Neighborhood socioeconomic conditions and depression: a systematic review and meta-analysis

Richardson, Robin; Westley, Tracy; Gariépy, Geneviève; Austin, Nichole; Nandi, Arijit

doi:10.1007/s00127-015-1092-4

Cited by 138 publications

(124 citation statements)

References 33 publications

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“…The first is that we have shown an inverse relationship between household income (an objective measure of socioeconomic status) and depressive symptoms in South Africa, a LMIC country characterized by extreme material deprivation and poverty; a finding that has been previously demonstrated in high-income country contexts (Weich & Lewis, 1998; Lorant et al, 2003; Richardson et al, 2015). This provides further support for evidence showing that poverty is a major risk factor for mental health disorders in LMICs (Patel & Kleinman, 2003; Author et al, 2010; Author et al, 2011; Author, 2014).…”

Section: Discussionsupporting

confidence: 52%

“…It has long been recognized that social determinants such as socioeconomic status (SES) have a direct relationship with health outcomes (Marmot et al, 1991; Mackenbach et al, 1997) including mental disorders (Weich & Lewis, 1998; Lorant et al, 2003; Author et al, 2010; Richardson et al, 2015). Increasing absolute material wealth strongly predicts better mental health outcomes and life expectancy within countries, but does not explain differences in these outcomes between high-income countries (Wilkinson & Pickett, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Income inequality widens the existing income-related disparity in depression risk in post-apartheid South Africa: Evidence from a nationally representative panel study

2017

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Aim Income inequality (II) and poverty are major challenges in South Africa (SA) yet little is known about their interaction on population mental health. We explored relationships between district II, household income (HHI) and depressive symptoms in national panel data. Method We used 3 waves (2008, 2010, 2012) of the SA National Income Dynamics Study (n=25936) in adjusted mixed effects logistic regression to assess if the relationship between HHI and depressive symptoms is dependent on level of II. Depressive symptoms were assessed with Centre for Epidemiologic Studies Depression scale, and District inequality ratios (P10P90) derived from HHI distributions in 53 districts. Results Lower HHI and increasing II were associated with depressive symptoms. The interaction term between HHI and II on depressive symptoms was significant (β=0.01, 95% CI:<0.01–0.01); with increasing II and decreasing HHI, depression risk increased. Conclusion II widens income-related disparities in depression risk in SA, with policy implications for understanding socioeconomic determinants of mental health and informing global efforts to reduce disparities in high poverty and inequality contexts.

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Section: Discussionsupporting

confidence: 52%

Section: Introductionmentioning

confidence: 99%

Income inequality widens the existing income-related disparity in depression risk in post-apartheid South Africa: Evidence from a nationally representative panel study

2017

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“…We included complex variables, such as residency ZIP codes and attending doctor. ZIP codes can act as a surrogate of neighborhood socioeconomic characteristics, which has been shown to be associated with multiple disease and health behaviors (36–38). The performance of attending doctors can potentially be a factor of postoperative outcomes as well (39, 40).…”

Section: Discussionmentioning

confidence: 99%

Delirium Prediction using Machine Learning Models on Predictive Electronic Health Records Data

Davoudi

Ozrazgat‐Baslanti

Ebadi

et al. 2017

2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)

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Electronic Health Records (EHR) are mainly designed to record relevant patient information during their stay in the hospital for administrative purposes. They additionally provide an efficient and inexpensive source of data for medical research, such as patient outcome prediction. In this study, we used preoperative Electronic Health Records to predict postoperative delirium. We compared the performance of seven machine learning models on delirium prediction: linear models, generalized additive models, random forests, support vector machine, neural networks, and extreme gradient boosting. Among the models evaluated in this study, random forests and generalized additive model outperformed the other models in terms of the overall performance metrics for prediction of delirium, particularly with respect to sensitivity. We found that age, alcohol or drug abuse, socioeconomic status, underlying medical issue, severity of medical problem, and attending surgeon can affect the risk of delirium.

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“…Characteristics of patients’ neighborhoods such as elevated poverty rates, low educational attainment, or unstable housing may reflect psychosocial stressors and reduced access to resources (e.g, transportation, childcare) which can undermine treatment participation or moderate treatment response. Systematic reviews of epidemiologic studies have reported mixed findings regarding whether individuals who live in more socioeconomically disadvantaged neighborhoods are at increased risk for depression . However, neighborhood income levels have been associated with antidepressant adherence, which could result in improved treatment outcomes among patients from neighborhoods with higher incomes.…”

Section: Introductionmentioning

confidence: 99%

Individual and neighborhood characteristics as predictors of depression symptom response

Panaite

Bowersox

Zivin

et al. 2019

Health Services Research

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Objective Assess whether neighborhood characteristics predict patient‐reported outcomes for depression. Data Sources VA electronic medical record data and U.S. census data. Study Design Retrospective longitudinal cohort. Data Extraction Methods Neighborhood and individual characteristics of patients (N = 4,269) with a unipolar depressive disorder diagnosis and an initial Patient Health Questionnaire (PHQ‐9) score ≥10 were used to predict 50 percent improvement in 4‐8‐month PHQ‐9 scores. Principal Findings The proportion of a patient's neighborhood living in poverty (OR = 0.98; 95% CI: 0.97‐.1.00; P = 0.03) was associated with lower likelihood of depression symptom improvement in addition to whether the patient was black (OR = 0.76; 95% CI:0.61‐0.96; P = 0.02) had PTSD (OR = 0.59; 95% CI:0.50‐0.69; P < 0.001) or had any service‐connected disability (OR = 0.73; 95% CI:0.61‐0.87; P < 0.001). Conclusions Neighborhood poverty should be considered along with patient characteristics when determining likelihood of depression improvement.

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Neighborhood socioeconomic conditions and depression: a systematic review and meta-analysis

Abstract: We found inconsistent evidence in support of a longitudinal association between NSEC and depression, and heterogeneity according to the length of follow-up time might partly explain the mixed evidence observed in the literature on NSEC and depression.

Cited by 138 publications

References 33 publications

Income inequality widens the existing income-related disparity in depression risk in post-apartheid South Africa: Evidence from a nationally representative panel study

Income inequality widens the existing income-related disparity in depression risk in post-apartheid South Africa: Evidence from a nationally representative panel study

Delirium Prediction using Machine Learning Models on Predictive Electronic Health Records Data

Individual and neighborhood characteristics as predictors of depression symptom response

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