Childhood mental health: an ecological analysis of the effects of neighborhood characteristics

Research on childhood mental illness traditionally examines risk factors most proximal to the child. However, current trends reflect growing interest in how broader contextual factors contribute to psychopathology risk. In this study, we examined neighborhood‐level indicators as potential sources of chronic strain in a sample of 156 mother–child dyads; children were 8 to 12 years old. For most neighborhood indicators, data were collected at the level of census tracts using publicly available data sets. We hypothesized that these indicators would be both associated with greater overall mental health symptoms and specifically predictive of childhood symptoms of depression. We also examined potential mediators (maternal functioning and family cohesion) and moderators (maternal depression). Neighborhood indicators correlated with parents’ ratings of children's overall mental health problems, but did not correlate with children's self‐report of depression symptoms. Maternal functioning mediated neighborhood effects on children's overall mental health problems. Implications and directions for future research are presented.The current work was supported by the following grants from the National Institutes of Health, National Institute of Mental Health MH066077, MH082861, PI: Martha C. Tompson, Ph.D. and MH082861S1, PI: Gail N. Kemp, M.A., M.P.H. (MH066077 - National Institutes of Health, National Institute of Mental Health; MH082861 - National Institutes of Health, National Institute of Mental Health; MH082861S1 - National Institutes of Health, National Institute of Mental Health)Published versio

Maternal depression and youth internalizing and externalizing symptomatology: severity and chronicity of past maternal depression and current maternal depressive symptoms

Maternal depression is a well-documented risk factor for youth depression, and taking into account its severity and chronicity may provide important insight into the degree of risk conferred. This study explored the degree to which the severity/chronicity of maternal depression history explained variance in youth internalizing and externalizing symptoms above and beyond current maternal depressive symptoms among 171 youth (58 % male) ages 8 to 12 over a span of 3 years. Severity and chronicity of past maternal depression and current maternal depressive symptoms were examined as predictors of parent-reported youth internalizing and externalizing symptomatology, as well as youth self-reported depressive symptoms. Severity and chronicity of past maternal depression did not account for additional variance in youth internalizing and externalizing symptoms at Time 1 beyond what was accounted for by maternal depressive symptoms at Time 1. Longitudinal growth curve modeling indicated that prior severity/chronicity of maternal depression predicted levels of youth internalizing and externalizing symptoms at each time point when controlling for current maternal depressive symptoms at each time point. Chronicity of maternal depression, apart from severity, also predicted rate of change in youth externalizing symptoms over time. These findings highlight the importance of screening and assessing for current maternal depressive symptoms, as well as the nature of past depressive episodes. Possible mechanisms underlying the association between severity/chronicity of maternal depression and youth outcomes, such as residual effects from depressive history on mother–child interactions, are discussed.The current work was supported by grants from the National Institutes of Health (MH066077, PI: Martha C. Tompson, PhD; MH082861, PI: Martha C. Tompson, PhD;). (MH066077 - National Institutes of Health; MH082861 - National Institutes of Health)Published versio

UK open source crime data: accuracy and possibilities for research

In the United Kingdom, since 2011 data regarding individual police recorded crimes have been made openly available to the public via the police.uk website. To protect the location privacy of victims these data are obfuscated using geomasking techniques to reduce their spatial accuracy. This paper examines the spatial accuracy of the police.uk data to determine at what level(s) of spatial resolution – if any – it is suitable for analysis in the context of theory testing and falsification, evaluation research, or crime analysis. Police.uk data are compared to police recorded data for one large metropolitan Police Force and spatial accuracy is quantified for four different levels of geography across five crime types. Hypotheses regarding systematic errors are tested using appropriate statistical approaches, including methods of maximum likelihood. Finally, a “best-fit” statistical model is presented to explain the error as well as to develop a model that can correct it. The implications of the findings for researchers using the police.uk data for spatial analysis are discussed

A randomized clinical trial comparing family-focused treatment and individual supportive therapy for depression in childhood and early adolescence

OBJECTIVE: Despite the morbidity and negative outcomes associated with early-onset depression, few studies have examined the efficacy of psychosocial treatment for depressive disorders during childhood. Integrating family in treatment could have particularly salutary effects during this developmental period. This trial compared immediate posttreatment effects of family-focused treatment for childhood depression (FFT-CD) with those of individual supportive psychotherapy (IP) for children 7 to 14 years old with depressive disorders. METHOD: Children were randomized to 15 sessions of FFT-CD (n = 67) or IP (n = 67) over 4 months. The primary treatment outcome was adequate clinical depression response, defined as at least a 50% decrease in score on the Children's Depression Rating Scale-Revised (CDRS-R). Additional outcomes included patient-centered outcomes (parent- and child-reported treatment satisfaction), remission (defined as CDRS-R score ≤28), change in continuous CDRS-R score, and change in child and parent reports of depressive and non-depressive symptoms and social adjustment. RESULTS: Significant improvement was evident across groups for depressive and non-depressive symptoms, global response, and functioning and social adjustment. Compared with children randomized to IP, children randomized to FFT-CD showed higher rates of adequate clinical depression response (77.7% versus 59.9%; number needed to treat = 5.72; odds ratio 2.29; 95% CI 1.001-5.247; t = 1.97, p = .0498). Across treatments, families reported high satisfaction; compared with IP families, FFT-CD families reported greater knowledge and skills for managing depression. There were no significant differences between treatment arms on secondary outcomes. CONCLUSION: Results support the value of psychosocial intervention, emphasize the important role that families play, and highlight the potential for FFT-CD for supporting recovery in children with depression. Clinical trial registration information-Systems of Support Study for Childhood Depression; http://clinicaltrials.gov; NCT01159041.R01 MH082856 - NIMH NIH HHS; R01 MH082861 - NIMH NIH HH

Family-focused treatment for childhood depression: model and case illustrations

Although the evidence base for treatment of depressive disorders in adolescents has strengthened in recent years, less is known about the treatment of depression in middle to late childhood. A family-based treatment may be optimal in addressing the interpersonal problems and symptoms frequently evident among depressed children during this developmental phase, particularly given data indicating that attributes of the family environment predict recovery versus continuing depression among depressed children. Family-Focused Treatment for Childhood Depression (FFT-CD) is designed as a 15-session family treatment with both the youth and parents targeting two putative mechanisms involved in recovery: (a) enhancing family support, specifically decreasing criticism and increasing supportive interactions; and (b) strengthening specific cognitive-behavioral skills within a family context that have been central to CBT for depression, specifically behavioral activation, communication, and problem solving. This article describes in detail the FFT-CD protocol and illustrates its implementation with three depressed children and their families. Common themes/challenges in treatment included family stressors, comorbidity, parental mental health challenges, and inclusion/integration of siblings into sessions. These three children experienced positive changes from pre- to posttreatment on assessor-rated depressive symptoms, parent- and child-rated depressive symptoms, and parent-rated internalizing and externalizing symptoms. These changes were maintained at follow-up evaluations 4 and 9 months following treatment completion.K23 MH101238 - NIMH NIH HHS; R01 MH082856 - NIMH NIH HHS; R01 MH082861 - NIMH NIH HH

A latent class analysis of parental bipolar disorder: examining associations with offspring psychopathology

Bipolar disorder (BD) is highly heterogeneous, and course variations are associated with patient outcomes. This diagnostic complexity challenges identification of patients in greatest need of intervention. Additionally, course variations have implications for offspring risk. First, latent class analysis (LCA) categorized parents with BD based on salient illness characteristics: BD type, onset age, polarity of index episode, pole of majority of episodes, rapid cycling, psychosis, anxiety comorbidity, and substance dependence. Fit indices favored three parental classes with some substantively meaningful patterns. Two classes, labeled “Earlier-Onset Bipolar-I” (EO-I) and “Earlier-Onset Bipolar-II” (EO-II), comprised parents who had a mean onset age in mid-adolescence, with EO-I primarily BD-I parents and EO-II entirely BD-II parents. The third class, labeled “Later-Onset BD” (LO) had an average onset age in adulthood. Classes also varied on probability of anxiety comorbidity, substance dependence, psychosis, rapid cycling, and pole of majority of episodes. Second, we examined rates of disorders in offspring (ages 4–33, Mage=13.46) based on parental latent class membership. Differences emerged for offspring anxiety disorders only such that offspring of EO-I and EO-II parents had higher rates, compared to offspring of LO parents, particularly for daughters. Findings may enhance understanding of BD and its nosologyThis study was funded by two Brain & Behavior Research Foundation (formerly NARSAD) Independent Investigator Awards (PI: Nierenberg), a Brain & Behavior Research Foundation Young Investigator Award (PI: Henin) generously supported in part by the SHINE Initiative, and an MGH Claflin Award (PI: Henin). We thank David A. Langer, Ph.D., Thomas M. Olino, Ph.D., and Meredith Lotz Wallace, Ph.D. for their consultation. (Brain & Behavior Research Foundation; Brain & Behavior Research Foundation Young Investigator Award; SHINE Initiative; MGH Claflin Award)Accepted manuscrip

Dynamics of a self--gravitating magnetized source

We consider a magnetized degenerate gas of fermions as the matter source of a homogeneous but anisotropic Bianchi I spacetime with a Kasner--like metric. We examine the dynamics of this system by means of a qualitative and numerical study of Einstein-Maxwell field equations which reduce to a non--linear autonomous system. For all initial conditions and combinations of free parameters the gas evolves from an initial anisotropic singularity into an asymptotic state that is completely determined by a stable attractor. Depending on the initial conditions the anisotropic singularity is of the ``cigar'' or ``plate'' types.Comment: 7 pages, 1 figur

Addressing antibiotic use: insights from social science around the world

Antimicrobial resistance (AMR) is a major threat to global health and economies, the harmful effects of which are disproportionately experienced by those living in Low- and Middle-Income Countries (LMICs). Tackling this complex problem requires multidisciplinary and multisectoral responses. In the last few years, there has been a growing acknowledgement of the vital role of social science in understanding and intervening on antibiotic use, a key driver of AMR. Existing reviews summarise evidence of specific aspects of antibiotic use and specific intervention types. The growing concern that our off-the-shelf toolkit for addressing antibiotic use is insufficient in the face of rising use across humans, animals and plants, requires that we take a fresh look at the ways we are understanding this problem and possibilities for solutions. The ambition of this report is to provide a timely intervention into this global debate, by formulating a conceptual map of the insights from the growing body of social science research on addressing antibiotic use conducted in a diverse range of economic, social, and health system settings around the world. A series of panel presentations and discussions was held in 2020 with leading social scientists working on antibiotic use in different settings. Analysis of the proceedings of these panels, together with a literature review which snowballed from the work of the 76 researchers profiled through the antimicrobialsinsociety.org community of practice, led to a grouping of the key points of entry for recommendations to act on antibiotic use. The report identifies three main areas of focus of social science recommendations to address antibiotic use: Practices, Structures and Networks. The Practices grouping, in which the majority of the social research on antibiotic use has been carried out over the years, focuses on addressing end user antibiotic use. It shows how scholarship has moved away from knowledge deficit models to embracing an ‘ecological’ approach and to considering practice as embedded in lives and livelihoods. This body of work emphasizes the centrality of the local context to identify possible targets for intervening to change practice. The Structures grouping assembles the growing body of work that understands antibiotic use as a product of economic and political conditions. This research draws from political economical perspectives to identify the ways antibiotics have taken on critical roles in modern societies. Based on research investigating water, hygiene, sanitation (WASH), health systems and the political economy, the report considers how interventions that target these societal structures might reduce recourse to antibiotics as a ‘quick fix’. The Networks grouping collates recent work that draws attention to the mundane networks of logics, classifications and flows within which antibiotics are entangled. Research exploring agricultural and development imperatives, global health architectures, and circulating discourses has revealed the material and meaningful connections between human and non-human actors – animals, medicines, microbes, technologies, for example – that extend through time and space far beyond the moment of antibiotic use. These studies help render visible for action the apparatus such as clinical guidelines, delivery chains and models of care that have previously been overlooked when studying and addressing antibiotic use. The domains for action on antibiotic use presented in this report raise important questions for the AMR community. First, how can we move from standardised approaches to developing, refining, and monitoring impacts of interventions locally? Second, what time horizons should we set for interventions that aim to address AMR, and what other impacts should we expect of efforts to optimise antibiotic use? Third, what forms of evidence are most relevant, and what professional and infrastructural investment is required for this to support meaningful and responsive evaluation? The analysis in this report suggests new forms of transnational and intranational engagements to address this pressing bio-social-political issue could provide a platform for widening the options for addressing antibiotic use and its associated challenges

Absence of Street Lighting May Prevent Vehicle Crime, but Spatial and Temporal Displacement Remains a Concern

OBJECTIVES: This paper estimates the effect of changes in street lighting at night on levels of crime at street-level. Analyses investigate spatial and temporal displacement of crime into adjacent streets. METHODS: Offense data (burglaries, robberies, theft of and theft from vehicles, and violent crime) were obtained from Thames Valley Police, UK. Street lighting data (switching lights off at midnight, dimming, and white light) were obtained from local authorities. Monthly counts of crime at street-level were analyzed using a conditional fixed-effects Poisson regression model, adjusting for seasonal and temporal variation. Two sets of models analyzed: (1) changes in night-time crimes adjusting for changes in day-time crimes and (2) changes in crimes at all times of the day. RESULTS: Switching lights off at midnight was strongly associated with a reduction in night-time theft from vehicles relative to daytime (rate ratio RR 0.56; 0.41–0.78). Adjusted for changes in daytime, night-time theft from vehicles increased (RR 1.55; 1.14–2.11) in adjacent roads where street lighting remained unchanged. CONCLUSION: Theft from vehicle offenses reduced in streets where street lighting was switched off at midnight but may have been displaced to better-lit adjacent streets. Relative to daytime, night-time theft from vehicle offenses reduced in streets with dimming while theft from vehicles at all times of the day increased, thus suggesting temporal displacement. These findings suggest that the absence of street lighting may prevent theft from vehicles, but there is a danger of offenses being temporally or spatially displaced

Multi-view Face Detection Using Deep Convolutional Neural Networks

In this paper we consider the problem of multi-view face detection. While there has been significant research on this problem, current state-of-the-art approaches for this task require annotation of facial landmarks, e.g. TSM [25], or annotation of face poses [28, 22]. They also require training dozens of models to fully capture faces in all orientations, e.g. 22 models in HeadHunter method [22]. In this paper we propose Deep Dense Face Detector (DDFD), a method that does not require pose/landmark annotation and is able to detect faces in a wide range of orientations using a single model based on deep convolutional neural networks. The proposed method has minimal complexity; unlike other recent deep learning object detection methods [9], it does not require additional components such as segmentation, bounding-box regression, or SVM classifiers. Furthermore, we analyzed scores of the proposed face detector for faces in different orientations and found that 1) the proposed method is able to detect faces from different angles and can handle occlusion to some extent, 2) there seems to be a correlation between dis- tribution of positive examples in the training set and scores of the proposed face detector. The latter suggests that the proposed methods performance can be further improved by using better sampling strategies and more sophisticated data augmentation techniques. Evaluations on popular face detection benchmark datasets show that our single-model face detector algorithm has similar or better performance compared to the previous methods, which are more complex and require annotations of either different poses or facial landmarks.Comment: in International Conference on Multimedia Retrieval 2015 (ICMR