Spatial distribution of the risk for metabolic complications

An application in south-east Brazil, 2006–2007

Dados Bibliográficos

ID15093005
AutoresLuciana Bertoldi Nucci (0000-0002-5140-3622, Universidade Estadual de Campinas (UNICAMP), autor correspondente), Lia TO Zangirolani (Pontifícia Universidade Católica de Campinas), Ana Carolina Cintra Nunes Mafra (0000-0001-9004-7176, Universidade Estadual de Campinas (UNICAMP)), Ana Carolina CN Mafra, Maria Angélica Tavares De Medeiros (0000-0002-8982-7084, Universidade Federal de São Paulo), Ricardo Carlos Cordeiro (0000-0002-0437-1066, Universidade Estadual de Campinas (UNICAMP)), Ricardo Cordeiro (0000-0001-6624-3070)
Ano2012
Volume15
Fascículo6
Páginas1008-1014
Data de publicação2012-06-01
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoPublic Health Nutrition (JOURNAL)
Identificadores do periódicoISSN: 1368-9800 • E-ISSN: 1475-2727
EditoraCambridge University Press (CUP) (PUBLISHER)
DOI10.1017/s1368980011003363
PMID22217669
OpenAlexW2123763016
IdiomaEN
Referências citadas38

Objective To identify spatial variation in the risk for metabolic complications (RMC) by means of a semi-parametric approach for multinomial data. Design Cross-sectional study. Setting We visited 730 households selected in the first of a two-stage sample in South health district in Campinas, São Paulo, Brazil, 2006–2007. Subjects We interviewed 651 individuals and obtained their respective anthropometric measures and geographical coordinates of their house location. They were classified according to a combination of BMI and abdominal circumference as having no risk, increased, high or very high RMC. Results Gender, age and schooling were associated with RMC. Crude spatial risk for the three levels of RMC in relation to the absence of risk suggested different patterns in each level. Adjusted spatial risk for the RMC showed smaller significant areas, but the pattern remained similar to crude risk. Conclusions Spatial point analysis with a multinomial approach improves the understanding of differences in RMC found, as we could identify specific areas in which to intervene. The public health significance of these findings may lie in the additional evidence provided that spatial location and its features can influence patterns of RMC

Anthropometry · Environmental health · Geography · Multinomial distribution · Multinomial logistic regression · Statistics · Demography · Health disparities and outcomes · Mathematics · Medicine · Obesity, Physical Activity, Diet · Urban Transport and Accessibility