Incidence of Obesity Among Young US Children Living in Low-Income Families, 2008–2011

OBJECTIVE: To examine the incidence and reverse of obesity among young low-income children and variations across population subgroups. METHODS: We included 1.2 million participants in federally funded child health and nutrition programs who were 0 to 23 months old in 2008 and were followed up 24 to 35 months later in 2010–2011. Weight and height were measured. Obesity at baseline was defined as gender-specific weight-for-length \u3e/=95th percentile on the 2000 Centers for Disease Control and Prevention growth charts. Obesity at follow-up was defined as gender-specific BMI-for-age \u3e/=95th percentile. We used a multivariable log-binomial model to estimate relative risk of obesity adjusting for gender, baseline age, race/ethnicity, duration of follow-up, and baseline weight-for-length percentile. RESULTS: The incidence of obesity was 11.0% after the follow-up period. The incidence was significantly higher among boys versus girls and higher among children aged 0 to 11 months at baseline versus those older. Compared with non-Hispanic whites, the risk of obesity was 35% higher among Hispanics and 49% higher among American Indians (AIs)/Alaska Natives (ANs), but 8% lower among non-Hispanic African Americans. Among children who were obese at baseline, 36.5% remained obese and 63.5% were nonobese at follow-up. The proportion of reversing of obesity was significantly lower among Hispanics and AIs/ANs than that among other racial/ethnic groups. CONCLUSIONS: The high incidence underscores the importance of earlylife obesity prevention in multiple settings for low-income children and their families. The variations within population subgroups suggest that culturally appropriate intervention efforts should be focused on Hispanics and AIs/ANs

Functional anonymisation: Personal data and the data environment

Anonymisation of personal data has a long history stemming from the expansion of the types of data products routinely provided by National Statistical Institutes. Variants on anonymisation have received serious criticism reinforced by much-publicised apparent failures. We argue that both the operators of such schemes and their critics have become confused by being overly focused on the properties of the data themselves. We claim that, far from being able to determine whether data are anonymous (and therefore non-personal) by looking at the data alone, any anonymisation technique worthy of the name must take account of not only the data but also their environment. This paper proposes an alternative formulation called functional anonymisation that focuses on the relationship between the data and the environment within which the data exist (their data environment). We provide a formulation for describing the relationship between the data and their environment that links the legal notion of personal data with the statistical notion of disclosure control. Anonymisation, properly conceived and effectively conducted, can be a critical part of the toolkit of the privacy-respecting data controller and the wider remit of providing accurate and usable data