Identifying risk profiles for childhood obesity using recursive partitioning based on individual, familial, and neighborhood environment factors

International Journal of Behavioral Nutrition and Physical Activity

Tracie A Barnett⁵  Mélanie Henderson³  Yan Kestens²  Lise Gauvin²  Marie-Hélène Roy-Gagnon¹  Andraea Van Hulst⁴ 

[1] Department of Epidemiology and Community Medicine, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada;Centre de Recherche Léa-Roback sur les Inégalités Sociales de Santé de Montréal, Université de Montréal, Montreal, Quebec, Canada;Division of Endocrinology, Department of Pediatrics, Centre Hospitalier Universitaire Sainte-Justine and Université de Montréal, Montreal, Quebec, Canada;Centre de recherche du Centre Hospitalier Universitaire Sainte-Justine, 5757 Avenue Decelles, suite 100, Montréal H3S 2C3, Québec, Canada;Epidemiology and Biostatistics Unit, INRS-Institut Armand-Frappier, Université du Québec, Laval, QC, Canada

关键词: Socioecological model;    Recursive partitioning analysis;    Physical activity;    Obesity;    Neighborhood characteristics;    Food environment;    Familial risk;    Body mass index;    Built environment;   

Others  :  1135859
DOI  :  10.1186/s12966-015-0175-7

 received in 2014-09-05, accepted in 2015-01-29,  发布年份 2015

【 摘 要 】

Background

Few studies consider how risk factors within multiple levels of influence operate synergistically to determine childhood obesity. We used recursive partitioning analysis to identify unique combinations of individual, familial, and neighborhood factors that best predict obesity in children, and tested whether these predict 2-year changes in body mass index (BMI).

Methods

Data were collected in 2005–2008 and in 2008–2011 for 512 Quebec youth (8–10 years at baseline) with a history of parental obesity (QUALITY study). CDC age- and sex-specific BMI percentiles were computed and children were considered obese if their BMI was ≥95^th percentile. Individual (physical activity and sugar-sweetened beverage intake), familial (household socioeconomic status and measures of parental obesity including both BMI and waist circumference), and neighborhood (disadvantage, prestige, and presence of parks, convenience stores, and fast food restaurants) factors were examined. Recursive partitioning, a method that generates a classification tree predicting obesity based on combined exposure to a series of variables, was used. Associations between resulting varying risk group membership and BMI percentile at baseline and 2-year follow up were examined using linear regression.

Results

Recursive partitioning yielded 7 subgroups with a prevalence of obesity equal to 8%, 11%, 26%, 28%, 41%, 60%, and 63%, respectively. The 2 highest risk subgroups comprised i) children not meeting physical activity guidelines, with at least one BMI-defined obese parent and 2 abdominally obese parents, living in disadvantaged neighborhoods without parks and, ii) children with these characteristics, except with access to ≥1 park and with access to ≥1 convenience store. Group membership was strongly associated with BMI at baseline, but did not systematically predict change in BMI.

Conclusion

Findings support the notion that obesity is predicted by multiple factors in different settings and provide some indications of potentially obesogenic environments. Alternate group definitions as well as longer duration of follow up should be investigated to predict change in obesity.

【 授权许可】

   
2015 Van Hulst et al.; licensee BioMed Central.

【 图 表 】

Figure 1.

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