Urban Health Indicator Tools of the Physical Environment: a Systematic Review
Urban health indicator (UHI) tools provide evidence about the health impacts of the physical urban environment which can be used in built environment policy and decision-making. Where UHI tools provide data at the neighborhood (and lower) scale they can provide valuable information about health inequalities and environmental deprivation. This review performs a census of UHI tools and explores their nature and characteristics (including how they represent, simplify or address complex systems) to increase understanding of their potential use by municipal built environment policy and decision-makers. We searched seven bibliographic databases, four key journals and six practitioner websites and conducted Google searches between January 27, 2016 and February 24, 2016 for UHI tools. We extracted data from primary studies and online indicator systems. We included 198 documents which identified 145 UHI tools comprising 8006 indicators, from which we developed a taxonomy. Our taxonomy classifies the significant diversity of UHI tools with respect to topic, spatial scale, format, scope and purpose. The proportions of UHI tools which measure data at the neighborhood and lower scale, and present data via interactive maps, have both increased over time. This is particularly relevant to built environment policy and decision-makers, reflects growing analytical capability and offers the potential for improved understanding of the complexity of influences on urban health (an aspect noted as a particular challenge by some indicator producers). The relation between urban health indicators and health impacts attributable to modifiable environmental characteristics is often indirect. Furthermore, the use of UHI tools in policy and decision-making appears to be limited, thus raising questions about the continued development of such tools by multiple organisations duplicating scarce resources. Further research is needed to understand the requirements of built environment policy and decision-makers, public health professionals and local communities regarding the form and presentation of indicators which support their varied objectives.
KeywordsUrban metrics Built environment Indicator Indices Policy Urban health Evidence Urban planning Healthy cities Social determinants of health
Both the global increase in non-communicable diseases and improved understanding of the social determinants of health have contributed to an increased awareness of the influence of built environment policies on health and wellbeing [1, 2, 3]. Estimates vary, but recent research attributes 23% of global deaths to the environments in which people live . The urban environment, including air pollution, noise, housing and transport, plays a significant role in people’s health, and improvements should involve collaboration between health and built environment professionals [2, 5]. Other social determinants, such as employment and education, are also influenced by urban planners, increasing the importance of their work for population health . Municipal built environment practitioners can improve health through policies and decisions which identify the need for and design of new infrastructure, development and regeneration programmes.
Benchmark progress at local, regional, national or international levels
Set targets for improvement
Demonstrate performance to residents
Prioritise funding allocation/bid for funding
Act as an ‘early warning’ of potential problems
Involve the public in prioritisation and definition of policy goals
Identify strengths and weaknesses in a community
The intended use of indicator tools is likely to inform their composition and characteristics, elements which are often represented in a taxonomy . Taxonomies have been developed for mental health and ecological indicators by identifying and classifying user requirements such as spatial scale and decision-making context [20, 21]. Yet, research addressing how indicators are used and how they can be standardised is missing, providing two main reasons why an improved understanding of UHI tool characteristics and an associated taxonomy may help indicator producers and users.
First, indicator researchers have tended to focus on the development and validation of indicator tools, rather than investigating how such tools are used by policy- and decision-makers . The production of new indicator tools is often a duplication of previous research efforts. However, there is recognition that locally developed tools may increase acceptability and allow for tailoring of indicators to local needs [19, 22, 23]. In fact, some have argued that the process of indicator development is at least as important in achieving change as the eventual use of indicators [16, 22]. Increased understanding of the characteristics of UHI tools which meet the needs of policy and decision-makers could reduce wasted efforts by indicator producers and increase usability for indicator users.
Second, despite the large amount of research on indicator development, there is still a lack of consensus on how to measure the urban environment’s impact on health and related concepts. Standardising the development of urban health indicators is a topic of ongoing debate [23, 24]. Despite the large number of UHI tools already available, researchers continue to contribute new international indicator sets whilst implicitly supporting greater standardisation (see [25, 26]). Salvador-Carulla and colleagues argued that there is a lack of international consensus on indicators and that indicator tools ‘lack adequate semantic interoperability’ . A taxonomy which describes the general characteristics of UHI tools would provide a useful step toward standardisation, resulting in reduction of duplicated efforts and easier identification of appropriate UHI tools.
To our knowledge, there exist three reviews of relevant indicators. The Prasad et al. systematic review of urban health metrics highlighted the lack of available data for metrics in low and middle income countries and questioned the translation of evidence gained through using such metrics into policy and decision-making . Rothenberg et al. conducted a non-systematic review of urban health indicators and metrics which found that indicator sets focus on large-area comparisons (nations, states) and that small-area comparisons (cities, neighborhoods) are relatively underdeveloped . They also observed similarity in the domains measured across compilations. The Badland et al. review of urban liveability indicators for the Australian urban planning policy context found inconsistency in how domains were measured, a relative lack of validated indicators and a lack of information on how to apply indicators to inform urban policy and practice .
This systematic review examines a specific type of indicator compilation which could inform municipal built environment policy and decision-makers about the social determinants of health, defined as ‘urban health indicator tools’. The review has two distinct parts, as outlined in a previously published protocol [28, 29]. Part A seeks to conduct a census of UHI tools to describe their characteristics and develop a taxonomy of such tools. Part B seeks to explore the perceptions and use of UHI tools by built environment policy and decision-makers. Both parts examine how UHI tools address the complexity of urban health and how this complexity affects policy and decision-making. This paper reports the findings of Part A.
Databases, websites and journals searched for the review, including years hand-searched for journals
Applied Social Sciences Index and Abstracts (ASSIA)
Social Policy and Practice
Web of Science Core Collection (includes the Social Sciences Citation Index)
Town and Country Planning Association (UK)
Royal Town Planning Institute (UK)
Planning Institute of Australia
American Planning Association
Built Environment and Public Health Clearinghouse (USA)
World Health Organization Europe, Urban Health, Healthy Cities
Annual Review of Public Health (5 years)
Social Science and Medicine (3 years)
BMC Public Health (1 year)
Social Indicators Research (3 years)
A UHI tool was defined as ‘a collection of summary measures about the physical urban environment’s contribution to human health and wellbeing’ . A combination of indicators can be referred to as a set, compilation, collection or tool [19, 30, 31]. We selected the term ‘tool’ because it reflects a utility or intention to support policy and decision-making. Tools which sought to measure the related concepts of quality of life (QOL), wellbeing and liveability were also included. During the screening stage, we decided to include tools which measured the impact of the physical urban environment on walkability/physical activity (PA) as this is an important contribution of the built environment toward promoting good health [32, 33]. Any UHI tool which met the definition was referred to in peer-reviewed or grey literature documents (including websites) and was published in English was included in the review. UHI tools needed to measure at least two different aspects of the physical urban environment to be included (e.g. housing and air quality).
All documents were screened by the principal investigator (HP) and a random sample of 10% of documents were screened by a second reviewer (KG) at the title and abstract and full paper screening stages. Differences were resolved through discussion. Eppi-Reviewer software was used to manage all documents and screening.
Data Extraction and Analysis
Topic: concept that the UHI tool measured (e.g. health or liveability)
Main source of data (e.g. municipal datasets or resident surveys)
Indicator type: subjective or objective (as defined in Lowe et al. [36 p. 136])
Whether the tool had been used beyond research
The last point was informed by the Google search of each indicator tool. If this search produced evidence of case studies, policy documents or other uses beyond the original research paper, this was marked as ‘used beyond research’. The others were marked as ‘unknown’.
We modified approaches used by Salvador-Carulla et al.  and Wardrop et al.  to develop our taxonomy. Salvador-Carulla and colleagues developed key topics for their taxonomy by reviewing published literature and indicator lists. Then they discussed these topics with expert groups. Wardrop and colleagues developed their taxonomy on the basis of characteristics of environmental indicators which would be useful for environmental managers using a survey of government officials. We combined and modified these approaches. We used relevant literature [9, 19, 27] and the data gathered in the review to identify five key characteristics of UHI tools for built environment professionals: spatial scale, purpose, topic, scope and format. These became the highest level category within the taxonomy, denoted as ‘class’. Data were extracted on each of the five classes. The second order in the taxonomy, ‘sub-class’, was developed during the analysis of data extracted in the review, noting differences within each class and categorising these in an iterative process. UHI tools may have characteristics from multiple sub-classes (they are not mutually exclusive). Indicator domains (listed as sub-classes under ‘scope’) were selected using a set of domains identified from previous reviews [9, 19]. For analysis purposes, all 8006 indicators were standardised to this list of domains. It is possible to divide these domains into smaller groups (e.g. chronic diseases and injuries could be sub-domains under the domain of health outcomes).
During data analysis the term neighborhood was grouped with other sub-city spatial scales including ward and district. Lower than neighborhood scales were also grouped together, representing street or household scale for example. Given variation in the meaning of terms like ‘district’ or ‘post-code’, scales were assigned on the basis of authors’ descriptions.
Spatial scale: multiple (county, zip code)
General geography: country
Specific geography: USA
Taxonomy of UHI Tools
Definitions and explanations of quality of life, liveability and wellbeing concepts from selected papers included in the systematic review or citations found therein
Quality of life
‘The wellbeing of individuals within the context of their environment’ 
‘Closely aligned with the social determinants of health’ 
‘The human requirement for social amenity, health and wellbeing and it includes both individual and community wellbeing’ 
“Associated with concepts such as happiness, life satisfaction and social capital, all of which fall under the rubric of a ‘social quality of life’” 
‘Reflect a community’s health status and its basic quality of life’ 
Number of indicators in each domain across UHI tool topic areas, sorted by H&W
Indicator domains grouped by scope across all UHI tools (total of 8006 indicators)
Number of indicators
Services and utilities
Public open space
Crime and safety
Leisure and culture
Health and social services
Employment and income
UHI tools measured between 3 and 286 individual indicators (average 56). Across the 145 UHI tools, 3 did not report the full list of indicators.
Other Characteristics of UHI Tools
This portion of the results section presents additional characteristics of UHI tools which were not used to form the taxonomy. See the protocol for the full list of items extracted and the Supplementary Material section for additional details and results.
Research institutions were the largest producer of UHI tools (54.5%, 79/145). Many of the tools produced by research institutions were not found to have been used beyond research (62.7%, 37/59). The funding source was often not stated (46%, 67/145). Where reported, the largest funder of UHI tools was government (17.9%, 26/145). Of the UHI tools, 86.9% (126/145) reported some information about the methodology. Evidence which informed the methodology or indicator selection was reported in 99/145 cases (68.3%). Peer-reviewed literature was the largest primary source of evidence used in 52.4% (76/145) of tools. The majority of tools (57.9%, 84/145) used existing datasets from multiple organisations to measure the indicators.
Indicators/indices can simplify or mask the complexity of the concepts being measured
The urban environment impact on health and behaviour is complex
Measuring the urban environment’s impact on health is complex
The process of policy and decision-making is complex
Eleven UHI tools stated that indicators or composite indices can simplify the complexity of the concepts being measured. In relation to the City of Winnipeg Quality of Life Indicators, Hardi and Pintér explained: ‘[i]ndicators are used to simplify information about complex phenomena, such as sustainable development or, in this case, QOL, in order to make communication easier and quantification possible’ . This was contrasted by the opposing view that indicators/indices can mask complexity (two instances). The authors of the London Quality of Life Indicators stated: ‘[a]lthough the Commission have sought to identify and report on 20 headline indicators, to constitute a popular ‘barometer’ for London’s quality of life, it is clear that single figure measures can mask a much more complex situation’ .
Three UHI tools referred to the complex process of policy and decision-making, sometimes in recognition that indicators may not inform policy due to this complexity. For example, Hunt and Lewin commented that ‘policy action may not easily follow the identification of environmental health problems [through indicators], which is due both to the large numbers of other factors that also affect health and to the complexity of the policy process’ .
UHI tools rarely explained strategies used to help account for complexity. Feneri et al. used Multi-criteria Decision Analysis to ‘conceptualize the complex issue of evaluating quality of life’ . They specified the use of Analytical Hierarchy Process to prioritise indicators. The AARP Livability Index used a high number of indicators to address complexity, stating: ‘[s]imple questions about livability [sic] can have complex answers. This is why the index includes a large number of metrics’ .
This review identified great diversity in the purpose and characteristics of urban health indicator tools making it difficult to draw simple conclusions. However, the review generated novel findings about UHI tools as they relate to the needs of built environment policy and decision-makers. Our taxonomy demonstrates the importance of considering users’ needs when developing indicator tools to ensure they can be used to support built environment practitioners. Our main findings are summarised here with implications discussed below. First, we found that the proportion of tools with data aggregation/measurement at the neighborhood and lower scale and presentation of data via digital interactive maps have both increased over time. Second, we highlighted that the majority of UHI tools intend to inform policy and decision-making, yet it is unclear whether a significant number achieve this aim. Third, we found that the majority of UHI tools are evidence-based and therefore provide a potential route from research through to policy. Fourth, we have explored the nature of how UHI tool methodologies address complexity, identifying specific strategies. Finally, we have shown that there is a degree of similarity in the domains measured across UHI tool topics.
In comparison to existing reviews of indicators which measure the urban environment’s impact on health, this review casts a wider net by including measures of health, QOL, liveability, wellbeing, and walkability/physical activity. This has enabled a detailed analysis of a large number of indicator tools and their respective characteristics, including 8006 individual indicators. The review was limited to English language publications, potentially excluding many UHI tools from non-English language countries. The method used to classify whether a tool had been used beyond research was simplistic and may have underestimated those tools which were indeed used beyond research.
The increasing number of UHI tools with data aggregation at neighborhood or lower scale is of significance for built environment policy and decision-makers. In 2002, Talen questioned the usefulness of indicators to inform urban planning because the majority were comparing cities (inter-city) rather than neighborhoods (intra-city) . Neighborhood and lower scale of measurement or data aggregation is more appropriate for identifying health inequalities and environmental deprivation which may contribute to poor health . Indicators at this scale can be used to inform neighborhood development/regeneration policies and monitor the impact of these over time. Data visualisation is also frequently noted as a helpful feature of UHI tools for built environment policy and decision-makers, particularly in relation to displaying data on maps [19, 27]. The growing numbers of UHI tools which present data on interactive maps at the neighborhood or lower scale are likely to be a powerful source of information for built environment policy and decision-makers.
A number of tools (31.7%, 46/145) did not explain the evidence used for indicator selection, creating questions over the suitability of their use in policy and decision-making. Although the validity of individual indicators (association between exposure and outcome) was not assessed by this review, the range of methods for selecting indicators demonstrated that this process was not always informed by evidence about environmental exposures and health effects. Badland et al. called for further research about the validity of indicators within UHI tools (specifically in relation to liveability indicators) . However, we would suggest that there is a large selection of validated indicators in the published literature and research efforts may be better directed toward understanding how existing indicators are used to guide the policy and decision-making process.
The distinction of whether UHI tools are used beyond research is of interest when considering transfer of research knowledge to practitioners. We were unable to confirm whether 45/120 tools (37.5%) which intended to inform policy/decision-making achieved this aim. There could be a delay between research and use or this may also point to other knowledge translation issues. UHI tool producers should consider the needs of their audience and may benefit from wider strategies to increase research use by policy and decision-makers (see ). The apparent low use of many UHI tools leads us to consider whether greater standardisation of indicators is required rather than development of new indicator tools.
Standardisation of UHI tools may be aided by our finding that there is significant overlap across domains measuring health-related topics such as QOL, liveability and wellbeing. Rothenberg et al. also found similarities in indicator domains across urban health indicator compilations . Guidance on developing indicators of health and the determinants of health is supported by specific frameworks (e.g. DPSEEA) that emphasise the requirement for an evidence-based, often causal relationship between environmental exposures and specific health outcomes [24, 51]. This formality may increase the acceptability of a standardised set of indicators. However, lack of consensus over how to define and measure related topics like QOL, wellbeing and liveability (despite similarity in existing UHI tools) may mean that standardisation for these topics is harder to achieve.
A standardised set of global indicators would mean that rather than developing new UHI tools, researchers and practitioners could choose from an internationally published set of evidence-based indicators. Local selection of indicators would likely be based on data availability, health priorities and community opinion. The WHO’s Urban Health Index provides methods for local public health data analysts to produce local indices (including instructions for mapping the results) [52, 53]. Such a tool is valuable to avoid duplicated effort when selecting appropriate indicator aggregation methods. However, we suggest that a set of global evidence-based indicators, which the WHO’s Urban Health Index currently lacks, would be of great value to local indicator projects. Given that many global UHI tools are already available, a standardised set would need to be widely promoted and supported to achieve impact and avoid further duplication of effort. Further research is needed to determine whether a standardised set of urban health indicators could be promoted globally and accepted locally (such as the Sustainable Development Goals).
Whilst some indicator producers recognised that indicators could help explain complex phenomena, other authors noted that they may not be effective at influencing a complex policy and decision-making process. This topic will be explored further in a subsequent paper related to this systematic review which will synthesise qualitative data from studies exploring the use of UHI tools in the built environment policy and decision-making process.
Observing the similarity across indicator measures, there is a question about whether some data are included simply because they are easy to measure (or commonly measured as a part of routine statistics), whilst other more difficult topics are excluded. For example, although noise is known to impact multiple health outcomes , it is less frequently measured in UHI tools, reflecting the difficulty of measuring this exposure. This is an area for further investigation. The growth of city datasets emerging from open data initiatives may increase the need for indicators to help interpret and make sense of data. This may also support increased small-scale spatial comparisons, improving usability by built environment policy and decision-makers. New data from smartphones, social media and other sources are also likely to increase available datasets for UHI tools and may be a useful way to increase citizen participation in generating and evaluating indicator data.
HP is an industry-sponsored PhD student funded by the Building Research Establishment Ltd and the BRE Trust. In her paid work for BRE she was responsible for creating an index of urban health indicators which was not published at the time of submission. HR was supported by the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care (CLAHRC) North Thames at Bart’s Health NHS Trust.
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