Danielle Talita Dos Santos1, Luana Seles Alves2, Marcos Augusto Moraes Arcoverde2, Luiz Henrique Arroyo2, Thaís Zamboni Berra2, Antônio Carlos Vieira Ramos2, Felipe Lima Dos Santos2, Ricardo Alexandre Arcêncio3, Carla Nunes4. 1. Ribeirão Preto College of Nursing of the University of São Paulo, Ribeirão Preto Campus (EERP/USP), Avenue dos Bandeirantes, 3900, 14040-902, Ribeirão Preto, São Paulo, Brazil; NOVA National School of Public Health, Universidade NOVA de Lisboa, Avenue Padre Cruz, 1600-560 Lisbon, Portugal. 2. Ribeirão Preto College of Nursing of the University of São Paulo, Ribeirão Preto Campus (EERP/USP), Avenue dos Bandeirantes, 3900, 14040-902, Ribeirão Preto, São Paulo, Brazil. 3. Ribeirão Preto College of Nursing of the University of São Paulo, Ribeirão Preto Campus (EERP/USP), Avenue dos Bandeirantes, 3900, 14040-902, Ribeirão Preto, São Paulo, Brazil. Electronic address: ricardo@eerp.usp.br. 4. NOVA National School of Public Health, Universidade NOVA de Lisboa, Avenue Padre Cruz, 1600-560 Lisbon, Portugal; Public Health Research Centre, Universidade NOVA de Lisboa, Avenue Padre Cruz, 1600-560 Lisbon, Portugal.
Abstract
BACKGROUND: Tuberculosis (TB) is one of the top 10 causes of death worldwide; in 2016, over 95% of TB deaths occurred in low- and middle-income countries. Although the incidence and deaths from TB have decreased in Brazil in recent years, the disease has increased in the vulnerable population, whose diagnosis is more delayed and the chances for abandonment and deaths are significantly higher. This study aimed to identify high-risk areas for TB mortality and evidence their social determinants through a sensitive tailored social index, in a context of high inequality in South Brazil. METHODS: A multistep statistical methodology was developed, based on spatial clustering, categorical principal components analysis, and receiver operating characteristic curves (ROC). This study considered 138 spatial units in Curitiba, South Brazil. TB deaths (2008-2015) were obtained from the National Information Mortality System and social variables from the Brazilian Human Development Atlas (2013). RESULTS: There were 128 TB deaths recorded in the study: the mortality rate was 0.9/100,000 inhabitants, minimum-maximum: 0-25.51/100,000, with a mean (standard deviation) of 1.07 (2.71), and 78 space units had no deaths. One risk cluster of TB mortality was found in the south region (RR=2.64, p=0.01). Considering the social variables, several clusters were identified in the social risk indicator (SRI): income (899.82/1752.94; 0.024), GINI Index (0.41/0.45; 0.010), and overcrowding (25.07/15.39; 0.032). The SRI showed a high capacity to discriminate the TB mortality areas (area under ROC curve 0.865, 95% CI: 0.796-0.934). CONCLUSIONS: A powerful risk map (SRI) was developed, allowing tailored and personalised interventions. The south of Curitiba was identified as a high-risk area for TB mortality and the majority of social variables. This methodological approach can be generalised to other areas and/or other public health problems.
BACKGROUND:Tuberculosis (TB) is one of the top 10 causes of death worldwide; in 2016, over 95% of TBdeaths occurred in low- and middle-income countries. Although the incidence and deaths from TB have decreased in Brazil in recent years, the disease has increased in the vulnerable population, whose diagnosis is more delayed and the chances for abandonment and deaths are significantly higher. This study aimed to identify high-risk areas for TBmortality and evidence their social determinants through a sensitive tailored social index, in a context of high inequality in South Brazil. METHODS: A multistep statistical methodology was developed, based on spatial clustering, categorical principal components analysis, and receiver operating characteristic curves (ROC). This study considered 138 spatial units in Curitiba, South Brazil. TBdeaths (2008-2015) were obtained from the National Information Mortality System and social variables from the Brazilian Human Development Atlas (2013). RESULTS: There were 128 TBdeaths recorded in the study: the mortality rate was 0.9/100,000 inhabitants, minimum-maximum: 0-25.51/100,000, with a mean (standard deviation) of 1.07 (2.71), and 78 space units had no deaths. One risk cluster of TBmortality was found in the south region (RR=2.64, p=0.01). Considering the social variables, several clusters were identified in the social risk indicator (SRI): income (899.82/1752.94; 0.024), GINI Index (0.41/0.45; 0.010), and overcrowding (25.07/15.39; 0.032). The SRI showed a high capacity to discriminate the TBmortality areas (area under ROC curve 0.865, 95% CI: 0.796-0.934). CONCLUSIONS: A powerful risk map (SRI) was developed, allowing tailored and personalised interventions. The south of Curitiba was identified as a high-risk area for TBmortality and the majority of social variables. This methodological approach can be generalised to other areas and/or other public health problems.
Authors: Jasper Nidoi; Winters Muttamba; Simon Walusimbi; Joseph F Imoko; Peter Lochoro; Jerry Ictho; Levicatus Mugenyi; Rogers Sekibira; Stavia Turyahabwe; Raymond Byaruhanga; Giovanni Putoto; Simone Villa; Mario C Raviglione; Bruce Kirenga Journal: BMC Public Health Date: 2021-11-26 Impact factor: 3.295