, Lima RB, Rabello Neto DL. Microcefalia no Brasil: prevalência e caracterização dos casos a partir do Sistema de Informações sobre Nascidos Vivos (Sinasc), 2000-2015. Epidemiol Serv Saude 2016; 25(4):701-712.^,2020 Garcia LP. Epidemia do vírus Zika e microcefalia no Brasil: Emergência, evolução e enfrentamento. Brasília: IPEA; 2018. (Texto para Discussão 2368).^,2222 Brasil. Ministério da Saúde (MS). Secretaria de Vigilância em Saúde. Situação epidemiológica da infecção pelo vírus Zika no Brasil, de 2015 a 2017 [página na Internet]. Boletim Epidemiológico 2018; 49(47):1-10. Disponível em: https://www.saude.gov.br/images/pdf/2018/novembro/12/2018-034.pdf
https://www.saude.gov.br/images/pdf/2018... .

Among the determinants of ZIKV evolution, Garcia²⁰20 Garcia LP. Epidemia do vírus Zika e microcefalia no Brasil: Emergência, evolução e enfrentamento. Brasília: IPEA; 2018. (Texto para Discussão 2368). includes the difficulties in vector control, poor family planning measures, the lack of maternal-infant care, in addition to significant inequalities that characterize the country. These aspects contribute to the Zika virus and its most devastating consequence, namely, microcephaly in babies, becoming endemics that affect primarily low-income families living in underdeveloped regions.

The relationship between average household income and the AIR of ZIKV in 2015-2016 has been reported in studies that found poor socioeconomic conditions associated with an increase in ZIKV incidence and its comorbidities, such as microcephaly. These studies highlight the fact that poor socioeconomic conditions may compromise fetal development, due to lack of access to health services and social vulnerability in addition to mothers’ difficulties understanding the real needs of their baby¹⁰10 Araújo TVB, Ximenes RAA, Miranda-Filho DB, Souza WV, Montarroyos UR, Melo APL, Valongueiro S, Albuquerque MFPM, Braga C, Filho SPB, Cordeiro MT, Vazquez E, Cruz DDCS, Henriques CMP, Bezerra LCA, Castanha PMDS, Dhalia R, Marques-Júnior ETA, Martelli CMT, Rodrigues LC, Investigators from the Microcephaly Epidemic Research Group, Brazilian Ministry of Health, Pan American Health Organization, Instituto de Medicina Integral Professor Fernando Figueira, State Health Department of Pernambuco. Association between microcephaly, Zika virus infection, and other risk factors in Brazil: final report of a case-control study. Lancet Infect Dis 2018; 18(3):328-336.^,2121 Fontoura FC, Cardoso MVLML. Association between congenital malformation and neonatal and maternal variables in neonatal units of a Northeast Brazilian city. Texto Contexto Enferm 2014; 23(4):907-914.^,2323 Abreu TT, Novais MCM, Guimarães ICB. Crianças com microcefalia associada a infecção congênita pelo vírus Zika: características clínicas e epidemiológicas num hospital terciário. Rev Cien Med Biol 2016; 15(3):426-433..

Studies underscore the importance of not disregarding the influence of social, economic and environmental factors related to the ZIKV, given that knowing the profile and cause of diseases in an area is essential to help plan public health policies and strengthen the National Health System (SUS), which could reduce the incidence of disease in the region under study⁷7 Nunes J, Pimenta DN. A epidemia de Zika e os limites da saúde global. Lua Nova 2016; 98:21-46.^,2424 Moraes CH, Loose EB, Girardi IMT. Dengue, Zika e Chikungunya: Análise da cobertura do risco de doenças associadas às mudanças climáticas sob a ótica do Jornalismo Ambiental. Disertaciones: Anuario Electrónico Estudios Comunicación Social 2017; 10(2):16.^,2525 Almeida LS, Cota ALS, Rodrigues DF. Saneamento, Arboviroses e Determinantes Ambientais: Impactos na saúde urbana. Cien Saude Colet [periódico na Internet]. 2019. Disponível em: http://www.cienciaesaudecoletiva.com.br/artigos/saneamento-arboviroses-e-determinantes-ambientais-impactos-na-saude-urbana/17113
http://www.cienciaesaudecoletiva.com.br/... .

One of the factors that has contributed to the correlation between a high AIR of ZIKV in 2015-16 and a high average household income is the fact that these municipalities with better incomes (capitals and metropolitan regions) also have better case notification. Ishitani et. al.²⁶26 Ishitani LH, Franco GC, Perpétuo IHO, França E. Desigualdade social e mortalidade precoce por doenças cardiovasculares no Brasil. Rev Saude Publica 2006; 40(4):684-691. also observed this characteristic, which justifies this relationship with better technical-operational conditions in the epidemiological surveillance system to detect, notify and investigate cases²⁷27 Assis VC, Amaral MPH, Mendonça AE. Análise da qualidade das notificações de dengue informadas no sistema de informação de agravos de notificação, na epidemia de 2010, em uma cidade pólo da zona da mata do estado de Minas Gerais. Rev APS 2014; 17(4):429-437.^,2828 Fernandes LA, Gomes MMF. Análise dos dados do SINAN sobre dengue nos municípios da Área Metropolitana de Brasília (AMB)/Analysis of SINAN data on dengue in the municipalities of the Metropolitan Area of Brasília (AMB). Braz J Health Rev 2018; 1(2):314-322..

These technical-operational conditions may explain another finding of the present study, namely the high rates of violence with a high AIR of ZIKV in the East Potiguar mesoregion that encompasses metropolitan Natal, the state capital, with health services better able to detect and notify cases.

Studies suggest that violence is associated with socioenvironmental factors such as high population density, unequal resource distribution, low family income, among other situations, and that these factors result in social inequities in terms of access to essential consumer goods such as health services²⁹29 Cardia N, Schiffer S. Violência e desigualdade social. Cien Cultura 2002; 54(1):25-31.

30 Macedo AC, Paim JS, Silva LMV, Costa MCN. Violência e desigualdade social: mortalidade por homicídios e condições de vida em Salvador, Brasil. Rev Saude Publica 2001; 35(6):515-522.

31 Resende JP, Andrade MV. Crime social, castigo social: desigualdade de renda e taxas de criminalidade nos grandes municípios brasileiros. Estudos Econom 2011; 41(1):173-195.^-3232 Morais IRD, Valentim RAM, Costa SM. Produção do espaço urbano e políticas públicas: a operacionalização do observatório do aedes aegypti no Rio Grande do Norte/RN. Hygeia 2018; 14(30):17-28..

According to Ferri et al.³³33 Ferri CP, Mitsuhiro SS, Barros MCM, Chalem E, Guinsburg R, Patel V, Prince M, Laranjeira R. The impact of maternal experience of violence and common mental disorders on neonatal outcomes: a survey of adolescent mothers in Sao Paulo, Brazil. BMC Public Health 2007; 7(1):209., violence is linked to disturbances over the course of life that result in negative neonatal outcomes when they occur during pregnancy. The incidence of violence is a public health problem with repercussions for individual and collective health, requiring the creation of specific practices and services. This becomes even more evident given the incidence of violence in RN, with the highest homicide rate in Northeastern Brazil (53.4 murders per 100,000 inhabitants in 2016). This reinforces the importance of investing in governance tools that allow the states to implement pacification policies³⁴34 Instituto de Pesquisa Econômica Aplicada (IPEA). Fórum Brasileiro de Segurança Pública (FBSP). Atlas da violência. Rio de Janeiro: IPEA; 2018.

35 Mello Jorge MHP, Koizumi MS, Tono VL. Causas Externas: O que são, como afetam o setor saúde, sua medida e alguns subsídios para a sua prevenção. Rev Saude-UNG-Ser 2007; 1(1):37-47.^-3636 Dahlberg LL, Krug EG. Violência: um problema global de saúde pública. Cien Saude Colet 2006; 11(Sup.):1163-1178..

However, one of the limitations of this study was to not georeference information using more detailed geographic levels (studies with data points, neighborhoods and census blocks) that could determine the existence of vulnerable areas in these municipalities due to the effects of scale and area aggregation. Furthermore, the coefficients of correlation may be different from those at the individual level, causing the so-called “ecological fallacy”³⁷37 Câmara G, Carvalho MS, Cruz OG, Correia V. Análise Espacial de Áreas . In: Druck S, Carvalho MS, Câmara G, Monteiro AVM, editores. Análise Espacial de Dados Geográficos. Brasília: EMBRAPA; 2004..

Another limitation is the disadvantage of working with a secondary database, namely the collection date. For the independent variables, updated data for the same period as the ZIKV outbreak were not available in the DATASUS and IBGE databases. However, data closest to the analysis period of the present study (ZIKV incidence) were collected. According to Freitas et. al.³⁸38 Freitas RMS, Smiderle JJ, Dias SA, Souza RM, Zidde C. Medindo o saneamento: potencialidades e limitações dos bancos de dados brasileiros. FGV CERI; 2018., this limitation occurs because these variables are obtained from demographic censuses, which causes data discontinuity, since they are only collected every ten years.

Moreover, despite exhibiting ecological correlations, the present study will help create public policies aimed at preventive measures that can be used as criteria for equitable distribution of public resources, prioritizing regions with the worst social inequalities in health.

Conclusion

This study demonstrated that municipalities in the East Potiguar mesoregion, such as Natal and its metropolitan area, with more notifications of violence and favorable average income, exhibit higher AIR of ZIKV cases, possibly due to better access to health services, which improves the notification of ZIKV cases in these municipalities.

These findings may also reflect the demographic density of these municipalities, given that high population density may shed more light on socioenvironmental problems.

The findings of the present study indicate a correlation between socioenvironmental determinants and ZIKV cases in RN state. This may prompt new discussions on the issue, which is currently highlighted by the damage it causes, making essential to understand both the life cycle and transmission of the ZIKV.

In addition, the Health Information Systems for Epidemiological Surveillance are vital for analyzing social inequalities in health, under the premise that this knowledge is essential when devising health planning and management strategies to cope with this disorder and its consequences.

References

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