The spatial analysis of the associations between built environment and lung health outcomes in children from Drakenstein, Western Cape: a nested case control study - Meyer, Demi Almaz (Department of Public Health and Family Medicine, 2026)
Aim To assess if there is any relationship between the social and environmental risk factors (built environment) and spatial distribution of child lung health outcomes in Drakenstein. Research question In children residing in Drakenstein, what are the risk factors associated with spatial distribution of respiratory disease outcomes? Hypothesis We hypothesize that the observed spatial distribution of child lung health outcomes is not associated with the social and environmental risk factors (built environment). Objectives 1. To describe spatial patterns of childhood lung health outcomes in terms of geographical areas in Drakenstein. 2. To describe built environment in terms of geographical areas in Drakenstein. 3. To determine the relationship between spatial distribution of childhood lung health outcomes and social and environmental risk factors (built environment) using spatial analysis. Background In South Africa, childhood lung diseases continue to be a serious public health issue. Lower respiratory tract infections (LRTIs) and tuberculosis (TB) are among the childhood lung disorders that have a diverse geographic distribution. However, information on the condition of children's lungs and related risk factors is typically only provided at the province, district, or subdistrict levels and is lacking at a local level in South Africa. Child lung health is associated with a complex combination of social and environmental risk factors. The built environment impacts public health by influencing human exposure to airborne pollutants. Overcrowding: which is often a result of poor infrastructure is directly linked to the built environment; facilitate the spread of childhood lung diseases. The geographical analysis of the epidemiology and risk factors for child lung diseases can guide the targeting of health programs that work to address this issue. This study aims to assess if there is any relationship between the social and environmental risk factors (built environment) and spatial distribution of child lung health outcomes in Drakenstein, Western Cape, South Africa. Methods The parent study is an ongoing birth cohort of approximately 1000 mother and child pairs however, only 844 mother and child pairs consented to household spatial coordinate collection. Demographics, household, environmental exposures, and child lung health outcomes data was collected in this study. This study's placement inside an ongoing longitudinal birth cohort study offers a novel opportunity to define children's lung health through in space, allowing for insightful inferences about the factors influencing children's health in South Africa. The overall cohort was spatially subsampled into distance matched case control groups. We examined the relationships between individual-, household, and community-level risk factors and child lung diseases (namely LRTIs and TB). To do this, we created and mapped the spatial data using the geo-location of study participants' households, known community-level risk factors, and key built environments. The built environment was categorized into distance between case/ control and built environment in kilometers and number of built environment categories within a 500-meter radius. The analysis makes use of multivariable logistic regression to determine the association between lung health (namely LRTI and LTBI) cases and controls and the social and environmental risk factors (built environment). Results The subsampled distance matched cohort included 408 LRTI cases (male children: 58%) and 408 LRTI controls (male children: 44%) (1:1 ratio) and 75 LTBI cases (male children: 65%) and 375 LTBI controls (male children: 49%) (1:5 ratio). The LRTI population is equally spread between Mbekweni (50%) and TC Newman (50%) neighbourhoods whereas the LTBI population is higher in the TC Newman neighbourhoods at 65% compared to 35% in Mbekweni neighbourhoods. In the LRTI and LTBI models, the number of significant differences between cases and controls vary. The main built environment types that were associated with LRTI were spaza shops [OR = 1.05, 95%CI (1.02; 1.08), p = 0.01], formal liquor stores [OR = 2. 63, 95%CI (1.20; 5.90); p = 0.02] and agriculture (farming places) [aOR = 0.34, 95%CI, 0.11; 0.82 p = 0.03]. The main built environment types that were associated with LTBI were formal [OR = 0.26, 95%CI (0.09; 0.68), p = 0.01] and informal liquor stores [OR = 0.27, 95%CI (0.08; 0.85), p = 0.01] and religious (places of worship) [OR = 11.81, 95%CI (1.51; 101.88), p = 0.02]. Conclusions To conclude, in Drakenstein, the associations between some of the social and environmental risk factors (built environment) and the spatial distribution of child lung health outcomes are present after distance-matching cases and controls and adjusting for covariates. The observed associations are indicative of the SES and community circumstances. Thus, creation of more green spaces or designing of buildings with better air flow would be useful and actionable by the government and local authorities as potential strategies to reduce the burden of childhood LRTI and LTBI in LMICs. Keywords Lower Respiratory Tract Infections; Tuberculosis; Built Environment; Spatial Analysis.