Introduction

The world population is rapidly ageing mostly due to a higher life expectancy and declining fertility rates [1,2]. This demographic transition raises concerns over several consequences of ageing such as social, economic and health issues. Regarding health, challenges relate to the need of maintaining good health among older adults (people 60 years or older, according to United Nations) and to an increase in the number of older adults affected by mental health problems [2]. As to older adults’ mental health, it is known that depression is the most frequent mental disorder in this age group, and its estimated prevalence can be up to 30% in 65 year old people and older, although it can vary according to sex and setting [3,4]. The importance of depression in the elderly is not solely due to its high prevalence rates, but also to its association with higher morbidity, mortality and disability, and with a higher risk of suicide compared to other age groups [5].

As to the factors associated with depression in older adults, a growing body of literature indicates that the neighborhood environment appears to influence older adults’ mental health [6–11]. The relationship between neighborhood characteristics and older adults’ health (mental and physical) can be explained on the basis that older adults are more vulnerable to the neighborhood’s environment due to multiple reasons [9]. First, not only could older adults be exposed longer to potential harms of the environment, but also the effect of these unhealthy contexts may be more severe in them secondary to elevated biological and psychological vulnerability. Second, neighborhood becomes the most relevant environment for older adults since there is a decrease in the number of other contexts they can move in (work, recreation, etc.); and third, the narrowing social networks of older adults make them rely more intensely on the immediate sources of integration the neighborhood can offer them [9].

In a similar way, the mechanisms by which neighborhood can affect the mental and physical health of older adults are through the modification of health behaviors and levels of psychosocial stress [6]. This effect may come from the two major components of a neighborhood: 1) the material/physical environment (structural dimension) and 2) the psychosocial/social environment (social processes). The first of them refers to the built environment and to the socioeconomic status (SES) of the neighborhood and includes measures of land use type, street connectivity, characteristics of urban design, residential income, education, occupational status, among others [6,10]. It has been documented that older adults living in neighborhoods and localities (which are the smallest administrative division level of the Mexican territory) with a high rate of poverty and deprivation had more depressive symptoms than those who lived in less poor areas or in more affluent environments [8,12]. Besides, not only might the built environment and physical order of the neighborhood promote healthy outdoor physical activities [6,13,14], but socioeconomic advantages of the environment might favor success of its residents [13].

On the other hand, social environment encompasses social capital, social cohesion and collective efficacy [6,15]. Social capital refers to a resource that emerges from the structure of social relationships, “shared norms, and mutual trust that facilitate coordination and cooperation for mutual benefit” [16], and is closely related to social cohesion, which is “the absence of social conflict coupled with the presence of strong social bonds and mutual trust” [17]. These two constructs are protective psychosocial resources against stress and depression by providing affective support and respect among neighbors; by allowing the sharing and transmission of behaviors; and by enabling social and stress control [6,10]. Another related term that might seem very similar to social capital is collective efficacy, which refers to the “linkage of mutual trust and shared expectations for intervening on behalf of the common good”; however, collective efficacy focuses on the shared trust and engagement of the residents of a neighborhood to keep local social control [17].

Conversely to social resources, social disorder can be defined as the “disintegration of processes and structures that maintain order, civility, and safety” [6]. Nevertheless, social disorder is not the only type of disorder, it also exists physical disorder and it should be noted that both of them can result from disadvantage in the neighborhood given by a deficiency of social and economic resources [13]. The lack of order (physical or social) in a neighborhood is reflected by abandoned buildings, filth, graffiti, vandalism, noise, high crime rates, conflicts among residents, public drug use, etc.; and these situations in turn produce a threatening environment suggestive of high risk of danger and disrespect of people and their belongings [13]. Social disorder can lead to depression through several mechanisms such as the continuously increased levels of stress, which trigger a sustained adrenal response; the discouraging of physical activity because of fear of being assaulted; and the resulting limited social contact among residents [13].

This brief review revealed that research about neighborhood effects on older adults’ mental health is scarce [11,18] and that most studies are cross-sectional and have been conducted in high-income countries [19]. This results in evidence with reverse causality limitations and in a low availability of investigations of this type in low- and middle-income countries like Mexico, where the social context and the composition of the neighborhoods are different [20]. In addition, most of available studies do not consider a range of neighborhood-level exposures, which could be important for mental health outcomes among older adults, such as social cohesion, trust and safety.

There are mixed findings for studies examining the association between neighborhood characteristics and older adults’ mental health, which may be explained, at least partly, by the neighborhood definition used, since it’s been demonstrated that this aspect influences the measure of neighborhood exposure [21]. Previously, it has to be noted that neighborhood is a vague and “amorphous” concept that can always be redefined because its construction is based on context, personal experiences and ideals of what a neighborhood should be; therefore, it can vary even among people living in the same space [22,23]. From the point of view of urban sociology, a neighborhood can be defined as a “subsection of a larger community, a collection of both people and institutions occupying a spatially defined area influenced by ecological, cultural, and sometimes political forces” [24]. Another definition regards neighborhoods as “geographical places that can have social and cultural meaning to residents and nonresidents alike and are subdivisions of large places” [25].

From an operational perspective, a neighborhood can be defined through several methods such as using administrative defined area units (census tracts) or egocentric areas like circular buffers, street network buffers or polygon-based buffers [26]. Of the egocentric options, the street network buffer, defined as a “buffer of a specified width placed around a line-based road network”, has several advantages compared to the others [26]. For example, it may assess more accurately the built environment’s factors that influence walking; it is more useful in areas without grid street networks; and is less sensitive to the skew that produces the single use of land (e.g., commercial and industrial) because it does not include inaccessible areas by walking [26]. The latter contrasts with the circular buffer and the polygon method, where a large proportion of these measures may be represented by an area that is not accessible to the pedestrian, but is still used to estimate the built environment measures [21,26].

Additionally, neighborhood effects studies on older adults’ mental health showed that studies about this subject have consistent results regarding some structural characteristics, although there are diverse and even contradictory results about some other features. For example, studies agree on the significant association between disadvantaged neighborhoods, given by greater poverty and less affluence, and depressive symptoms and less social activity in older adults [7,8,11]; but conversely, there are mixed findings in aspects like neighborhood ethnic composition, social environment and perceived environment problems [19]. For all these reasons, the aim of this study was to examine the association between structural and social characteristics of the neighborhood, defined by egocentric buffers, and depression in Mexican older adults.

Methods

Results

Applying the inclusion and exclusion criteria yielded a sample of 1524 participants, although 996 (65.35%) constituted the analytical sample because they had complete data. This analytical sample was distributed in 31 groups at the state level, with the lowest and highest number of observations within groups being 4 and 111, respectively. In addition, the average number of observations per group was 35.19, and there were no singletons. Additionally, most of the sample was female (56.83%; CI 95% 53.72–59.88%), was 60–69 years old (46.29% CI 95% 43.20–49.40%), had elementary education (65.96%; CI 95% 62.96–68.85%) and a permanent partner (65.66%; CI 95% 62.65–68.55%). The overall cumulative incidence of depression was 10.64% (CI 95% 8.87–12.72%). As to the health-related variables, 28.92% (CI 95% 26.18–31.82%) and 38.76% (CI 95% 35.77–41.83%) had multimorbidity and functional limitations, respectively. Regarding the measures of the neighborhood, it was found that the highest mean values of length were those of space for pedestrian traffic (mean = 408.00; sd = 218.75); for free access to people (mean = 332.74; sd = 250.86), and without peddlers (mean = 309.99; sd = 233.62). Contrarily, the lowest average length was that of space restricted to vehicles (mean = 15.04; sd = 25.28). With respect to the social environment measurements, most participants reported a low degree of social capital (80.52%; CI 95% 77.94–82.87%) and a high level of safety (58.53%; CI 95% 55.44–61.56%), while only 44.08% (CI 95% 41.01–47.18%) of individuals reported to trust their neighbors. Table 1 shows the complete univariate analysis.

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Table 1. Sociodemographic, health-related and neighborhood characteristics of the Mexican older adults from SAGE Waves 1 and 2, 2009–2014.

https://doi.org/10.1371/journal.pone.0219540.t001

The bivariate analysis showed a significant association between the incidence of depression and sex (p<0.001), work status (p = 0.03), multimorbidity (p<0.01) and functional limitations (p<0.01). There were no significant associations between depression and any of the built and social environment measures of the neighborhood, except for the total length of space with public lighting, which was marginally significant (p = 0.08).

Regarding the multivariable analysis of the cohort (Table 2), it was found that none of the built environment variables were associated with the cumulative incidence of depression in older adults in the global model. However, the point estimates of the space with sidewalks, restricted to vehicles and with trees varied largely when stratifying by area of residence, with the space with vehicle restriction being a marginally significant protector in the urban area (OR: 0.98; CI 95% 0.97–1.00). In addition, it was seen that the built environment measures were not predictive in the rural setting, so it was decided to perform a further analysis of the previously listed variables only in the urban area.

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Table 2. Association between the neighborhood physical and social environment and the incidence of depression in Mexican older adults from SAGE Waves 1 and 2, 2009–2014.

https://doi.org/10.1371/journal.pone.0219540.t002

To achieve this, several steps were followed: 1) the linear prediction of the incidence of depression (logit) was predicted for the models using the space with sidewalks, with trees and restricted to vehicles as main predictors. 2) Those linear predictions were plotted against their correspondent predictor in its continuous form (S1–S3 Figs). 3) The cutoff points to create the categories for the built-environment variables were chosen based on the distribution of the scatter points along the x-axis; this way a regression model was performed in each category, yielding very different slopes from the ones obtained in the general model (S4–S6 Figs). Additionally, different cut-off points were tested, but the ones that allowed a sufficient sample within each category, had the lowest log-likelihood and made sense for practical use were the ones that were kept for display in Table 3. Anyhow, the other tested cutoff points yielded similar results and pointed towards the same conclusions (S1 Table of the Supporting Information file). The scatter plots used to select the cutoff points to create the categories for each variable and the sensitivity analysis of these are shown in the Supporting Information file.

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Table 3. Association between three physical environment measurements and the incidence of depression in Mexican older adults from the urban area from SAGE Waves 1 and 2, 2009–2014.

https://doi.org/10.1371/journal.pone.0219540.t003

When conducting the models with the categories of the aforementioned variables, it was evidenced that the space with vehicle restriction showed a protective association with the incidence of depression (Table 3). Even more, the model suggested a possible dose-response relationship, so that the more space restricted to vehicles, the more protection it displayed, although only one category had statistical significance (OR₁: 0.45, CI 95% 0.24–0.85; OR₂: 0.51, CI 95% 0.17–1.55; OR₃: 0.30, CI 95% 0.06–1.54). The space with trees also indicated a protective effect, though it was not significant, and the space with sidewalks did not show a clear direction of the association, nor it was significant.

As to the measurements of the social environment of the neighborhood (Table 2), none of these variables were statistically associated with the incidence of depression in older adults, although the analysis suggested that higher levels of social capital had a possible protective association in the urban area (OR_medium: 0.61, CI 95% 0.25–1.47; OR_high: 0.78, CI 95% 0.29–2.14). Similarly, trusting in neighbors appeared to be protective in the overall and stratified-by-area models. On the other hand, lower levels of safety showed higher risk of depression in the overall sample and in the rural and urban settings, with the medium level of safety being marginally significant in the overall (OR: 1.48; CI 95% 0.96–2.30) and in the rural (OR: 3.44; CI 95% 0.95–12.45) models. The estimations in the latter were imprecise due to the scarce sample in the rural setting.

Finally, the analysis to explore potential interactions was carried out, although it is worth mentioning that performing stratified analysis to explore interactions reduces the available sample for modeling estimations, which results in imprecise estimates. All the results from this last step of the analysis can be seen in the Supporting Information file.

The sex-stratified analysis evidenced that the space restricted to vehicles seemed to reduce the likelihood of depression only in women, although it was marginally significant and the magnitude of the association was quite small and very close to the null (OR: 0.99; CI 95% 0.97–1.00). In addition, the point estimates suggested that higher levels of social capital and trust in neighbors were protective against depression only in women; while lower levels of safety increased the chances of depression in both men and women. However, neither of these variables reached statistical significance in any case (S2 Table of the Supporting Information file).

On the other hand, for performing the age-stratified analysis, the young old and oldest old categories were created based on the United Nations definition, which classifies individuals 80 years old and older as “oldest old” [2]. Based on the above, the analysis revealed that the space restricted to vehicles seemed to reduce the likelihood of depression in the oldest old group (OR: 0.88; CI 95% 0.77–1.01), although it should be noted that the estimates from the oldest old group were very imprecise due to its very small sample (n = 70). Contrarily, none of the evaluated variables showed a significant association with depression in the young old group (S3 Table).

Lastly, in the analysis stratified by socioeconomic status, categories were created by grouping quintiles 1 and 2 into the “low” category; quintile 3 remained as “medium”; and quintiles 4 and 5 were grouped to form the “high” category. This way, it was found that space with free access to people (OR: 1.00; CI 95% 1.00–1.01), with public lighting (OR: 1.00; CI 95% 1.00–1.01) and covered in concrete (OR: 1.00; CI 95% 0.99–1.00) were statistically and marginally significant respectively. However, the point estimates and the CI 95% were the null value or very close to it. On the other hand, a high level of social capital significantly decreased the likelihood of depression in individuals from a low socioeconomic status (OR: 0.08; CI 95% 0.01–0.53); and lower levels of safety increased the odds of depression in older adults from a high socioeconomic status, with the medium level being statistically significant compared to a high level of safety (OR: 2.90; CI 95% 1.09–7.71). Results shown in S4 Table.

Discussion

With the aim of examining the association between physical and social characteristics of the neighborhood and depression in Mexican older adults, the present research found that vehicular restriction was a protective factor against the incidence of depression in older adults from urban areas. Even more, it seemed to be a possible dose-response relationship, so that larger lengths of space restricted to vehicles showed a higher protection. These results are consistent with previous research that have found significant associations between mental well-being and traffic safety [40,43] and unsafe traffic and lack of help within neighbors [40]. Furthermore, a qualitative study showed that older adults identified traffic safety as a very important factor that affected their health [44]. Contrarily, the present findings contrast with other investigation where green areas were associated with depressive symptoms [45].

There are multiple potential mechanisms for the found association and they come from the benefits of pedestrianization, which refers to impede the access of motor vehicles to a determined area [46]. For example, the space restricted to vehicles promotes physical activity (reflected in the increasing number of journeys on foot), which in turn has been shown to alleviate depressive symptoms [47–49]. Additionally, the pedestrianization of streets stimulates the practice of other activities since different land-use other than for traffic can be implemented, so it becomes an opportunity to personally interact and communicate with other people, thereby increasing social interaction [46,47,50,51]. It has to be reminded that social networks and social contact are important factors that influence the onset of depression [52,53]. As to the personal safety perception, restriction of vehicles improves it because it reduces the rates of crime and of traffic accidents involving pedestrians, so the elderly can comfortably and safely walk, which derives in the reduction of stress levels while walking [46,47]. Finally, other pathways by which the vehicle restriction might improve mental health (and depression) is through the reduction of air pollution and noise, since these factors are associated with cardiovascular diseases, which at the same time are highly associated with depression [47,51,52,54].

In addition to the previously described, exploration of interactions showed that women and “oldest-old” individuals seemed to benefit more from the space restricted to vehicles compared to men and young olds, respectively; although the reduction of the likelihood of depression was more evident in the age-stratified analysis. Explanations for these findings can also come from the benefits of pedestrianization, specifically, from the increase of social interaction and of safety. Firstly, it has been described that gender differences in contextual effects might be due to 1) different perceptions of their environment; 2) different exposure to certain aspects of the neighborhood; and 3) differential vulnerability to those aspects [55]. For example, women are reported to have a higher social interaction and more social networks within their neighborhoods [56], therefore, they might experience a greater benefit from a neighborhood characteristic that promotes social interaction and communication, which are protective factors against depression. This would be in accordance with previous research that have found that neighborhood social features, such as social cohesion and social capital mediated the association between neighborhood disadvantage/physical environment and depression [57,58]. On the other hand, ageing comes with a decline in health and changes in functioning, such as diminished cognitive capacity and senses, which accentuates at oldest ages, so that the elderly (especially the oldest old) are more prone to traffic accidents [59,60]. This way, the enhanced safety given by the space with vehicle restriction might alleviate the described situation, since it reduces the rate of traffic accidents where pedestrians are involved, which allows older adults to walk without fearing a traffic accident.

Moreover, the analysis also suggested a possible association between the incidence of depression and some features of the social environment in both urban and rural areas, although the estimations did not have statistical significance, possibly due to a problem of lack of power. In particular, individuals who trusted their neighbors seemed to have a lower risk of depression, while people who perceived higher levels of crime in their neighborhoods appeared to have a higher risk of depression compared to those who consider it safe. The lack of significant associations contrasts with other investigations where crime, unwillingness of neighbors to help each other, neighborhood safety and social cohesion were significantly associated with higher and lower depressive symptoms in older adults, respectively [40,41,61,62].

However, when stratifying by socioeconomic status, a high level of social capital was a significant protector against depression in low socioeconomic status older adults, while a medium level of safety significantly increased the likelihood of depression in high socioeconomic status individuals. These findings might relate to the adequacy of social and economic resources of people from different socioeconomic statuses, so that different aspects of social environment could be more or less beneficial in certain settings. However, this should be further investigated to extend the knowledge on this aspect.

Considering all of the above, it appears that the social environment of the neighborhood could have a larger influence on mental health outcomes, such as depression. This could be hypothesized since not only the analysis suggested that social capital and safety were associated with the likelihood of depression in Mexican older adults from low and high socioeconomic status, respectively; but also it showed that a built environment characteristic that promotes social interaction reduced the likelihood of depression in older adults from urban areas. Additionally, it has been described that aspects from the social environment, such as social capital and social cohesion mediate the influence of structural characteristics of the neighborhood on the health of its residents [63]. This way, it could be suggested that a neighborhood that enhances social ties and networks, and provides social resources that help buffer stress levels might have a better impact on mental health. Moreover, the decay of the built environment (which has been associated with depressive symptoms in previous research) can reflect a problematic social environment that lacks social control and where social disorder prevails [10,13]. On the contrary, physical activity and other outcomes related to the fulfillment of it, i.e. obesity, hypertension, diabetes, might be more influenced by the “walkability” of the neighborhood, in which the built environment plays a major role [10,64].

Since conducting randomized trials for studying neighborhood effects is largely difficult, natural experiments become a very useful tool, in spite of the intervention not being randomly assigned [65,66]. Therefore, for future investigations, researchers should take advantage of the natural experiments occurring within neighborhoods, especially of those related to the transformation or intervention of the built environment [67]. The above in order to evaluate the impact on health indicators, including the ones related to mental health; in the specific case of Mexico, research should focus on the impact of the infrastructure modification in the health of its inhabitants.

This study has important strengths and limitations. With respect to the strengths of this research, they relate to the inclusion of both built and social environment features, so that built environment and social context were considered in the analysis; also, the measurements of the built environment were done objectively through the usage of GIS information. Furthermore, the individual information of participants was obtained through the application of previously validated and calibrated instruments, which allowed adjusting for the main individual-level confounders in the multivariable analysis. Additionally, since several hypothesis tests were conducted in the multivariable analysis, the obtained results were reviewed and re-analyzed controlling for the False Discovery Rate and our conclusions remained the same. Notwithstanding, we consider that further investigation should be done to confirm the findings from this study and continue this line of research. On the other hand, this is a longitudinal study, so that the 5-year incidence of major depression was modelled in the analysis. Lastly, this research not only adds up to the few available researches about neighborhood effects on human health in Latin America with a nationally representative sample [68], but is also the first investigation to our knowledge about neighborhood effects and mental health with a nationally representative sample in Mexico, since the other studies about neighborhood effects in Mexico are focused on different outcomes [69,70]. Moreover, the study of neighborhood effects on mental health is an emergent field as most of studies are conducted on physical activity, obesity or chronic disease. In addition, the present investigation contributes filling the gap regarding longitudinal studies on neighborhood effects in a Latin sample of older adults, as has been described in previous research [61].

Regarding the limitations of this study, the main concern arises from the fact that the used data had very few observations within each neighborhood, that is, there were scarce participants coming from the same neighborhood. This situation is due to the design of the primary study (SAGE) because its sample was intended to be nationally representative, which does not guarantee representativity at lower levels such as the town-level and the neighborhood-level. The described lack of sufficient individuals derives in a larger within-variance than the between-variance, which hinders the correct distinction between contextual and compositional effects of the neighborhood on the mental health of older adults. Additionally, the lack of sufficient individuals within the same neighborhood made us use the state as the second aggregation level, making it the Primary Sampling Unit (PSU) for this specific research. This, in turn, differs from the PSU established for SAGE, which prevented us from being able to account for the complex design of the original research simultaneously to the multilevel analysis. However, a sensitivity analysis was performed by running multiple logistic regression models after having specified the complex sampling design of SAGE. While few of the results were sensitive to the choice of incorporating sampling design, our overall conclusions remained unchanged. Besides, it can be considered that the most important aspect about this investigation was to account for the correlation structure within clusters of the second aggregation level (the states), not for the PSU of the original study, which was accomplished by means of the multilevel analysis. Furthermore, there was a reduction of 34.65% in the sample due to missing data. This limitation could be a result of the low response rate from wave 1, which was caused by a short time for fieldwork, so there were not many opportunities to revisit participants in case they were not found during the first contact [28]. Based on the above, it is reasonable to think that the losses were random. However, a sensitivity analysis was performed to compare individuals with and without complete data, so that those variables that were found to be associated with the completeness of information were adjusted for in the multivariable analysis. Additionally, neighborhood was defined through a street-network buffer, which is less sensitive to destinations people actually go to and to the real environmental factors older adults are exposed to in their neighborhoods, as demonstrated by previous studies performed with a neighborhood definition based on GPS data, since it is a presumed residential area where individuals mobilize [14,71]. Notwithstanding, the street-network buffer has several advantages over the other residential buffers [26]. Furthermore, a problem of lack power to detect associations might be present since the incidence of the depression cases was not as high as expected and because the built environment measurements lacked variability and tended to be skewed to the left (towards the zero), especially in the rural settings. This last data difficulty, along with the fact that built-environment measures from the urban area are not entirely appropriate for studying neighborhood effects in the rural area [42,72,73], hindered the correct analysis in the rural setting. Therefore, the obtained results relating to the physical context in the urban area might not be applicable to rural areas. It should also be noted that depression was partly measured with an algorithm based on depressive symptoms, so that there might be misclassification of the outcome since it was not possible to perform a clinical assessment of participants. Moreover, confounding might be present in the estimations since it was not possible to include data on some variables that the literature underscores as important when performing the research, such as the type of land use that predominates, the number of years living in the neighborhood, and the proportion of older adults living in the neighborhood of the participants. In addition, a direct measure of the socioeconomic status of the neighborhood, like the deprivation index specific to the neighborhood, could not be used in the analysis given the limited availability of this information. Nonetheless, adjustment was made with the deprivation index of the municipality, which is the second administrative level in Mexico and had a correlation of 0.36 (p = <0.001) with a sample of the deprivation index of the neighborhood. Finally, the analysis could not incorporate an objective measure of safety or crime of the neighborhoods because that information was not available to use.

Conclusions

Investigating neighborhood effects on older adults’ mental health is an emergent field as most of studies are conducted on physical activity, obesity or chronic diseases; however, more research on this aspect is needed since not only world population is rapidly ageing, but also depression is one of the largest contributors of burden disease in this age group. In addition, the study of depression tends to be done at the individual-level, neglecting that the environment where a person develops also plays a key role in the risk of having depression. Therefore, it might be beneficial to not just individually treat each depressed patient, but also make interventions at the neighborhood-level that improve or complement the treatment of this mental disease and prevents its onset. The present investigation contributes to reinforce the knowledge about the association between the built-environment (specifically, traffic safety) and depression in older adults and to fill the gap regarding longitudinal studies on neighborhood effects in a Latin sample of older adults, as has been described in previously (38). Nonetheless, further research on this subject is needed, especially in low- and middle-income countries, to keep evaluating neighborhood characteristics that might be related to older adults’ mental health in order to help guiding policies related to urban planning and neighborhood interventions.

Supporting information

Acknowledgments

This paper uses data from the WHO World Health Surveys / Multi-Country Survey Study (as appropriate) and from WHO's Study on Global Ageing and Adult Health (SAGE). SAGE is supported by the US National Institute on Aging through Interagency Agreements OGHA 04034785; YA1323-08-CN-0020; Y1-AG-1005-0) and through research grants R01-AG034479 and R21-AG034263.