ABSTRACT
Background: Infant mortality rate (IMR) is globally identified by the policymakers as the marker of health of a population. Objectives: This study aimed to detect the change in hotspots of IMR in Indian states from the year 2000 to 2012, identify hotspots of IMR at district level in selected states from each of the six regions of India and determine the potential predictors of IMR after accounting for spatial autocorrelation. Methods: Ecological study design was used to analyze state and district level data on IMR of India. For the first objective, the data were obtained from Sample Registration System. For the second objective, we classified India into six regions and selected a state in each region that had the highest IMR. The district level data on IMR and potential predictors were obtained from surveys, namely, Annual Health Survey, District Level Household and Facility Survey and Census. Spatio-temporal hotspots of IMR were examined using local indicators of spatial association statistic. Spatial regression was used to identify the potential predictors of IMR after accounting for spatial autocorrelation. Results: Temporal hotspots of IMR were found in the central part of India. Spatial hotspots were identified in districts of Uttar Pradesh. A negative association of IMR existed with female literacy rate, mothers receiving antenatal checkup (%), and people living in urban areas (%). Conclusion: IMR continues to be a problem in the states that have previously shown to be poor performing. Certain districts within these states need emphasis for focused activities.
ABSTRACT
Resumo Este trabalho analisa o padrão espacial da tuberculose no período de 2005 a 2008 identificando variáveis socioeconômicas relevantes para a ocorrência da doença através de modelos estatísticos espaciais. É um estudo ecológico realizado no Rio de Janeiro com casos novos. Utilizou-se o setor censitário como unidade de análise. Foram calculadas as taxas de incidência e usado o método Bayesiano Empírico Local. Foi constatada a autocorrelação espacial com Índice de Moran e LISA. Usando teste de Spearman, as variáveis com correlação estatisticamente significativas a 5% foram utilizadas nos modelos. No modelo de regressão multivariado clássico as variáveis Proporção de responsável com renda entre 1 e 2 salários-mínimos, Proporção de analfabetos, Proporção de domicílios com pessoas que moram sozinhas e Renda média do responsável se ajustaram melhor. Essas variáveis foram inseridas nos modelos Spatial Lag e Spatial Error e os resultados comparados. O primeiro apresentou os melhores parâmetros: R2 = 0,3215, Log da Verossimilhança = -9228, AIC = 18468 e SBC = 18512. Os métodos estatísticos apresentaram-se eficientes na identificação de padrões espaciais e definição de determinantes da doença dando uma visão da heterogeneidade no espaço, possibilitando uma atuação mais direcionada a populações específicas.
Abstract The present study analyses the spatial pattern of tuberculosis (TB) from 2005 to 2008 by identifying relevant socioeconomic variables for the occurrence of the disease through spatial statistical models. This ecological study was performed in Rio de Janeiro using new cases. The census sector was used as the unit of analysis. Incidence rates were calculated, and the Local Empirical Bayesian method was used. The spatial autocorrelation was verified with Moran's Index and local indicators of spatial association (LISA). Using Spearman's test, variables with significant correlation at 5% were used in the models. In the classic multivariate regression model, the variables that fitted better to the model were proportion of head of family with an income between 1 and 2 minimum wages, proportion of illiterate people, proportion of households with people living alone and mean income of the head of family. These variables were inserted in the Spatial Lag and Spatial Error models, and the results were compared. The former exhibited the best parameters: R2 = 0.3215, Log-Likelihood = -9228, Akaike Information Criterion (AIC) = 18,468 and Schwarz Bayesian Criterion (SBC) = 18,512. The statistical methods were effective in the identification of spatial patterns and in the definition of determinants of the disease providing a view of the heterogeneity in space, allowing actions aimed more at specific populations.
Subject(s)
Humans , Tuberculosis/epidemiology , Models, Statistical , Socioeconomic Factors , Brazil/epidemiology , Regression Analysis , Risk Factors , Bayes Theorem , Statistics, Nonparametric , Spatial Analysis , Spatial RegressionABSTRACT
Objective To investigate the temporal and spatial distribution of Schistosoma infection of population and its risk factors in Eastern Dongting Lake area in 2012 and 2014,so as to provide the reference for formulating effective intervention mea-sures. Methods Junshan District was selected as a study field in Eastern Dongting Lake area. The method of spatial autocorre-lation analysis was applied to analyze the change of spatial distribution of Schistosoma infection in Junshan District in 2012 and 2014. The spatial regression model was fitted to detect the risk factors for human infection. Results The livestock infection rate in 2013 was lower than that in 2011. The average infection rate of schistosome was reduced to 0.55%in 2014. The spatial auto-correlation existed on the distribution of schistosomiasis in Junshan District in both 2012 and 2014 and 4 high incidence villages were identified. The results of the spatial error model showed that the prevalence of human infection was positively correlated with the infection rate of the livestock and the area of the susceptible environment in 2012. The spatial lag model showed that the prevalence of human schistosomiasis was positively correlated with the area of the susceptible environment ,but not with the in-fection rate of livestock. Conclusion The measures involving grazing prohibition and phasing out cattle and sheep are remark-ably effective and should continue on the basis of the current spatial distribution of schistosomiasis in this area.
ABSTRACT
Objective To understand the spatial distribution of hepatitis C in Chongqing and its influencing factors.Methods The surveillance data of hepatitis C in 38 counties in Chongqing from January 2010 to December 2014 were collected,and spatial autocorrelation analysis and spatial regression analysis were conducted respectively by using software GeoDa 1.6.7.Results The reported incidence of hepatitis C in Chongqing ranged from 7.3/100 000 to 13.6/100 000 during 2010-2014,with the annual reported incidence of 10.3/100 000.The global Moran' s I values were 0.478,0.503,0.529,0.438,0.406 respectively (P＜0.05).The local spatial autocorrelation analysis indicated there were 6,4,7,5 and 6 areas with high incidences of hepatitis C in 2010,2011,2012,2013 and 2014 respectively.Spatial regression analysis revealed that the reported incidence of hepatitis C in Chongqing was associated with the urbanization rate (Z=2.126,P=0.033).Conclusions The spatial distribution of hepatitis C in Chongqing from 2010 to 2014 was highly clustered.The hot spot of hepatitis C were mainly in the core areas and extended areas with well-developed economy,however the cold spot were in southeastern ecological reserve area with less developed economy.Urbanization had a certain positive influence on the distribution of hepatitis C in Chongqing.