Assessing Trade-Offs and Optimal Ranges of Density for Life Expectancy and 12 Causes of Mortality in Metro Vancouver, Canada, 1990-2016.

Jessica Yu,Michael Brauer,Martino Tran,Paul Gustafson

doi:10.3390/ijerph19052900

Abstract

Background: Understanding and managing the impacts of population growth and densification are important steps for sustainable development. This study sought to evaluate the health trade-offs associated with increasing densification and to identify the optimal balance of neighbourhood densification for health. Methods: We linked population density with a 27-year mortality dataset in Metro Vancouver that includes census-tract levels of life expectancy (LE), cause-specific mortalities, and area-level deprivation. We applied two methods: (1) difference-in-differences (DID) models to study the impacts of densification changes from the early 1990s on changes in mortality over a 27-year period; and (2) smoothed cubic splines to identify thresholds of densification at which mortality rates accelerated. Results: At densities above ~9400 persons per km2, LE began to decrease more rapidly. By cause, densification was linked to decreased mortality for major causes of mortality in the region, such as cardiovascular diseases, neoplasms, and diabetes. Greater inequality with increasing density was observed for causes such as human immunodeficiency virus and acquired immunodeficiency syndrome (HIV/AIDS), sexually transmitted infections, and self-harm and interpersonal violence. Conclusions: Areas with higher population densities generally have lower rates of mortality from the major causes, but these environments are also associated with higher relative inequality from largely preventable causes of death.

Highlights

70% of the world’s population will be living in urban centers by 2050 [1].Understanding and managing the impacts of population growth and densification are, important steps to implementing the 2030 Agenda for Sustainable Development.Growth management plans (GMPs) or urban containment policies, such as the ‘greenbelt’in Ontario, Canada, and urban growth boundaries in Oregon, United States, have managed residential development outside or inside pre-determined urban–rural boundaries often to support sustainability and climate change agendas
Using almost three decades of repeated cross-sectional data, we analysed the change in density over the change in life expectancy (LE) using DID models to determine what relation, if any, density had with LE and selected cause-specific mortality outcomes
The treatment group of the high-socioeconomic status (SES) CTs started with the lowest LE (2.7 years less than the control group) and the treatment group of the low SES CTs started with a higher LE (0.7 years difference compared to the control group)

Summary

Introduction

70% of the world’s population will be living in urban centers by 2050 [1].Understanding and managing the impacts of population growth and densification are, important steps to implementing the 2030 Agenda for Sustainable Development.Growth management plans (GMPs) or urban containment policies, such as the ‘greenbelt’in Ontario, Canada, and urban growth boundaries in Oregon, United States, have managed residential development outside or inside pre-determined urban–rural boundaries often to support sustainability and climate change agendas. Understanding and managing the impacts of population growth and densification are, important steps to implementing the 2030 Agenda for Sustainable Development. Growth management plans (GMPs) or urban containment policies, such as the ‘greenbelt’. In Ontario, Canada, and urban growth boundaries in Oregon, United States, have managed residential development outside or inside pre-determined urban–rural boundaries often to support sustainability and climate change agendas. From an energy efficiency perspective, GMPs can help reduce car dependency, support development of public transportation, preserve ecological features, and reduce air pollution [2]. GMPs have some potential downsides and constraints, such as potential gentrification, public opposition (e.g., NIMBY—‘not in my backyard’), and land economics that support sprawl [3]. Theories have emerged that suggest there are net benefits and costs for population health from densification.

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Results

Discussion

Conclusion