Tracking health and the environment: a pilot test of environmental public health indicators.
Effective environmental health tracking requires a coordinated approach that identifies hazards, evaluates exposures, and tracks population health (Litt, Tran, Malecki, Neff, Resnick, & Burke, 2000). According to the Environmental Health Tracking Project Team of the Pew Environmental Health Commission, "'Tracking' is synonymous with CDC's [the Centers for Disease Control and Prevention's] concept of public health surveillance, which is defined as "the ongoing, systematic collection, analysis and interpretation of health data essential to the planning, implementation and evaluation of public health practice ... (Thacker et al.)" (Environmental Health Tracking Project Team, 2000, page 14; Thacker & Berkelman, 1988).
Summary measures, or indicators, of environmental conditions and public health outcomes are the foundation of environmental health tracking. To advance indicator development and use, the Johns Hopkins Center for Excellence in Environmental Public Health Tracking (JHU Tracking Center) evaluated three pilot indicator pairs: 1) air toxics and leukemia in New Jersey, 2) mercury emissions and fish advisories in the United States, and 3) urban sprawl and obesity in New Jersey. These pilots illustrate the feasibility of creating environmental hazard and health outcome indicators, examining temporal and geographic trends in the indicators, and identifying temporal and geographic relationships. The results highlight how existing environmental health data can be used to create meaningful indicator measures and facilitate hypothesis generation. Visualizing indicators spatially, temporally, and in relation to one another can provide critical assistance to state and local public health agencies trying to create and prioritize interventions, and to researchers seeking to better understand environment-related diseases.
1. Air Toxics and Leukemia in New Jersey
Air toxics and leukemia indicators have been developed at the county level for the purpose of tracking leukemia incidence rates, emissions of three air toxics associated with leukemia--benzene; 1,3 butadiene; and ethylene oxide--and the relationships between them (Hughes, Meek, Walker & Beauchamp, 2003; Kirman et al., 2004; Snyder, 2000).
The leukemia indicator was incidence, with elevation defined as incidence greater than the national average (12.3 per 100,000) (National Cancer Institute [NCI], 2004). County leukemia incidence rates for 1986-1996 were obtained from the New Jersey State Cancer Registry, and the national average leukemia incidence for 1997-2001 came from NCI (NCI, 2004; New Jersey Department of Health and Senior Services [NJDHSS], 1998).
The air toxics indicator was risk ratios (relative risks) summed across the three chemicals; levels >1 were defined as high. Emissions data for benzene; 1,3 butadiene; and ethylene oxide were obtained from the 1990 National Air Toxics Assessment (NATA). We used data from 1990 to allow for the approximately 10-year latency period for leukemia associated with chemical exposure (Kirman et al., 2004). Air toxics risk ratios were calculated according to the Assessment System for Population Exposure Nationwide (ASPEN) dispersion model (U.S. EPA, 1990).
Descriptive analyses were carried out to determine the average and range of leukemia incidence rates and air toxic risk ratios in New Jersey counties.
Trend and Linkage: Temporal Trend analysis depicted leukemia incidence rates statewide in white males and females from 1986 to 1996.
Trend and Linkage: Geographic
Geographic trend analysis using ArcGIS 8.0 identified counties with both high leukemia incidence and high emissions.
The average leukemia incidence rate across New Jersey counties between 1997 and 2001 was 12.3 cases per 100,000, compared with the national average of 11.2 per 100,000 (NCI, 2004; NJDHSS, 1998). Average risk ratios across New Jersey counties for benzene; 1,3 butadiene; and ethylene oxide were 25.1, 43.7, and 1, respectively. Risk ratios for benzene and 1,3 butadiene suggested that the magnitude of the risk of developing cancer or noncancer health outcomes was much higher for individuals living in New Jersey than for individuals not living in New Jersey (25.1 times and 43.7 times respectively).
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Figure 1 shows the leukemia time trend analysis for white males and females between 1986 and 1996. Rates decreased gradually for both genders, a result consistent with the national trend (NCI, 2004). Maps did not reveal geographic trends in air toxics or leukemia.
Bar charts depicting relationships between high air toxic emissions and high leukemia incidence rates were created. Individual charts for each of the three air toxics did not show apparent associations with leukemia risk. A combined chart is presented in Figure 2. Counties are arranged in ascending order along the x-axis according to cumulative emissions of benzene; 1,3 butadiene; and ethylene oxide. The y-axis displays leukemia incidence rates per 100,000 for each county. No apparent relationship was observed.
The average leukemia incidence rate across New Jersey counties was 12.3 per 100,000, compared with the national rate of 11.2 per 100,000, suggesting that people in New Jersey are at a greater risk of leukemia than elsewhere in the nation. Furthermore, all counties except Atlantic, Cumberland, Union, and Hudson had rates above the national average. Many New Jersey counties had air toxics risk ratios >1 for both benzene and 1,3 butadiene, but not for ethylene oxide. Average risk ratios for benzene and 1,3 butadiene were 25.1 and 43.7, respectively, suggesting the potential for adverse health effects. Counties with high air toxic risk ratios did not, however, appear to have higher leukemia incidence rates.
There are some important limitations to take into account with respect to the analyses. Incomplete reporting of leukemia incidence in certain counties may have prevented a relationship between air toxics and leukemia from being observed. In addition, the use of the 1990 NATA data, which provided only modeled estimates of exposure at the county level as opposed to real-time data at a smaller geographic scale, may have had an impact on the findings of our study. The use of a smaller geographic scale and real-time air quality monitoring would allow for greater sensitivity in future studies to detect a relationship between leukemia incidence and air toxics exposure. The analysis also did not take into account potentially important variables, including time of diagnosis and exposure to leukemia-causing agents such as tobacco smoke. The use of statistical methods to control for confounders, especially tobacco use, would strengthen future research.
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The indicators in this study provided important geographic information about the distribution of leukemia and air toxics. On the basis of such information, practitioners can develop hypotheses about risk factors and can target intervention investments such as screening, awareness building, and communication with regulators about pollution sources. From a research perspective, looking geographically at the two indicators provides the opportunity to hypothesize, for example, about the different leukemia-protective factors operating in rural and urban counties.
2. Mercury Emissions and Fish Advisories in the United States
Mercury air emissions and fish advisory indicators were developed to examine the geographic and temporal distribution, trends, and relationships in U.S. states with high mercury emissions and those with high fish advisory levels. Adverse health effects from mercury exposure include neurotoxic effects in the developing fetus and cardiovascular effects in men (U.S. EPA, 1997; Guallar et al., 2002). Linking mercury emission sources with deposition (measured by fish advisories) improves understanding of the link between mercury air emissions and health outcomes.
The mercury indicator was state air emissions in pounds. States were considered to have elevated mercury emissions if the measure exceeded the national average (796 lbs in 2002, the selected year of interest). Mercury emissions in pounds for air, surface water, land, and underground releases were obtained from U.S. EPA's Toxics Release Inventory (TRI) (U.S. EPA, 2004).
The fish advisory indicator was the percentage of lake acres and river miles under advisory for each state. Levels were considered elevated if the range exceeded national averages (30.5 percent of lake acres and 18.0 percent of river miles in 2002). Fish advisory data for mercury were obtained for all 50 states, for the years 1993 to 2002, from U.S. EPA's NLFWA database (U.S. EPA, 2004).
Bar charts examined total air emissions, surface water discharges, land releases, and on-site disposal for all 50 states. The percentage of lake acres and river miles under mercury fish advisory also was examined for all 50 states in 2002.
Trends and Linkage: Temporal
Line graphs analyzed trends in national mercury air emissions from 1988 to 2002 and percentage of lake acres and river miles under mercury fish advisory from 1993 to 2002.
Trends and Linkage: Geographic
The geographic relationship between distribution of mercury air emissions and fish advisory locations by mapping, using ArcGIS 8.0.
In 2002, the average state mercury emission was 796 lbs. The highest-emitting states were Ohio, Alabama, and Utah. In 2002,30.5 percent of lake acres and 18.0 percent of river miles nationwide were under mercury fish advisory.
In 1997 and in 1999, U.S. EPA changed the TRI reporting requirements, causing jumps in the data between 1997-1998 and 1999-2000. As shown in Figure 3, however, mercury air emissions decreased over each time period for which reporting requirements were constant. It is surmised that overall, mercury air emissions have decreased from 1988 to 2002. By contrast, the percentage of lake acres and river miles covered by fish advisories has increased over time from 1993 to 2002.
Mercury air emissions tend to be released from states in the South to the Mid-Atlantic, particularly Texas and Ohio. As U.S. EPA models of mercury transport and deposition predicted, mercury fish advisories were concentrated in northern states (Great Lake states and Ohio River valley), northeastern states, and Florida (Figure 4). Ohio and North Carolina had both high mercury emissions and high fish advisories.
The decline in mercury air emissions may be explained by factors including federal bans on mercury additives in paints and pesticides, mercury reduction in batteries, decreased coal use, and state regulations (U.S. EPA, 1997). Even though air emissions are declining, water deposition may still occur at high rates because past mercury emissions continue to be cycled between air, land, and water. Emissions from other countries may also be transported globally and deposited in U.S. lakes and rivers. In addition, increased water testing and rising awareness of mercury health effects may have resulted in an increase in state-issued fish advisories (U.S. EPA, 1997).
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A number of limitations must be taken into account in the analyses. Lake and river advisories are issued in states predicted by U.S. EPA to have high mercury deposition rates; however, in the absence of modeled data, it is not known how the geographic locations of fish advisories are related to the geographic distribution of mercury deposition. The relationship may be confounded by global mercury transport patterns and natural emissions sources. In addition, states are responsible for developing their own advisory programs and determining when to issue advisories. There is thus considerable variability between states in the extent of monitoring, sampling frequency, and mercury threshold for issuing advisories, making it difficult to draw conclusions about national trends (U.S. EPA, 2004).
The databases were also limited. TRI reports did not comprehensively describe mercury emissions, and data breaks hampered trend assessment. The data were also not as complete in earlier years. U.S. EPA's National Listing of Fish and Wildlife Advisories (NLFWA) database relies on state testing and reporting; however, measurement variability between states hinders comparisons. Further research with more complete data is needed for a better understanding of the mercury cycle, deposition patterns, and global transport.
Despite these limitations, the indicators developed in the project described here help with geographic and temporal tracking of air emissions and fish advisories. The information is useful for understanding and evaluating the impact of regulations and other factors on emissions generation and for evaluating the varying policy responses to emissions. It can help states with the development of interventions beyond fishing advisories. The fact that the geographic distributions of mercury emissions and fish advisories in this analysis follow U.S. EPA model predictions strengthens the joint use of these indicators. Their combination provides one way of illustrating how mercury emissions in one area affect health and lifestyle elsewhere.
3. Urban Sprawl and Obesity in New Jersey
Indicators of obesity and urban sprawl were created because obesity rates have reached epidemic proportions throughout the United States and are related to a lack of physical activity in the population (U.S. Department of Health and Human Services [U.S. DHHS], 2001. Studies have found a relationship between urban sprawl, obesity, and participation in physical activity (Ewing, Schmid, Killingsworth, Zlot, & Raudenbush, 2003).
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Because one indication of urban sprawl is increased land development in low-density areas, urban sprawl was defined as the percentage of housing unit development in a county between 1990 and 2000 that occurred in low-density municipalities. Low-density municipalities were defined as being below the state median housing unit density. County data on housing unit density for the state of New Jersey were obtained from the 1990 and 2000 U.S. censuses (U.S. Census Bureau, 2002; United States Census Bureau, 2002a, 2000b).
As an obesity indicator, the proportion of the population expected to be overweight or obese was estimated by county on the basis of the Behavioral Risk Factor Surveillance System (BRFSS) sample after the age, race, gender, and income of participants in the BRFSS were controlled for by logistic regression. No more refined sources for population level estimates of obesity exist. Data on body mass index (BMI) were obtained from the 1996-2000 BRFSS (CDC, 1996-2000). These years were used because for a number of counties in New Jersey, data on BMI were not available for earlier time periods.
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The range of urban sprawl and the increase in obesity in New Jersey counties was analyzed.
Trend and Linkage: Temporal
To perform temporal analysis of the obesity measure, we plotted county values in relation to their year. The urban sprawl indicator did not allow for temporal trend analysis, because although 10 years were covered, the indicator was a single value representing the change in housing unit density over those years.
Trend and Linkage: Geographic
Urban sprawl and obesity indicators were mapped by county with ArcGIS 8.0. To examine the relationship between urban sprawl and change in the obesity indicator, we compared the change in the proportion of overweight and obese county residents between 1996 and 2000 and the amount of urban sprawl in each county between 1990 and 2000.
Counties in New Jersey had levels of urban sprawl ranging from 0 percent to 100 percent between 1990 and 2000. In 2000, the proportion of residents expected to be overweight or obese in New Jersey counties ranged from 53 percent to 76 percent.
The geographic analysis of the urban sprawl indicator, shown in Figure 5, revealed that Morris and Cumberland counties experienced the greatest amount of urban sprawl between 1990 and 2000, while Hudson and Union had the least amount. Counties in northeastern New Jersey and along the coast had less sprawl overall between 1990 and 2000 than other New Jersey counties.
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Geographic analysis of the obesity indicator found that the New York and Philadelphia metro areas had slightly higher proportions of overweight or obese residents than other parts of New Jersey. The change in the proportion of overweight and obese residents in New Jersey counties between 1996 and 2000 was not found to exhibit a spatial pattern. Temporal analysis revealed that all counties for which data were available, except Mercer County, had increased obesity and overweight between 1996 and 2000.
Figure 6 shows a bar graph depicting the level of sprawl calculated for each county from 1990 to 2000 and the change in the proportion of the population expected to be overweight or obese between 1996 and 2000. There was not a consistent association between these indicators. A geographic analysis of this association was also carried out, and a consistent pattern was not observed.
The urban sprawl indicator identified counties with rising consumption of undeveloped land. Such information can be used to target areas for land preservation efforts. The overweight-and-obesity indicator identified the counties with the greatest increases in overweight and obese residents between 1996 and 2000--knowledge that can be used to target interventions to curtail overweight and obesity.
Further analyses using the urban-sprawl indicator developed in this paper should be carried out in other states to see if any connections with overweight and obesity can be established. In addition, the association of this indicator with physical activity and automobile use should be examined. Such information would help in designing and tracking intervention programs and policies to support the design of communities that foster physical activity participation amongst residents.
As with the other two pilot indicator projects, the conclusions drawn from this analysis were limited by a number of factors. By 1990, many counties in New Jersey had already experienced a great deal of urban sprawl, making exposure to sprawled development patterns high throughout the state. In addition, it is likely that a lag period between sprawled development and effects on the population's health exists. Thus, in order to see the effects of urban sprawl between 1990 and 2000 on the population's health, it may be necessary to examine obesity and overweight changes over the decade after sprawl occurred. Further research should be carried out using statistical analyses that make it possible to account for a lag between urban sprawl and obesity. In addition, examination of the relationship between urban sprawl and obesity on a smaller geographic scale may allow for a relationship to be observed.
Together, the pilot projects described in the paper demonstrate the utility and feasibility of using indicators for environmental public health tracking efforts. They show how indicators combining environmental monitoring and public health tracking data can be valuable tools for examining temporal and geographic trends that describe potential environmental relationships of concern. Such information is essential for quantification of environmental hazards, exposures, and health outcomes and identifying priority areas for intervention. These projects demonstrate that indicator linkage projects can successfully be carried out with publicly available data sources and can be used to establish trends, generate hypotheses, and identify future research needs with respect to environmental exposures and public health outcomes. That information can then be used by practitioners for the development of interventions and research programs.
Several lessons can be drawn from the development of the indicators presented in this paper. First, indicator development is restricted by the availability, reliability, and consistency of data. Second, multidisciplinary expertise and collaboration are needed to design and track indicators that will be useful for policy. Finally, because indicator projects may not be controlled studies, their results are often difficult to interpret. Great care must be taken in the communication of findings about environmental exposure and disease relationships to the public. Words should be carefully chosen, caveats should be highlighted and repeated, and clear legends should be placed on every graphic. For linkage indicators examining both hazard/exposure and outcomes, it must be emphasized that conjunction or lack thereof provides only exploratory and potentially suggestive data about distributions and trends. Controlled analyses are generally needed to draw firmer conclusions.
Indicators are the foundation of environmental public health tracking. Increased use and development of indicators is necessary for establishment of a nationwide environmental public health tracking network capable of tracking and linking environmental exposures and health outcomes. As the nationwide environmental public health tracking network develops, researchers must ensure that data needed for the development of relevant environmental and health indicators are collected in a usable format in the smallest geographic aggregations feasible. Without such data, indicators cannot be developed for the study of connections between health and the environment. The lessons learned from the indicator projects described in this paper demonstrate the feasibility and utility of using national and county level indicators to study relationships between environmental hazards, exposures, and health outcomes.
Acknowledgement: Our research was supported by the Johns Hopkins Center for Excellence in Environmental Public Health Tracking through a grant from the Centers for Disease Control and Prevention.
Corresponding author: Erin Dreyling, Johns Hopkins Bloomberg School of Public Health, Department of Health Policy and Management, c/o Thomas Burke, 624 N. Broadway, Rm. 492, Baltimore, MD 21205. E-mail: email@example.com.
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Erin Dreyling, Ph.D.
Elizabeth J. Dederick, M.A., M.H.S.
Ramya Chari, M.P.H.
Beth Resnick, M.P.H.
Kristen Chossek Malecki, M.P.H., Ph.D.
Thomas Burke, M.P.H., Ph.D.
Roni Neff, Sc.M., Ph.D.
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|Author:||Dreyling, Erin; Dederick, Elizabeth J.; Chari, Ramya; Resnick, Beth; Malecki, Kristen Chossek; Burke|
|Publication:||Journal of Environmental Health|
|Article Type:||Author abstract|
|Date:||Dec 1, 2007|
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