Introduction

The study of depression in older adults (OA) has acquired critical importance in that it ranks as the second most common mental disorder worldwide, surpassed only by dementia [1]. According to estimates, by 2030, depression will have escalated to first place in the global burden of disease, accounting for 6% of the total disability-adjusted life years [2]. Current prevalence of clinical depression among OA fluctuates between 1% and 9% [1], exerting a high impact on quality of life, perception of health, functionality, occurrence of comorbidity and economic productivity [1].

In 2005, prevalence of clinical depression in Mexico was estimated at 5% and 9.5% among men and women, respectively [3]. Alongside major clinical depression, depressive symptoms [4] have drawn considerable attention from public health initiatives. While they share a number of probable etiological explanations with clinical depression [5]–[6] and have been directly associated with mortality and suicide in this age group [6]–[7], it is estimated that their prevalence may eventually affect four times as many individuals as clinical depression [8]. In the case of Mexico, the prevalence of subclinical depression in OA over 65 years, using the Geriatric Examination for Computer Assisted Taxonomy (GMS–AGECA), was estimated at 31.9% and 32.2% in urban and rural areas, respectively, which is more than twice that the estimated prevalence using DSM-IV or ICD-10 criteria [9].

It has been amply documented that the social determinants of health (mainly contextual poverty, income inequality and social cohesion across the social organization – from the neighborhood up to the national level-) play a prominent role in a wide array of health outcomes, including maternal and child mortality, cardiovascular disease and infectious diseases [10]. However, only recently has research recognized the impact of social determinants on mental health indicators, particularly on depression [11].

According to the ecosocial theory, the health-disease process is part of a hierarchical system where causal interactions flow organically across organization levels [12] and in consequence, social influences become literally “embodied” into individuals [13]. Kim has recently adapted this model to analyze the etiology of the depression [14] by organizing the individual and contextual factors hierarchically under an explanatory model. By interweaving the two, Kim's perspective now makes it possible to understand depression as an interaction between the social context and individual susceptibility [12], [14].

Various studies have analyzed the individual and contextual aspects of depression across a variety of OA populations, with the literature identifying the following risk factors: being female, mourning as a result of a significant life event, especially the loss of a partner, functional limitations, existence of a prior depressive episode, physical pain, presence of other pathologies, an inadequate social support network, a low educational level, living alone, social isolation, a perception of being inadequately cared for, limited access to health services and, especially, a precarious economic situation [1], [15]–[18]. On the other hand, with regard to contextual factors, it has been observed that socioeconomic disadvantage and, in general, different measures of poverty and deprivation regarding the place of residence (neighborhood, community or locality) correlate with depression in adults overall and, particularly, in OA [14].

It has been advanced that, in general, economically precarious contexts tend to generate hostile social environments (marked by insecurity and few suitable areas for recreation) [19]–[21], thus heightening exposure to everyday risks. In turn, these conditions are known to breed psychosocial stress, depleted social capital, limited access to resources and low-quality health services, all of which interact with individual susceptibility as catalysts of depression [14], [19]–[21].

Acknowledging that the different levels of contextual conditions are relevant to depression not only expands the explanatory framework for this disorder by incorporating the role of social determinants, but also provides evidence for creating interventions that transcend the individual realm. The objective of this study was to estimate the association of income inequality and socioeconomic deprivation at place of residence with the occurrence of depressive symptoms in OA aged 60 and over in Mexico.

Methods

Results

Of the 8,874 OA who participated in ENSANUT 2012, we excluded those who were unable to respond to the interview personally. The principal reasons for failure to respond were hearing or speech impairment (694 OA) and memory loss (320 OA). The final analytical sample thus consisted of 7,867 OA, of whom 53.4% were female, with an average age of 69.3 years (standard error (S.E).  =  0.15) and 6.1 years of formal education (S.E.  =  0.11). Finally, 22.3% had difficulty performing at least one of the basic activities of daily living and 18.0% at least one of the instrumental activities.

The prevalence of depressive symptoms was 35.6% (CI95%: 33.8%–37.4%). Table 1 displays the characteristics of the sample according to the presence of depressive symptoms. The following variables showed a significant association with depressive symptoms: female, absence of an intimate partner, paid job, few years of formal education, difficulty in performing basic or instrumental activities of daily living, presence of comorbidity, not being consulted about household decisions, occurrence of violent incidents or accidents in the previous 12 months, living alone, and a low household socioeconomic status (p<0.05). The bivariate analysis yielded no statistically significant relationship among the different income inequality levels. The estimated ICC values were: 6.3% (CI95%: 3.9%–9.6%) at the locality level; 4.0% (CI95%: 2.0%–7.7%) at the municipal level, and 1.5% (CI95%: 0.07%–3.3%) at the state level.

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Table 1. Sociodemographic characteristics according to the presence of depressive symptoms in the study sample.^‡

https://doi.org/10.1371/journal.pone.0108127.t001

Table 2 presents the results of the multilevel logistic regression models. As the exploratory analysis indicated that the household asset and locality deprivation indices were not linearly associated with to the logarithm of odds on the presence of depressive symptoms, both variables were incorporated as tertiles in the regression models. Additionally, we adjusted all models using the locality and municipality population sizes as well as to the percentage of rural population at both levels.

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Table 2. Multilevel logistical regression models for the association between the presence of depressive symptoms and contextual variables: income inequality and socioeconomic deprivation^‡.

https://doi.org/10.1371/journal.pone.0108127.t002

In general, the association of the outcome variable with the individual and household variables was consistent across estimated models, in particular with regard to the following variables: female, absence of an intimate partner, not being consulted on household decisions, few years of schooling, difficulty in performing the basic or instrumental activities of daily living, presence of comorbidities and the tertiles of asset index. In comparing the three estimated models, the third (with random intercepts for the three levels) demonstrated the best fit, not only by the log-likelihood and AIC values, but also and most importantly, by the LRT results.

The third model showed a statistically significant association between the presence of depressive symptoms and the following individual variables: female (OR = 1.37; CI95%:1.23–1.54), absence of an intimate partner (OR = 1.16; CI95%; 1.03–1.32), not being consulted about household decisions (OR = 1.64; CI95%: 1.37–1.97), years of schooling (OR = 0.97; CI95%: 0.95–0.98), presence of multimorbidity (OR = 1.32; CI95%: 1.25–1.38), difficulty in performing at least one instrumental activity of daily living (OR = 1.45; CI95%: 1.26–1.68), difficulty in performing at least one basic activity of daily living (OR = 1.85; CI95%: 1.62–2.10) and self-reported accidents (OR = 1.53; CI95%: 1.26–1.85) or violent incident(s) (OR = 2.41; CI95%: 1.57–3.69) during the previous 12 months. Finally, at the household level, we observed a significant and protective association between the outcome variable and a higher socioeconomic status (OR = 0.79; CI95%: 0.66–0.94, when comparing tertiles 1 and 3).

Regarding the association of the contextual variables with the presence of depressive symptoms, on comparing tertiles 3 versus 1, the association of socioeconomic deprivation index was marginally significant at the locality level (OR = 1.28; p<0.10), and significant at the municipality level (OR = 1.16, p<0.01). While income inequality showed no association at any of the aggregation levels analyzed (locality, municipality or state). Furthermore, no evidence was found to support the use of random slopes at either the municipality (LRT = 0.10; p = 0.99) or the state (LRT = 0.22; p = 0.89) levels.

Discussion

In line with other recent studies on OA, we found significant association between the presence of depressive symptoms and a number of variables at the individual level, in particular: female [17], [40]–[43], absence of an intimate partner [17], [41]–[43], a low educational level or illiteracy [17], [41]–[42], lack of empowerment in household decision-making (especially with regard to financial matters) [43]–[44], disability or functional limitations [40]–[41], presence of comorbidity [40]–[43], and chronic exposure to psychosocial stress [42]. The latter could be an important factor in the strong connection we found between depressive symptoms and self-reported incidents of physical violence or accidents in the previous 12 months. This is consistent with a recent Brazilian study [45], which found a strong association between exposure to violent, traumatic events and the prevalence of various psychiatric disorders, including major clinical depression. Other studies have established an association between a low socioeconomic status at either the individual or household level and depression among OA [15]–[17], [43]–[48].

Our principal finding was a strong link between the presence of depressive symptoms and socioeconomic deprivation at both the locality and municipality levels. Other studies have reported such a link at even more immediate aggregation levels, namely, the neighborhood [49]–[55]. In this regard, a recent theoretical framework for exploring the role of the social context has suggested that the highly adverse social conditions in the areas immersed in steepest socioeconomic deprivation –specifically, crime and restricted access to resources of all sorts, such as nutritional diversity, an adequate physical environment and health care– erode interpersonal relationships and social capital, interacting with individual susceptibility to heighten the risk of depression [14]. This explanatory framework could well apply to higher-level conditions, such as those characterizing the localities [43] and municipalities inhabited by our sample of OA.

The link between socioeconomic deprivation at the locality level and the presence of depressive symptoms in Mexico has already been described. OA living in deprived environments routinely confront more adverse conditions leading to feelings of impotence and hopelessness, both, recognized factors in depression [43]. Multilevel analyses in Belgium [56] and Japan [57] have reported connections between socioeconomic deprivation and depression in OA at the municipality level, while another work has correlated poverty and depression in the United States at the county level [58]. While clearly recognizing the importance of the individual-level factors identified in this study, we emphasize the need to comprehend the impact of socioeconomic disadvantages at place of residence as a critical social determinant of depression in OA.

The association found by other studies between income inequality and the presence of depressive symptoms at the state level was not encountered in our work [59]. The failure of this and other studies to find this association may stem from the fact that income inequality and socioeconomic deprivation have separate causal pathways. While socioeconomic deprivation mainly refers to the material conditions of the environment and access to various social services, income inequality is a more complex social determinant because it refers to not only the deficit but also the social distribution of available resources and the social consequences of the inequality [59].

In consequence, the effect of the first determinant would be more direct, as suggested in the Kim model [14]; while, in contrast, the effect of socioeconomic marginalization on mental health could be explained by more indirect etiologies, such as the ecological effects of social disorganization [59]. In one of the most explored theoretical models, inequality generates social corrosion, corruption and alterations in social capital, thereby creating a hostile social environment, which produces a risk to overall health, including mental health [60]. However, these pathways have not been fully elucidated and, therefore, this constitutes a promising field of research. In addition, its associations reported for this determinant vary by level and tend to be particularly salient at the aggregation levels most proximate to the event being studied.

The findings of this work can be clearly understood under the theory of the social determinants of health, particularly under the ecosocial theory [12]. The social context - measured in our study by socioeconomic deprivation at the locality and municipal levels - is a fundamental factor in the incidence of numerous chronic illnesses, such as cardiovascular disease, obesity, cancer and infant mortality [19], [61]. In our work, locality and municipal level deprivation is a multidimensional construct indicative of restricted access to essential public services, such as education, housing and income, which, in turn, can be important determinants of health outcomes. Disaggregating the potential impact of the deprivation index on the presence of depressive symptoms in OA at the municipal level would probably provide more conclusive data, given that, as a summary indicator, the deprivation index simultaneously reflects the combined association of numerous interrelated social determinants –social capital, physical environment and violence - that are also known to exert an impact on depressive symptoms [40], [43], [49]. In fact, alternative contextual variables incorporated into our secondary analyses –homicide rates, levels of health coverage, criminality indicators, and government spending at the municipality and locality levels– revealed no significant association with the presence of depressive symptoms. On the other hand, the multidimensional character of the index may precisely constitute its strength, since it expands the conceptual horizon to encompass a host of interlocking associations, including that of depression and socioeconomic deprivation at the locality and municipal levels.

The results of our study suggest that the current public health approach to depression needs to be expanded. Policy makers might consider that transforming the social environment and generating economic development in the poorest areas could be important strategies that would have both a multidimensional impact on health promotion and protect OA from the social risks to mental health. Additionally, providing some resources in the immediate environment– such as access to health services and recreational and social support – would strengthen the individual resilience of OA to mental disorders [14].

This study has several limitations. One is related with its cross-sectional design, which does not permit drawing firm conclusion about causal associations. Our findings are thus limited to suggesting potential connections requiring more in-depth analysis in subsequent studies. Another limitation derives from the different timing of the various measurements, with the contextual and individual/household measurements conducted in 2010 and 2012, respectively. Nonetheless, it can be expected that, on the one hand, relative differences in the deprivation index would hold for two years, and most importantly, on the other, a potential effect at the municipal level would correlate more with prolonged exposure to socioeconomic deprivation than with a simultaneous event. A final weakness in our study is the fact that, due to lack of information, we were unable to explore other important factors in OA depression, such as significant life events and social networks, which have been recognized as crucial in the etiology of depression.

Conclusions

The results of this study suggest that deprivation at the municipal and locality levels is associated with depressive symptoms, thus inviting proposals for comprehensive public policy actions that view the problem of depression in older adults also as an expression or symptom of more generalized social malaise. Evidence on the role of socioeconomic deprivation will gain scientific and political importance only to the extent that epidemiologic findings translate into a transformation of the social systems [62]. Recognizing that poverty at the municipal and locality levels impacts the health of older adults should galvanize our society to undertake the profound changes required in the social structure.