The Impacts of Housing Characteristics and Built-Environment Features on Mental Health

The impact of urbanization on youth mental health in Hong Kong

PURPOSE OF REVIEW

Urbanization is a multifaceted process that can have both positive and negative effects on mental health, especially in adolescents. This paper attempts to summarize the impact of urbanization on youth mental health in Hong Kong.

RECENT FINDINGS

Several studies have shown that urbanization is associated with an increased risk of youth mental health problems in Hong Kong. Psychosocial factors like perceived stress, social isolation, and exposure to environmental pollutants may contribute to a negative association between urbanization and mental health issues. Academic pressure, poor transparency and accessibility to mental health services in Hong Kong further exacerbate youth's mental health wellbeing. Environmental factors like housing conditions, green spaces, and visible urban greenery have been found to influence mental health outcomes. Existing empirical studies have found a positive association between urbanization and mental health problems, while others have found no association or even a negative association.

SUMMARY

Further research is warranted to investigate the complex relationship between urbanization and youth mental health in Hong Kong. Exploration of effective interventions is necessary to mitigate the negative impacts of urbanization on youth mental health. Understanding this relationship can inform health policy-making and formulate interventions to promote youth's mental health well being in the short-and long run.

Impact of Environment on Pain among the Working Poor: Making Use of Random Forest-Based Stratification Tool to Study the Socioecology of Pain Interference

Pain interferes with one's work and social life and, at a personal level, daily activities, mood, and sleep quality. However, little research has been conducted on pain interference and its socioecological determinants among the working poor. Noting the clinical/policy decision needs and the technical challenges of isolating the intricately interrelated socioecological factors' unique contributions to pain interference and quantifying the relative contributions of each factor in an interpretable manner to inform clinical and policy decision-making, we deployed a novel random forest algorithm to model and quantify the unique contribution of a diverse ensemble of environmental, sociodemographic, and clinical factors to pain interference. Our analyses revealed that features representing the internal built environment of the working poor, such as the size of the living space, air quality, access to light, architectural design conducive to social connection, and age of the building, were assigned greater statistical importance than other more commonly examined predisposing factors for pain interference, such as age, occupation, the severity and locations of pain, BMI, serum blood sugar, and blood pressure. The findings were discussed in the context of their benefit in informing community pain screening to target residential areas whose built environment contributed most to pain interference and informing the design of intervention programs to minimize pain interference among those who suffered from chronic pain and showed specific characteristics. The findings support the call for good architecture to provide the spirit and value of buildings in city development.

The interplay among individuals' distress, daily activities, and perceptions of COVID-19 and neighborhood cohesion: A study using network analysis

The reduction of social interactions through non-pharmaceutical interventions (NPIs) has been shown to effectively curb COVID-19 transmission. However, these control measures were often accompanied by changes in people's daily routines and constraints on their activity space, which could lead to mental distress (i.e., anxiety and depression). This study examined the interplay among individuals' anxiety, depression, daily activities, and perceptions of COVID-19 and neighborhood cohesion. Taking Hong Kong as an example, an online survey (N = 376) was conducted to collect data from participants between March 14 to May 11, 2022. The data include respondents' self-reported anxiety and depressive symptoms, daily activities (e.g., smartphone use), perceptions of COVID-19 (e.g., the possibility of infecting COVID-19), and perceptions of neighborhood cohesion. Using network analysis, we found that excessive smartphone use, life disturbance by COVID-19, and a community with people getting along well with each other were significant factors associated with participants' anxiety and depression. Using critical path analysis, we observed that NPIs reduced human mobility, led to delayed bedtime, and increased smartphone use, which were associated with participants' mental distress. We also found that NPIs and COVID-19 were associated with people's perceptions of infection and the severity of COVID-19 and human mobility flexibility, which may further lead to mental distress. Our results also demonstrated that people with high education levels were vulnerable. These results provided important insights for designing appropriate interventions without generating deleterious impacts on people's mental health in the future.

A happy home? Socio-economic inequalities in depressive symptoms and the role of housing quality in nine European countries

BACKGROUND

This study examines the prevalence of and socio-economic inequalities in depressive symptoms in nine high-income European countries, focusing in particular on the role of housing quality.

METHODS

Using the European Social Survey, a concentration index of depressive symptoms in each country is estimated. The role of housing quality is assessed by examining the risk factors associated with the concentration index, using the Recentred Influence Function method. To contextualise the housing quality results, other predictors of inequalities in depressive symptoms inequalities are also quantified and discussed.

RESULTS

Our results indicate that inequalities in depressive symptoms are concentrated among poorer respondents both in each country and in total. Austria and Belgium have the lowest inequalities and France has the highest. No geographic pattern is evident. Housing problems are associated with higher inequalities in six of the nine countries in the sample. While no association is evident for indicators of socio-economic status such as years of education and income, financial strain is significant.

CONCLUSIONS

This study is the first to estimate the degree of socio-economic inequality in depressive symptoms across European countries. The association between poor housing and poorer inequalities suggests that housing has a role to play lowering depressive symptoms inequalities.

Nonstationary relationships among individuals' concurrent exposures to noise, air pollution and greenspace: A mobility-based study using GPS and mobile sensing data

Individuals are often exposed to multiple environmental factors simultaneously. Understanding their joint effects is essential for developing effective public health policies. However, there has been a lack of research examining individuals' concurrent exposures to multiple environmental factors during people's daily mobility. To address this gap, this study investigated the relationships between and geographic patterns of individual exposures to air pollution (PM2.5), noise and greenspace using individual-level real-time GPS and mobile sensing data collected in outdoor environments. The findings indicate that the relationships between individual exposures to air pollution, noise and greenspace vary across different value ranges of exposures. The study also reveals that people's concurrent exposures to multiple environmental factors exhibit spatial nonstationary and strong clustering patterns. These results highlight the importance of considering spatial nonstationary and spatial heterogeneity of environmental exposures in understanding the relationships between multiple exposures in environmental health research.

How People's COVID-19 Induced-Worries and Multiple Environmental Exposures Are Associated with Their Depression, Anxiety, and Stress during the Pandemic

This study investigates how people's perceived COVID-19 risk, worries about financial hardship, job loss, and family conflicts, and exposures to greenspace, PM2.5, and noise (in people's residential neighborhoods and daily activity locations) are related to their depression, anxiety, and stress during the COVID-19 pandemic. Using a two-day activity-travel diary, a questionnaire, and real-time air pollutant and noise sensors, a survey was conducted to collect data from 221 participants living in two residential neighborhoods of Hong Kong during the COVID-19 pandemic. Linear regression was conducted to explore the relationships. Significant associations between people's COVID-19-related worries and exposures to grassland and PM2.5 with depression, anxiety, and stress were found in the results. These associations with depression, anxiety, and stress vary depending on people's demographic attributes. These results can help direct the public authorities' efforts in dealing with the public mental health crisis during the COVID-19 pandemic.

Associations between COVID-19 risk, multiple environmental exposures, and housing conditions: A study using individual-level GPS-based real-time sensing data

Few studies have used individual-level data to explore the association between COVID-19 risk with multiple environmental exposures and housing conditions. Using individual-level data collected with GPS-tracking smartphones, mobile air-pollutant and noise sensors, an activity-travel diary, and a questionnaire from two typical neighborhoods in a dense and well-developed city (i.e., Hong Kong), this study seeks to examine 1) the associations between multiple environmental exposures (i.e., different types of greenspace, PM_2.5, and noise) and housing conditions (i.e., housing types, ownership, and overcrowding) with individuals' COVID-19 risk both in residential neighborhoods and along daily mobility trajectories; 2) which social groups are disadvantaged in COVID-19 risk through the perspective of the neighborhood effect averaging problem (NEAP). Using separate multiple linear regression and logistical regression models, we found a significant negative association between COVID-19 risk with greenspace (i.e., NDVI) both in residential areas and along people's daily mobility trajectories. Meanwhile, we also found that high open space and recreational land exposure and poor housing conditions were positively associated with COVID-19 risk in high-risk neighborhoods, and noise exposure was positively associated with COVID-19 risk in low-risk neighborhoods. Further, people with work places in high-risk areas and poor housing conditions were disadvantaged in COVID-19 risk.

Inconsistent Association between Perceived Air Quality and Self-Reported Respiratory Symptoms: A Pilot Study and Implications for Environmental Health Studies

As public awareness of air quality issues becomes heightened, people's perception of air quality is drawing increasing academic interest. However, data about people's perceived environment need scrutiny before being used in environmental health studies. In this research, we examine the associations between people's perceptions of air quality and their self-reported respiratory health symptoms. Spearman rank correlation coefficients were estimated and the associations were tested at the 95% confidence level. Using data collected from participants in two representative communities in Hong Kong, the results indicate a weak but significant association between people's perceived air quality and their self-reported frequency of respiratory symptoms. However, there are disparities in such an association between different genders, age groups, household income levels, education levels, marital statuses, and geographic contexts. The most striking disparities are between genders and geographic contexts. Multiple significant associations were observed for male participants (correlation coefficients: 0.169~0.205, p-values: 0.021~0.049), while none was observed for female participants. Besides, multiple significant associations were observed in the old town (correlation coefficients: 0.164~0.270, p-values: 0.003~0.048), while none was observed in the new town. The results have significant implications for environmental health research using social media data, whose reliability depends on the association between people's perceived or actual environments and their health outcomes. Since inconsistent associations exist between different groups of people, researchers need to scrutinize social media data before using them in health studies.