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Urban health indicators and indices—current status
BMC Public Healthvolume 15, Article number: 494 (2015)
Though numbers alone may be insufficient to capture the nuances of population health, they provide a common language of appraisal and furnish clear evidence of disparities and inequalities. Over the past 30 years, facilitated by high speed computing and electronics, considerable investment has been made in the collection and analysis of urban health indicators, environmental indicators, and methods for their amalgamation. Much of this work has been characterized by a perceived need for a standard set of indicators. We used publication databases (e.g. Medline) and web searches to identify compilations of health indicators and health metrics. We found 14 long-term large-area compilations of health indicators and determinants and seven compilations of environmental health indicators, comprising hundreds of metrics. Despite the plethora of indicators, these compilations have striking similarities in the domains from which the indicators are drawn—an unappreciated concordance among the major collections. Research with these databases and other sources has produced a small number of composite indices, and a number of methods for the amalgamation of indicators and the demonstration of disparities. These indices have been primarily used for large-area (nation, region, state) comparisons, with both developing and developed countries, often for purposes of ranking. Small area indices have been less explored, in part perhaps because of the vagaries of data availability, and because idiosyncratic local conditions require flexible approaches as opposed to a fixed format. One result has been advances in the ability to compare large areas, but with a concomitant deficiency in tools for public health workers to assess the status of local health and health disparities. Large area assessments are important, but the need for small area action requires a greater focus on local information and analysis, emphasizing method over prespecified content.
“When we look at health problems on a world scale, we see bewildering diversity.” John Bryant’s classic 1969 work, Health and the Developing World,  begins with a dictum no less true today. Early in the book, he cites a composite index of human resource development based entirely on two measures of education (enrollment in the second level of education plus enrollment at the third level of education times five ), and stresses that “no weight should be put on the precise location of any one country in this ranking.” Thus, nearly 50 years ago, some of the chief problems with indicators and indices were well understood.
Against a backdrop of chaos and development, improvement in data systems and technology made health data more available in the ensuing decades, but the problems of summarization and interpretation persist. Scale is one of the critical factors in developing indicators and indices. The type and number of indicators, how they are presented, transformed and combined, the size of the targeted area, the relative placement of geographic units—all are scalable factors in the construction of an overall assessment. Indeed, the audience for the assessment is also scalable—from neighborhood groups to global agencies. Issues of scale, and the tension between multiple indicators and single statistics, suggest the need for a variety of alternative approaches.
A recent compendium of composite measures of human progress provides an exhaustive listing of extant indices from many areas of human endeavor . This review, whose content overlaps in part with that compendium, will focus on indicators and indices that are relevant to urban health and urban health disparities in countries with advanced economies as well as low and middle income countries (LMICs). The emphasis will be on the types of urban metrics that are extant, the measures and methodologies used to assess health disparities, the comparability of these measures, and the extent to which single (vs. multiple vs. parsimonious) measures have been used to assess urban health and health disparities. Though of substantial importance in the construction of metrics, the statistical methodology has been discussed and reviewed in detail, and will not be a major focus here. Nor will specific disparities be featured. But in light of the urban orientation of the review, measures of health and environment will be paramount, together with mechanisms that have been used to amalgamate them.
Measurement of health and disparities
A convenient framework for classifying the available measures establishes three levels of measurement: Rubrics, Domains, and Indicators (Fig. 1). The descriptive names used here—there are many valid alternatives—are a convenience for stressing the distinction among logical types. Rubrics represent societal-level factors that affect health, either directly, or as determinants. Domains are specific factors within a Rubric for which measurements are available. For example, the Rubric “Environment” includes the Domain “Air Quality” that contains a set of Indicators (e.g. “Proportion of households living within 300 m of major industrial stationary sources of air pollution”) from which potential disparities can be derived. To complete the vocabulary, for this review we will use “Index” to refer to a single measure, figure or picture that is constructed from Indicators. The term “metric” is used generically to refer to any measurement.
General properties of indicators
Soon after the Millennium Declaration, a Health Metrics Network,  funded by the Gates Foundation, was established to assist member nations in developing and interpreting health data. This partnership has recognized the key relationship between an indicator of health and an indicator of health disparity, and has provided leadership in deriving the latter from the former. An important contribution of those involved in the Network is a concise summary of the methodologies available to examine indicators and assess inequalities (first published in Spanish  and subsequently in English; [6–8] this discussion was based on Mackenbach and Kunst;  see also Houweling et al. ).
The authors point out that the vast majority of Indicators are based on data aggregated by geopolitical unit. A subset are cumulative markers (Gross National Product (GNP), percent of literacy, unemployment rate) that lack meaning at the individual level . It is readily inferred that most analysis of Indicators is ecological, and thus constrained by the statistical limitations of correlational analysis. For example, in constructing an Index, indicators that co-vary do not necessarily increase the amount of information in the Index (though in some instances, such as a latent variable construct, they may). But conversely, indicators that are not correlated render interpretation of the Index problematic, since the level and trajectory of the Index then results from the complex interaction of disparate measures.
Capacity for demonstrating disparity
Perhaps more important, the authors  point out that indicator selection should be predicated on the ability to demonstrate disparity. By using information on the total population, and being sensitive to the size and distribution of the population along socioeconomic groupings, an overall indicator (say, GNP) can be transformed into a measure of inequality. In these articles, the authors then describe the major ways in which disparities are expressed (see Table 1). In fact, all of these are some form of ratio or difference, manipulated to highlight certain aspects of the contrast. For example, the measures that are based on the slope of a regression line (the ratio of a change in a variable compared to a unit change in another) simply provide a model-based contrast as opposed to the simple empirical observation of say, the ratio of highest percentile to lowest. The Lorenz Curve and the Concentration Curve are more complicated ratio measures, and have been shown to be specific examples of the class known as Relative Distribution Measures. Such measures raise a fundamental question for all methods of representing disparities. A simple ratio is dimensionless, and can thus be used for the comparison of many populations. A simple difference is in units of the underlying measures (money, frequency, incidence, area, etc.); some of these units permit direct comparability and others do not. Other measures of disparity compare observed data to an absolute standard (such as one of no disparity, as with the Lorenz curve and Gini coefficient). Still others use a standard embedded in the total data (say, highest or mean value). Relative distribution measures, [11, 12] on the other hand, compare two distributions directly so that, for example, the level of disparity in two urban areas can be directly described. Other measures permit this, but may require extra steps. In addition, the common practice of rank ordering, if performed without reporting the actual disparities, would not be sufficient to provide the actual difference between two areas since the space between ranks is not uniform.
More recently, Talih  introduced the symmetrized Renyi Index, based on prior work using entropy measures to assess disparities. An important advantage of this measure is its invariance with respect to a reference group (say, the population average or the least well off group). In addition, population-weighted and equal-weighted versions can be calculated, and an “aversion” parameter can be included that reflects the investigator’s judgment as to values that society attributes to inequality .
Criteria for indicators
The choice of indicators, from the myriad available, should be predicated on some agreed upon set of criteria. Flowers et al.  provide a checklist of 20 facets of a proposed indicator: several are descriptive (title, origin, rationale, routine or special collection, frequency); others deal with general characteristics (strengths, weaknesses, perverse incentives, influence on practice or behavior). A simpler, and perhaps more forceful summary of the ideal characteristics of indicators is provided by Etches et al.  whose keywords bear repeating: consensual, conceptual, valid, sensitive, specific, feasible, reliable, sustainable, understandable, timely, comparable, and flexible. These authors stress as well the need for a conceptual framework , Fig. 1, p.34 from which the appropriate indicators can be drawn and indices can be constructed. Such a framework can be the basis for multilevel modeling  and for causal analysis . Though neither of these approaches is necessarily involved in the formation of indices, they are part of the intellectual basis for prior and subsequent analysis.
Pitfalls and problems
Several statistical problems bedevil indicators. The Will Rogers phenomenon, for example, is the paradox observed “when moving an item from one set to another moves the average values of both sets in the same direction,” , p.243 and refers to migration of an item to a group vastly different from its own. Such a situation obtains when the highest value in one population is less than the lowest value in another, so that movement of the highest item lowers the mean of both groups. Indicators are also subject to regression to the mean, a phenomenon that reflects the random distribution of measurement error. A more extreme value will likely be followed by one less extreme because, based on typical distributions, the error in measurement of the second value is likely to be less extreme than that of the first value. As noted earlier, indicators or indices are often presented as ranks, which are ordinal rather than interval or ratio quantities despite the use of integers. Ranks convey a sense of better or worse that may not be merited by the underlying data. In addition, entities are not equally separated, and some may be bunched so that ties are resolved by resorting to a non-meaningful number of significant digits. Most indicator assessments and ranking procedures do not contain an appropriate estimate of uncertainty, and an assumed difference may be spurious. Flowers et al.  suggest the use of such devices as funnel plots (a standard part of the meta-analysis armamentarium) to detect aberrations in the distribution of values that may point to real differences.
Aggregation of indicators
As described by Saltelli et al.  indices are composite statistics that have generated polarized views of their value: either a mashed together collection of unrelated numbers or a usable distillation of reality. But these authors go on to point out that such statistics are really mathematical models developed through a social process: the community of scientists, policy makers, and practitioners must largely agree on their makeup and utility. The European Commission Joint Research Centre group on Composite Indicators  has explored the mathematical, political, social, and economic aspects of composite indicators in detail [19, 21–26]. This complex analysis provides a rigorous basis for combining criteria and for legitimate ranking schemes. As these and other investigators point out, linear aggregation, with either equal weighting or some other weighting scheme, is simplest, most commonly used, and often least reproducible, in that it does not derive from pre-established criteria, but rather from experience and negotiation. Geometric aggregation, usually by multiplying the nth root of n items, has been used successfully by the UNDP Human Development Index  but does require higher technical capacity. The most complicated of the approaches—multi-criteria analysis [28, 29] —is less adaptable for use on the local level, but a toolbox of techniques has been developed, [21, 22] and the use of this approach is a good example of the potential value of an academic and public health partnership .
The major compilations of indicators
The current large collections of indicators differ substantially in genesis and purpose (Table 2). WHO’s Urban HEART,  the Michigan Critical Health Indicators,  and San Francisco’s Healthy Development Measurement Tool (now renamed the Sustainable Communities Index)  were all constructed, in part, to permit local areas to assemble and assess their own data. The United States’ Healthy People 2020  was constructed as a mechanism for tracking progress toward national health goals, and focuses predominantly on individual risk. The Community Health Status Indicators  are an interactive tool for localities to assess their situation. Cities Environment Reports on the Internet (CEROI),  the CDC’s Environmental Health Indicators,  and California’s Environmental Health Indicators  focus primarily on environmental measures, many of which are urban. Women’s Health Indicators  are a compendium from many sources whose focus is how the indicators apply to women. Similarly, UNICEF’s compilation applies to children, and is a tool for tracking Millennium Developmental Goals . The WHO Indicator Compendium  (on a large scale), and the Social Health of the States  (on a smaller scale), are general sets of measures that includes elements of both personal and environmental health. The World Bank’s World Development Indicators  is primarily economic and political in orientation but has considerable information on health and urban development. Global Cities Indicators, a set of measures on 20 themes that measure city services and quality of life, have been developed by the Global Cities Institute of the University of Toronto, and is available to member cities only .
With such diversity of purpose, it is no surprise that there is little concordance in the naming of Rubrics, Domains, or Indicators, or in the number of indicators. World Development Indicators, for example, has collected a set of 508 indicators on 217 countries for the period 1960 to 2013. Seventy-six of these relate directly to health and the urban environment. At the other end of the spectrum, the UN Habitat Agenda Indicators number 26, and provide a good example of the type of informed choices that are made. Under a Domain heading that they call “Social Development and the Eradication of Poverty,” they choose six Indicators in order to capture the essence of the Domain: Under-5 mortality, Homicides, HIV prevalence, Literacy rates, School enrollment, and Women Councilors. In a similar vein, the WHO Kobe Center’s Urban HEART lists 12 “core” indicators, and 18 “strongly recommended” measures. Its rough analogy to the UN Habitat Agenda Indicators is a Domain called “Core indicators: health determinants” that contains: access to safe water; access to improved sanitation; completion of primary education; skilled birth attendance; fully immunized children; prevalence of tobacco smoking; unemployment; and government spending on health. Both lists of indicators are worthy, but they clearly take different routes to a similar goal. A cursory look at the remaining Indicator projects reinforces the sense of plethora rather than parsimony. But a more detailed look suggests a somewhat different picture. If similar or identical Domains in each major compilation are given a common name, a pattern of concordance emerges. Nine Domains appear in more than half of the aggregations, and three of them (health care, infant mortality, and education) appear in more than two-thirds. The qualitative impression is that there is a vast array of specific indicators, with little commonality among projects, but a relatively limited number of Domains that appear in many, if not most projects. These Domains deal largely with health care outcomes, though several social determinants of health (for example, education, poverty and environment) are represented as well. Thus, despite disagreement about detail, there is some evidence of agreement about basic content. This observation augurs well for the construction of more flexible indices that permit interchangeability of indicators.
The properties of indices
There are only a few indices that are specifically urban in orientation, but a substantial number of congeners have been developed for other purposes. Consideration of the range of indices provides some insight into the appropriate methodologies for construction and validation (Table 3).
In its simplest form, an index is constructed from a set of indicators that have been transformed (standardized, normalized, scaled) so that they are directly comparable, and then added together. Simple arithmetic combination, often mistakenly called “unweighted,” implies that each indicator is given the same unit weight. The resulting Index may be bounded (such as a proportion or percent) or unbounded at one or both ends. A simple example is the Social Health of the States,  a long-running Index from the Institute for Innovation in Social Policy. It combines 16 indicators that have been scaled and averaged so that the worst possible score is 50 (smaller is better). The difference between a state’s actual average and 50 is then expressed as a percentage of 50. The states are rank-ordered and grouped in quintiles (1–10 are excellent; 41–50 are poor). Similarly, the Michigan Index of Urban Prosperity —one of the specifically urban indices—combines nine indicators from multiple sources (crime rate; property value change; median household income; employment rate; employment change; graduation rate; Michigan Education Assessment Program passing rate; young adults; population change). It uses the ratio of each site-specific indicator to the overall state indicator (actually, to the overall mean) and averages them, deriving a number in the vicinity of 1.0. A somewhat more complicated urban metric is the Index of Resident Economic Well Being,  which combines indicators from five Domains (unemployment rate; poverty rate; labor force participation; median household income; per capita income) by using a linear combination of N-scores (deviations from the median, as opposed to z-scores, which are standard deviations from the mean).
More complex indices
Perhaps the most important of these is the Human Development Index (HDI)  now in its 25th year, published by the United Nations Development Program (UNDP). The measure is constructed from life expectancy at birth, measures of schooling and expected years of schooling, and gross national income per capita. Each of these is standardized by taking the country value as a percent of the range of the most extreme values for any participating country over the past 20 years compared to subsistence value: ([country value – subsistence value]/[maximum value – subsistence value]). The resulting value is a proportion between 0.0 and 1.0. The two education values are combined by taking their arithmetic mean and combining them with the other two measures using their geometric mean ([life expectany1/3 x schooling1/3 x income1/3]). A country’s HDI is then rank-ordered among all the others, and its place over time can provide the size and direction of relative progress (“relative” because a country’s change in rank may not reflect its change in absolute values). Since the standards for “best” and the “worst” are fixed, and each nation’s values are placed on a scale with that range, the concept of disparity is an integral part of the measure. Though urbanicity is not the focus of the HDI, its approach and methodology are suited to the development of a measure of urban health and disparities. In addition, the UNDP introduced an inequality-adjusted HDI,  a measure that accounts for inequality by adjusting each indicator’s value by its level of inequality, based on work by Atkinson  wherein he used the analogy between ranking inequality distributions and ranking probability distributions based on utility. Each of the three indicators is adjusted by the ratio of the geometric mean of the distribution to its arithmetic mean. Using a similar statistical approach, they have also introduced a Gender Inequality Index that captures the difference in reproductive health, empowerment and the labor market for men and women. A third measure—the Multidimensional Poverty Index—diverges from the Human Development Index by using microdata from household surveys. Each person is classified as poor or non-poor based on his or her family deprivation and the data are aggregated to form a national index. The actual computation bears considerable resemblance to previously discussed aggregations of indicators (weighted linear combinations), though the mechanism for combining information on the 10 indicators used is complex.
Another example of a more complex measure, the Bertelsmann Transformation Index (BTI)  takes a wholly different approach. In their process, 17 criteria (“Domains”) are represented by 52 questions (“Indicators”) that are answered in a report completed by 128 participating nations. The answers go through two levels of review and calibration (not further defined) by experts in the responding country and by the BTI board. Scores are combined by linear aggregation (not further defined), and an overall score and sub-domain scores are calculated. The approach may be described as a modified, interactive, Delphi technique that is heavily dependent on expert opinion, and may or may not be reproducible. Such an approach, however, recognizes, and in fact embraces, the political process that is an important part of index development.
A third approach is typified by the Corruption Perception Index  produced by Transparency International that ranks countries by the perception of corruption in their public sector. They collect information from a variety of sources (of which the BTI is one) and use at least three different sources for each country. This approach represents a substantial divergence from most of the others in that a uniform data set is not used for each country. Rather, they subject available information to substantial mathematical manipulation: data are standardized by using matching percentiles (reminiscent of the relative distribution methods), then undergo beta transformation, and a linear average of the transformed values is taken. The final index and ranking are substantially removed from the raw information. This approach acknowledges presumed exchangeability of indicators after mathematical manipulation.
Still another approach might be termed the “organic” Index, one that grows, shape-shifts, and is tested for its credibility and consistency. An example is the Deprivation Index, first proposed by Townsend  in 1987 and Carstairs  in 1989. These were constructed as the sum of four standardized variables. The Townsend Deprivation Index used percentage of unemployed people in the active population, percentage of not-owner-occupied households, percentage of households without a car, and percentage of overcrowded households. The Carstairs Index replaced no-owner-occupied households with the percent of low social class persons (a measure available in England based largely on occupation). In a subsequent review of Deprivation Indices , Carstairs describes other variations, such as the Jarman Underprivileged Score , constructed from rankings by general practitioners and subsequently used as part of a reimbursement scheme. Carstairs demonstrates that the Deprivation Index, as she developed it, was strongly correlated with measures such as overall mortality and cancer registrations.
In more recent years, the Deprivation concept has been retained, but the details altered. Sivakuman  proposed a Human Deprivation Index based on percent below the poverty line, infant mortality, and illiteracy rate. One-third of each is added together to form the Index. Messer et al.  constructed a Deprivation Index based on five sociodemographic domains: income/poverty, education, employment, housing, and occupation. They used principal components analysis, taking the first principal component as representative of neighborhood deprivation, an assertion supported by the consistency of component loading across study areas. Rey et al.  explored the properties of their previously developed Index, FDep99, which had been constructed from: median household income; the percentage high school graduates in the population aged 15 years and older; the percentage blue collar workers in the active population; and the unemployment rate. This measure was also constructed using principal components analysis, and the first principal component accounted for 68 % of total variation in mortality. The authors provided an empirical analysis that purported to show that FDep99 was superior to their slightly altered versions of the Townsend and Carstairs Indices.
The aforementioned Indices are instructive in providing a typology, but only touch on the extant composites that have been developed. In a systematic review, Kaltenthaler and colleagues  described 18 health indices culled from the literature from 1966 to 2000, and summarized information on their origin (US, UK, Canada, and Europe), characteristics, purpose, types of indicators, methods of aggregation, data sources, and validation. Several major points emerged. First, only four of the indicators had been validated, two by professional judgment, two by inference. Second, the user groups were not clearly defined, so that the target geopolitical level was not always clear. Reasons for choice of indicator were opaque. Weights appeared to be arbitrary, or at least not justified by standard criteria. The data upon which many of these indices were based were not always publically or universally available. The authors concluded that this set of indices would not be suitable for health policy makers in the United Kingdom (the place of origin of the study). Nonetheless, the authors reaffirm “the need for a population-based health index at either national or local level.” , p. 254.
Unfortunately, the literature on Indices that reflect urban health specifically is sparse. Those that include both urbanicity and health tend to focus on the former. As an example, Shane and Graedel  propose a set of indicators that includes a measure each for air, water, soils, transportation, energy, resource use, population, urban ecology, livability, and general environmental management. They do not include health measures per se, but do use the Human Development Index as an environmental measure. Instead of a composite index, they propose a novel graphic: a triangle made of four layers (planning, waste, resource, human factors). Each of the 10 metrics is represented in the triangle by a grey scale corresponding to its adequacy (high, middle or low rating). The resultant “picture” can be compared to triangles from other areas, and can be used as a marker for evaluation over time.
An exercise in index construction was conducted by Stephens et al.  who used a workshop environment to build an Index of Deprivation that compared Accra, Ghana with Sao Paolo, Brazil. Interestingly, groups working on the two areas devolved on the same Domains (income, education of head of household, number of persons per room, sanitation, and safe water access), but had to use different Indicators within those Domains. The collected data produced an overall picture that concealed substantial differences between the two areas. Those differences were demonstrated, however, by a simple choropleth map comparing the two cities by using four levels of socio-environmental conditions. Nonetheless, the authors felt that the data and resulting indices did not fully capture the political and social complexity of the cities. They do, however, cite several positive policy changes that resulted from the exercise. An important message from the study is the need for greater flexibility in the choice of indicators that make up an Index, since their true function may be as a catalyst for local change.
We have recently published  an Urban Health Index (UHI) that focused more on method than content. Adopting approaches used for the Human Development Index, [27, 47] the UHI permits construction of a variety of composite indices related to urban health, urban health disparities, and health determinants, and is coupled to a technique for mapping that provides visual display of disparities for contiguous small areas. Indices are standardized by transforming the values for each small area into a proportion of the range for the overall location, and are then combined by taking their geometric mean. The method, still under empirical investigation, may be of use in demonstrating health disparities and the geographic distribution of inequalities. It is an example of the reorientation of composite indices from methods for ranking to flexible tools for use by local public health workers to assess health status, needs, and disparities. In addition, it highlights the need to collect data as an integral part of the construction of indices. Small area data—differing only in scale from the more routinely collected large area data—are critical for understanding the urban microenvironment.
Measuring the urban environment
The urban milieu has produced its own set of indicators, many of which are tied to health determinants. They are in a separate sphere of research, however, largely because of a differing measurement methodology, but also because of the well-known complexities of associating specific environmental hazards to health . Recently, researchers estimated that almost 25 % of all disease burden can be attributed to the environment. The burden is estimated to be even greater—34 %--in children under 15 years of age, and to be of far greater consequence in LMICs compared to more developed countries [63, 64]. There is a growing need to be able to measure and use indicators of environmental health since they are a crucial link in the data and decision-making process , Ch. 3. The purpose of the indicators is to express linkage between an environmental condition and health effect relevant at the policy level which may then facilitate effective decision-making.
Two general types of environmental health indicators have been described: exposure-based indicators and effect-based indicators  Ch. 3. Exposure-based indicators measure environmental exposures with established health effects such as particulate matter with respiratory disease. Effect-based indicators typically measure a health effect that is commonly associated with an environmental exposure: for example, diarrheal disease and drinking water quality. Corvalan and colleagues  have suggested that environmental indicators must meet a dual standard: to be scientifically valid, and politically relevant. The latter would include being related to conditions that can be changed, easy to understand, acceptable to all stakeholders, and temporally cogent.
A variety of frameworks has been developed to assist with indicator creation and use. The most commonly cited framework for environmental health indicators is the “Driving forces, Pressures, State, Exposures, Effects and Action” or DPSEEA framework . While based in part on the simpler pressure-state-response framework, this modified version has expanded to include the role of driving forces which are thought to be the key components that push environmental processes forward. As presented by Briggs (Fig. 1 in his publication ) the framework can provide a guide for the development of appropriate environmental health indicators for a range of situations. It also provides a tool to consider the various levels of environmental health interventions and how they may have impact on the different components of the model as provided in the “Action” component of the framework.
Over the last thirty years multiple projects have been undertaken to develop environmental health indicators. A composite set of indicators has not been developed although as evidenced by the comparisons of the other indicator sets, often many sets of indicators overlap. Even where these indicators overlap, few have been specific to urban environments. Lawrence  reviewed the body of work on environmental health indicators (Table 4) with a specific emphasis on those that have focused on cities. Lawrence puts forward a new research agenda for urban health indicators. He suggests that researchers “use indicators to identify sets of contextually defined components of each human settlement and its neighborhoods.” He also recommends identifying comparable sets of indicators that are useful for comparison across different types of “human settlements.” Finally, he stresses the need for spatial and temporal measures at the local level.
The compilation of environmental urban indicators has many features in common with the corresponding health indicators. There are many variations (Indicators) on several themes (Domains). A host of individual indicators have been considered, but many of them are potentially interchangeable. Little empirical information, however, is available on their co-variation or their exchangeability. The exact balance of environmental indicators, social and economic determinants, and health outcomes in creating Indices is still an open issue though there appears to be general agreement that all should be part of such an Index.
Looking ahead—geospatial measurement of health and health disparity
A complementary approach to the assessment of health disparities is the burgeoning field of geovisualization. The growing armamentarium of data and geographic tools has given rise to alternate methods for measuring disparities, some of which can be married to the just described indicators and indices. For example, measures of urban design (enclosure, scale, transparency, complexity) can be obtained directly from digital sources and used to define urban space that may house the disadvantaged . Remote sensing has been coupled with GIS methods (in Bangladesh, for example ) to demonstrate concentrations of poverty and the heterogeneity within impoverished areas. Techniques for assessing access to parks and other environmental landmarks have been used to provide measures of the availability of health activities within an urban space . Google StreetView makes possible measures of local food availability with easy connection to population density and other factors that may affect disparities .
A series of studies from Australia demonstrate the potential melding of health and environmental indicators and geovisualization. Badland and colleagues identified 11 domains for “liveability” that included 61 usable indicators, and developed a framework that connected these indicators to social determinants of health . They applied this concept to demonstrate the connection between Public Open Spaces (POS) and the mechanisms by which they influence health . Similarly, a set of public transport indicators were developed, and their pathways of connection to population health explored . This work-in-progress promises to bring environmental factors (open spaces, transport) together with health determinants in real space, and to serve as a complex metric for identifying health disparities.
Despite the plethora of domains and indicators there are substantial commonalities among the major projects that have attempted to characterize health and disparities. These domains deal largely with health, irrespective of geo-location, and are usually at the regional or national level. Those that focus on urban issues often include environmental markers that affect health as well. The commonalities suggest that investigators share a common set of priorities but differ over the available welter of detail. An important area for further investigation is to explore that common ground, and determine—empirically, if not theoretically—the extent of correlation and exchangeability among indicators. Local urban areas would then have flexibility in the formation of indices based on locally available data.
There are commonalties among the approaches to Index formation as well. Several techniques for amalgamation of indicators are available, from simple linear combination to more sophisticated mathematical transformations and combinations. Many of these methods are transparent, and would be available to practitioners at the local level as well.
Measures that use indicators and indices to demonstrate disparities have been more elusive. Though considerable statistical development has gone into measures of disparity (see Table 1), those measures are largely a calculation created after the fact. (An example of an exception would be the inequality-weighted Human Development Index,  a valid and sophisticated measure, but one whose complexity hides raw differences.) The issue of demonstrating disparity re-invokes the question of scale. When applied globally, the disparity implicit in rank ordering of nations simply reports the difference between rich and poor. Attention to the detail within such ranking ignores Bryant’s admonition from 50 years ago. It ignores, as well, the spectrum of data and approaches required by the continuum from affluence to indigence. Issues of consummate importance for the latter (environmental quality, resource availability, public services, basic sanitation) have less immediacy for developed urban areas, though the microenvironment of some presumably affluent urban areas may well be substantially disadvantaged. Perhaps the real power of Indicators and Indices is to demonstrate disparity on the local level—a place where significant change may be possible. Locally collected data and simple, flexible tools for amalgamation, rather than fixed packages, may be a fruitful approach to understanding health disparity.
Cities environment reports on the internet
Gross National Product
Human development index
Health equity assessment and response tool
United Nations Development Program.
Bryant J. Health and the Developing World. Ithaca and London: Cornell University Press; 1969.
Harbison F, Myers CA. Education, manpower, and economic growth. New York: McGraw-Hill; 1964.
Yang L. An inventory of composite measures of human progress. http://hdr.undp.org/sites/default/files/inventory_report_working_paper.pdf, 2015 (Accessed April 12, 2015).
Vidaurre-Arenas M, Martinez-Piedra R. Health metrics network: a global partnership to improve access to information for health care practitioners and policy makers. Epidemiol Bull. 2005;26(2):1–8.
Schneider MC, Castillo-Salgado C, Bacallao J, Loyola E, Mujica OJ, Vidaurre-Arenas M, et al. Métodos de medición de las desigualdades de salud. Rev Panam Salud Publica. 2002;12(6):398–415.
Schneider MC, Castillo-Salgado C, Bacallao J, Loyola E, Mujica OJ, Vidaurre-Arenas M, et al. Methods for measuring health inequalities (Part I). Epidemiol Bull. 2004;25(2):12–4.
Schneider MC, Castillo-Salgado C, Bacallao J, Loyola E, Mujica OJ, Vidaurre-Arenas M, et al. Methods for measruing health inequalities (Part II). Epidemiol Bull. 2005;26(1):5–10.
Schneider MC, Castillo-Salgado C, Bacallao J, Loyola E, Mujica OJ, Vidaurre-Arenas M, et al. Methods for measuring health inequalities (Part III). Epidemiol Bull. 2005;26(2):12–5.
Mackenbach JP, Kunst A. Measuring the magnitude of socio-economic inequalities in health: and overview of available measures illustrated with two examples from Europe. Soc Sci Med. 2010;44(6):757–71.
Houweling TAJ, Kunst AE, Mackenbach JP. World Health Report 2000: inequality index and socioeconomic inequalities in mortality. Lancet. 2001;357:1671–2.
Handcock M, Morris M. Relative distribution methods. Sociol Methodol. 1998;28:53–97.
Handcock M, Morris M. Relative distribution methods in the social sciences. New York: Springer-Verlag, Inc.; 1999.
Talih M. A reference-invariant health disparity index based on Renyi divergence. Ann Appl Stat. 2013;7(2):1217–43.
Rossen LM, Talih M. Social determinants of disparitiesin weight among US children and adolescents. Ann Epidemiol. 2014;24:705–13.
Flowers J, Hall P, Pencheon D. Public health indicators. Public Health. 2005;119:239–45.
Etches V, Frank J, DiRuggiero E, Manuel D. Measuring population health: a review of indicators. Annu Rev Public Health. 2006;27:29–55.
Diez-Roux A. Bringing context back into epidemiology: variables and fallacies in multilevel analysis. Am J Public Health. 1998;88(2):216–22.
Fleischer NL, Diez-Roux A. Using directed acyclic graphs to guide analyses of neighbourhood health effects: an introduction. J Epidemiol Community Health. 2007;62:842–6.
Saltelli A, Nardo M, Saisana J, Tarantola S. Composite indicators: the controversy and the way forward. (http://www.oecd.org/dataoecd/40/50/33841312.doc). 2005.
European Commission Joint Research Centre. Composite indicators. https://ec.europa.eu/jrc/en/coin, 2013 (Accessed May 4, 2014).
Nardo M, Saisana M, Saltelli A, Tarantola S, Hoffman A, Giovannini E. Handbook on constructing composite indicators: methodology and user guide. OECD Statistics Working Papers, 2005/3, OECD Publishing 2005.
Nardo M, Saisana J, Saltelli A, Tarantola S. Tools for composite indicators building. from European Commission: Joint Research Centre. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.114.4806&rep=rep1&type=pdf, 2005 (Accessed March 10, 2015).
Saisana J, Saltelli A, Tarantola S. Uncertainty and sensitivity analysis techniques as tools for the quality assessment of composite indicators. J R Stat Soc A. 2005;168(Part 2):307–23.
Saltelli A, Funtowicz S. The precautionary principle: implications for risk management strategies. Int J Occup Med Environ Health. 2004;17(1):47–57.
Tarantola S, Saltelli A. Composite indicators: the art of mixing apples and oranges. http://kolloq.destatis.de/2007/tarantola.pdf, 2007 (Accessed March 10, 2015).
Paruolo P, Saisana M, Saltelli A. Ratings and rankings: voodoo or science? J R Stat Soc. 2012;176(3):609–34.
United Nations Development Programme. Human Development Report 2013. http://hdr.undp.org/en/2013-report, 2013 (Accessed March 10, 2015).
Munda G. Social multi-criteria evaluation: methodological foundations and operational consequences. Eur J Oper Res. 2004;158:662–77.
Saltelli A, Tarantola S, Chan K. A role for sensitivity analysis in presenting the results from MCDA studies to decision makers. J Multi Crit Decis Anal. 1999;8(3):139–45.
Murray CJL, Lopez AD. Production and snalysis of health indicators: the role of Academia. PLoS Med. 2010;7(11):e1001004.
World Health Organization: Centre for Health Development. Urban Health Equity Assessment and Response Tool (Urban HEART). http://www.who.int/kobe_centre/measuring/urbanheart/en/, 2014 (Accessed March 10, 2015).
Michigan Department of Community Health. Michigan Critical Health Indicators. http://www.michigan.gov/mdch/0,4612,7-132-2944_5327_47055---,00.html, 2011 (Accessed March 10, 2015).
Wall M, Farhang L, Bhatia R. Sustainable Communities Index. http://www.sustainablecommunitiesindex.org/, 2012 (Accessed March 10, 2015).
Office of Disease Prevention and Health Promotion, US Department of Health and Human Services. Health People 2020. http://www.healthypeople.gov/2020/default.aspx, 2015 (Accessed March 10, 2015).
US Department of Health and Human Services. Community Health Status Indicators. http://wwwn.cdc.gov/communityhealth/, 2015 (Accessed March 10, 2015).
United Nations Environment Program (UNEP). Cities Environment Reports on the Internet (CEROI). www.unep.org/ieacp/files/pdf/Geo_Cities_Manual_ECCA.pdf, 2014 (Accessed May 2, 2015).
U.S. Department of Health and Human Services, Centers for Disease Control and Prevention. Environmental Hazards and Health Effects: Environmental Public Health Indicators Project. http://ephtracking.cdc.gov/showIndicatorsData.action, 2013 (Accessed March 10, 2015).
California Department of Health Services, Environmental Health Investigations Branch. California Environmental Health Indicators. http://www.ehib.org/papers/health_indicators.pdf, 2002 (Accessed March 10, 2015).
Office on Women’s Health, US Department of Health and Human Services. Quick Health Data Online. http://www.healthstatus2020.com/index.html, 2014 (Accessed March 10, 2015).
UNICEF. Data: Monitoring the situation of Children and Women. http://data.unicef.org/index.php?section=unicef_aboutus, 2014 (Accessed March 10, 2015).
World Health Organization. World Health Statistics 2013. Indicator compendium. http://www.who.int/gho/publications/world_health_statistics/WHS2013_IndicatorCompendium.pdf, 2013 (Accessed March 10, 2015).
Opdycke S, Miringoff M-L. The Social Health of the States 2008. http://iisp.vassar.edu/SocialHealthofStates.pdf, 2008 (Accessed March 10, 2015).
The World Bank. World Development Indicators. http://data.worldbank.org/products/wdi, 2014 (Accessed March 10, 2015).
Global Cities Institute. Global City Indicators. http://www.cityindicators.org/Default.aspx#, 2014.
Michigan Department of Community Health. Index of Urban Prosperity. http://blogpublic.lib.msu.edu/index.php/state_of_michigan_cities_an_index_of_urb?blog=5, 2015 (Accessed March 10, 2015).
Wolman HL, Ford CC, Hill E. Evaluating the success of urban success stories. Urban Studies. 1994;31(6):835–50.
United Nations Development Programme. Human Development Indices: technical notes. http://hdr.undp.org/sites/default/files/hdr14_technical_notes.pdf, 2015 (Accessed March 10, 2015).
Atkinson AB. On the measurement of inequality. J Econ Theory. 1970;2:244–63.
Bertelsmann Stiftung. Bertelsmann Transformation Index. http://www.bertelsmann-transformation-index.de/en/, 2015 (Accessed March 10, 2015).
Transparency International. Corruption Perceptions Index 2010. http://www.transparency.org/cpi2014/in_detail, 2015 (Accessed March 10, 2015).
Townsend P. Deprivation. Int Soc Pol. 1987;16(2):125–48.
Carstairs V, Morris R. Deprivation: explaining differences in mortality between Scotland and England and Wales. Br Med J. 1989;299(6704):886–9.
Carstairs V. Deprivation indices: their interpretation and use in relation to health. J Epidemiol Community Health. 1995;49(Suppl2):S3–8.
Jarman B. Identification of underprivileged areas. Br Med J. 1983;286:1705–9.
Sivakuman M. Human Deprivation Index: a measure of multidimensional poverty. MPRA Paper No. 22337, posted 26. April 2010 / 13:27. http://mpra.ub.uni-muenchen.de/22337/, 2010 (Accessed March 10, 2015).
Messer LC, Laraia BA, Kaufman JS, Eyster J, Holzman C, Culhane JEI, et al. The development of a standardized neighborhood deprivation index. J Urban Health. 2006;83(6):1041–62.
Rey G, Jougla E, Fouillet A, Hemon D. Ecological association between a deprivation index and mortality in France over the period 1997 – 2001: variations with spatial scale, degree of urbanicity, age, gender and cause of death. BMC Public Health. 2009;9. doi:10.1186/1471-2458-9-33.
Kaltenthaler E, Maheswaran R, Beverley C. Population-based health indexes: a systematic review. Health Policy. 2004;68(2):245–55.
Shane AM, Graedel TE. Urban environmental sustainability metrics: a provisional set. J Environ Plann Manage. 2000;43(5):643–63.
Stephens C, Akerman M, Avle S, Maia PB, Companario P, Doe B, et al. Urban equity and urban health: using existing data to understand inequalities in health and environment in Accra, Ghana and Sao Paulo, Brazil. Environ Urban. 1997;9:181–202.
Rothenberg RB, Weaver SR, Dai D, Stauber C, Prasad A, Kano M. A flexible urban health index for small area disparities. J Urban Health. 2014;91(5):823–35.
Spielman SE, Yoo EH. The spatial dimensions of neighborhood effects. Soc Sci Med. 2009;68:1098–105.
Pruss-Ustun A, Corvalan C. How much disease burden can be prevented by environmental interventions? Epidemiology. 2007;18(1):167–78.
Pruss-Ustun A, Bonjour S, Corvalan C. The impact of the environment on health by country: a meta-synthesis. Environmental Health 2008;7(7). doi:10.1186/1476-069X-7-7.
Corvalan C, Briggs D, Zielhuis G. Decision-making in Environmental Health. London: Taylor & Francis Group; 2000.
Briggs D. Environmental Health Indicators: Framework and Methodologies. WHO/SDE/OEH/99.10. http://whqlibdoc.who.int/hq/1999/WHO_SDE_OEH_99.10.pdf, 1999 (Accessed May 4, 2014).
Lawrence RJ. Urban environmental health indicators: appraisal and policy directives. Rev Environ Health. 2008;23(4):299–326.
Purciel M, Neckerman KM, Lovasi GS, Quinn JW, Weiss C, Bader MDM et al. Creating and validating GIS measures of urban design for health research. J Environ Psychol. 2009;29:457–66.
Angeles G, Lance P, Barden-O’Fallon J, Islam N, Mahbub AQM, Nazem NI. The 2005 census and mapping of slums in Bangladesh: Design, select results and application. International J Health Geographics 2009;8(32). doi:10.1186/1476-072X-8-32.
Maroko AR, Maantay JA, Sohler NL, Grady KL, Arno PS. The complexities of measuring access to parks and physical activity sites in New York City: A quantitative and qualitative approach. International Journal of Health Geographics 2009;8(34). doi:10.1186/1476-072X-8-34.
Clarke P, Ailshire J, Melendez R, Bader M, Morenoff J. Using Google Earth to conduct a neighborhood audit: reliability of a virtual audit instrument. Health Place. 2010;16:1224–9.
Badland H, Whitzman C, Lowe M, Davern M, Aye L, Butterworth I, et al. Urban liveability: emerging lessons from Australia for exploring the potential for indicators to measure the social determinants of health. Soc Sci Med. 2014;111:64–73.
Villanueva K, Badland H, Hooper P, Koohsari MJ, Movoa S, Davern M, et al. Developing indicators of public open space to promote health and well being in communities. Appl Geogr. 2015;57:112–9.
Badland H, Mavoa S, Villanueva K, Roberts R, Davern M, Giles-Corti B. The development of policy-relevant transport indicators to monitor health behaviours and outcomes. J Transp Health. 2015. Epub. http://dx.doi.org/10.1016/j.jth.2014.07.005i.
Claussen B, et al. EURO-URHIS 2, Urban Health Monitoring and Analysis System to Inform Policy. http://www.urhis.eu/, 2010 (Accessed March 10, 2015).
United Nations Human Settlements Programme. Urban indicators guidelines: monitoring the habitat agenda and the millennium development goals. http://ww2.unhabitat.org/programmes/guo/documents/urban_indicators_guidelines.pdf, 2004 (Accessed March 10, 2015).
United Nations Development Programme. Human Development Reports. http://hdr.undp.org/en/statistics/hdi, 2014 (Accessed March 10, 2015).
ICLEI. Local Governments for Sustainability. http://www.iclei.org/our-activities/our-agendas/sustainable-city.html, 2014 (Accessed March 10, 2015).
European Commission. European Common Indicators. http://ec.europa.eu/environment/urban/common_indicators.htm, 2014 (Accessed March 10, 2015).
UK Department for Environment Food and rural Affairs. Sustainable Development Indictors. https://www.gov.uk/government/collections/sustainable-development-indicators, 2014 (Accessed March 10, 2015).
WHO European Healthy Cities Network. http://www.euro.who.int/en/health-topics/environment-and-health/urban-health/activities/healthy-cities/who-european-healthy-cities-network, 2014 (Accessed March 10, 2015).
This study was supported by a grant from the WHO Center of Health Development (the WHO Kobe Center). Research support was also supported by the National Institute of Minority Health and Health Disparities of the National Institutes of Health under award number 1P20MD004806. The authors would like to acknowledge the participation of Jeremy Crampton, PhD, in the initial versions of this work. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the World Health Organization.
The authors declare that they have no competing interests.
RR carried out the initial literature review and wrote a portion of the original draft. CS wrote a portion of the initial draft and provided environmental assessment. SW editing the draft and confirmed the references. DD edited the draft and provided consultation on geographic issues. AP and MK helped to conceptualize the review, provided fugitive references, and edited the final draft. All authors approved the final draft.