Child and Maternal Mental Health Before and During the COVID-19 Pandemic: Longitudinal Social Inequalities in a Brazilian Birth Cohort.

OBJECTIVE: The coronavirus disease 2019 (COVID-19) pandemic has caused major stress for families and children, particularly in the context of prolonged school closures. Few longitudinal studies are available on young children's mental health, including data both before and during the pandemic. This study examined experiences that might increase risk for mental health problems among caregivers and young children during the COVID-19 pandemic and inequalities driven by pre-pandemic disadvantage. METHOD: This prospective, population-based birth cohort study in Pelotas, Brazil, analyzed 2,083 children and caregivers with data from before the pandemic in 2019, when children were 4 years old, and again in 2020, when schools were closed for a long period during the pandemic. Child conduct problems, emotional problems, and hyperactivity-inattention problems were assessed using the Strengths and Difficulties Questionnaire. Family financial hardship, relationship difficulties, caregiver mental health, parenting practices, and child fears and isolation were considered as potential risk factors. RESULTS: Across the whole population, the only significant increase in mental health problems from before to during the pandemic was found for maternal depression. However, poorer families were at far greater risk of experiencing serious financial problems, food shortages, increased conflict in adult relationships, parenting problems, and child worries about food availability during the pandemic. In turn, these difficulties were associated with increases in multiple mental health problems for both caregivers and children. Increased child mental health problems were most strongly associated with concurrent maternal anxiety (ß > 0.20, p < .001, for each of child conduct, emotional, and hyperactivity problems), maternal depression (ß = 0.26, p < .001, for child emotional problems), partner criticism (ß = 0.21, p < .001, for child conduct problems), and harsh parenting (ß > 0.20, p < .001, for both child conduct and hyperactivity problems). Child worry about COVID-19 was associated with increased emotional problems (ß = 0.14, p < .001), but children's isolation was not associated with their mental health. CONCLUSION: Overall, the impact of the COVID-19 pandemic on mental health is a mixed picture, but for families in poverty, marked material and interpersonal difficulties were associated with increases in mental health problems among children and caregivers.

Effects of Laws Expanding Civilian Rights to Use Deadly Force in Self-Defense on Violence and Crime: A Systematic Review.

Background. Since 2005, most US states have expanded civilian rights to use deadly force in self-defense outside the home. In most cases, legislation has included removing the duty to retreat anywhere one may legally be, commonly known as stand-your-ground laws. The extent to which these laws affect public health and safety is widely debated in public and policy discourse.Objectives. To synthesize the available evidence on the impacts and social inequities associated with changing civilian rights to use deadly force in self-defense on violence, injury, crime, and firearm-related outcomes.Search Methods. We searched MEDLINE, Embase, PsycINFO, Scopus, Web of Science, Sociological Abstracts, National Criminal Justice Reference Service Abstracts, Education Resources Information Center, International Bibliography of the Social Sciences, ProQuest Dissertations and Theses, Google Scholar, National Bureau of Economic Research working papers, and SocArXiv; harvested references of included studies; and consulted with experts to identify studies until April 2020.Selection Criteria. Eligible studies quantitatively estimated the association between laws that expanded or restricted the right to use deadly force in self-defense and population or subgroup outcomes among civilians with a comparator.Data Collection and Analysis. Two reviewers extracted study data using a common form. We assessed study quality using the Risk of Bias in Nonrandomized Studies of Interventions tools adapted for (controlled) before-after studies. To account for data dependencies, we conducted graphical syntheses (forest plots and harvest plots) to summarize the evidence on impacts and inequities associated with changing self-defense laws.Main Results. We identified 25 studies that estimated population-level impacts of laws expanding civilian rights to use deadly force in self-defense, all of which focused on stand-your-ground or other expansions to self-defense laws in the United States. Studies were scored as having serious or critical risk of bias attributable to confounding. Risk of bias was low across most other domains (i.e., selection, missing data, outcome, and reporting biases). Stand-your-ground laws were associated with no change to small increases in violent crime (total and firearm homicide, aggravated assault, robbery) on average across states. Florida-based studies showed robust increases (24% to 45%) in firearm and total homicide while self-defense claims under stand-your-ground law were more often denied when victims were White, especially when claimants were racial minorities.Author's Conclusions. The existing evidence contradicts claims that expanding self-defense laws deters violent crime across the United States. In at least some contexts, including Florida, stand-your-ground laws are associated with increases in violence, and there are racial inequities in the application of these laws.Public Health Implications. In some US states, most notably Florida, stand-your-ground laws may have harmed public health and safety and exacerbated social inequities. Our findings highlight the need for scientific evidence on both population and equity impacts of self-defense laws to guide legislative action that promotes public health and safety for all.Trial Registration. Open Science Framework (https://osf.io/uz68e).

Predicting Barriers to Treatment for Depression in a U.S. National Sample: A Cross-Sectional, Proof-of-Concept Study.

OBJECTIVE: Even though safe and effective treatments for depression are available, many individuals with a diagnosis of depression do not obtain treatment. This study aimed to develop a tool to identify persons who might not initiate treatment among those who acknowledge a need. METHODS: Data were aggregated from the 2008-2014 U.S. National Survey on Drug Use and Health (N=391,753), including 20,785 adults given a diagnosis of depression by a health care provider in the 12 months before the survey. Machine learning was applied to self-report survey items to develop strategies for identifying individuals who might not get needed treatment. RESULTS: A derivation cohort aggregated between 2008 and 2013 was used to develop a model that identified the 30.6% of individuals with depression who reported needing but not getting treatment. When applied to independent responses from the 2014 cohort, the model identified 72% of those who did not initiate treatment (p<.01), with a balanced accuracy that was also significantly above chance (71%, p<.01). For individuals who did not get treatment, the model predicted 10 (out of 15) reasons that they endorsed as barriers to treatment, with balanced accuracies between 53% and 65% (p<.05 for all). CONCLUSIONS: Considerable work is needed to improve follow-up and retention rates after the critical initial meeting in which a patient is given a diagnosis of depression. Routinely collected information about patients with depression could identify those at risk of not obtaining needed treatment, which may inform the development and implementation of interventions to reduce the prevalence of untreated depression.