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The U.S. foreclosure crisis intensified markedly during the Great Recession of 2007-09, and currently an estimated five percent of U.S. residential properties are more than 90 days past due or in the process of foreclosure. Yet there has been no systematic assessment of the effects of foreclosure on health and mental health. I applied systematic search terms to PubMed and PsycINFO to identify quantitative or qualitative studies about the relationship between home foreclosure and health or mental health. After screening the titles and abstracts of 930 publications and reviewing the full text of 76 articles, dissertations, and other reports, I identified 42 publications representing 35 unique studies about foreclosure, health, and mental health. The majority of studies (32 [91%]) concluded that foreclosure had adverse effects on health or mental health, while three studies yielded null or mixed findings. Only two studies examined the extent to which foreclosure may have disproportionate impacts on ethnic or racial minority populations. Home foreclosure adversely affects health and mental health through channels operating at multiple levels: at the individual level, the stress of personally experiencing foreclosure was associated with worsened mental health and adverse health behaviors, which were in turn linked to poorer health status; at the community level, increasing degradation of the neighborhood environment had indirect, cross-level adverse effects on health and mental health. Early intervention may be able to prevent acute economic shocks from eventually developing into the chronic stress of foreclosure, with all of the attendant benefits this implies for health and mental health status. Programs designed to encourage early return of foreclosed properties back into productive use may have similar health and mental health benefits.

Summary Points * Despite hundreds of mHealth pilot studies, there has been insufficient programmatic evidence to inform implementation and scale-up of mHealth. * We discuss what constitutes appropriate research evidence to inform scale up. * Potential innovative research designs such as multi-factorial strategies, randomized controlled trials, and data farming may provide this evidence base. * We make a number of recommendations about evidence, interoperability, and the role of governments, private enterprise, and researchers in relation to the scale up of mHealth. Interventions categorized under the rubric \"mobile health\" or \"mHealth\"--broadly defined as medical and public health practice supported by mobile devices [2]--span a variety of applications ranging from the use of mobile phones to improve point of service data collection [3], care delivery [4], and patient communication [5] to the use of alternative wireless devices for real-time medication monitoring and adherence support [6].

Although the theory of syndemics is principally a theory about population health, the past two decades' worth of quantitative literature motivated by the theory has generally focused on studying individuals rather than populations--and consequently has had very little to say about population health. Our aim in this Viewpoint is to critically review the literature on syndemics and to introduce key concepts for measuring their effects on population health.

Social distancing measures to address the US coronavirus disease 2019 (COVID-19) epidemic may have notable health and social impacts. We conducted a longitudinal pretest-posttest comparison group study to estimate the change in COVID-19 case growth before versus after implementation of statewide social distancing measures in the US. The primary exposure was time before (14 days prior to, and through 3 days after) versus after (beginning 4 days after, to up to 21 days after) implementation of the first statewide social distancing measures. Statewide restrictions on internal movement were examined as a secondary exposure. The primary outcome was the COVID-19 case growth rate. The secondary outcome was the COVID-19-attributed mortality growth rate. All states initiated social distancing measures between March 10 and March 25, 2020. The mean daily COVID-19 case growth rate decreased beginning 4 days after implementation of the first statewide social distancing measures, by 0.9% per day (95% CI -1.4% to -0.4%; P < 0.001). We did not observe a statistically significant difference in the mean daily case growth rate before versus after implementation of statewide restrictions on internal movement (0.1% per day; 95% CI -0.04% to 0.3%; P = 0.14), but there is substantial difficulty in disentangling the unique associations with statewide restrictions on internal movement from the unique associations with the first social distancing measures. Beginning 7 days after social distancing, the COVID-19-attributed mortality growth rate decreased by 2.0% per day (95% CI -3.0% to -0.9%; P < 0.001). Our analysis is susceptible to potential bias resulting from the aggregate nature of the ecological data, potential confounding by contemporaneous changes (e.g., increases in testing), and potential underestimation of social distancing due to spillover effects from neighboring states. Statewide social distancing measures were associated with a decrease in the COVID-19 case growth rate that was statistically significant. Statewide social distancing measures were also associated with a decrease in the COVID-19-attributed mortality growth rate beginning 7 days after implementation, although this decrease was no longer statistically significant by 10 days.

Atypical antipsychotic medications are widely prescribed for the adjunctive treatment of depression, yet their total risk-benefit profile is not well understood. We thus conducted a systematic review of the efficacy and safety profiles of atypical antipsychotic medications used for the adjunctive treatment of depression. We included randomized trials comparing adjunctive antipsychotic medication to placebo for treatment-resistant depression in adults. Our literature search (conducted in December 2011 and updated on December 14, 2012) identified 14 short-term trials of aripiprazole, olanzapine/fluoxetine combination (OFC), quetiapine, and risperidone. When possible, we supplemented published literature with data from manufacturers' clinical trial registries and US Food and Drug Administration New Drug Applications. Study duration ranged from 4 to 12 wk. All four drugs had statistically significant effects on remission, as follows: aripiprazole (odds ratio [OR], 2.01; 95% CI, 1.48-2.73), OFC (OR, 1.42; 95% CI, 1.01-2.0), quetiapine (OR, 1.79; 95% CI, 1.33-2.42), and risperidone (OR, 2.37; 95% CI, 1.31-4.30). The number needed to treat (NNT) was 19 for OFC and nine for each other drug. All drugs with the exception of OFC also had statistically significant effects on response rates, as follows: aripiprazole (OR, 2.07; 95% CI, 1.58-2.72; NNT, 7), OFC (OR, 1.30, 95% CI, 0.87-1.93), quetiapine (OR, 1.53, 95% CI, 1.17-2.0; NNT, 10), and risperidone (OR, 1.83, 95% CI, 1.16-2.88; NNT, 8). All four drugs showed statistically significant effects on clinician-rated depression severity measures (Hedges' g ranged from 0.26 to 0.48; mean difference of 2.69 points on the Montgomery-Asberg Depression Rating Scale across drugs). On measures of functioning and quality of life, these medications produced either no benefit or a very small benefit, except for risperidone, which had a small-to-moderate effect on quality of life (g = 0.49). Treatment was linked to several adverse events, including akathisia (aripiprazole), sedation (quetiapine, OFC, and aripiprazole), abnormal metabolic laboratory results (quetiapine and OFC), and weight gain (all four drugs, especially OFC). Shortcomings in study design and data reporting, as well as use of post hoc analyses, may have inflated the apparent benefits of treatment and reduced the apparent incidence of adverse events. Atypical antipsychotic medications for the adjunctive treatment of depression are efficacious in reducing observer-rated depressive symptoms, but clinicians should interpret these findings cautiously in light of (1) the small-to-moderate-sized benefits, (2) the lack of benefit with regards to quality of life or functional impairment, and (3) the abundant evidence of potential treatment-related harm. Please see later in the article for the Editors' Summary.

The theory of syndemics has received increasing attention in clinical medicine since the onset of the COVID-19 pandemic, due to the synergistic interactions of the disease with pre-existing political, structural, social and health conditions. In simple terms, syndemics are synergistically interacting epidemics that occur in a particular context with shared drivers. When policymakers ask why some communities have higher death rates from COVID-19 compared with other communities, those working from a syndemics framework argue that multiple factors synergistically work in tandem, and populations with the highest morbidity and mortality experience the greatest impact of these interactions. In this Perspective, we use specific case examples to illustrate these concepts. We discuss the emergence of syndemics, how epidemics interact, and what scientists, clinicians and policymakers can do with this information. This Perspective delivers an introduction to syndemic thinking, and provides insights into how epidemics interact and what scientists, clinicians and policymakers can do with this information.

University administrators and mental health clinicians have raised concerns about depression and anxiety among Ph.D. students, yet no study has systematically synthesized the available evidence in this area. After searching the literature for studies reporting on depression, anxiety, and/or suicidal ideation among Ph.D. students, we included 32 articles. Among 16 studies reporting the prevalence of clinically significant symptoms of depression across 23,469 Ph.D. students, the pooled estimate of the proportion of students with depression was 0.24 (95% confidence interval [CI], 0.18–0.31; I 2  = 98.75%). In a meta-analysis of the nine studies reporting the prevalence of clinically significant symptoms of anxiety across 15,626 students, the estimated proportion of students with anxiety was 0.17 (95% CI, 0.12–0.23; I 2  = 98.05%). We conclude that depression and anxiety are highly prevalent among Ph.D. students. Data limitations precluded our ability to obtain a pooled estimate of suicidal ideation prevalence. Programs that systematically monitor and promote the mental health of Ph.D. students are urgently needed.

Violence against women by intimate partners remains unacceptably common worldwide. The evidence base for the assumed psychological impacts of intimate partner violence (IPV) is derived primarily from studies conducted in high-income countries. A recently published systematic review identified 13 studies linking IPV to incident depression, none of which were conducted in sub-Saharan Africa. To address this gap in the literature, we analyzed longitudinal data collected during the course of a 3-y cluster-randomized trial with the aim of estimating the association between IPV and depression symptom severity. We conducted a secondary analysis of population-based, longitudinal data collected from 1,238 pregnant women during a 3-y cluster-randomized trial of a home visiting intervention in Cape Town, South Africa. Surveys were conducted at baseline, 6 mo, 18 mo, and 36 mo (85% retention). The primary explanatory variable of interest was exposure to four types of physical IPV in the past year. Depression symptom severity was measured using the Xhosa version of the ten-item Edinburgh Postnatal Depression Scale. In a pooled cross-sectional multivariable regression model adjusting for potentially confounding time-fixed and time-varying covariates, lagged IPV intensity had a statistically significant association with depression symptom severity (regression coefficient b = 1.04; 95% CI, 0.61-1.47), with estimates from a quantile regression model showing greater adverse impacts at the upper end of the conditional depression distribution. Fitting a fixed effects regression model accounting for all time-invariant confounding (e.g., history of childhood sexual abuse) yielded similar findings (b = 1.54; 95% CI, 1.13-1.96). The magnitudes of the coefficients indicated that a one-standard-deviation increase in IPV intensity was associated with a 12.3% relative increase in depression symptom severity over the same time period. The most important limitations of our study include exposure assessment that lacked measurement of sexual violence, which could have caused us to underestimate the severity of exposure; the extended latency period in the lagged analysis, which could have caused us to underestimate the strength of the association; and outcome assessment that was limited to the use of a screening instrument for depression symptom severity. In this secondary analysis of data from a population-based, 3-y cluster-randomized controlled trial, IPV had a statistically significant association with depression symptom severity. The estimated associations were relatively large in magnitude, consistent with findings from high-income countries, and robust to potential confounding by time-invariant factors. Intensive health sector responses to reduce IPV and improve women's mental health should be explored.

Depression is recognized globally as a leading cause of disability. Early-life adverse childhood experiences (ACEs) have been shown to have robust associations with poor mental health during adulthood. These effects may be cumulative, whereby a greater number of ACEs are progressively associated with worse outcomes. This study aimed to estimate the associations between ACEs and adult depression and suicidal ideation in a cross-sectional, population-based study of adults in Uganda. Between 2016 and 2018, research assistants visited the homes of 1,626 adult residents of Nyakabare Parish, a rural area in southwestern Uganda. ACEs were assessed using a modified version of the Adverse Childhood Experiences-International Questionnaire, and depression symptom severity and suicidal ideation were assessed using the Hopkins Symptom Checklist for Depression (HSCL-D). We applied a validated algorithm to determine major depressive disorder diagnoses. Overall, 1,458 participants (90%) had experienced at least one ACE, 159 participants (10%) met criteria for major depressive disorder, and 28 participants (1.7%) reported suicidal ideation. We fitted regression models to estimate the associations between cumulative number of ACEs and depression symptom severity (linear regression model) and major depressive disorder and suicidal ideation (Poisson regression models). In multivariable regression models adjusted for age, sex, primary school completion, marital status, self-reported HIV status, and household asset wealth, the cumulative number of ACEs was associated with greater depression symptom severity (b = 0.050; 95% confidence interval [CI], 0.039-0.061, p < 0.001) and increased risk for major depressive disorder (adjusted relative risk [ARR] = 1.190; 95% CI, 1.109-1.276; p < 0.001) and suicidal ideation (ARR = 1.146; 95% CI, 1.001-1.311; p = 0.048). We assessed the robustness of our findings by probing for nonlinearities and conducting analyses stratified by age. The limitations of the study include the reliance on retrospective self-report as well as the focus on ACEs that occurred within the household. In this whole-population, cross-sectional study of adults in rural Uganda, the cumulative number of ACEs had statistically significant associations with depression symptom severity, major depressive disorder, and suicidal ideation. These findings highlight the importance of developing and implementing policies and programs that safeguard children, promote mental health, and prevent trajectories toward psychosocial disability.

[...]economic outcomes in the same population subgroups have worsened as well. Since the late 1970s, growth in wages and incomes stagnated for most US residents [5]. [...]income inequality has increased dramatically over this period, reaching levels not seen since the eve of the 1929–1939 Great Depression [10]. [...]the deregulation of the financial industry has contributed to increasing “financialization” of the economy, which has both increased the relative importance of financial firms—and the wages of those employed within them—and exposed small businesses and individuals to greater financial risk [20]. [...]emerging evidence suggests that deunionization has reduced the bargaining power of workers without college degrees, thereby undermining union-associated earnings premia and worsening income inequality [22,23].