Exploring the associations between neighbourhood food environment, household food insecurity and child weight-related outcomes in socio-economically and racially/ethnically diverse families.

OBJECTIVE: To examine associations among neighbourhood food environments (NFE), household food insecurity (HFI) and child's weight-related outcomes in a racially/ethnically diverse sample of US-born and immigrant/refugee families. DESIGN: This cross-sectional, observational study involving individual and geographic-level data used multilevel models to estimate associations between neighbourhood food environment and child outcomes. Interactions between HFI and NFE were employed to determine whether HFI moderated the association between NFE and child outcomes and whether the associations differed for US-born v. immigrant/refugee groups. SETTING: The sample resided in 367 census tracts in the Minneapolis/St. Paul, MN metropolitan area, and the data were collected in 2016-2019. PARTICIPANTS: The sample was from the Family Matters study of families (n 1296) with children from six racial/ethnic and immigrant/refugee groups (African American, Latino, Hmong, Native American, Somali/Ethiopian and White). RESULTS: Living in a neighbourhood with low perceived access to affordable fresh fruits and vegetables was found to be associated with lower food security (P < 0·01), poorer child diet quality (P < 0·01) and reduced availability of a variety of fruits (P < 0·01), vegetables (P < 0·05) and whole grains in the home (P < 0·01). Moreover, residing in a food desert was found to be associated with a higher child BMI percentile if the child's household was food insecure (P < 0·05). No differences in associations were found for immigrant/refugee groups. CONCLUSIONS: Poor NFE were associated with worse weight-related outcomes for children; the association with weight was more pronounced among children with HFI. Interventions aiming to improve child weight-related outcomes should consider both NFE and HFI.

Early Detection of Exposure to Toxic Chemicals Using Continuously Recorded Multi-Sensor Physiology.

Early detection of exposure to a toxic chemical, e.g., in a military context, can be life-saving. We propose to use machine learning techniques and multiple continuously measured physiological signals to detect exposure, and to identify the chemical agent. Such detection and identification could be used to alert individuals to take appropriate medical counter measures in time. As a first step, we evaluated whether exposure to an opioid (fentanyl) or a nerve agent (VX) could be detected in freely moving guinea pigs using features from respiration, electrocardiography (ECG) and electroencephalography (EEG), where machine learning models were trained and tested on different sets (across subject classification). Results showed this to be possible with close to perfect accuracy, where respiratory features were most relevant. Exposure detection accuracy rose steeply to over 95% correct during the first five minutes after exposure. Additional models were trained to correctly classify an exposed state as being induced either by fentanyl or VX. This was possible with an accuracy of almost 95%, where EEG features proved to be most relevant. Exposure detection models that were trained on subsets of animals generalized to subsets of animals that were exposed to other dosages of different chemicals. While future work is required to validate the principle in other species and to assess the robustness of the approach under different, realistic circumstances, our results indicate that utilizing different continuously measured physiological signals for early detection and identification of toxic agents is promising.