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

In this study, we examined the relationships between housing characteristics, neighborhood built-environment features, and people's mental health in Hong Kong, an Asian city well known for its high-density and high-rise housing. The potential mediating effects of people's perceived living environment were also considered in the analysis. We collected data from 221 participants from two communities in Hong Kong, i.e., Sham Shui Po (SSP) and Tin Shui Wai (TSW), using a stratified random sampling approach. Big datasets were also used to derive relevant built-environment features at the street block level. We used structural equation modeling to explore the complex relationships among housing characteristics, built-environment features, and mental health. The results indicate that the associations between built-environment quality and people's mental health are weak. For communities with relatively poor housing conditions (i.e., SSP in this study), the impact of housing characteristics on mental health may be more direct; for communities with relatively good housing conditions (i.e., TSW in this study), the effect of housing characteristics on mental health may be indirect. Our findings shed light on the importance of considering different contexts in developing policies related to housing and built environment and mental health.

Associations Between Household Socioeconomic Status, Car Ownership, Physical Activity, and Cardiorespiratory Fitness in South African Primary Schoolchildren Living in Marginalized Communities

BACKGROUND

Little is known whether physical activity (PA)-promoting environments are equally accessible to children with divergent socioeconomic status (SES) in low-/middle-income countries. The authors, therefore, examined whether South African children from poorer versus wealthier families living in marginalized communities differed in moderate to vigorous PA and cardiorespiratory fitness. We also tested associations between family car ownership and PA/cardiorespiratory fitness.

METHODS

Parents/guardians of 908 children (49% girls, mean age = 8.3 [1.4] y) completed a survey on household SES. PA was assessed via 7-day accelerometry, parental and child self-reports, and cardiorespiratory fitness with the 20-m shuttle run test.

RESULTS

Based on accelerometry, most children met current moderate to vigorous PA recommendations (≥60 min/d). About 73% of the children did not engage in structured physical education lessons. Whereas children of the lowest SES quintile accumulated higher levels of device-based moderate to vigorous PA, peers from the highest SES quintile engaged in more sedentary behaviors, but self-reported higher engagement in sports, dance, and moving games after school. Families' car ownership was associated with higher parent/self-reported leisure-time PA.

CONCLUSIONS

A deeper understanding is needed about why wealthier children are more sedentary, but simultaneously engage in more leisure-time PA. The fact that access to structural physical education is denied to most children is critical and needs to be addressed.

A Pilot Study to Examine Exposure to Residential Radon in Under-Sampled Census Tracts of DeKalb County, Georgia, in 2015

While DeKalb County, Georgia, offers free radon screening for all eligible residents, portions of the county remain relatively under-sampled. This pilot study focused on 10% of the census tracts in the county with the lowest proportion of radon testing; most were in southern DeKalb County. In total, 217 households were recruited and homes were tested for indoor radon concentrations on the lowest livable floor over an eight-week period from March-May 2015. Tract-level characteristics were examined to understand the differences in socio-demographic and economic factors between the pilot study area and the rest of the county. The pilot study tracts had a higher proportion of African Americans compared to the rest of DeKalb County (82% versus 47%). Radon was detected above 11.1 Bq/m³ (0.3 pCi/L) in 73% of the indoor samples and 4% of samples were above 148 Bq/m³ (4 pCi/L). Having a basement was the strongest predictive factor for detectable and hazardous levels of radon. Radon screening can identify problems and spur homeowners to remediate but more research should be done to identify why screening rates vary across the county and how that varies with radon levels in homes to reduce radon exposure.

Urban-rural differences in daily time-activity patterns, occupational activity and housing characteristics

BACKGROUND

There is evidence that rural residents experience a health disadvantage compared to urban residents, associated with a greater prevalence of health risk factors and socioeconomic differences. We examined differences between urban and rural Canadians using data from the Canadian Human Activity Pattern Survey (CHAPS) 2.

METHODS

Data were collected from 1460 respondents in two rural areas (Haldimand-Norfolk, Ontario and Annapolis Valley-Kings County, Nova Scotia) and 3551 respondents in five urban areas (Vancouver, Edmonton, Toronto, Montreal, and Halifax) using a 24-h recall diary and supplementary questionnaires administered using computer-assisted telephone interviews. We evaluated differences in time-activity patterns, occupational activity, and housing characteristics between rural and urban populations using multivariable linear and logistic regression models adjusted for design as well as demographic and socioeconomic covariates. Taylor linearization method and design-adjusted Wald tests were used to test statistical significance.

RESULTS

After adjustment for demographic and socioeconomic covariates, rural children, adults and seniors spent on average 0.7 (p < 0.05), 1.2 (p < 0.001), and 0.9 (p < 0.001) more hours outdoors per day respectively than urban counterparts. 23.1% (95% CI: 19.0-27.2%) of urban and 37.8% (95% CI: 31.2-44.4%) of rural employed populations reported working outdoors and the distributions of job skill level and industry differed significantly (p < 0.001) between urban and rural residents. In particular, 11.4% of rural residents vs. 4.9% of urban residents were employed in unskilled jobs, and 11.5% of rural residents vs. <0.5% of urban residents were employ in primary industry. Rural residents were also more likely than urban residents to report spending time near gas or diesel powered equipment other than vehicles (16.9% vs. 5.2%, p < 0.001), more likely to report wood as a heating fuel (9.8% vs. <0.1%; p < 0.001 for difference in distribution of heating fuels), less likely to have an air conditioner (43.0% vs. 57.2%, p < 0.001), and more likely to smoke (29.1% vs. 19.0 %, p < 0.001). Private wells were the main water source in rural areas (68.6%) in contrast to public water systems (97.6%) in urban areas (p < 0.001). Despite these differences, no differences in self-reported health status were observed between urban and rural residents.

CONCLUSIONS

We identified a number of differences between urban and rural residents, which provide evidence pertinent to the urban-rural health disparity.

Predictors of endotoxin levels in U.S. housing

BACKGROUND

The relationship of domestic endotoxin exposure to allergy and asthma has been widely investigated. However, few studies have evaluated predictors of household endotoxin, and none have done so for multiple locations within homes and on a national scale.

OBJECTIVES

We assayed 2,552 house dust samples in a nationwide study to understand the predictors of household endotoxin in bedroom floors, family room floors, beds, kitchen floors, and family room sofas.

METHODS

Reservoir house dust from five locations within homes was assayed for endotoxin and demographic and housing information was assessed through questionnaire and onsite evaluation of 2,456 residents of 831 homes selected to represent national demographics. We performed repeated-measures analysis of variance (rANOVA) for 37 candidate variables to identify independent predictors of endotoxin. Meteorologic data were obtained for each primary sampling unit and tested as predictors of indoor endotoxin to determine if wetter or warmer microclimates were associated with higher endotoxin levels.

RESULTS

Weighted geometric mean endotoxin concentration ranged from 18.7 to 80.5 endotoxin units (EU)/mg for the five sampling locations, and endotoxin load ranged from 4,160 to 19,500 EU/m(2). Bivariate analyses and rANOVA demonstrated that major predictors of endotoxin concentration were sampling location in the home, census division, educational attainment, presence of children, current dog ownership, resident-described problems with cockroaches, food debris, cockroach stains, and evidence of smoking observed by field staff. Low household income entered the model if educational attainment was removed.

CONCLUSION

Increased endotoxin in household reservoir dust is principally associated with poverty, people, pets, household cleanliness, and geography.