Street-view greenspace exposure and objective sleep characteristics among children

Greenspace may benefit sleep by enhancing physical activity, reducing stress or air pollution exposure. Studies on greenspace and children's sleep are limited, and most use satellite-derived measures that do not capture ground-level exposures that may be important for sleep. We examined associations of street view imagery (SVI)-based greenspace with sleep in Project Viva, a Massachusetts pre-birth cohort. We used deep learning algorithms to derive novel metrics of greenspace (e.g., %trees, %grass) from SVI within 250m of participant residential addresses during 2007–2010 (mid-childhood, mean age 7.9 years) and 2012–2016 (early adolescence, 13.2y) (N = 533). In early adolescence, participants completed >5 days of wrist actigraphy. Sleep duration, efficiency, and time awake after sleep onset (WASO) were derived from actigraph data. We used linear regression to examine cross-sectional and prospective associations of mid-childhood and early adolescence greenspace exposure with early adolescence sleep, adjusting for confounders. We compared associations with satellite-based greenspace (Normalized Difference Vegetation Index, NDVI). In unadjusted models, mid-childhood SVI-based total greenspace and %trees (per interquartile range) were associated with longer sleep duration at early adolescence (9.4 min/day; 95%CI:3.2,15.7; 8.1; 95%CI:1.7,14.6 respectively). However, in fully adjusted models, only the association between %grass at mid-childhood and WASO was observed (4.1; 95%CI:0.2,7.9). No associations were observed between greenspace and sleep efficiency, nor in cross-sectional early adolescence models. The association between greenspace and sleep differed by racial and socioeconomic subgroups. For example, among Black participants, higher NDVI was associated with better sleep, in neighborhoods with low socio-economic status (SES), higher %grass was associated with worse sleep, and in neighborhoods with high SES, higher total greenspace and %grass were associated with better sleep time. SVI metrics may have the potential to identify specific features of greenspace that affect sleep

Perceived barriers in family-based behavioural treatment of paediatric obesity – Results from the FABO study

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.Background: To date, few studies have investigated perceived barriers among those who participate in and drop out of family-based behavioural treatment (FBT) for paediatric obesity. Examining experienced barriers during treatment, and their role in participation and completion of treatment has important implications for clinical practice. Objectives: To compare perceived barriers to participating in a family-based behavioural social facilitation treatment (FBSFT) for obesity among families who completed and did not complete treatment. Methods: Data were analysed from 90 families of children and adolescents (mean (M) age = 12.8 years, standard deviation (SD) = 3.05) with severe obesity enrolled in a 17-session FBSFT program. After completing 12 sessions or at the time of dropout, parents and therapists completed the Barriers to Treatment Participation Scale (BTPS), a 5-point Likert scale (1 = never a problem, 5 = very often a problem) which includes four subscales: 1. Stressors and obstacles that compete with treatment, 2. Treatment demands and issues, 3. Perceived relevance of treatment, 4. Relationship with the therapist. Results: Families who did not complete treatment scored significantly higher on the BTPS subscales stressors and obstacles that compete with treatment (M = 2.03, SD = 0.53 vs. M = 1.70, SD = 0.42), p = 0.010 and perceived relevance of treatment (M = 2.27, SD = 0.48 vs. M = 1.80, SD = 0.50), p < 0.001 than families who completed treatment. No other significant differences between groups were observed. Conclusion: Families are more likely to drop out of FBSFT when experiencing a high burden from life stressors or when treatment is not meeting the expectations and perceived needs of the family.publishedVersio

Family-based treatment of children with severe obesity in a public healthcare setting: Results from a randomized controlled trial

To compare the effectiveness of family-based behavioural social facilitation treatment (FBSFT) versus treatment as usual (TAU) in children with severe obesity. Parallel-design, nonblinded, randomized controlled trial conducted at a Norwegian obesity outpatient clinic. Children aged 6–18 years referred to the clinic between 2014 and 2018 were invited to participate. Participants were randomly allocated using sequentially numbered, opaqued, sealed envelopes. FBSFT (n = 59) entailed 17 sessions of structured cognitive behavioural treatment, TAU (n = 55) entailed standard lifestyle counselling sessions every third month for 1 year. Primary outcomes included changes in body mass index standard deviation score (BMI SDS) and percentage above the International Obesity Task Force cut-off for overweight (%IOTF-25). Secondary outcomes included changes in sleep, physical activity, and eating behaviour. From pre- to posttreatment there was a statistically significant difference in change in both BMI SDS (0.19 units, 95% confidence interval [CI]: 0.10–0.28, p < .001) and %IOTF-25 (5.48%, 95%CI: 2.74–8.22, p < .001) between FBSFT and TAU groups. FBSFT participants achieved significant reductions in mean BMI SDS (0.16 units, (95%CI: −0.22 to −0.10, p < .001) and %IOTF-25 (6.53%, 95% CI: −8.45 to −4.60, p < .001), whereas in TAU nonsignificant changes were observed in BMI SDS (0.03 units, 95% CI: −0.03 to 0.09, p = .30) and %IOTF-25 (−1.04%, 95% CI: −2.99 to −0.90, p = .29). More FBSFT participants (31.5%) had clinically meaningful BMI SDS reductions of ≥0.25 from pre- to posttreatment than in TAU (13.0%, p = .021). Regarding secondary outcomes, only changes in sleep timing differed significantly between groups. FBSFT improved weight-related outcomes compared to TAU.publishedVersio

Interactional justice at work is related to sickness absence: a study using repeated measures in the Swedish working population

Background: Research has shown that perceived unfairness contributes to higher rates of sickness absence. While shorter, but more frequent periods of sickness absence might be a possibility for the individual to get relief from high strain, long-term sickness absence might be a sign of more serious health problems. The Uncertainty Management Model suggests that justice is particularly important in times of uncertainty, e.g. perceived job insecurity. The present study investigated the association between interpersonal and informational justice at work with long and frequent sickness absence respectively, under conditions of job insecurity. Methods: Data were derived from the 2010, 2012, and 2014 biennial waves of the Swedish Longitudinal Occupational Survey of Health (SLOSH). The final analytic sample consisted of 19,493 individuals. We applied repeated measures regression analyses through generalized estimating equations (GEE), a method for longitudinal data that simultaneously analyses variables at different time points. We calculated risk of long and frequent sickness absence, respectively in relation to interpersonal and informational justice taking perceptions of job insecurity into account. Results: We found informational and interpersonal justice to be associated with risk of long and frequent sickness absence independently of job insecurity and demographic variables. Results from autoregressive GEE provided some support for a causal relationship between justice perceptions and sickness absence. Contrary to expectations, we found no interaction between justice and job insecurity. Conclusions: Our results underline the need for fair and just treatment of employees irrespective of perceived job insecurity in order to keep the workforce healthy and to minimize lost work days due to sickness absence

Variation in mitochondrial function in hypoxia-sensitive and hypoxia-tolerant human glioma cells

We have shown previously that human glioblastoma multiforme cells vary in their ability to survive under hypoxic conditions. Under oxygen limiting conditions, hypoxia-tolerant cells decrease their oxygen consumption rate whereas hypoxia-sensitive cells continue to consume oxygen at a relatively steady rate until the oxygen supply becomes exhausted. We now show that hypoxia-tolerant and hypoxia-sensitive cells exhibit distinct patterns of mitochondrial function in response to hypoxic challenge. Hypoxia-tolerant cell lines retain stable mitochondrial membrane potential and ATP concentration when incubated under oxygen limiting conditions. In addition, hypoxia-tolerant cell lines are consistently more sensitive to a wide spectrum of inhibitors of mitochondrial function than are hypoxia-sensitive cells. In contrast, the hypoxia-sensitive cells are unable to maintain stable mitochondrial membrane potential and ATP levels when incubated at reduced oxygen tension. These results demonstrate significant differences in the mitochondrial function between these two phenotypes and reinforce previous data that suggest a regulatory role for mitochondria in the development of hypoxia tolerance

Estimating the current and future cancer burden in Canada: Methodological framework of the Canadian population attributable risk of cancer (ComPARe) study

Introduction The Canadian Population Attributable Risk of Cancer project aims to quantify the number and proportion of cancer cases incident in Canada, now and projected to 2042, that could be prevented through changes in the prevalence of modifiable exposures associated with cancer. The broad risk factor categories of interest include tobacco, diet, energy imbalance, infectious diseases, hormonal therapies and environmental factors such as air pollution and res

Conditional meta-analysis stratifying on detailed HLA genotypes identifies a novel type 1 diabetes locus around TCF19 in the MHC

The human leukocyte antigen (HLA) class II genes HLA-DRB1, -DQA1 and -DQB1 are the strongest genetic factors for type 1 diabetes (T1D). Additional loci in the major histocompatibility complex (MHC) are difficult to identify due to the region’s high gene density and complex linkage disequilibrium (LD). To facilitate the association analysis, two novel algorithms were implemented in this study: one for phasing the multi-allelic HLA genotypes in trio families, and one for partitioning the HLA strata in conditional testing. Screening and replication were performed on two large and independent datasets: the Wellcome Trust Case–Control Consortium (WTCCC) dataset of 2,000 cases and 1,504 controls, and the T1D Genetics Consortium (T1DGC) dataset of 2,300 nuclear families. After imputation, the two datasets have 1,941 common SNPs in the MHC, of which 22 were successfully tested and replicated based on the statistical testing stratifying on the detailed DRB1 and DQB1 genotypes. Further conditional tests using the combined dataset confirmed eight novel SNP associations around 31.3 Mb on chromosome 6 (rs3094663, p = 1.66 × 10−11 and rs2523619, p = 2.77 × 10−10 conditional on the DR/DQ genotypes). A subsequent LD analysis established TCF19, POU5F1, CCHCR1 and PSORS1C1 as potential causal genes for the observed association

Interdisciplinary-driven hypotheses on spatial associations of mixtures of industrial air pollutants with adverse birth outcomes

Background: Adverse birth outcomes (ABO) such as prematurity and small for gestational age confer a high risk of mortality and morbidity. ABO have been linked to air pollution; however, relationships with mixtures of industrial emissions are poorly understood. The exploration of relationships between ABO and mixtures is complex when hundreds of chemicals are analyzed simultaneously, requiring the use of novel approaches. Objective: We aimed to generate robust hypotheses spatially linking mixtures and the occurrence of ABO using a spatial data mining algorithm and subsequent geographical and statistical analysis. The spatial data mining approach aimed to reduce data dimensionality and efficiently identify spatial associations between multiple chemicals and ABO. Methods: We discovered co-location patterns of mixtures and ABO in Alberta, Canada (2006–2012). An ad-hoc spatial data mining algorithm allowed the extraction of primary co-location patterns of 136 chemicals released into the air by 6279 industrial facilities (National Pollutant Release Inventory), wind-patterns from 182 stations, and 333,247 singleton live births at the maternal postal code at delivery (Alberta Perinatal Health Program), from which we identified cases of preterm birth, small for gestational age, and low birth weight at term. We selected secondary patterns using a lift ratio metric from ABO and non-ABO impacted by the same mixture. The relevance of the secondary patterns was estimated using logistic models (adjusted by socioeconomic status and ABO-related maternal factors) and a geographic-based assignment of maternal exposure to the mixtures as calculated by kernel density. Results: From 136 chemicals and three ABO, spatial data mining identified 1700 primary patterns from which five secondary patterns of three-chemical mixtures, including particulate matter, methyl-ethyl-ketone, xylene, carbon monoxide, 2-butoxyethanol, and n-butyl alcohol, were subsequently analyzed. The significance of the associations (odds ratio > 1) between the five mixtures and ABO provided statistical support for a new set of hypotheses. Conclusion: This study demonstrated that, in complex research settings, spatial data mining followed by pattern selection and geographic and statistical analyses can catalyze future research on associations between air pollutant mixtures and adverse birth outcomes