Can Early Intervention Policies Improve Well-being? Evidence from a randomized controlled trial *

Many authors have proposed incorporating measures of well-being into evaluations of public policy. Yet few evaluations use experimental design or examine multiple aspects of well-being, thus the causal impact of public policies on well-being is largely unknown. In this paper we examine the effect of an intensive early intervention program on maternal well-being in a targeted disadvantaged community. Using a randomized controlled trial design we estimate and compare treatment effects on global well-being using measures of life satisfaction, experienced well-being using both the Day Reconstruction Method (DRM) and a measure of mood yesterday, and also a standardized measure of parenting stress. The intervention has no significant impact on negative measures of well-being, such as experienced negative affect as measured by the DRM and global measures of well-being such as life satisfaction or a global measure of parenting stress. Significant treatment effects are observed on experienced measures of positive affect using the DRM, and a measure of mood yesterday. The DRM treatment effects are primarily concentrated during times spent without the target child which may reflect the increased effort and burden associated with additional parental investment. Our findings suggest that a maternal-focused intervention may produce meaningful improvements in experienced well-being. Incorporating measures of experienced affect may thus alter cost-benefit calculations for public policies

Can Early Intervention Improve Maternal Well-Being? Evidence from a Randomized Controlled Trial

Objective   This study estimates the effect of a targeted early childhood intervention program on global and experienced measures of maternal well-being utilizing a randomized controlled trial design. The primary aim of the intervention is to improve children’s school readiness skills by working directly with parents to improve their knowledge of child development and parenting behavior. One potential externality of the program is well-being benefits for parents given its direct focus on improving parental coping, self-efficacy, and problem solving skills, as well as generating an indirect effect on parental well-being by targeting child developmental problems.  Methods   Participants from a socio-economically disadvantaged community are randomly assigned during pregnancy to an intensive 5-year home visiting parenting program or a control group. We estimate and compare treatment effects on multiple measures of global and experienced well-being using permutation testing to account for small sample size and a stepdown procedure to account for multiple testing.  Results  The intervention has no impact on global well-being as measured by life satisfaction and parenting stress or experienced negative affect using episodic reports derived from the Day Reconstruction Method (DRM). Treatment effects are observed on measures of experienced positive affect derived from the DRM and a measure of mood yesterday.  Conclusion   The limited treatment effects suggest that early intervention programs may produce some improvements in experienced positive well-being, but no effects on negative aspects of well-being. Different findings across measures may result as experienced measures of well-being avoid the cognitive biases that impinge upon global assessments

Estimating multivariate similarity between neuroimaging datasets with sparse canonical correlation analysis:an application to perfusion imaging

An increasing number of neuroimaging studies are based on either combining more than one data modality (inter-modal) or combining more than one measurement from the same modality (intra-modal). To date, most intra-modal studies using multivariate statistics have focused on differences between datasets, for instance relying on classifiers to differentiate between effects in the data. However, to fully characterize these effects, multivariate methods able to measure similarities between datasets are needed. One classical technique for estimating the relationship between two datasets is canonical correlation analysis (CCA). However, in the context of high-dimensional data the application of CCA is extremely challenging. A recent extension of CCA, sparse CCA (SCCA), overcomes this limitation, by regularizing the model parameters while yielding a sparse solution. In this work, we modify SCCA with the aim of facilitating its application to high-dimensional neuroimaging data and finding meaningful multivariate image-to-image correspondences in intra-modal studies. In particular, we show how the optimal subset of variables can be estimated independently and we look at the information encoded in more than one set of SCCA transformations. We illustrate our framework using Arterial Spin Labeling data to investigate multivariate similarities between the effects of two antipsychotic drugs on cerebral blood flow

Influence of a low-carbohydrate diet on endothelial microvesicles in overweight women

Low-carbohydrate diets (LCD) are increasing in popularity, but their effect on vascular health has been questioned.Endothelial microvesicles (EMV) are membrane-derived vesicles with the potential to act as a sensitive prognostic biomarker of vascular health and endothelial function. The aim of this study was to examine the influence of a LCD on EMV and other endothelial biomarkers of protein origin. Twenty-four overweight women (age, 48.4 ± 0.6 years; height, 1.60 ± 0.07 m; body mass, 76.5 ± 9.1 kg; body mass index, 28.1 ± 2.7 kg·m−2; waist circumference, 84.1 ± 7.4 cm; mean ± standard deviation) were randomisedto either 24 weeks on their normal diet (ND) or a LCD, after which they crossed over to 24 weeks on the alternative diet.Participants were assisted in reducing carbohydrate intake, but not below 40 g·day−1. Body composition and endothelial biomarkers were assessed at the crossover point and at the end of the study. Daily carbohydrate intake (87 ± 7 versus 179 ± 11 g) and the percentage of energy derived from carbohydrate (29% versus 44%) were lower (p < 0.05) on the LCD compared to the ND, but absolute fat and saturated fat intake were unchanged. Body mass and waist circumference were 3.7 ± 0.8 kg and 3.5 ± 1.0 cmlower (p < 0.05), respectively, after the LCD compared with the ND phases. CD31+CD41−EMV, soluble (s) thrombomodulin, sE-selectin, sP-selectin, serum amyloid A and C-reactive protein were lower (p < 0.05) after the LCD compared to the ND, but serum lipids and apolipoproteins were not different. EMV along with a range of endothelial and inflammatory biomarkers are reduced by a LCD that involves modest weight loss

The cortical thickness phenotype of individuals with <i>DISC1</i> translocation resembles schizophrenia

BACKGROUND. The disrupted in schizophrenia 1 (DISC1) gene locus was originally identified in a Scottish pedigree with a high incidence of psychiatric disorders that is associated with a balanced t(1;11)(q42.1;q14.3) chromosomal translocation. Here, we investigated whether members of this family carrying the t(1;11)(q42.1;q14.3) translocation have a common brain-related phenotype and whether this phenotype is similar to that observed in schizophrenia (SCZ), using multivariate pattern recognition techniques. METHODS. We measured cortical thickness, cortical surface area, subcortical volumes, and regional cerebral blood flow (rCBF) in healthy controls (HC) (n = 24), patients diagnosed with SCZ (n = 24), patients diagnosed with bipolar disorder (BP) (n = 19), and members of the original Scottish family (n = 30) who were either carriers (T+) or noncarriers (T–) of the DISC1 translocation. Binary classification models were developed to assess the differences and similarities across groups. RESULTS. Based on cortical thickness, 72% of the T– group were assigned to the HC group, 83% of the T+ group were assigned to the SCZ group, and 45% of the BP group were classified as belonging to the SCZ group, suggesting high specificity of this measurement in predicting brain-related phenotypes. Shared brain-related phenotypes between SCZ and T+ individuals were found for cortical thickness only. Finally, a classification accuracy of 73% was achieved when directly comparing the pattern of cortical thickness of T+ and T– individuals. CONCLUSION. Together, the results of this study suggest that the DISC1 translocation may increase the risk of psychiatric disorders in this pedigree by affecting neurostructural phenotypes such as cortical thickness. FUNDING. This work was supported by the National Health Service Research Scotland, the Scottish Translational Medicine Research Collaboration, the Innovative Medicines Initiative (IMI), the Engineering and Physical Sciences Research Council (EPSRC), The Wellcome Trust, the National Institute of Health Research (NIHR), and Pfizer

Personalized Medication Response Prediction for Attention-Deficit Hyperactivity Disorder: Learning in the Model Space vs. Learning in the Data Space.

Attention-Deficit Hyperactive Disorder (ADHD) is one of the most common mental health disorders amongst school-aged children with an estimated prevalence of 5% in the global population (American Psychiatric Association, 2013). Stimulants, particularly methylphenidate (MPH), are the first-line option in the treatment of ADHD (Reeves and Schweitzer, 2004; Dopheide and Pliszka, 2009) and are prescribed to an increasing number of children and adolescents in the US and the UK every year (Safer et al., 1996; McCarthy et al., 2009), though recent studies suggest that this is tailing off, e.g., Holden et al. (2013). Around 70% of children demonstrate a clinically significant treatment response to stimulant medication (Spencer et al., 1996; Schachter et al., 2001; Swanson et al., 2001; Barbaresi et al., 2006). However, it is unclear which patient characteristics may moderate treatment effectiveness. As such, most existing research has focused on investigating univariate or multivariate correlations between a set of patient characteristics and the treatment outcome, with respect to dosage of one or several types of medication. The results of such studies are often contradictory and inconclusive due to a combination of small sample sizes, low-quality data, or a lack of available information on covariates. In this paper, feature extraction techniques such as latent trait analysis were applied to reduce the dimension of on a large dataset of patient characteristics, including the responses to symptom-based questionnaires, developmental health factors, demographic variables such as age and gender, and socioeconomic factors such as parental income. We introduce a Bayesian modeling approach in a "learning in the model space" framework that combines existing knowledge in the literature on factors that may potentially affect treatment response, with constraints imposed by a treatment response model. The model is personalized such that the variability among subjects is accounted for by a set of subject-specific parameters. For remission classification, this approach compares favorably with conventional methods such as support vector machines and mixed effect models on a range of performance measures. For instance, the proposed approach achieved an area under receiver operator characteristic curve of 82-84%, compared to 75-77% obtained from conventional regression or machine learning ("learning in the data space") methods

Predicting Carotid Artery Disease and Plaque Instability from Cell-derived Microparticles

ObjectivesCell-derived microparticles (MPs) are small plasma membrane-derived vesicles shed from circulating blood cells and may act as novel biomarkers of vascular disease. We investigated the potential of circulating MPs to predict (a) carotid plaque instability and (b) the presence of advanced carotid disease.MethodsThis pilot study recruited carotid disease patients (aged 69.3 ± 1.2 years [mean ± SD], 69% male, 90% symptomatic) undergoing endarterectomy (n = 42) and age- and sex-matched controls (n = 73). Plaques were classified as stable (n = 25) or unstable (n = 16) post surgery using immunohistochemistry. Blood samples were analysed for MP subsets and molecular biomarkers. Odds ratios (OR) are expressed per standard deviation biomarker increase.ResultsEndothelial MP (EMP) subsets, but not any vascular, inflammatory, or proteolytic molecular biomarker, were higher (p < .05) in the unstable than the stable plaque patients. The area under the receiver operator characteristic curve for CD31+41− EMP in discriminating an unstable plaque was 0.73 (0.56–0.90, p < .05). CD31+41− EMP predicted plaque instability (OR = 2.19, 1.08–4.46, p < .05) and remained significant in a multivariable model that included transient ischaemic attack symptom status. Annexin V+ MP, platelet MP (PMP) subsets, and C-reactive protein were higher (p < .05) in cases than controls. Annexin V+ MP (OR = 3.15, 1.49–6.68), soluble vascular cell adhesion molecule-1 (OR = 1.64, 1.03–2.59), and previous smoking history (OR = 3.82, 1.38–10.60) independently (p < .05) predicted the presence of carotid disease in a multivariable model.ConclusionsEMP may have utility in predicting plaque instability in carotid patients and annexin V+ MPs may predict the presence of advanced carotid disease in aging populations, independent of established biomarkers

Factors associated with breastfeeding initiation:A comparison between France and French-speaking Canada

Background: Breastfeeding is associated with multiple domains of health for both mothers and children. Nevertheless, breastfeeding initiation is low within certain developed countries. Furthermore, comparative studies of initiation rates using harmonised data across multiple regions is scarce. Objective: The aim of the present study was to investigate and compare individual-level determinants of breastfeeding initiation using two French-speaking cohorts. Methods: Participants included ~ 3,900 mothers enrolled in two cohort studies in Canada and France. Interviews, questionnaires, and medical records were utilised to collect information on maternal, family, and medical factors associated with breastfeeding initiation. Results: Rates of breastfeeding initiation were similar across cohorts, slightly above 70%. Women in both Canada and France who had higher levels of maternal education, were born outside of their respective countries and who did not smoke during pregnancy were more likely to initiate breastfeeding with the cohort infant. Notably, cohort effects of maternal education at the university level were found, whereby having 'some university' was not statistically significant for mothers in France. Further, younger mothers in Canada, who delivered by caesarean section and who had previous children had reduced odds of breastfeeding initiation. These results were not found for mothers in France. Conclusions and Implications for Practice: While some similar determinants were observed, programming efforts to increase breastfeeding initiation should be tailored to the characteristics of specific geographical regions which may be heavily impacted by the social, cultural and political climate of the region, in addition to individual and family level factors.European Commission - Seventh Framework Programme (FP7