Sarkar Chinmoy, Webster Chris, Gallacher John
Healthy High Density Cities Lab, HKUrbanLab, University of Hong Kong, Hong Kong Special Administrative Region, China.
Healthy High Density Cities Lab, HKUrbanLab, University of Hong Kong, Hong Kong Special Administrative Region, China.
Lancet Planet Health. 2017 Oct;1(7):e277-e288. doi: 10.1016/S2542-5196(17)30119-5. Epub 2017 Oct 5.
Obesity is a major health issue and an important public health target for urban design. However, the evidence for identifying the optimum residential density in relation to obesity has been far from compelling. We examined the association of obesity with residential density in a large and diverse population sample drawn from the UK Biobank to identify healthy-weight-sustaining density environments.
For this full-data, cross-sectional analysis, we used UK Biobank data for adult men and women aged 37-73 years from 22 cities across the UK. Baseline examinations were done between 2006 and 2010. Residential unit density was objectively assessed within a 1 km street catchment of a participant's residence. Other activity-influencing built environment factors were measured in terms of density of retail, public transport, and street-level movement density, which were modelled from network analyses of through movement of street links within the defined catchment. We regressed adiposity indicators of body-mass index (BMI; kg/m), waist circumference (cm), whole body fat (kg), and obesity (WHO criteria of BMI ≥30 kg/m) on residential density (units per km), adjusting for activity-influencing built environment factors and individual covariates. We also investigated effect modification by age, sex, employment status, and physical activity. We used a series of linear continuous and logistic regression models and non-linear restricted cubic spline models as appropriate.
Of 502 649 adults in the prospective cohort, 419 562 (83·5%) participants across 22 UK Biobank assessment centres met baseline data requirements and were included in the analytic sample. The fitted restricted cubic spline adiposity-residential density dose-response curve identified a turning point at a residential density of 1800 residential units per km. Below a residential density of 1800 units per km, an increment of 1000 units per km was positively related with adiposity, being associated with higher BMI (β 0·19 kg/m, 95% CI 0·14 to 0·24), waist circumference (β 0·41 cm, 0·28 to 0·54), and whole body fat (β 0·40 kg, 0·30 to 0·50), and with increased odds of obesity (odds ratio [OR] 1·10, 1·07 to 1·14). Beyond 1800 units per km, residential density had a protective effect on adiposity and was associated with lower BMI (β -0·22 kg/m, -0·25 to -0·20), waist circumference (β -0·54 cm, -0·61 to -0·48), and whole body fat (β -0·38 kg, -0·43 to -0·33), and with decreased odds of obesity (OR 0·91, 0·90 to 0·93). Subgroup analyses identified more pronounced protective effects of residential density among individuals who were younger, female, in employment, and accumulating higher levels of physical activity, except in the case of whole body fat, for which the protective effects were stronger in men.
Housing-level policy related to the optimisation of healthy density in cities might be a potential upstream-level public health intervention towards the minimisation and offsetting of obesity; however, further research based on accumulated prospective data is necessary for evidencing specific pathways. The findings might mean that governments, such as the UK Government, who are attempting to prevent suburban densification by, for example, prohibiting the subdivision of single lot housing and the conversion of domestic gardens to housing lots, will potentially have the effect of inhibiting the conversion of suburbs into more healthy places to live.
University of Hong Kong, UK Biobank, and UK Economic & Social Research Council.
肥胖是一个主要的健康问题，也是城市设计的一个重要公共卫生目标。然而，确定与肥胖相关的最佳居住密度的证据远不具有说服力。我们在从英国生物银行抽取的一个规模庞大且多样化的人群样本中，研究了肥胖与居住密度之间的关联，以确定有助于维持健康体重的密度环境。
对于这项全数据横断面分析，我们使用了英国生物银行中来自英国22个城市的37至73岁成年男性和女性的数据。基线检查在2006年至2010年期间进行。居住单元密度是在参与者住所1公里街道集水区内客观评估的。其他影响活动的建成环境因素是根据零售密度、公共交通密度和街道层面的活动密度来衡量的，这些是通过对定义集水区内街道链接的通行情况进行网络分析建模得到的。我们将体重指数（BMI；kg/m）、腰围（cm）、全身脂肪（kg）和肥胖（世界卫生组织BMI≥30 kg/m的标准）等肥胖指标对居住密度（每公里单元数）进行回归分析，并对影响活动的建成环境因素和个体协变量进行了调整。我们还研究了年龄、性别、就业状况和身体活动的效应修正。我们酌情使用了一系列线性连续和逻辑回归模型以及非线性受限立方样条模型。
在前瞻性队列的502649名成年人中，来自22个英国生物银行评估中心的419562名（83.5%）参与者满足基线数据要求并被纳入分析样本。拟合的受限立方样条肥胖 - 居住密度剂量反应曲线在居住密度为每公里1800个居住单元处确定了一个转折点。在每公里居住密度低于1800个单元时，每公里增加1000个单元与肥胖呈正相关，与更高的BMI（β 0.19 kg/m，95%CI 0.14至0.24）、腰围（β 0.41 cm，0.28至0.54）和全身脂肪（β 0.40 kg，0.30至0.50）相关，并且肥胖几率增加（优势比[OR] 1.10，1.07至1.14）。超过每公里1800个单元后，居住密度对肥胖有保护作用，与较低的BMI（β -0.22 kg/m，-0.25至-0.20）、腰围（β -0.54 cm，-0.61至-0.48）和全身脂肪（β -0.38 kg，-0.43至-0.33）相关，并且肥胖几率降低（OR 0.91，0.90至0.93）。亚组分析表明，居住密度对年龄较小、女性、就业且身体活动水平较高的个体具有更明显的保护作用，但全身脂肪除外，其保护作用在男性中更强。
与城市健康密度优化相关的住房层面政策可能是一项潜在的上游公共卫生干预措施，有助于将肥胖最小化并抵消其影响；然而，需要基于积累的前瞻性数据进行进一步研究，以证明具体途径。这些发现可能意味着，诸如英国政府等试图通过例如禁止单户住房细分以及将家庭花园转变为住房地块等方式来防止郊区密度增加的政府，可能会产生抑制郊区转变为更健康居住场所的效果。
香港大学、英国生物银行和英国经济与社会研究理事会。
