Food insecurity and mental health outcomes among homeless adults: a scoping review

2020 ◽  
pp. 1-12 ◽  
Author(s):  
Elizabeth I Loftus ◽  
James Lachaud ◽  
Stephen W Hwang ◽  
Cilia Mejia-Lancheros
Keyword(s):  
Mental Health ◽  
Food Security ◽  
Food Insecurity ◽  
Scoping Review ◽  
Health Conditions ◽  
Homeless Adults ◽  
Mental Health Conditions ◽  
Household Food ◽  
Potential Association ◽  
Health Related

Abstract Objective: This review summarises and synthesises the existing literature on the relationship between food insecurity (FS) and mental health conditions among adult individuals experiencing homelessness. Design: Scoping review. Papers published between 1 January 2008 and 2 November 2018, searched in PubMed, Web of Science, Scopus, PsycINFO, Cochrane Library and CINAHL, using homelessness, food security and mental health keywords. Setting: Global evidence. Participants: Homeless adults aged 18 years or more. Results: Nine articles (eight cross-sectional and one longitudinal) were included in the present review. FS was measured using the Household Food Insecurity Access Scale, the United States Department of Agriculture Household Food Security Survey Module, as well as single-item or constructed measures. Depression and depressive symptoms were the most common mental health conditions studied. Other mental health conditions assessed included alcohol and substance use, emotional disorders, mental health problems symptoms severity and psychiatric hospitalisations. Composite measures such as axis I and II categories and a cluster of severe mental conditions and mental health-related functioning status were also analysed. FS and mental health-related problems were considered as both exposure and outcome variables. The existing evidence suggests a potential association between FS and several mental health conditions, particularly depression, mental health symptoms severity and poor mental health status scores. Conclusions: This review suggests the potential association between some mental health conditions and FS among homeless adults. However, there is a need for more longitudinal- and interventional-based studies, in order to understand the nature and directionality of the links between FS and mental health in this population group.

26 Supporting the Transition to Postsecondary for Students with Mental Health Conditions: A Scoping Review

2021 ◽  
Vol 26 (Supplement_1) ◽  
pp. e19-e21
Author(s):  
Dan Devoe ◽  
Thomas Lange ◽  
Pauline MacPherson ◽  
Dillon Traber ◽  
Rosemary Perry ◽  
...  
Keyword(s):  
Mental Health ◽  
Postsecondary Education ◽  
Scoping Review ◽  
Life Stage ◽  
Subject Area ◽  
Health Conditions ◽  
Future Research ◽  
Level Of Evidence ◽  
Cross Sectional ◽  
Mental Health Conditions

Abstract Primary Subject area Mental Health Background The transition from high school to postsecondary is a critical milestone for independence and empowerment. This life stage frequently coincides with the emergence of most mental health conditions (MHCs). Without adequate support to assist with the transition to postsecondary education, the mental health of arriving students with existing MHCs is likely to decline or remain unmet. Declining mental health is strongly associated with students withdrawing from both secondary and postsecondary education. However, a scoping review of interventions aiming to support youth with MHCs transition to postsecondary has not been conducted. Objectives The objectives of this scoping review were to identify: (1) researched interventions that support youth with MHCs during the transition to postsecondary; (2) best practices used to support this transition; (3) methods of evaluating these interventions and any limitations; and (4) gaps where future research is warranted. Design/Methods A database search of MEDLINE, PsycINFO, Embase, SocINDEX, ERIC, CINHAL, and Education Research Complete was undertaken. Two reviewers independently screened studies and extracted the data. Thematic analysis and risk-of-bias assessment were conducted on included studies. Results Nine studies were included in this review, describing eight unique interventions (Figure 1). Sixty-two percent of interventions were nonspecific in the MHCs that they were targeting in postsecondary students. These interventions were designed to support students upon arrival to postsecondary. Peer mentorship, student engagement, and interagency collaboration were found to be beneficial approaches to supporting youth transitioning into postsecondary (Table 1). The overall quality and level of evidence in these studies was low. Three knowledge gaps were found: evidence was not generalizable to the diversity of MHCs, intervention studies were mostly cross-sectional in nature and lacked follow-up data, and sustaining intervention funding remained a challenge for postsecondary institutions. Conclusion The volume of research identified was limited but indicated overall that offering support during the transition to postsecondary was beneficial for students with MHCs. Further evidence is needed that is generalizable across the mental health spectrum, and that assesses intervention outcomes in relation to intervention costs.

scholarly journals Household food insecurity, income loss, and symptoms of psychological distress among adults following the Cyclone Amphan in coastal Bangladesh

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259098
Author(s):  
Ahmed Hossain ◽  
Bayes Ahmed ◽  
Taifur Rahman ◽  
Peter Sammonds ◽  
Shamrita Zaman ◽  
...  
Keyword(s):  
Mental Health ◽  
Food Security ◽  
Psychological Distress ◽  
Food Insecurity ◽  
Psychological Symptoms ◽  
Household Food Insecurity ◽  
Income Loss ◽  
Coastal Bangladesh ◽  
Household Food ◽  
High Prevalence

Introduction Cyclone Amphan swept into Bangladesh’s southwestern coast at the end of May 2020, wreaking havoc on food security and economic stability, as well as possibly worsening mental health. We studied the prevalence of post-cyclone stressors in adults following the cyclone and its association with symptoms of psychological distress. Methods We conducted a cross-sectional study in a coastal district of Bangladesh. A five-item brief symptom rating scale was used to measure the symptoms of psychological distress. Household food insecurity was measured using the USAID Household Food Insecurity Access Scale questionnaire. We estimated adjusted prevalence ratios (aPRs) using robust log-linear models adjusted for potential confounders. Results A total of 478 adults (mean [SD] age, 37.0[12.6] years; 169[35.4%] women) participated in the study. The prevalence of moderate-to-severe psychological symptoms and suicidal ideation was 55.7% and 10.9%, respectively. Following the cyclone, 40.8% of the adults reported severe food insecurity, and 66% of them reported moderate-to-severe mental health symptoms. Also, 54.4% of women and 33.7% of men reported severe food insecurity in the households. Moreover, 25.5% of respondents reported no income or a significant income loss after the cyclone, and 65.5% of them had moderate-to-severe psychological symptoms. Also, 13.8% of respondents reported housing displacement because of severely damaged houses, and 68.2% of them reported moderate-to-severe psychological symptoms. The high prevalence of mental health symptoms was found in women (aPR = 1.41, 95% CI = 1.06–1.82), people with severe food insecurity (aPR = 1.63, 95% CI = 1.01–2.64), and people who lost jobs or lost a major income source (aPR = 1.25, 95% CI = 1.02–1.54). Conclusion Following cyclone Amphan, many low-income individuals saw their income drop drastically while others were unemployed and living with severe food insecurity. The result suggests gender inequalities in food-security after the cyclone. Immediate action is needed to ensure household food-security for reducing the burden of mental illness. Rising opportunities of paid-jobs and decreasing income-loss, especially for the poor people, can have a protective impact on psychological distress. However, due to the high prevalence of severe psychological symptoms, long-term mental health services are required among the population of coastal Bangladesh.

scholarly journals Trajectories of depressive symptoms among vulnerable groups in the UK during the COVID-19 pandemic

2020 ◽  
Author(s):  
Philipp Frank ◽  
Eleonora Iob ◽  
Andrew Steptoe ◽  
Daisy Fancourt
Keyword(s):  
Mental Health ◽  
Social Support ◽  
Depressive Symptoms ◽  
Human Condition ◽  
Health Conditions ◽  
Vulnerable Groups ◽  
Mental Health Conditions ◽  
Growth Mixture Modelling ◽  
Health Related ◽  
The Uk

ABSTRACTObjectiveThe coronavirus disease 2019 (COVID-19) pandemic has affected many aspects of the human condition, including mental health and psychological wellbeing. This study examined trajectories of depressive symptoms (DST) over time among vulnerable individuals in the UK during the COVID-19 pandemic.MethodsThe sample consisted of 51,417 adults recruited from the COVID-19 Social Study. Depressive symptoms were measured on seven occasions (21st March - 2nd April), using the Patient Health Questionnaire (PHQ-9). Sociodemographic vulnerabilities included non-white ethnic background, low socio-economic position (SEP), and type of work (keyworker versus no keyworker). Health-related and psychosocial vulnerabilities included pre-existing physical and mental health conditions, experience of psychological and/or physical abuse, and low social support. Group-based DST were derived using latent growth mixture modelling and multivariate logistic regression models were fitted to examine the association between these vulnerabilities and DSTs. Model estimates were adjusted for age, sex, and suspected COVID-19 diagnosis.ResultsThree DSTs were identified: low [N=30,850 (60%)] moderate [N=14,911 (29%)], and severe [N=5,656 (11%)] depressive symptoms. DSTs were relatively stable across the first 6 weeks of lockdown. After adjusting for covariates, experiences of physical/psychological abuse (OR 13.16, 95% CI 12.95-13.37), pre-existing mental health conditions (OR 13.00 95% CI 12.87-13.109), pre-existing physical health conditions (OR 3.41, 95% CI 3.29-3.54), low social support (OR 12.72, 95% CI 12.57-12.86), and low SEP (OR 5.22, 95% CI 5.08-5.36) were significantly associated with the severe DST. No significant association was found for ethnicity (OR 1.07, 95% 0.85-1.28). Participants with key worker roles were less likely to experience severe depressive symptoms (OR 0.66, 95% 0.53-0.80). Similar but smaller patterns of associations were found for the moderate DST.ConclusionsPeople with psychosocial and health-related risk factors, as well as those with low SEP seem to be most vulnerable to experiencing moderate or severe depressive symptoms during the COVID-19 pandemic.

scholarly journals Machine learning in mental health: a scoping review of methods and applications

2019 ◽  
Vol 49 (09) ◽  
pp. 1426-1448 ◽  
Author(s):  
Adrian B. R. Shatte ◽  
Delyse M. Hutchinson ◽  
Samantha J. Teague
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Scoping Review ◽  
Latent Dirichlet Allocation ◽  
Health Conditions ◽  
Support Vector ◽  
Mental Health Conditions ◽  
Big Data Applications ◽  
Study Results ◽  
Detection And Diagnosis

AbstractBackgroundThis paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice.MethodsWe employed a scoping review methodology to rapidly map the field of ML in mental health. Eight health and information technology research databases were searched for papers covering this domain. Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type, and study results. Articles were then synthesised via narrative review.ResultsThree hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis; (ii) prognosis, treatment and support; (iii) public health, and; (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's disease. ML techniques used included support vector machines, decision trees, neural networks, latent Dirichlet allocation, and clustering.ConclusionsOverall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration. With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to other areas of psychology and mental health. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field.

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