Elsevier

Clinical Psychology Review

Volume 38, June 2015, Pages 65-78
Clinical Psychology Review

Healthcare interventions for depression in low socioeconomic status populations: A systematic review and meta-analysis

https://doi.org/10.1016/j.cpr.2015.03.001Get rights and content

Highlights

  • 17 interventions were identified to treat DD in low-SES populations.

  • There was a significant reduction in overall depressive symptoms at short-term.

  • The overall effect slightly decreased at long-term.

  • The effectiveness can be maximized through cultural training for providers.

  • Booster sessions seem to reduce depressive symptoms at long-term.

Abstract

The prevalence and impact of depressive disorders in developed countries are associated with certain population characteristics, including socioeconomic status. The aim of this systematic review and meta-analysis was to identify, characterize and analyze the short- and long-term effectiveness of healthcare interventions for depressive disorders in low socioeconomic status populations. The main biomedical databases were searched and fifteen articles assessing seventeen interventions were included in the review. Most interventions were implemented in the US (n = 11) and culturally adapted (n = 11). We conducted two meta-analyses for assessing both the short- (n = 11) and long-term effectiveness (n = 12) of interventions. There was a statistically significant reduction in overall depressive symptoms (− 0.58, 95% CI [− 0.74, − 0.41]) at short-term (up to three months after the intervention), especially for combined and psychotherapeutic interventions. The overall effect slightly decreased at long-term (− 0.42, 95% CI [− 0.63, − 0.21]). Those interventions including culturally specific training for providers and booster sessions seemed to be more effective in reducing depressive disorders at short and long term, respectively. In conclusion, healthcare interventions are effective in decreasing clinically significant depressive disorders in low socioeconomic status populations. Future interventions should take into account the key characteristics identified in this review.

Introduction

It has been estimated that one in four people will have a mental disorder during their lifetime (World Health Organization, 2001). Mood disorders are one of the most common types of mental disorder (Alonso et al., 2004, Kessler et al., 2005, King et al., 2008). In terms of their prevalence, it has been calculated that ranges from 6.6% to 11.9% (Baumeister & Härter, 2007), of which about three quarters are depressive disorders, which makes them a leading cause of disability worldwide (Ferrari et al., 2013, Kessler et al., 2003, Marcus et al., 2012, Moussavi et al., 2007).

There is a considerable body of evidence suggesting that the prevalence and impact of depressive disorders in developed countries are associated with certain population characteristics, including socioeconomic status (SES) (Lorant et al., 2007, Missinne and Bracke, 2012, Ruiz-Perez et al., 2011, Wang et al., 2005).

Socioeconomic inequality in depression is heterogeneous and varies according to the way mental health disorder is measured, to the definition and measurement of SES, and to contextual features such as region and time (Lorant et al., 2003). Previous work has indicated that having a low SES, as ascertained by the use of proxies such as education and income, increases not only the risk of onset of depressive disorders but also their persistence (Lorant et al., 2003). In this sense, inequalities in healthcare provision are at least partially responsible for the observed health disparities. When compared with the general population, those with low-SES are less likely to be diagnosed with depression and to either seek or to receive treatment (Alegria et al., 2008, Cabassa et al., 2006, Chung et al., 2003, Gallo et al., 1995, Grote et al., 2008, Pingitore et al., 2001), further contributing to the maintenance or exacerbation of the disorder.

However, the evidence regarding the effectiveness of interventions aimed at decreasing inequalities in mental health care is sparse and limited to specific population subgroups, for instance by focusing on women (Levy & O'Hara, 2010) or ethnic minorities (Beach et al., 2006). Levy and O'Hara (2010) studied psychotherapeutic interventions targeted at low income depressed women (USA, Mexico and Chile), offering a series of recommendations for clinical practice, including the need to adapt these interventions to the specific characteristics of the target population. Cabassa and Hansen (2007) conducted a systematic review of depression treatments for Latino adults in the primary care setting, observing that collaborative care models achieved the best results in reducing treatment inequalities, which was reiterated in a subsequent study including a low-SES population (Bao et al., 2011).

Other studies have also assessed the effectiveness of culturally adapted interventions for ethnic minorities, finding that interventions that were adapted to the targeted group were more likely to be successful (Benish et al., 2011, Griner and Smith, 2006). According to the authors, cultural adaptations to mental health interventions consist of identifying how the client's culture and context might interact with the intervention and then systematically integrating his or her values and worldviews into treatment which can be done by explicitly discussing the client's cultural values and beliefs, or by using the client's preferred language during treatment, among other strategies (Benish et al., 2011).

Taken together, the existing literature indicates that low SES populations are more likely to develop and maintain depressive disorders (Lorant et al., 2003), while also being less likely to seek or receive treatment (Alegria et al., 2008). Previous work has also indicated that culturally adapted interventions might bridge the gap and increase the effectiveness of mental health interventions for populations from certain disadvantaged groups, such as ethnic and racial minorities (Benish et al., 2011, Griner and Smith, 2006). However, the evidence for interventions aimed at populations with low SES is scarcer and often entangled with other characteristics such as gender or ethnicity.

The aim of this systematic review and meta-analysis was to identify, characterize and analyze the short- and long-term effectiveness of healthcare interventions for depressive disorders specifically targeted to low SES populations.

Section snippets

Methods

The present study is part of a broader systematic review, which aimed to identify and analyze healthcare interventions to reduce social inequalities in depressive disorders. The review and its procedures were planned, conducted, and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Moher, Liberati, Tetzlaff, & Altman, 2009).

Identification of articles

Search results are summarized in the PRISMA flow diagram (Fig. 1). The initial search identified a total of 4091 citations, 1154 of which were duplicated. Title and abstract of the remaining 2937 citations were screened; as a result 580 citations were selected for further assessment. After examination of full text articles, 15 articles assessing 17 interventions were included, as two articles assessed two active arms each (Gater et al., 2010, Miranda et al., 2003).

Study characteristics

The characteristics of the

Discussion

This systematic review identified 17 healthcare interventions aiming at ameliorating depressive disorders and reducing clinically significant depression symptoms in patients with low SES. The interventions were based on different approaches, namely psychotherapy and collaborative care, and most of them were culturally adapted to the target population. Our meta-analyses suggested that interventions reduced depressive symptoms both at short and long term. In addition we observed that the most

Role of funding source

Funding for this study was provided by the National Institute of Health Carlos III (Study PS09/00747). The funding source had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript.

Contributors

ARG and IRP designed the study. ARG and GP selected the articles and extracted relevant data. ARG and MRB conducted the statistical analysis. ARG, IRC, and DCG drafted the article. All authors provided input during the preparation of the manuscript, and approved the final version.

Conflict of interests

The authors declare that they have no conflict of interests.

Acknowledgments

The authors thank Victor Sarmiento (Andalusian Agency for Health Technology Assessment, Andalusia, Spain) for designing the bibliographic searches.

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