Health Vulnerability Index (HVI) and its association with Dengue fever in Brazil

1. FEDERAL UNIVERSITY OF MINAS GERAIS VETERINARY SCHOOL DEPARTMENT OF PREVENTIVE VETERINARY MEDICINE Belo Horizonte, Brazil Health Vulnerability Index (HVI) and its association with Dengue fever in Brazil Misael Enrique Oviedo Pastrana1*; Rachel Lage Brito2; Rafael Romero Nicolino1; Camila Stefanie Fonseca de Oliveira1; João Paulo Amaral Haddad3. 1PhD student in Veterinary Science / Epidemiology , Preventive Veterinary Departament, Federal University of Minas Gerais, Belo Horizonte, Brazil. 2Municipality of Pedro Leopoldo, Minas Gerais, Brazil.. 3Preventive Veterinary Departament, Federal University of Minas Gerais, Belo Horizonte, Brazil. *Corresponding author: mpastrana@outlook.com

2. Dengue Virus • It isanarbovirus, flavivirus genus • Transmittedbymosquitoes • There are 4 serotypes (Den-1, 2,3,4) • Co-circulation of these serotypes in the Western Hemisphere, has been observed in recent years, 2006–2010. • Clinical presentation: Cold • Dengue fever (DF) • Dengue hemorrhagic fever (DHF) • Dengue shock syndrome (DSS) Death Pan American Health Organization In Laughlin et al 2012. Perera and Kuhn, 2008

3. Distribution of global dengue risk Determination of risk status based on combined reports from WHO, the United States Centers for Disease Control and Prevention, Gideon online, ProMED, DengueMap, Eurosurveillance and published literature (Simmons CP et al, 2012). WHO, 2012

4. Average number of dengue and severe dengue cases reported to WHO annually in 1955–2007 and number of cases reported in recent years, 2008–2010 WHO, 2012

5. Average number of dengue cases in 30 most highly endemic countries/territories as reported to WHO, 2004–2010 WHO, 2012

6. Dengue Fever In Brazil, the Zoonoses Control Centers are the institution responsible for the management of the dengue. Aedes aegypti Is the most common epidemic vector in America; It can be easily identified; But, this is also the main transmitting agent of the urban yellow fever.

7. Health Impact Are among the most important reemerging infectious diseases globally 50 – 100 million annual infections 500,000 cases of DHF/DSS 20,000 – 25,000 deaths Cause public alarm and stress in the public health control systems Laughlin et al 2012.

8. Health Vulnerability Index (HVI) • HVI is an official indicator . • Was created with the purpose of revealing inequalities between the epidemiological profiles of different social groups . • It’s composed of five variables: • HVI • HVI has been used in epidemiological studies characterizing and correlating population groups with the distribution of diseases. • Association between the HVI and dengue could identify sites for action priority, facilitating decision-making in the execution of more efficient actions to controlling. GEEPI, 2003; Araújo, 2011; Barbosa, 2011

9. Methods • Selecting a municipality. • Pedro Leopoldo (PL), located in the Metropolitan Region of Belo Horizonte, state of Minas Gerais, Brazil. • The unit of analysis was the census sector. • Sixty-nine census tracts were evaluated in PL. • PL has a population of 54,596 inhabitants

10. Methods • Dengue cases were identified and spatially located using a GPS. • The evaluation was from 2009 to 2011. • The incidence rates were determined for the 69 census sectors • Instability problems were identified by: • Errors in the identification of individuals. • Underreporting of cases • Small population sizes of some census tracts. Geographical location of cases

11. Methods • Instability corrections: • with support of Bayesian statistics, using the local empirical Bayes estimator. • Comparison between the original incidence rates and Bayesian rates. • Kernel density, global autocorrelation (Moran’s I) and local autocorrelation (Anselin Local Moran’s I) were used to understand the distribution of dengue and their association with HVI. • Were used: TerraView 4.2 (National Institute for Space Research -INPE, Brazil) and ArcGIS 9.3 (ESRI, USA)

12. Results Cases, population and number of census tracts analyzed in the different districts of the municipality of Pedro Leopoldo, state of Minas Gerais, Brazil.

13. Results Comparisons between the distributions of original incidence and Bayesian incidence, in the year 2009 (A) 2010 (B) and 2011 (C). A In the original rates were selected the census tracts with values ​​in the 2nd , 3rd , 4th and 5th quantile Could be observed how these same points were revalued by the Bayesian method, passing some other quantiles and reducing the instability. The Local Empirical Bayes Estimator allowed the generation of new corrected incidence rates The Bayesian distribution was the most appropriate and probable. B C

14. Results Kernel density allowed estimating the intensity for the spatial distribution of the HVI and Bayesian incidence rates. • Spatial distribution of the HVI (A). • Conglomerate with high HVI are identified in the north of the municipality. In contrast, these area had the lowest incidence rates. • Spatial distribution of the Bayesian incidence rates (B,C, and D). • The distribution of Bayesian incidence rates showed similarity in the three years. • Clusters of high incidence in the central • area were identified.

15. Results Spatial autocorrelation (Global Moran's Index)

16. Results Local autocorrelation allowed to Identify clusters in the spatial distribution of the HVI and Bayesian incidence rates. • Spatial distribution of the HVI (A). • HVI clusters: • Cluster, type HH, was identified in the north. • Clusters, types LL, were identified in the central part of the city. • Spatial distribution of the Bayesian incidence rates (B,C, and D). • Incidence clusters. • Cluster, type HH was identified for each year in the central area. • The incongruity between HVI and Bayesian incidence rates demonstrated that HVI is not a good indicator to estimate the risk of dengue in the city of Pedro Leopoldo.

17. Conclusions • The Local Empirical Bayes Estimator proved to be an important tool for generating new rates corrected and smoothed. • The application of kernel density and local spatial autocorrelation correctly identified the areas of greatest health risk. • However, this vulnerability does not reflect the site where the highest incidence rate and raw number of cases of dengue occur. • HVI is not an efficient indicator to be adopted for the control and identification of risk areas for dengue in the municipality of Pedro Leopoldo. • New hypotheses have to be considered.

18. References • Araujo, V.E.M. Analysis of spatio-temporal distribution of visceral leishmaniosisand clinical-epidemiological profile of the cases and deaths, Belo Horizonte, Minas Gerais. Doctoral thesis. ICB/UFMG, 2011. 208 p., il. • Barbosa, AD, Characterization and spatial distribution of scorpion stings in Belo Horizonte, Minas Gerais, 2005 to 2009. Master's Dissertation. EV/UFMG, 2011.87 p., il. • World Health Organization ,2012.Global strategy for dengue prevention and control 2012-2020. • GEEPI - Epidemiology and Information Office. Health Vulnerability Index. Municipal Government of Belo Horizonte. 2003, 10 p. • Laughlin CA; MorensDM;Cassetti MC; 1 Costero-Saint AD; San Martin JL; Whitehead SS; Fauci AS. Dengue Research Opportunities in the Americas. The Journal of Infectious Diseases 2012;206:1121–7. • PereraR, Kuhn RJ. Structural proteomics of dengue virus. CurrOpinMicrobiol, 2008 11(4): 369–377.

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