Environmental health indicators and a case study of air pollution in Latin American cities

Environmental Research 111 (2011) 57–66

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Review

Environmental health indicators and a case study of air pollution in Latin American cities Michelle L. Bell a,n, Luis A. Cifuentes b, Devra L. Davis c, Erin Cushing a, Adriana Gusman Telles d, Nelson Gouveia e a

School of Forestry and Environmental Studies, Yale University, 195 Prospect St., New Haven, CT 06511, USA Pontificia Universidad Catolica de Chile, Industrial and Systems Engineering Department, Santiago, Chile c University of Pittsburgh, Center for Environmental Oncology, Pittsburgh, PA, USA d Harvard University School of Public Health, Department of Environmental Health, Boston, MA, USA e ~ Paulo, Brazil University of Sa~ o Paulo, Faculty of Medical Sciences, Department of Preventive Medicine, Sao b

a r t i c l e in f o

abstract

Article history: Received 13 February 2010 Received in revised form 14 October 2010 Accepted 16 October 2010 Available online 13 November 2010

Environmental health indicators (EHIs) are applied in a variety of research and decision-making settings to gauge the health consequences of environmental hazards, to summarize complex information, or to compare policy impacts across locations or time periods. While EHIs can provide a useful means of conveying information, they also can be misused. Additional research is needed to help researchers and policy-makers understand categories of indicators and their appropriate application. In this article, we review current frameworks for environmental health indicators and discuss the advantages and limitations of various forms. A case study EHI system was developed for air pollution and health for urban Latin American centers in order to explore how underlying assumptions affect indicator results. Sixteen cities were ranked according to five indicators that considered: population exposed, children exposed, comparison to health-based guidelines, and overall PM10 levels. Results indicate that although some overall patterns in rankings were observed, cities’ relative rankings were highly dependent on the indicator used. In fact, a city that was ranked best under one indicator was ranked worst with another. The sensitivity of rankings, even when considering a simple case of a single pollutant, highlights the need for clear understanding of EHIs and how they may be affected by underlying assumptions. Careful consideration should be given to the purpose, assumptions, and limitations of EHIs used individually or in combination in order to minimize misinterpretation of their implications and enhance their usefulness. & 2010 Elsevier Inc. All rights reserved.

Keywords: Environmental health indicators Air pollution Public health

Contents 1. 2. 3. 4.

5.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Types of environmental indicators related to human health . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Approaches to environmental indicators related to human health . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A case study in children’s health and air pollution in Latin America . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. Development of case study indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3. Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

57 58 60 61 61 61 63 64 64

1. Introduction

n

Corresponding author. E-mail address: [email protected] (M.L. Bell).

0013-9351/$ - see front matter & 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.envres.2010.10.005

Decision-makers aiming to improve air quality face a wealth of information on health and environmental conditions that may be overwhelming and difficult to interpret in a meaningful way.

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A common approach to make data more manageable is Environmental Health Indicators (EHIs), which provide quantitative measures of environmental conditions linked with health outcomes. EHIs summarize complex information and provide a common framework allowing comparison across locations or time. Indicators can help identify trends, establish priorities, and monitor progress to assist in policy formation. Further, policies can be systematically evaluated with their estimated indicator values to help compare the consequences of various decisions. However, the development of indicators necessarily involves assumptions regarding what information to include and methods for combining information. Use of an indicator without firm understanding of the inherent limitations and assumptions could lead to inappropriate conclusions. The first daily air quality indicator may have been an index introduced by the Allegheny County Health Department in 1971 to evaluate air quality in the Pittsburgh, Pennsylvania region (Longhurst, 2005). This indicator used a formula based on monitoring data for sulfur dioxide (SO2) and coefficient of haze and federal regulations for SO2 and particulate matter (PM). Index scores were categorized into ‘‘excellent,’’ ‘‘unsatisfactory,’’ ‘‘poor,’’ or ‘‘unhealthy,’’ and revised versions included more strata. This early index highlights challenges in the use of EHIs as local newspapers published graphical displays of the index without descriptive information. Debates arose regarding the proper interpretation of the index and the best method of dissemination (Longhurst, 2005). Here we review the application of EHIs, investigate categories of indicators, and construct a case study to explore how indicator values vary by underlying assumptions. For the case study, we selected air quality in urban centers of Latin America, one of the world’s most rapidly developing regions and home to one of the world’s youngest populations. Latin America faces growing air pollution problems with significant health consequences, ranging from restricted activity days to mortality (Romieu et al., 1990; ˜ ez Cifuentes et al., 2000; Rojas-Martinez et al., 2007; Escamilla-Nun et al., 2008; O’Neill et al., 2008; Arbex et al., 2009; de Medeiros et al., 2009). Many Latin American cities routinely experience poor air quality due to rapid unplanned urbanization, increased motor vehicle fleet, and outdated industrial technologies. The region has one of the world’s highest rates of urbanization with 475% of residents in urban areas, and hosts three of the 10 largest cities in the world (Mexico City, Mexico; Sa~ o Paulo, Brazil; Buenos Aires, Argentina) (London Times, 1999; United Nations Statistics Division, 2008). Asthma prevalence and associated health effects for some Latin American regions are higher than in the U.S., particularly in urban areas (Cooper et al., 2009). The combination of elevated air pollution and population density leads to a large number of people at risk. Recent estimates suggest that over 100 million people in Latin America, including a high percentage of children, are exposed to air pollution levels exceeding the World Health Organization (WHO) health-based guidelines (WHO, 2000). Therefore, improved air quality would provide significant health and economic benefits in this region (Bell et al., 2006a). Strategies to decrease emissions of greenhouse gases (GHG) could have co-benefits of reducing local air pollution, as many strategies for lowering GHG would simultaneously lower levels of pollutants with similar sources (Cifuentes et al., 2001; Bell et al., 2008). In recent decades, measures to control and prevent air pollution have been implemented in cities such as Mexico City, Sa~ o Paulo, and Santiago, Chile. Many Latin American countries have air quality regulations mirroring those of the U.S. (Rinco´n et al., 2007). Despite these efforts, air pollution levels frequently exceed national standards, especially for particulates and ozone (Cifuentes et al., 2005). This paper reviews current approaches for indicators that have been used to guide environmental policies intended to reduce the

consequent health burden. A case study is explored for Latin American cities with multiple indicators combining levels of PM with aerodynamic diameter o10 mm (PM10), WHO health-based air quality guidelines, and estimates of the population exposed, with an emphasis on children, to demonstrate the use of EHIs and various approaches. We discuss the advantages and limitations of various formations of indicators.

2. Types of environmental indicators related to human health Much effort has been focused on developing indicators to measure the environment’s impact on human health (Lawrence, 2008). Indicators serve a broad range of purposes, from assessing damage to ecological systems (Dinsdale and Harriot, 2004) to estimating environmental damages from industry (Siracusa et al., 2004). EHIs’ goals include:

 Help the understanding, evaluation, and comparison of health and environmental consequences of policy decisions.

 Quantify the state of environmental conditions (e.g., air quality) and related health responses.

 Aid comparison of environmental health impacts among multiple locations or time periods.

 Encourage public awareness of environmental health. Indicators can be used to evaluate the effectiveness of previous decisions (e.g., pollutants’ concentrations before and after implementation of a policy) and future decisions (e.g., estimated economic benefit from avoided mortality under a particular policy). These quantifications, which can take many forms, are powerful tools that can help policy-makers and the general public to conceptualize objectives, choose among alternatives, and adjust policies. Wills and Briggs (1995) defined two categories of EHIs. Healthrelated environmental indicators, or exposure-based indicators, are environmental conditions or trends that may cause adverse health effects. This type of indicator uses population exposure information to relay health implications based on environmental conditions. Environmental-related health indicators, or outcome-based indicators, use information about direct health outcomes, such as frequency of respiratory symptoms. Another approach to broadly categorize EHIs is to consider their purposes. A surrogate indicator serves as a proxy for unavailable information. The surrogate indicator is not a summary measure, but rather substitutes for variables with insufficient data. Values of a single variable are used to reflect a different or larger set of variables. An example is the use of mosses or spider webs as a substitute for data on metal pollution (Hose et al., 2002; Aceto et al., 2003) or the use of strontium and lead isotopes to detect trends of pollution and sources of contamination (Charalampides and Manoliadis, 2002). Surrogate indicators may be useful for regions or time periods with limited monitoring data. For example, Sa~ o Paulo, Brazil, began measuring PM10 in 1981, but measured PM2.5 (PM with aerodynamic diameter o2.5 mm) more recently (CETESB, 2008). While PM2.5 measurements are unavailable for all areas and timeframes of interest, PM10 can be used as a surrogate by adjusting by a known fraction (e.g.,  60%). Indicator chemical constituents of particles have also been applied (Dominici et al., 2010). PM2.5 selenium was used as a surrogate indicator of PM2.5 from coal combustion (Laden et al., 2000) and sulfate as an indicator of overall regional airborne particles (Sarnat et al., 2006). Distance to traffic served as a surrogate for vehicle-related air pollution (Hoek et al., 2002).

M.L. Bell et al. / Environmental Research 111 (2011) 57–66

Composite indicators link environment and health by synthesizing a large array of information by combining raw data from surveys and monitoring into statistics that are converted into indicators (von Schirnding, 2002). This provides an integrated, although incomplete, view of existing conditions and trends, and can thereby provide a better understanding of environmental impacts on health (Briggs et al., 1996; von Schirnding, 2002). These indicators are used when a complete analysis of all available information is prohibited by time, expertise, or other resources, or when more complete information is unnecessary. Additional classifications of indicators were developed by the European Environment Agency (von Schirnding, 2002):

 Descriptive indicators reflect state of the environment or human 

 

health conditions (e.g., concentration of pollutants). Performance indicators reflect how far a particular factor is from a reference target (e.g., difference between current pollution concentrations and regulatory standards, number of days on which an air quality standard is exceeded). Efficiency indicators relate to the efficiency of production and consumption processes (e.g., energy consumption per unit of output). Total welfare indicators merge economic, social and environmental aspects.

Indicators can also be categorized according to whether they are qualitative or quantitative and their spatial scale. They may be a formal indicator, or used without explicit recognition that an ‘‘indicator’’ is being applied. Further, an indicator could be used at any point in the environment and human health system, from emissions through exposure and health endpoint. Fig. 1 describes the relationship between local characteristics, environment and

Local Characteristics Development Status Transportation Industry Health care system Housing stock

Physical Characteristics Topography Meteorology

human health systems, and EHIs for air quality. Examples of indicators for air pollution include the following:

        

emissions sources (e.g., industrial activity); emissions levels (e.g., tons emitted/year); ambient concentration; indoor pollutant levels; exposure (e.g., concentrations from personal ambient monitors); population exposed (e.g., number of people exposed above a reference level); health consequences (e.g., number of respiratory-related hospital admissions); material and ecological consequences (e.g., lost crops); economic evaluation (e.g., cost of hospital visits)

Some of these measures, such as ambient concentrations, are both composites and surrogates as they reflect population-level exposure, although actual doses vary by individual. The list roughly follows the path from source to impacts from the physical and social drivers of air pollution (e.g., population growth) to socioeconomic impacts (e.g., lost work days) (Fig. 1). In fact, many of the indicators listed could be surrogates for one or more indicators listed afterward. For example, emission sources are a surrogate for pollutant concentrations. Each type of information requires different inputs and may be more or less appropriate in various circumstances. The health and ecological impacts at each point in this pathway depend on local characteristics, including development status. The area’s development status includes transportation characteristics, such as age and maintenance status of the vehicle fleet and the vehicle miles traveled. The location’s physical characteristics, such as topography and meteorology, will greatly

Environment and Health System

Example Indicators

Physical / Social Drivers

•Vehicle miles traveled/person •Composition of vehicle fleet

Pollutant Emissions

Ambient & Indoor Concentrations

Health, Material, & Ecological Damage

Population Characteristics Age distribution Baseline health status

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Socio-Economic Impacts

•Tons/yr •Tons/person/yr •Source profiles

•Annual pollutant concentrations •# of days with levels above regulatory standards •PM chemical composition

•Cases/year •Cases/population •Crops lost

•Monetary amount/yr •Monetary amount/person •% income •% GNP •DALYs

Fig. 1. Air quality environmental health indicators: relationship between local characteristics, the environment and human health systems, and indicators. Note: GNP stands for gross national product. DALYs stands for disability-adjusted life years. This figure provides examples of environmental health indicators based on the relationship between local characteristics that affect the environment and health system.

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affect air pollutant concentrations and resulting damage. For example, Sa~ o Paulo, which contains about a quarter of Brazil’s vehicles, is subject to frequent temperature inversions during winter due to its topography and meteorology (CETESB, 2008). The health consequences of air pollution can be affected by the baseline population, such as through nutritional status, access to health care, and the age distribution because the very young and elderly are particularly susceptible. The type of EHIs that would be most useful likely differs by region due to the varying nature of government, monitoring systems for pollution and health, and environmental problems. The design of EHIs may also differ based on whether primary concerns are local or global, rural or urban, or a new, future, or existing problem. For instance, while some areas may wish to address air pollution from existing transportation networks, many Latin American cities are currently undergoing enormous expansion of transportation networks. This generates high pollution levels and associated health consequences for populations that were not subject to such problems decades ago. The current development of these transportation systems allows different policy alternatives than would be possible in cities with existing systems. Key issues for Latin American EHIs are economic resources, tradeoffs between development and environmental or health damage, large cities with growing populations, and expanding transportation and industry.

3. Approaches to environmental indicators related to human health EHIs, including those meant to reflect overall air quality, have been used for decades (Ott and Thom, 1976; Dennis et al., 1983). Fig. 1 lists examples of commonly used indicators for air pollution that assess various points in the system, such as emissions or economic valuation. In fact, almost all air pollution policy decisions use indicators in some form. An example of a commonly used indicator, often applied without formal recognition that it is an indicator, is the annual ozone level, which summarizes more highly temporally resolved ozone information. Another example is a citywide PM2.5 average, which reflects a summary of more spatially resolved information. These are examples of simple quantities used to indicate overall air quality in relation to a particular pollutant for a given region and time period, as an indirect measure of exposure to air pollution. Yet this composite indicator does not fully capture the complex nature of air quality because it does not account for temporal or spatial differences, and therefore is a simplification of the air quality system (Bell et al., 2005a). In fact, all composite indicators obscure detail to some degree. For the indicator to be understood, and therefore useful to policy-makers, prior knowledge is necessary regarding the nature of the air pollutant in the region of interest as well as the health impacts from exposure to the specified level of the pollutant. Such an indicator is particularly useful when the temporally disaggregated and place-specific data are too cumbersome or irrelevant to the task at hand. Temporal issues related to indicators include the timescale (e.g., a single day versus a year) and whether indicator values are held constant over time. Stagnant indicators evaluate conditions at a distinct time, whereas trend indicators examine how risk changes over time. Spatial components of an indicator include whether they investigate a particular place (Marshall et al., 2009) or comparisons across space, such as different communities (Kyle et al., 2002). Spatial and temporal heterogeneity in the consequences of air quality could involve assessment of whether levels of air pollutants change over space and time, or whether the health risk of a given pollutant changes over space and time (Dominici et al., 2007; Shin et al., 2008; Wu et al., in press), such as due to differences in the composition of the pollutant mixture, which could be related to

health outcomes (Lippmann et al., 2006; Bell et al., 2007; Hopke, 2008; Dominici et al., 2010). Another difference among indicators is whether they permit a ‘‘threshold’’ effect, a ‘‘safe’’ level of exposure that does not influence health risks. Performance indicators that estimate deviation from a specified goal (e.g., regulation) imply no risk at lower levels. However, much of the scientific literature on air pollution and health indicates the absence of a threshold (Daniels et al., 2000; Bell et al., 2006b; White et al., 2009). Knowledge about risks from low levels is limited compared to more commonly experienced levels, and results are inconsistent (Kim et al., 2004), although analytical methods have been developed (Cakmak et al., 1999). Some proposed EHIs reflect the lack of threshold, such as an indicator based on mortality risk in Canadian cities from exposure to nitrogen dioxide (NO2), ozone, and PM2.5 (Stieb et al., 2008). Simple indicators have been developed that amalgamate statistics on different exposures to generate an overall air quality indicator. For instance, the U.S. Environmental Protection Agency’s Air Quality Index (AQI) assesses levels of several air pollutants to generate an overall measure of air quality and categorizes air quality from ‘‘Hazardous’’ to ‘‘Good’’ based on their anticipated health impacts (USEPA, 2003). Other efforts produced aggregate indices representing long-term exposure to particles, ozone, SO2, nitrogen dioxide (NO2), and carbon monoxide (CO) (Kyle et al., 2002). Most indicators capture impacts of single pollutants without consideration of simultaneous exposure to multiple pollutants. For example, an indicator based on SO2, NO2, ozone, PM10, PM2.5, and CO, and demonstrated with data from Cape Town, South Africa, considers health risk from all pollutants simultaneously (Cairncross et al., 2007). However, the indicator sums pollutants’ individual attributable risks based on single-pollutant science and does not address interaction between pollutants. Other efforts to develop indicators with multiple pollutants (Swamee and Tyagi, 1999; Khanna, 2000; Kyrkilis et al., 2007; Stieb et al., 2008) are similarly based on single-pollutant science in that they aggregate health effects of several individual pollutants, rather than use risk estimates based on exposure to a complex pollution mixture. The development of multi-pollutant indicators is hindered by the current scientific literature as most health studies consider consequences of a single pollutant, with other pollutants either omitted or incorporated as potential confounders. Although results have indicated synergism among pollutants (Mauderly and Samet, 2009), scientific understanding is limited. A multi-pollutant approach to EHIs necessitates an understanding of how the human body responds to a wide array of chemical mixtures. Currently, such research is underway but faces numerous challenges including inadequate data, exposure assessment, and statistical limitations (Dominici et al., 2010). At a broader scale, EHIs can be constructed to include measures of air quality, water quality, and other environmental factors that influence health, as well as ecosystem indicators. While we focus on EHIs with respect to air quality, indicators have been proposed for a broad range of purposes ranging from evaluating health differences among urban neighborhoods for use in policy-making (Landon, 1996) to gauging climate change impacts (Brown, 1993). Indicators have been widely used in many fields, such as economics, public health, ecology, oceanography, and geophysics (e.g., (Wcislo et al., 2002; Rice, 2003; Lawrence, 2008). One such index is the Environmental Performance Index (EPI) (Esty et al., 2008; EPI, 2009). Values of performance are generated for environmental stressors to human health, ecosystems, and natural resources based on 25 indicators, which are then aggregated into subcategories and overall EPI scores by country. The indicators cover six policy-related categories: environmental

M.L. Bell et al. / Environmental Research 111 (2011) 57–66

health, air pollution, water resources, biodiversity and habitat, productive natural resources, and climate change. The 2008 EPI scored Sweden the best out of 149 countries. The EPI’s developers acknowledge data and methodological limitations and recognize that these issues should be considered when interpreting EPI scores. Sensitivity analyses are used to evaluate how results shift due to known uncertainties (Esty et al., 2008). Several EHIs have been proposed by the WHO, World Resources Institute, and other organizations (Pastides, 1995; Wills and Briggs, 1995; Spiegel and Yassi, 1997; Davis and Saldiva, 1999; California Dept. of Health Services, 2002; Dalbokova and Kryzanowski, 2002; Samson, 2002; von Schirnding, 2002; WHO, 2003; WHO, 2004). In recent years, extensive efforts have been made to quantify the burden of disease attributable to known risk factors as a means of setting priorities and allocating resources (Murray and Lopez, 1997; Ezzati et al., 2002, 2003, 2005). The Global Burden of Disease project, first conducted in 1990, with updates in subsequent years (Murray and Lopez, 1996; WHO, 2004; Lopez et al., 2006), links risks including environmental and other factors to mortality and morbidity based on Disability-Adjusted Life Years (DALYs). The indicator of DALYs is a single metric of disease burden that estimates the loss of life years from premature death and poor health. A commonly used approach, created by the WHO, is the DPSEEA (Driving Forces–Pressure–State–Exposure–Effect–Action) framework based on the causal chain of events and systems that lead to ¨ and Corvala´n, 1995; Corvala´n environmental conditions (Kjellstrom et al., 1999). This model is an adaptation of the Pressure-State¨ by the Organization for Response framework developed Kjellstrom Economic Cooperation and Development (OECD), which in turn was based on earlier efforts by the Canadian government (Wills and Briggs, 1995; Briggs et al., 1996; Walz, 2000). Driving forces reflect social, economic, or political factors at the societal level that affect the environment. Driving forces result in pressure factors that alter environmental conditions, such as emissions of air pollutants (e.g., tons/year of a given pollutant for a specific region). The state refers to a resulting environmental condition (e.g., PM10 concentration). Exposure factors reflect interaction between humans and the environment, such as a population-weighted annual mean PM10. The effect factors refer to health responses (e.g., number of asthma hospitalizations for a given time period and location). Action refers to policy and other activities intended to protect human health and the environment at some point in the system, such as at the driving forces or exposure. An example of an action aimed at altering exposure is a warning system suggesting that sensitive subpopulations stay inside on high air pollution days. A modified DPSEEA framework was developed by adding a context factor representing individual-level social, economic, or demographic factors that can modify an individual’s exposure or that result in an adverse health response (WHO, 2004). The DPSEEA framework permits a comprehensive view of the relationship between environment and health. Because indicators are possible at all stages of the process, from human activities to resulting health outcomes, it allows interventions at different stages of the DPSEEA model. Following this framework, the WHO proposed several indicators to inform policy regarding air pollution’s health effects: road transport intensity (tons-kilometers, passenger-kilometers); emissions of air pollutants; population-weighted urban annual average concentration of NO2, PM10, PM2.5, and SO2; distribution of daily ozone; and years of expected life lost (YLL) attributable to long-term PM2.5 exposure. Table 1 summarizes several EHI efforts and their positions within the DPSEEA framework. The more commonly applied portions of the framework were state, exposure, and effect, although some efforts explicitly followed the DPSEEA system, covering all areas.

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4. A case study in children’s health and air pollution in Latin America 4.1. Development of case study indicators We generated multiple EHIs for air pollution and children’s health in large Latin American cities and applied them to rank cities from ‘‘best’’ to ‘‘worst.’’ The indicator design is intentionally simple with only a single pollutant and annual average levels. The population of interest is the total population or children o15 years, and the cities to be included are urban centers in Latin America. Despite this ostensibly basic approach, several assumptions and choices must be made. Different ways of combining population and air pollution data yield different indicators. Our goal with these examples is to explore how various approaches to a seemingly simple indicator may result in different implications. While the indicators can provide insight into the health burden from air pollution in major Latin American cities, the purpose of this work is not to provide definitive answers on this topic but rather to investigate the nature of EHIs. The case study included Latin American cities with population of one million or more based on data from the United Nations (United Nations Statistics Division, 2008). For each city, the percentage of children o15 years was estimated based on the city’s population and the percentage of persons o15 for that country. Air pollution data were obtained from previous work representing a collection of official reports (Cifuentes et al., 2005). Annual PM10 averages were available for several cities, and this pollutant was chosen as the basis for indicators. For cities that only measured Total Suspended Particles (TSP), that value was converted to PM10 assuming that approximately 60% of TSP is PM10, although the actual ratio varies (Cicero-Ferna´ndez et al., 1993; Li et al., 2004; Karar et al., 2006; Querol et al., 2007). The study period was 1997–2003, although data were not available for all years and locations. Cities without data for at least two years were excluded. Cities were ranked according to each of five indicators:

 EHI-A) long-term average PM10 concentration.  EHI-B) long-term PM10 concentration weighted by total population exposed.

 EHI-C) long-term PM10 concentration weighted by population o15 years exposed.

 EHI-D) population exposed to unhealthy levels of PM10, defined 

as 420 mg/m3 (WHO guideline) (WHO, 2006). EHI-E) children o15 years exposed to unhealthy levels of PM10, defined as levels above the WHO guideline.

The first three indicators consider only the level of pollutant, and assume that cities with higher levels are ‘‘worse’’ (i.e., should have worse rankings). The fourth and fifth indicators allow for a threshold effect, where all pollution levels below the WHO guideline are equivalent, and pollution levels above the guideline are equivalent. EHI-B through E incorporate the population exposed. EHI-C and EHI-E focus on children as opposed to the general population. Table 2 summarizes the features of the five indicators. 4.2. Results Table 3 and Fig. 2 show the ranking of 16 Latin American cities, where ‘‘1’’ represents the best rank and ‘‘16’’ the worst. EHI-B through E, which are population-weighted, provide rather consistent rankings. In some cases, a much different ranking is provided by EHI-A, which is not population-weighted and considers annual PM10 concentrations. The best-performing cities are Curitiba (1), Toluca (2) and Sa~ o Paulo (3), with the lowest PM10 levels, while the

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M.L. Bell et al. / Environmental Research 111 (2011) 57–66

Table 1 Examples of approaches to environmental health indicators. Notes

Environmental Health Indicator (EHI)

Spatial scale

Timeframe

Examples of indicators by position in DPSEEA framework (Driving Forces–Pressure–State– Exposure–Effect–Action)

EHIs for Brazil and other Latin American countries (Carneiro et al., 2006)

Regional/National (Brazil, Cuba, Canada, Argentina)

n/a

Driving force: Economic policy of healthy residences development. Pressure: Per capita consumption. State: Water supply services coverage. Exposure: Cumulative radiation dose. Effect: Incidence of melanoma.

Enteric diseases in young children in Mexico City (Cifuentes et al., 2002)

Local/Regional (Mexico City)

June–October 1999

State: Concentrations of fecal coliform in water. Effect: Occurrence of diarrheal disease in the previous two weeks.

Combined data on disease, water supply, and water samples. Designed to summarize the most important predictors of diarrheal disease in children.

Pew Environmental Health Tracking Project (Litt et al., 2004)

Multi-state (United States)

10 years (1986–1995)

Effects: Respiratory, skin, endocrine/ metabolic, neurologic, reproductive/fertility.

Describes multi-step approach to rebuild public health tracking systems.

Drinking water indicators in New Zealand (Khan et al., 2007)

National study of values at local level (Territorial Local Authorities)

5 years (1998–2002)

State: Proportion of drinking water samples with E. coli or fecal streptococci exceeding guideline values. Exposure: Percentage of population with public water supply to the home. Effect: Annual drinking waterborne disease rate per 100,000 population. Action: Number of valid water sample measurements per pollutant/capita/year.

Study design intended to validate the association between four indicators representing different stages of the DPSEEA framework.

Indicators for endemic arsenic in China (Peterson et al., 2001)

Regional (provinces) (China)

n/a

Multiple indicators in DPSEEA. Driving force: Arsenic in bedrock and minerals. Effect: Cancer incidence. Action: Community-awareness, water treatment, funding for scientific research.

Designed to aid decision-makers in managing the human health impacts of arsenic. Based on the pressure-state-impact-response framework.

EHIs for European children (Pond et al., 2007)

Multi-national (Europe)

n/a

Applied an exposure/health/policy framework. Exposure: Child population-weighted annual mean PM10 concentration. Health (Effect in DPSEEA): Percentage of adolescents who are overweight or obese. Policy (Action in DPSEEA): Policies to reduce children’s traffic mortalities.

Emphasizes children. Designed to support policy efforts and monitor trends. Indicators classified as exposure, health, or policy.

Ecosystem health indicators for Centro Habana (Spiegel et al., 2001)

Local (Centro Habana, Cuba)

n/a

Applied DPSEEA framework. Driving force: Urbanization growth rate. State: Microbiological contaminants in water supply. Exposure: Percent of persons without water supply. Effect: Incidence of gastrointestinal disease.

Designed to evaluate whether human health interventions were effective and efficient to aid future efforts. Emphasizes social environment as well as physical health.

Health-related environmental indices and environmental equity (Wheeler, 2004)

Regional / Small areas (wards) (England and Wales)

1 year (1991 for geographical data, 1999 for pollutant data)

Pressures: Atmospheric chemical releases from large-scale industrial processes, landfills and sites registered under Control of Major Accident Hazards Regulation. State: Ambient air quality.

Designed to investigate environmental justice/ environmental equity. Indices weighted by socio-economic status and compared for rural versus urban areas.

Indicators for endemic fluorosis in China (Yang et al., 2003)

Regional (provinces) (China)

1 year (annual average)

Multiple indicators in DPSEEA. Exposure: Population in a disease village. Effect: Occurrence of dental fluorosis, occurrence of skeletal fluorosis.

Based on the pressure-stateimpact-response framework. Developed health impact index and management capability index.

Note: The following provides summaries of efforts to develop environmental health indicators and examples of indicators as well as their position in the DPSEEA framework. Not all efforts explicitly followed a DPSSEA design.

worst-ranked cities by this measure are Montevideo (16), Lima (15), and Buenos Aires (14). EHI-B builds on EHI-A by incorporating the population exposed, which yielded notably different results from EHI-A, although Curitiba (1) remains the best-ranked city. A focus on children is incorporated into EHI-C, which values overall PM10 levels as well as the exposed population o15 years. Rankings are identical to those

for EHI-B, with the exception of reversed rankings for Mexico City and Buenos Aires. Indicators EHI-D and EHI-E are based on the exposed population with PM10 concentrations exceeding WHO guidelines, with EHI-D focusing on total population and EHI-E on children. All cities included in this analysis exceed WHO PM10 guidelines; therefore, rankings correspond to population size. Managua, Juarez, and

M.L. Bell et al. / Environmental Research 111 (2011) 57–66

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Table 2 Description of Environmental Health Indicators’ (EHIs’) characteristics for case study.

Description

Long-term PM10 levels Population Exposed Focus on Children Threshold Effect

EHI-A Average PM10

EHI-B Average PM10 weighted by population

EHI-C Average PM10 weighted by population o15 years

EHI-D Population exposed to unhealthy PM10 levels

EHI-E Population o 15 years exposed to unhealthy PM10 levels

X

X X

X X X

X

X X X

X

Note: The designations ‘‘EHI-A’’, ‘‘EHI-B’’, etc. are used to distinguish among the indicators.

Table 3 Rankings of cities by various environmental health indicators.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

EHI-A

EHI-B

EHI-C

EHI-D

EHI-E

Curitiba Toluca Sa~ o Paulo Puebla Guadalajara Managua Bogota Salvador Mexico City Juarez Monterrey Santiago Quito Buenos Aires Lima Montevideo

Curitiba Managua Toluca Juarez Puebla Quito Salvador Montevideo Guadalajara Monterrey Santiago Bogota Sa~ o Paulo Lima Mexico City Buenos Aires

Curitiba Managua Toluca Juarez Puebla Quito Salvador Montevideo Guadalajara Monterrey Santiago Bogota Sa~ o Paulo Lima Buenos Aires Mexico City

Managua Juarez Montevideo Quito Toluca Curitiba Puebla Salvador Monterrey Guadalajara Santiago Bogota Lima Sa~ o Paulo Buenos Aires Mexico City

Montevideo Managua Juarez Quito Curitiba Toluca Puebla Salvador Monterrey Guadalajara Santiago Bogota Lima Buenos Aires Sa~ o Paulo Mexico City

Toluca

Sao Paulo

Santiago

Puebla Quito Salvador

Montevideo

Mexico City Monterrey

Lima Managua

Juarez

Guadalajara

BuenosAires Curitiba

Bogota

Note: The city ranked 1 has the best indicator value; the city ranked 16 has the worst indicator value. EHI stands for environmental health indicator. The designations ‘‘EHI-A’’, ‘‘EHI-B’’, etc. are used to distinguish among the indicators.

(best) 1 2 3 4 5 6 Ranking

7 8 9 10 11 12 13 14 15

EHI-A EHI-B EHI-C EHI-D EHI-E

(Worst) 16 Fig. 2. Rankings of cities by various environmental health indicators.

Montevideo have, in slightly different orders, fewer persons exposed to unhealthy PM10 levels using these indicators. Sa~ o Paulo, Buenos Aires, and Mexico City are ranked the worst.

Depending on the criteria included in the EHIs, some changes in city ranking were observed. The most striking example is Montevideo, which rose from the worst in EHI-A (average PM10 concentration) to the best in EHI-E (children exposed to unhealthy PM10 levels). Although this city had the highest particulate levels, resulting in the poor ranking for EHI-A, the population was one of the lowest, resulting in better ranks for indicators considering the population exposed. For all indicators reflecting some combination of population and PM10, Montevideo received an intermediate rank. In contrast, Sa~ o Paulo, Brazil, was positively ranked in EHI-A as the third best city, but was among the worst four for the remaining indicators, being the second largest city in Latin America and having a higher number of people exposed. Despite changes in some cities’ ranks, general trends can be observed (Fig. 2). For example, Buenos Aires is in the worst three cities and Lima in the lowest four cities under all indicators. The cities of Curitiba and Managua consistently perform well, achieving one of the best two ranks in most indicators.

4.3. Implications The different rankings based on the indicators we created using a single dataset in relatively simple combinations demonstrate the complexity and flexibility of EHIs. Because EHIs are inherently based on assumptions and simplifications of data, they can be sensitive to misinterpretation. Our example necessitated several decisions that affected rankings and introduced limitations. The relative priorities of pollution levels and population exposed are reflected among the design of the EHIs, yet there are also assumptions and limitations that apply to all. Here, we explicitly describe some of these issues, which are often a part of the indicator development process, but not always highlighted. Our indicators compared Latin American cities, with population data compiled by the United Nations, and air pollution data collected by governmental agencies within each country. The quality and nature of data may differ by country, such as the frequency of data collection. The method of defining urban boundaries (e.g., city versus metropolitan area) can affect which data are included. Not all cities had data for all years of the case study. The number of children exposed was estimated based on country-specific data for the percentage of the total population o15 years; however, within-country variation for this percentage is likely and estimates are especially limited for countries with highly variable demographic profiles. While such assumptions may be valid for our case study, which is meant to serve as an exploration of EHIs, they may be inappropriate for other purposes, and data availability on sensitive subpopulations (e.g., children, elderly, those with pre-existing medical conditions) are limited for many regions. Only 16 cities were included in this analysis, based on availability of air pollution data. However, in 2003, 43 Latin American cities had populations of at least one million (United Nations Statistics Division, 2008). PM10 for some cities was based on a standard PM10/TSP ratio, although the actual ratio varies.

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The health impacts of particles are likely to differ by their chemical composition as individual components or sources have been linked to various health outcomes (Lippmann et al., 2006; Bell et al., 2007; Hopke, 2008). The public health burden also can differ by population characteristics, such as potential effect modification by socioeconomic status (O’Neill et al., 2003, 2008; Bell et al., 2005b; Bell and Dominici, 2008). Indicators based on environmental conditions do not account for how those conditions correspond to exposures and how exposures relate to doses. For example, activity patterns, such as movement from work to home and between indoor and outdoor settings, can affect how ambient levels correspond to personal exposure. The assumptions necessary for this comparatively simple case study highlight the complex assumptions in EHI development.

5. Discussion EHIs, when used appropriately, can be useful to raise awareness of environmental health, stimulate and inform interventions, or evaluate policies’ progress and effectiveness. However, the potential to misuse and misunderstand environmental indicators is considerable, as any indicator incorporates inherent limitations. Indicators, by design, do not fully reflect the state of the environmental health system. Composite indicators obscure details and information, whereas surrogate indicators are limited as they are not measuring the true factor of interest. In our case study, no indicators had identical rankings although some general trends were observed. Some rankings were quite different with a city ranked best with one indicator and worst with another. These results indicate the importance of fully understanding the indicator’s design, assumptions, and goals. No single EHI represents an absolute truth, but rather one of many ways of looking at the health consequences of environmental conditions. EHI development and implementation involves several choices including the level of aggregation (temporal, spatial, health outcomes, pollutants); practicality (availability of data, cost of assimilating and processing data, ease of use), and how closely the indicator reflects true values and issues of concern. In addition to deciding what information to include, the method to amalgamate various indices is far from straightforward as numerous methods are available and can produce different rankings (Hobbs, 1985). Other criteria that merit consideration in EHI’s development and implementation include scientific credibility, sensitivity, omission of important features, and potential bias. Some unique indicators have explicitly incorporated perceptions and values, such as public perceptions of environmental quality (Burger et al., 2003). A study of environmental data from 45 Italian municipalities included stakeholders’ priorities in a sustainability index (Clerici et al., 2004). The adequacy of EHIs might vary by local context (Pastides, ¨ 1995; Kahlmeier and Braun-Fahrlander, 2004; Calijuri et al., 2009), such as from differences in pollution or population characteristics. Some indicators have been developed specifically for a given region (Pong et al., 2002; Yang et al., 2003). A review of air quality indices found differences by country regarding approach, underlying data, and emphasis on health (van den Elshout et al., 2008). For example, the authors note that a given NO2 level would be considered very poor under a French index and moderate in a United Kingdom index. EHIs may be unsuitable to evaluate a particular program if they were not designed in accordance with that program’s goals ¨ (Kahlmeier and Braun-Fahrlander, 2004). Some potentially useful EHIs suffer from inconsistent protocols, such as chemical contaminants in breast milk for which data are affected by methods of sample selection, pooling, reporting, and analysis (Solomon and Weiss, 2002). The development of indicators should consider

whether they represent a causal relationship between the exposure and the health response and whether they validly approximate the causal relationship, such as through surrogate measures of the true variables of interest (Pastides, 1995). In general, the policy process involves dissimilar organizations and individuals with diverse perspectives who respond according to their own motivations, resources, and knowledge base. For instance, some policy-makers may be heavily guided by economic considerations, in which case indicators omitting economic outcomes have limited usefulness. If an indicator is intended to inform the public, community participation in the indicator design is valuable. Consideration should be given to whether an indicator directly addresses the concerns of and can be communicated to target users (Cole et al., 1999). Social and political context can be important for all stages of an indicator, from development to interpretation (Longhurst, 2005). Indicators that reflect actual health outcomes, or even exposure patterns and population density, are more likely to be of interest to the public and decision-makers than indicators further removed from valued endpoints. We therefore expect that indicators such as emissions levels would have smaller impact on perceptions and policy than, for example, the number of premature deaths. However, in many cases information on actual health consequences are unavailable, thus indirect measures are substituted. Further, the full nature of health impacts of a particular pollutant may not be fully understood, and stages earlier in the environmental system (e.g., emissions) may be easier for decision-makers to address. A study of over 200 projects from 1970 to 1990 found that only 1% of indicators were directly related to exposure (Wills and Briggs, 1995). In spite of limitations, EHIs will and should be used, because decision-makers and the public seldom have the time, expertise, or resources to consider the full array of environmental health consequences. However, the development and interpretation of indicators should involve considerable thought regarding the indicator’s purpose, position in the environmental system (e.g., source to pollutant to exposure to health), and ability to communicate risks. Results from the case study highlight the need to evaluate and clearly communicate indicators’ underlying assumptions and goals, as well as how changes in assumptions may affect relative indicator rankings.

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