Objective: to describe the profile of cases of tuberculosis and diabetes comorbidity in Brazil.
Methods: this is a descriptive study with data from the Brazilian Information System for Notifiable Diseases - tuberculosis (Sinan-TB) and from the System of Registration and Monitoring of Hypertension and Diabetes Mellitus (Hiperdia), from 2007 to 2011; probabilistic linkage was carried out with Reclink software.
Results: 24,443 cases of comorbidity were found, including 3,181 cases not registered on Sinan-TB; of the total number of recovered cases, mostly were males (57.2%), aged 40-59 years (52.3%), black/brown-skinned (68.4%), with five to eight years of schooling (78.4%), with no regular use of alcohol (86.5%) and negative serology for the HIV virus (91.8%).
Conclusion: the cases found had similar profile to those registered on Sinan-TB and the probabilistic linkage of data from different information systems enabled the detection of cases not captured by surveillance.
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http://dx.doi.org/10.5123/S1679-49742017000200013 | DOI Listing |
PLoS One
December 2024
Department of Operational and Implementation Research, ICMR- National Institute for Research in Reproductive and Child Health- HTA Regional Resource Hub, Mumbai, Maharashtra, India.
BMC Public Health
November 2024
Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, 33 Ursula Franklin Street, Toronto, ON, M5S 2S1, Canada.
Background: Biological sample collection and data linkage can expand the utility of population health surveys. The present study investigates factors associated with population health survey respondents' willingness to provide biological samples and personal health information.
Methods: Using data from the 2019 Centre for Addiction and Mental Health (CAMH) Monitor survey (n = 2,827), we examined participants' willingness to provide blood samples, saliva samples, probabilistic linkage, and direct linkage with personal health information.
BMC Med Res Methodol
November 2024
MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
Background: Bias from data missing not at random (MNAR) is a persistent concern in health-related research. A bias analysis quantitatively assesses how conclusions change under different assumptions about missingness using bias parameters that govern the magnitude and direction of the bias. Probabilistic bias analysis specifies a prior distribution for these parameters, explicitly incorporating available information and uncertainty about their true values.
View Article and Find Full Text PDFRev Soc Bras Med Trop
November 2024
Universidade de São Paulo, Faculdade de Medicina Veterinária e Zootecnia, Programa de Pós-Graduação em Epidemiologia e Saúde Única, São Paulo, SP, Brasil.
Background: This study aimed to identify COVID-19 cases among people living with HIV (PLWH) in Brazil in 2020, describe their clinical, sociodemographic, and epidemiological profiles, and evaluate the factors associated with disease severity.
Methods: This cross-sectional study used secondary data obtained from the Brazilian healthcare system. Probabilistic and deterministic data linkage methods were used to identify coinfected patients.
Epidemiol Serv Saude
November 2024
Universidade Federal de Mato Grosso, Faculdade de Enfermagem, Cuiabá, MT, Brasil.
Objective: To investigate factors associated with tuberculosis deaths in Mato Grosso state, Brazil, from 2011 to 2020.
Methods: Retrospective cohort study with data obtained from the Notifiable Health Conditions Information System and the Mortality Information System. Deaths were qualified using probabilistic linkage and analyzed using Poisson regression.
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