Urban Scaling of Health Outcomes: a Scoping Review

More than one half of the world population now lives in urban areas [1]. Cities present unique challenges for the well-being of their residents and their shared environment [2]. The United Nation’s New Urban Agenda further highlights the importance of urban health research in achieving Sustainable Development Goals such as ending poverty, hunger, and creating sustainable cities [3, 4]. In a world undergoing rapid urbanization, understanding how city-level factors change with city size can be instrumental in the creation of a unified theory of city living: a predictive framework for how urbanization and city growth affects society and the environment [5‐8]. This theory would allow, among other things, for a better understanding of how health outcomes vary across the continuum of city size, and how variations in these outcomes may be associated with city-level factors and underlying policies which are important to improve planetary health.

Cities are complex systems where the dynamics of population size and social interaction give rise to emergent phenomena known as urban scaling [9]. Urban scaling describes the processes by which urban features such as economic features, wealth, crime, pollution, consumption patterns, and energy expenditure vary with changes in city size (i.e., population growth) [6]. A linear scaling response indicates no relationship between the urban feature and city size. For example, the amount of energy consumption per household is relatively similar across cities of similar size [5, 7]. Some characteristics of cities, for example road infrastructure, show sublinear scaling which means that as cities grow in size, the amount of road length and gas stations, relative to population size, decreases [5, 7]. In contrast, other features of the urban environment such as the relative amount of wealth, innovation, crime, and pollution per capita increases as cities grow in size, a phenomenon known as superlinear scaling [5, 7]. The way cities grow is also relevant to the scaling phenomena. While often treated as a static feature of cities, city size is the result of dynamic processes that imply many different types and rates of growth [6‐8]. Figure 1 shows an example of three scaling responses for three hypothetical types of causes of death.

A large body of literature has explored urban-rural differences in health and has originated the urban penalty and urban advantage theories which posit deleterious or positive overall impacts of urban living for population health [2, 10]. However, urban-rural comparisons are often limited by the fact that cities are heterogenous in many features, including city population size. Additionally, while the urban-rural framework can provide convenient comparisons, the urban penalty and advantage theories are limited by the complexity and diversity of cities, which tend to vary across the globe; suggesting the benefits and risk of urban living are not uniform [2]. Given the complex and diverse nature of cities, there is an inherent need for a framework to outline and characterize the dynamic relationship between city characteristics and health.

Current literature applying the concept of urban scaling to health is scarce, with most research focusing on the scaling properties of factors that are determinants of health [5, 7, 11‐14]. Understanding urban population dynamics, and subsequent scaling laws, are the first steps toward developing theories that describe the relationship between city characteristics and population health, with many of these characteristics being meaningful policy levers in terms of sustainability, resource limits, and healthy governance [2‐4]. In this study, we review the evidence pertaining to the urban scaling of health outcomes, that is, how health outcomes scale with city size.

The main objective of this scoping review was to map the existing evidence pertaining to the urban scaling properties of health outcomes. We followed the framework of the Joanna Briggs Institute (JBI) [15] and reported methods and results using the Preferred Reporting Items for Systematic Review and Meta-Analysis Extension for Scoping Reviews (PRISMA ScR) guidelines [16]. More details on the scoping review methodology can be found in the review protocol [17].

In this scoping review, we mapped evidence regarding the associations between city size or growth and health outcomes, with a focus on studies with an explicit scaling framework. We highlight five key findings. First, we found a diverse literature from many different geographical and temporal settings and outcomes, that included heterogeneous city definitions and different operationalizations of city size (e.g., continuous, as is the case for all scaling studies and some non-scaling studies, as well as categorical). Second, we found evidence of an urban penalty with higher mortality and worse health outcomes in larger cities of high-income countries, at least during the nineteenth century, that shifted in the early twentieth century toward lower mortality in larger cities. Third, we found that two key diseases with an epidemic component, measles, and influenza, are influenced by city size in conjunction with other factors like geographic proximity and transmission potential, while other communicable diseases such as STIs, HIV, and dengue tend to occur more frequently in larger cities. Fourth, we found that NCDs show a heterogeneous pattern that depends on the specific outcome and context. Fifth, homicides and other crimes are more common in larger cities, suicides are more common in smaller cities, and traffic-related injuries show a less clear pattern that may differ by context and type of injury.

A majority of the studies in this review were set in high-income countries (75 out of 102, 74%). While we captured a few studies from low- and middle-income countries (LMIC), such as Brazil and Mexico, the absence of evidence examining the urban scaling in other settings is a clear gap in the literature. This lack of evidence is especially worrisome for low-income countries, where poor sanitation, inequalities in resource availability, and overcrowding are especially prevalent in urban areas and may have a large influence on scaling patterns [100]. Furthermore, most future urban population growth is expected to occur in LMICs, specifically in Latin America, Asia, and Africa, and understanding the consequences of urban growth in these settings is key to achieving the Sustainable Development Goals [101] and should be a priority of future research.

One key aspect of being able to compare cities is having a clear definition of their boundaries [102]. In this scoping review, we found large heterogeneity in the way cities are defined. There is no single universally accepted definition of a city, and more often than not, the way cities are defined varies across countries and regions. While administrative units were the most used city definition, their primary purpose is administrative, and they may not represent actual city boundaries. Understanding the consequences of different city definitions on the scaling properties of health outcomes is a key direction of future research, as previous studies have highlighted that the scaling laws of some city features may vary systematically by city definition [11]. In a small number of studies we were not able to even identify what the authors referred to as “city”, which creates issues for reproducibility. Future research on urban health should clearly define what is meant by “city” and how boundaries are defined.

Our second key finding is that an urban penalty was present in the nineteenth and early twentieth century for studies set in what are now high-income countries [19, 22‐27, 103], with a shift occurring during the first half of the twentieth century toward lower mortality, especially due to communicable diseases, in larger cities of high-income countries [23]. The shift in mortality is likely attributable to changes in both rural and urban areas [2]. However, the heterogeneity in outcomes observed for cities of similar size in most scaling studies points to other city characteristics that are driving health. The emergence of these characteristics depends not only on size, but also on differences in geographic context, connectivity, resource availability, and economic growth, among many other factors [2]. Aside from being complex, city populations are among the most diverse; and while urbanization can affect health, these effects are heterogeneous for different populations, resulting in inequities at multiple levels [104]. Additionally, the observed shift in mortality may be related to changes in the urbanization processes [2, 27], evident in present day LMICs where rapid urbanization and development may contribute to unsafe settlement conditions and poor access to services, which can further exacerbate the urban penalty [105]. Whether the shifts in disease burden that originally occurred in cities of high-income countries are being replicated currently in LMIC cities has yet to be studied, precluding a complete understanding of this phenomenon, so future studies should leverage cross-national comparisons of cities to understand the dynamic associations between urbanization and health in countries at different stages of development [106].

Our third finding identified complex associations of city size and growth with certain diseases such as measles and influenza, and superlinear associations with city size for other commonly studied communicable diseases such as STIs, HIV, and dengue fever. A number of studies examined the 1918 influenza pandemic, with mixed evidence regarding the role of city size, consistent with studies on seasonal influenza. On the other hand, for measles, city size has a clear effect on the size and shape of epidemic waves [69, 70], as factors such as the critical community size, spatial hierarchies, and fadeout probabilities are all related to city size [68]. Last, STIs follow a consistent superlinear scaling pattern [30], but the scaling behavior of specific STIs is heterogenous and may depend on variability in disease transmission [29]. The effect of transmission variability on disease dynamics has been reported before [107, 108], and currently represents a potential avenue of future research in understanding the dynamics of large outbreaks such as the COVID-19 pandemic [109, 110].

Our fourth finding was that NCDs show a heterogenous pattern which varies based on health outcome, geographic context, developmental stage, and other factors. This is evident in the findings that cardiometabolic conditions scaled differentially in cities of the USA, Brazil, and China, where the USA tends to display a sublinear behavior (outcomes more common in smaller cities) while other countries display superlinear behaviors (outcomes more common in larger cities). While NCDs were the second most common class of health outcome in the review, there is limited evidence about the urban scaling of NCDs to date.

Our fifth key finding, is that the scaling properties of injuries were mostly consistent, indicating that homicides and other serious crimes were more common in larger cities, and suicides were more common in smaller cities. However, the scaling properties of road-traffic injuries were less clear and seemed to vary by type of injury (e.g., pedestrian vs. other road users) [93]. This may also be due to underestimation resulting in relatively low counts of injuries, compared to broader causes of death, which may lead to statistical issues in estimating scaling coefficients when a number of cities have zero counts of a specific injury [92].

Our review identified a few directions for future research on the urban scaling of health outcomes. We found very little research examining population growth as an exposure. The study of population growth in longitudinal study designs allows better inferences regarding the possible causal link between city size and health than cross-sectional analyses of city size. Drawing inferences regarding the links between city size and city outcomes from cross-sectional analyses of city size relies on an important assumption: the absence of confounding by other factors associated with city size (i.e., differences between cities of different sizes are equivalent to differences associated with changes in city size for a given city over time, which holds at least time invariant city factors constant). This is also known as the assumption of ergodicity (i.e., lack of path dependence), or no impact of how the city arrived to that population number [111]. Recent studies have challenged this assumption, finding that the longitudinal scaling properties of urban features may differ from the cross-sectional properties [112‐114]. Better understanding of the links between city size and health requires longitudinal analyses that examine population growth within cities over time as well as attention to the type of city growth and the processes driving growth.

While scaling studies aim to describe changes in city outcomes with changes in city size, the scaling framework also allows for the differentiation between size-related and place-specific effects, as proposed by Bettencourt, Lobo, Strumsky & West [12]. This is achieved through the mapping and examination of regression residuals from the basic scaling equation, which contain deviations from the empirically estimated scaling power law. These residuals are dimensionless indicators, independent of city size, that can provide quantitative information about the performance of urban areas and allow for calculation of correlations with other city-level predictors. These other city-level predictors include city-level policies, social environment features (e.g., levels of poverty, inequality, segregation, etc.), and physical and built environment characteristics (e.g. climate, air pollution, urban landscape, street design, etc.), among others. The key contribution of a scaling analysis that includes an exploration of residuals would be the joint interpretation of both size-related patterns (e.g., a scaling coefficient above 1 indicating a higher homicide rate in larger cities) and city-specific effects derived from other city features independent of city size (e.g., cities with higher income inequality having a higher homicide rate).

Finally, all studies included in this review have a common objective of examining the relationship between city size and some health outcome(s); features characteristic of the urban scaling framework. However, we found heterogeneity in how these studies were conducted, in terms of definitions, operationalizations of city size, and the presence of (or lack thereof) adjustment variables. For example, we only found a few studies that examined how introducing adjustment variables changed scaling patterns [29‐31, 39, 71, 93]. Future research should be transparent about the inclusion of relevant covariates, as adequately controlling for these covariates can influence the scaling response and also provide meaningful evidence on the relationship between covariates (e.g., income, education, population density) and health in cities. For example, given the important role of age in driving mortality, studies of the scaling of deaths with city size should consider how adjusting for age may change scaling coefficients.

We acknowledge some limitations. First, our search strategy may have missed some studies on city size/growth and health, especially if they were published in journals not indexed in the databases we searched. This may be especially important for studies published in the early twentieth century, which may not be entirely captured in these databases. However, in order to increase the scope of the review, we also used a backwards search to identify references cited by included studies. Second, we did not complete a forward search (i.e., a search for papers citing included studies). Consequently, we may have missed studies relevant to our objectives. Additionally, the decision to search only two databases (PubMed and LILACS) may have excluded relevant studies. Last, given the broad scope of our review, we could not present the results of each study in detail. However, as is the goal of scoping reviews, our main objective was to map the available evidence and identify gaps for future research. We have also provided in Appendix Table 4 the full scope of our review, detailing all reviewed studies. We also acknowledge several strengths. This scoping review has provided an initial comprehensive map of evidence on the urban scaling of health outcomes. We reviewed 102 studies in total, drawing attention to several factors that may contribute to inconsistencies between studies, including exposure, and city definitions. The scoping review was not limited to a single language and was able to capture evidence in English, Spanish, and Portuguese.

In this scoping review, we have identified a rich and complex evidence landscape on the urban scaling of health outcomes and the relationship between city size and health. However, we have identified several gaps that merit future research, including a paucity of research in LMICs urban areas and across a variety of countries in different settings, along with a lack of clarity and consistency in city definitions, and how different definitions may lead to changes in inferences. We also identified several aspects where current research in scaling may help in understanding disease dynamics, including the exploration of the complexity of transmission of epidemic diseases, the recognition of the importance of studying population growth (i.e., longitudinal population size), the use of deviations from the scaling law to study predictors of health outcomes, and greater transparency about decisions regarding adjustment for important covariates. With growing urban populations worldwide, the continuous challenge of non-communicable diseases, the importance of injury mortality in premature mortality, and the (re-)emergence of infectious diseases, understanding the consequences of our urban world seems key in the design and planning of interventions to address unmet public health needs.