Objectives This study aimed to examine the associations between objectively assessed neighbourhood environmental attributes and depressive symptoms in Hong Kong Chinese older adults and the moderating effects of neighbourhood environmental attributes on the associations between living arrangements and depressive symptoms.
Design Cross-sectional observational study.
Setting Hong Kong.
Participants 909 Hong Kong Chinese community dwellers aged 65+ years residing in preselected areas stratified by walkability and socioeconomic status.
Exposure and outcome measures Attributes of participants’ neighbourhood environment were objectively assessed using geographic information systems and environmental audits. Depressive symptoms were measured using the Geriatric Depression Scale.
Results Overall, pedestrian infrastructure (OR=1.025; P=0.008), connectivity (OR=1.039; P=0.002) and prevalence of public transport stops (OR=1.056; P=0.012) were positively associated with the odds of reporting depressive symptoms. Older adults living alone were at higher risk of reporting any depressive symptoms than those living with others (OR=1.497; P=0.039). This association was moderated by neighbourhood crowdedness, perceptible pollution, access to destinations and presence of people. Residing in neighbourhoods with lower levels of these attributes was associated with increased deleterious effects of living alone. Living in neighbourhoods with lower public transport density also increased the deleterious effects of living alone on the number of depressive symptoms. Those living alone and residing in neighbourhoods with higher levels of connectivity tended to report more depressive symptoms than their counterparts.
Conclusions The level of access to destinations and social networks across Hong Kong may be sufficiently high to reduce the risk of depressive symptoms in older adults. Yet, exposure to extreme levels of public transport density and associated traffic volumes may increase the risk of depressive symptoms. The provision of good access to a variety of destinations, public transport and public open spaces for socialising in the neighbourhood may help reduce the risk of depressive symptoms in older adults who live alone.
- older adults
- built environment
- depressive symptoms
This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Statistics from Altmetric.com
Strengths and limitations of this study
A large range of neighbourhood environmental attributes were examined in relation to depressive symptoms.
The use of geographic information systems and environmental audits to quantify neighbourhood environmental attributes allowed to partially control for potential reverse causality due to depressed mood affecting individuals’ perceptions of environmental exposures.
A novel aspect of this study is the examination of the moderating effects of objectively assessed neighbourhood attributes on the associations between living arrangements and depressive symptoms.
This is a cross-sectional observational study and, hence, causal relations cannot be inferred.
Depression is a growing public health concern. According to the WHO, it is the leading cause of ill health and disability worldwide.1 More than 300 million people are estimated to be suffering from depression, corresponding to 4.4% of the global population.1 Depression is more common among older adults, with a prevalence of 7% and believed to be underestimated.2 As the world population ages, there will be a corresponding increase in the number of older adults with depressive symptoms and associated global health burden.3
Social-ecological models emphasise the importance of multilevel environmental factors for the health of entire populations.4–7 Older adults are more susceptible to the influence of their local environment and adverse neighbourhood conditions due to ageing-related decreases in physical function and mobility.8 The neighbourhood environment is deemed to impact on older adults’ health outcomes (eg, depressive mood) by interacting with their diminished physical functioning (eg, impaired mobility) and related maladaptive responses (eg, social isolation).9 For example, specific neighbourhood characteristics, such as access to age-friendly recreational facilities, may facilitate older adults’ engagement in physical and social activities which, in turn, may help develop adaptive responses (eg, resilience to negative affectivity) to declining physical capacity.
There is some evidence that neighbourhood social environmental attributes may influence depressive symptoms in older adults. Perceived neighbourhood disorder was found to be predictive of late-age depression,10 and higher social cohesion11–13 and neighbourhood-level socioeconomic status (SES)14–16 were associated with fewer depressive symptoms. Also, several studies have found associations between physical aspects of the neighbourhood environment and depression. For instance, availability of retail destinations was positively related to depression in older Australian men,17 whereas higher levels of perceived traffic safety in the neighbourhood were associated with fewer depressive symptoms in a sample of US older adults.18 More consistent associations have been found between social than physical aspects of the neighbourhood environment and depressive symptoms.19 These differences in patterns of associations may be due to the fact that physical aspects of the neighbourhood environment have been less frequently examined than their social counterparts using diverse measures.19 20
It is noteworthy that many studies that examined neighbourhood environmental correlates of depressive symptoms in older adults used self-report measures of neighbourhood attributes. In such case, there is a high risk of reverse causality whereby participants’ depressive mood may affect their perceptions of the environmental exposures of interest (eg, neighbourhood safety from crime).21 Environmental data collected using more objective measures of the neighbourhood environment, including geographic information systems (GIS)22 and environmental audits conducted by independent auditors,23 are likely to provide more robust estimates of the potential causal effects of neighbourhood environmental attributes on residents’ depressive symptoms.
Apart from emphasising the importance of environmental factors for health outcomes, social-ecological models also posit that individual-level factors interact with environmental factors to yield specific health outcomes.6 9 An important individual-level factor that has been associated with a higher risk of depressive symptoms is living alone as opposed to living with family members or others.24 25 Older adults who live alone are likely to be more socially isolated and, hence, at risk of depression. Living in a neighbourhood that facilitates engagement in various activities may reduce the risk of depressive symptoms especially in older adults who live alone. In fact, a recent study found that living alone was more highly associated with depression in mid-aged and older adults reporting low levels of perceived quality of social interactions with neighbours.26 However, to our knowledge, no studies have examined the moderating effect of objectively assessed neighbourhood environment attributes on the associations between living arrangements (living alone vs living with others) and older adults’ depressive symptoms. This is an important issue for Hong Kong as well as many other high-density urban areas experiencing rapid increases in number of older adults living alone.27
The primary aim of this study was to examine associations of objectively assessed neighbourhood environmental attributes with depressive symptoms in Hong Kong Chinese older adults. The secondary aim was to estimate the moderating effects of neighbourhood environmental attributes on the associations between living arrangements (living alone vs living with others) and older adults’ depressive symptoms. We hypothesised that (1) objective measures of availability/access to destinations, greenness and a pedestrian-friendly infrastructure would be negatively associated with depressive symptoms; (2) environmental stressors such as signs of crime/disorder, pollution, traffic-related variables and presence of stray dogs would be positively associated with depressive symptoms; (3) older adults living alone would report more depressive symptoms than their counterparts; (4) and the negative effects of living alone on depressive symptoms would be attenuated by better access/availability of destinations and lower levels of environmental stressors.
We used data from the Active Lifestyle and the Environment in Chinese Seniors (ALECS) project,28 an observational study of built and social neighbourhood environmental correlates of depressive symptoms and quality of life in Hong Kong Chinese community dwellers aged 65+ years.
Study design and neighbourhood selection
The ALECS project adopted a two-stage sampling method that involved the recruitment of participants living in selected areas (Tertiary Planning Units (TPUs)). TPUs, the smallest administrative area units in Hong Kong with census data, were stratified by SES (represented by TPU-level median household income) and walkability (a composite index of net residential density, intersection density and land use mix).29 30 TPUs with high and low levels of SES and walkability, classified into four types of neighbourhoods (ie, high SES/high walkable, high SES/low walkable, low SES/high walkable and low SES/low walkable), were preselected prior to participant recruitment to maximise the variability of environmental attributes (eg, dwelling density, access to destinations and safety from crime) in the sample. A total of 124 out of 289 TPUs were included.31 Such a sampling strategy has been previously used in single-country23 and multicountry32 studies that investigated associations of environmental attributes with behavioural outcomes (eg, physical activity) related to depression.33 34 Further details on the neighbourhood selection procedure are reported elsewhere.28 35
As Hong Kong Personal Data (Privacy) Ordinance36 restricts direct access to residential addresses and other contact details, participants were recruited in person from 11 Elderly Health Centres (EHCs) of the Department of Health, Hong Kong Special Administrative Region (HKSAR) and eight elderly community centres located in the preselected TPUs. The majority of participants (72%) were recruited through EHCs, which are distributed across all 18 Hong Kong districts. The EHCs were established in 1998 to provide comprehensive primary healthcare services, including health assessment, physical check-up and curative treatment, to persons aged 65 years or above. We used EHCs as recruitment sites because they provide health-related information that can be used for eligibility screening purposes, and their clients are usually willing to participate in health-related studies endorsed by the Department of Health, HKSAR.37 Although EHCs’ clients are representative of the general population of older adults in terms of age and SES,37 they tend to be more health conscious.38 To examine the potential bias (better mental health) associated with recruiting participants from the EHCs, we recruited approximately 30% of the sample (n=258) from elderly community centres with no formal provision of medical and health services. No significant differences between participants from the two types of centres were observed in age, physical health, marital status, living arrangements, type of neighbourhood of residence, type of housing and car in the household. Participants from the EHC tended to be more educated (P=0.018) and more likely to be men (P=0.010) than their counterparts.
Potential participants attending an EHC or community centre were invited to partake in the study and assessed for eligibility (Cantonese-speaking older adults aged ≥65 years, cognitively intact, able to walk without assistance for at least 10 m and having lived in preselected TPUs for at least 6 months). Nine hundred and nine older adults were recruited (response rate: 71%). Men, residents of less walkable TPUs and members of EHCs (all Ps<0.001) were more likely to refuse to participate in the study than their counterparts. On recruitment, participants provided written consent for participation in the study. Further details of recruitment procedures are available elsewhere.28 31
Measures and procedures
Exposures: neighbourhood attributes
Objective neighbourhood environmental attributes were assessed using GIS and environmental audits. GIS data were sourced from the Census and Statistics, Lands, and Planning Departments, HKSAR. Participant residential buffers, approximating neighbourhood boundaries, were created by tracing from the participants’ residential addresses through their unique street networks in all directions for 400 m and 800 m (see online supplementary table for definitions). GIS-derived environmental attributes were generated for each participant and each buffer size using Esri’s ArcGIS software (online supplementary table). Environmental audits, conducted infield by trained auditors, were used to quantify environmental attributes that were not assessable via GIS (eg, presence of people), and also where the available GIS data were outdated or incomplete. We used 400 m and 800 m residential buffers to delineate participants’ neighbourhoods because these are considered to be walkable distances and appropriate geographical scales for older adults living in high-density environments.35 39 40
Environmental audits were conducted using items from the Environment in Asian Scan Tool – Hong Kong (EAST-HK).41 These items assessed the presence or absence of the environmental attributes listed in the online supplementary table in each sampled street segment. A street segment was defined as a section of a street between intersections. To identify street segments for auditing, 400 m crow-fly buffers surrounding each participant’s residential address were created, and all segments of major roads/streets that were accessible to pedestrians were selected. If the number of selected major roads/street segments in a specific buffer was smaller than 25% of the total number of segments included in that buffer, additional segments (from minor roads) were randomly selected. A validation study of the EAST-HK suggested that 25% street segments were sufficient to obtain representative estimates of various environmental attributes in Hong Kong neighbourhoods.41 Environmental audits were limited to 400 m crow-fly buffers due to budgetary constraints. It should be, however, noted that in our previous study41 a 400 m crow-fly distance corresponded to a network distance from 400 to ~900 m.
Trained auditors were instructed to assess both sides of selected street segments and record destinations visible from the street. When assessing the presence of destinations in multifloor, mixed-use buildings, they consulted the directory of services in the building. Environmental attributes were measured by single or multiple EAST-HK items and aggregated by participant buffers. Scores on single-item measures denoted the percentage of audited street segments within a buffer with that particular attribute, while scores on multiple-item measures represented the percentage of the maximum obtainable score averaged across audited street segments within a buffer.23 For example, a buffer consisting of three audited street segments with respective scores on ‘presence of people’ (assessed by four items) of 4 (representing 100% of the maximum obtainable score), 3 (75%) and 3 (75%) was assigned an aggregate score of 83.3% (ie, the sum of 100%, 75% and 75% divided by three).23 In addition, a diversity score was computed for recreational destinations indicating the number of different types of recreational destinations present in a participant’s residential buffer.
Outcome: depressive symptoms
Depressive symptoms were measured via interviewer administration of the four-item Geriatric Depression Scale (GDS) with a yes/no response format.42 Two items were inversely scored, and the number of depressive symptoms was represented by the sum of the scores on the four items (ranging from 0 to 4). The GDS43 has been widely used for the assessment of depressive symptoms in older adults. This study employed a short, validated four-item version of the GDS to minimise participants’ burden.44
Information on the participants’ age, sex, educational attainment, marital status, living arrangement, housing type, availability of car in the household and the number of current diagnosed chronic health problems was collected via an interviewer-administered survey and medical records. These variables together with area-level SES and type of recruitment centre (EHC and elderly community centre) were included as covariates in the regression models.
Patient and public involvement
This study did not involve patients. Participants were community dwellers who, after participating in the study, received individualised feedback on their health-related lifestyle behaviours. The findings from this study will be disseminated to the wider public via local media and non-government organisations.
Descriptive statistics were computed for all variables. Generalised additive mixed models (GAMMs) were used to estimate confounder-adjusted associations of objectively assessed neighbourhood environmental attributes with depressive symptoms. GAMMs can model outcomes with various distributional assumptions, spatially correlated data and curvilinear relationships of unknown form.45 In this study, a large number of participants (n=574, 63%) reported no depressive symptoms. Therefore, we evaluated two sets of GAMMs. A set of GAMMs modelled the odds of reporting any versus no depressive symptoms. These GAMMs used binomial variance and logit link functions and yielded ORs. Another set of GAMMs with negative binomial variance and logarithmic link functions modelled the number of non-zero depressive symptoms and produced antilogarithms of regression coefficients representing the proportional difference in mean outcome (the number of non-zero depressive symptoms) associated with a 1-unit increase in the predictor.
We first estimated the multivariable associations of all covariates and living arrangements with the two depressive symptom outcomes. A second set of main effect GAMMs estimated the dose–response relationships of single environmental attributes with the two outcomes, adjusted for all covariates and living arrangements. Curvilinear relationships of environmental attributes with the outcomes were assessed with thin-plate smooth terms in GAMMs. Smooth terms failing to provide sufficient evidence of curvilinearity, defined as a 5-unit difference in Akaike information criterion, were replaced by linear terms.45
Moderating effects of environmental attributes on the associations between living arrangements and depressive symptoms were estimated by adding a two-way interaction term to the main effect GAMMs. Significant interactions (P<0.05) were probed using Johnson-Neyman procedures,46 whereby we estimated the range of values of the environmental attributes (also known as regions of significance) for which the effects of living arrangements (living alone vs living with others) on the depression outcomes were statistically significant.
All significant single environmental attributes and interaction terms were entered in multiple environmental attribute GAMMs adjusted for all covariates. Environmental attributes that were strongly correlated (r>0.50) were combined into composite variables as appropriate. Only those environmental attributes and interaction terms that showed a significant independent effect on the outcomes (P<0.05) were retained in the final multiple environmental attribute models. All analyses were conducted in R using the packages ‘mgcv’47 and ‘gmodels’.48
Table 1 reports the descriptive statistics for all variables relevant to this study. More than half of the sample were women, married or cohabiting with a partner, living in private housing and with less than secondary education. Nearly a quarter of participants reported living alone. The majority of the sample did not report any depressive symptoms (63.2%). Substantial levels of variability across residential buffers were observed for most of the examined environmental characteristics, with the exception of signs of crime/disorder. Overall, the presence of signs of crime/disorder and stray dogs/animals was low. On average, residential buffers scored relatively high on residential density, traffic safety, pedestrian infrastructure, presence of people, pollution and some measures of destination density/prevalence.
Older adults who were female (OR=2.294; 95% CI 1.616 to 3.257; P<0.001), living alone or with more current diagnosed health problems showed higher odds of having any depressive symptoms than their counterparts (table 2). The number of health problems was also positively associated with the number of non-zero depressive symptoms. Also, compared with those with no formal or postsecondary education, participants with secondary school education reported higher odds of having any versus no depressive symptoms but, on average, fewer symptoms among those with any symptoms. The type of recruitment centre was unrelated to depressive symptom outcomes.
Table 3 summarises the associations of single neighbourhood environmental attributes with depressive symptoms in older adults. No significant associations were observed between GIS-derived environmental attributes and the two depressive symptoms outcomes. Among the attributes measured using environmental audits, three significant linear associations were observed. Specifically, higher levels of pedestrian infrastructure, connectivity and prevalence of public transport stops were associated with increased odds of reporting one or more depressive symptoms.
The moderating effects of neighbourhood environmental attributes on the associations between living arrangements and depressive symptoms are summarised in table 4, where we report the ranges of values of the environmental attributes for which the associations between living arrangements and depressive symptoms were significant at the probability levels of 0.05, 0.01 or 0.001 (as appropriate). As expected, when compared with participants living with others, those living alone were more likely to report (any) depressive symptoms when living in neighbourhoods with poorer access to civic/institutional destinations, retail, food/eating outlets, public transport stops and health clinics/services, lower levels of crowdedness and fewer people on the streets (table 4). They were also more likely to experience depressive symptoms when living in areas with lower levels of perceptible pollution (noise and odours). For example, among residents of neighbourhoods with a (relatively low) pollution score of 21.4, the odds of reporting any depressive symptoms in those living alone were 109% higher than the odds observed in those living with others (table 4). However, for higher levels of pollution (>43.7 points), there was no significant difference in the odds of reporting any versus no depressive symptoms between those living alone and those living with others. At high levels of access to public transport stops (≥59.7 transit points per km2), health clinics/services (≥18.8 destinations in residential buffers) and crowdedness (~2 standard deviations above average), participants living alone were significantly less likely to report any depressive symptoms than their counterparts. The same was observed with regards to the effects of public transport density on the number of depressive symptoms among those reporting any. In contrast, those living alone tended to report more depressive symptoms than those living with others, if residing in neighbourhoods with high levels of connectivity.
In models of multiple neighbourhood environmental attributes (table 5), connectivity and prevalence of public transport stops remained positively associated with the odds of reporting any depressive symptoms. Presence of people (OR=0.982; 95% CI 0.966 to 0.999; P=0.036) and a composite destination index (consisting of sum of z-scores of variables related to access to civic and institutional and retail destinations, food/eating outlets, health clinics/services, and public transport stops; OR=0.921; 95% CI 0.854 to 0.994; P=0.034) were the only significant moderators of the associations between living arrangements and the odds of any depressive symptoms. Specifically, participants living alone were more likely to report depressive symptoms in neighbourhoods with poor access to multiple destinations and fewer people on the street, compared with those living with others. Among those with any depressive symptoms, the moderating effects of connectivity (e b =1.017; 95% CI 1.001 to 1.003; P=0.032) and public transport density of 800 m buffer (e b =0.985; 95% CI 0.971 to 0.999; P=0.035) remained significant. Participants living alone tended to report more depressive symptoms in neighbourhoods with high levels of connectivity (above average) and less depressive symptoms in neighbourhoods with better access to public transports (≥9.2 transit points per km2) than their counterparts.
The main aim of this study was to quantify the associations of depressive symptoms with a wide range of objectively assessed neighbourhood attributes in Hong Kong Chinese older adults. Only 3 of the 21 examined categories of neighbourhood environmental attributes were found to be significantly associated with depressive symptoms in the whole sample. This lack of associations may be explained by the fact that Hong Kong is generally characterised by a well-developed public transport system and high levels of density and access to retail/services,35 which are known to promote a physically49 and socially active lifestyle.50 Also, 75% of the sample reported living with others. Hence, the proportion of participants potentially suffering from loneliness (a major contributor to depression)51 due to social isolation stemming from living alone in a neighbourhood with limited opportunities for social contacts was relatively low. We also expected that access to parks and greenery would be negatively, and adverse neighbourhood conditions (ie, crime/disorder) positively, related to depressive symptoms. However, these hypotheses were not confirmed. Park quality rather than presence of parks may be a more important contributor to older adults’ mental well-being.52 53 The failure to observe an association between signs of crime/disorder and depressive symptoms in this study is likely due to the extremely low levels of crime/disorder found in the sampled street segments and, generally, in Hong Kong.30 41
The three environmental attributes that were found to be associated with increased odds of reporting one and more depressive symptoms were the prevalence of public transport stops, street connectivity and pedestrian infrastructure. Although this small number of statistically significant associations (3 out of 70) might have arisen by chance, there are several plausible mechanisms that might explain them. High levels of public transport density are usually accompanied by higher levels of traffic-related noise and air pollution, especially in urban environments typified by a concentration of tall buildings.54 Both excessive traffic-related noise and air pollution have been linked to stress, inability to psychologically restore and depression.55 56 One of the main features included in the measure of street connectivity used in this study was the presence of bridges, overpasses or tunnels. Pedestrian bridges and overpasses are highly prevalent in Hong Kong and commonly found in crowded, built-up areas with high traffic volumes and lack of sitting facilities and public spaces to meet with others. These areas are also typically characterised by a developed pedestrian infrastructure with well-maintained pavements and indoor pedestrian passageways through buildings.57 This may explain why a positive association between depressive symptoms and pedestrian infrastructure was observed in the single environmental variable but not in the multiple environmental variable models adjusted for street connectivity. Multisite studies expanding the level of variability in exposures may be needed to accurately characterise the dose–response relationships between characteristics of the neighbourhood environment and depressive symptoms in older adults.58 59
The secondary aims of this study were to examine the association between depressive symptoms and living arrangements and to quantify the moderating effects of neighbourhood environmental attributes on this association. Older adults living alone showed a higher likelihood of reporting at least one depressive symptom compared with those living with family members or others, which was consistent with earlier studies.24 25 Living alone and loneliness are established risk factors for depression and depressive symptoms in older adults. These effects are thought to be due to lower levels of social support in those who live alone.60 Although the association between living arrangements and depressive symptoms was in the expected direction, it was not strong. Previous studies on Northeast Asian populations noted that living with children and grandchildren may increase the level of stress and, hence, the risk of depression.25 This is especially the case in modern Asian societies where respect for privacy and independence are becoming increasingly important values due to the assimilation of Western culture and lifestyles.61 Consequently, it is possible that, in this study, a certain percentage of older adults who reported living with family members or others might have been exposed to higher level of stress leading to experiencing depressive symptoms due to living in a crowded household with their children and grandchildren. Unfortunately, this study did not collect detailed data on household composition enabling the estimation of the effect of different categories of living arrangements on depressive symptoms among those who reported living with family members or others.
An analysis of the moderating effects of neighbourhood environmental attributes on the associations between living arrangement and depressive symptoms revealed that, as expected, those living alone were more likely to report (any) depressive symptoms than their counterparts when residing in neighbourhoods with poorer access to destinations (eg, services and retail) and fewer people on the street. Having good access to destinations and people in the neighbourhood may help offset the negative effects of living alone by providing opportunities for socialising and engagement in a variety of activities. It is interesting that at higher levels of access to public transport and crowdedness, those living alone were less likely to report any depressive symptoms than those living with others. Older adults living in ultra-dense overcrowded urban environments with high levels of traffic-related noise and pollution may benefit from daily periods of restoration and quiet. These may be more easily attainable if living alone than if living in a small apartment with others, which is a common housing condition in Hong Kong.62 In fact, household crowding has been found to contribute to psychological distress.63
Apart from being respectively negatively and positively associated with depressive symptoms, perceptible pollution and street connectivity also respectively attenuated and increased the deleterious effects of living alone. However, the moderating effect of pollution was no longer significant in the multivariable model likely due, as explained above, to it being a by-product of high density of/access to destinations and presence of people. Street connectivity remained a significant moderator in the multivariable models. As mentioned above, the presence of pedestrian bridges/overpasses is common in ultra-dense neighbourhoods of Hong Kong with high volumes of traffic. The latter neighbourhood characteristic has been identified as a risk factor for depression.55 64
This study has several strengths and limitations. Unlike previous investigations,19 we examined a large range of neighbourhood environmental attributes plausibly related to depressive symptoms. Also, we used objective approaches to quantify neighbourhood attributes that allowed us to partially control for potential reverse causality due to depressed individuals tending to exhibit negative cognitive bias resulting in negative thoughts and perceptions.65 Residential self-selection bias is likely to be a trivial source of reverse causality in this study because Hong Kong’s high levels of population density (6760 people/km2) and low percentage of developed land (less than 25%)66 limit most residents’ choice of accommodation and 37% of Hong Kong older adults live in public rental housing.67 Given the satisfactory response rate and the level of similarity in depressive symptoms and sociodemographic characteristics of participants recruited from two types of recruitment centres, the findings from this study are likely to be generalisable to the population of Chinese Hong Kong older adults matching the study eligibility criteria and other populations of older adults living in similar ultra-dense metropolises of Southeast Asia. Yet, we need to consider that the lower response rates among residents of low-walkable neighbourhoods might have introduced some bias. If respondents with depressive symptoms are less likely to participate in surveys and low walkable neighbourhoods increase the risk of depressive symptoms,19 the observed associations between environmental attributes and depressive symptom outcomes may have been attenuated (biased downwards).
Limitations also include the cross-sectional nature of the study and inability to employ a more comprehensive sampling frame for participant recruitment due to privacy ordinance restrictions. Future research may need to focus on longitudinal studies and natural experiments that provide more robust estimates of causal influences of the neighbourhood environment on depressive symptoms. However, small changes in the neighbourhood environment across short time periods (<5 years) are a challenge in longitudinal research as they provide low statistical power to detect associations. Future studies may also benefit from the use of both objective and self-report measures of the environment allowing the examination of the mediating role of environmental perceptions in the relationships between objective measures of the environment and depressive symptoms.
Overall, our findings shed some light on the complex relationships between the neighbourhood characteristics of ultra-dense cities and older adults’ depressive symptoms in an Asian context. The level of access to destinations and social networks across Hong Kong neighbourhoods may be sufficiently high to reduce the risk of depressive symptoms attributable to social isolation in the general population of older adults. Traffic-related noise and air pollution associated with extreme levels of public transport density may increase the likelihood of depressive symptoms in residents of ultra-dense cities such as Hong Kong. Measures to reduce traffic-related air pollution and noise, such as the upgrade of bus fleets, policies promoting the reduction of car and bus idling and the installation of vegetative barriers may help attenuate this environmental risk factor. Particular neighbourhood attributes, such as access to destinations and presence of people on the street, may facilitate engagement in stress-buffering behaviours (eg, socialising with others68 and engaging in physical activity)35 in people living alone. Providing good access to facilities and public open spaces for socialising in neighbourhoods with high prevalence of older adults living alone should be considered as an important aspect of mental health promotion.
Contributors CJPZ drafted the manuscript, coordinated the study, computed the GIS variables and contributed to data collection and data analyses. EC critically reviewed the manuscript, conceptualised and secured funding for the study and performed the analyses. AB contributed to the conceptualisation of the manuscript and the study. CHPS contributed to the conceptualisation of the study, translation of surveys and assisted in data collection. PL contributed to the conceptualisation of the study and processing of the GIS variables. JMJ facilitated the data collection and organisation. RSYL assisted in the coordination of the study, data collection and the conceptualisation of the study. All authors read, edited or revised the manuscript for important intellectual content and approved the final version.
Funding This study received a General Research Fund grant from the University Grant Committee, Hong Kong (HKU 741511H). EC is supported by an Australian Research Council Future Fellowship (FT14010085).
Competing interests None declared.
Patient consent Detail has been removed from this case description/these case descriptions to ensure anonymity. The editors and reviewers have seen the detailed information available and are satisfied that the information backs up the case the authors are making.
Ethics approval The University of Hong Kong Human Research Ethics Committee for Non-Clinical Faculties (EA270211) and the Department of Health (Hong Kong SAR).
Provenance and peer review Not commissioned; externally peer reviewed.
Data sharing statement This study used data from clients of the Elderly Health Service, the Department of Health, HKSAR. Access to the data is limited by the Department of Health, HKSAR, to the staff of the Department of Health, HKSAR and the research investigators.
If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.