Identifying socioeconomic, epidemiological and operational scenarios for tuberculosis control in Brazil: an ecological study

Abstract

ObjectivesTo identify scenarios based on socioeconomic, epidemiological and operational healthcare factors associated with tuberculosis incidence in Brazil.DesignEcological study.SettingsThe study was based on new patients with tuberculosis and epidemiological/operational variables of the disease from the Brazilian National Information System for Notifiable Diseases and the Mortality Information System. We also analysed socioeconomic and demographic variables.ParticipantsThe units of analysis were the Brazilian municipalities, which in 2015 numbered 5570 but 5 were excluded due to the absence of socioeconomic information.Primary outcomeTuberculosis incidence rate in 2015.Data analysisWe evaluated as independent variables the socioeconomic (2010), epidemiological and operational healthcare indicators of tuberculosis (2014 or 2015) using negative binomial regression. Municipalities were clustered by the k-means method considering the variables identified in multiple regression models.ResultsWe identified two clusters according to socioeconomic variables associated with the tuberculosis incidence rate (unemployment rate and household crowding): a higher socioeconomic scenario (n=3482 municipalities) with a mean tuberculosis incidence rate of 16.3/100 000 population and a lower socioeconomic scenario (2083 municipalities) with a mean tuberculosis incidence rate of 22.1/100 000 population. In a second stage of clusterisation, we defined four subgroups in each of the socioeconomic scenarios using epidemiological and operational variables such as tuberculosis mortality rate, AIDS case detection rate and proportion of vulnerable population among patients with tuberculosis. Some of the subscenarios identified were characterised by fragility in their information systems, while others were characterised by the concentration of tuberculosis cases in key populations.ConclusionClustering municipalities in scenarios allowed us to classify them according to the socioeconomic, epidemiological and operational variables associated with tuberculosis risk. This classification can support targeted evidence-based decisions such as monitoring data quality for improving the information system or establishing integrative social protective policies for key populations.

Dichotomy in Fatal Outcomes in a Large Cohort of People Living with HTLV-1 in São Paulo, Brazil

“…Third, low income and living outside the large Metropolitan area of São Paulo (the largest city in the Southern hemisphere with an estimated 12 million inhabitants), poses a major hurdle for people living with HTLV-1, and HAM/TSP patients in particular, to comply with yearly visits and hence may influence our results through increased loss of follow-up. The profound economic crisis in Brazil over the last five years might have aggravated this [38], for example, the unemployment rate rose from 7% in 2014 to 13% of the working population in 2017 [39]. Fourth, we do not have data on adherence to antiretroviral therapy (ART) among HIV-coinfected individuals in our cohort.…”

Cadastro Nacional de Estabelecimentos de Saúde como ferramenta de análise da descentralização do atendimento da tuberculose para a atenção básica

“…Tendo em vista que o Brasil é marcado por disparidades sociais que o dividem em dois cenários socioeconômicos, agrupando as regiões Norte e Nordeste no menos favorecido e as demais no mais favorecido 13,14 , esperava-se que a descentralização para a atenção básica fosse distribuída de forma heterogênea entre os municípios brasileiros por macrorregiões, o que não foi constatado em 2016. Nesse processo, enfatiza-se, após décadas de modelo assistencial centralizado no hospital, o papel indutor do governo central para a transferência aos municípios da responsabilidade com a atenção básica.…”

Dichotomy in Fatal Outcomes in a Large Cohort of People Living with HTLV-1 in São Paulo, Brazil

“…Third, low income and living outside the large Metropolitan area of São Paulo (the largest city in the Southern hemisphere with an estimated 12 million inhabitants), poses a major hurdle for people living with HTLV-1, and HAM/TSP patients in particular, to comply with yearly visits and hence may influence our results through increased loss of followup. The profound economic crisis in Brazil over the last five years might have aggravated this [42], for example, the unemployment rate rose from seven percent in 2014 to 13% of the working population in 2017 [43]. Fourth, we do not have data on adherence to antiretroviral therapy (ART) among HIV-co-infected individuals in our cohort.…”