Identification and Characterization of Populations Living Near High-Voltage Transmission Lines: A Pilot Study Daniel Wartenberg, 1,2 Michael Grenberg, 1'3 and Richard Lathrop4 1Environmental and Occupational Health Sciences Institute, Piscataway, NJ 08854 USA; 2Department of Environmental and Community Medicine, UMDNJ-Robert Wood Johnson Medical School, Piscataway, NJ 08854 USA; 3Department of Urban Studies and Community Health and 4Department of Natural Resources, Rutgers University, New Brunswick, NJ 08903 USA Exposure to electric and magnetic fields has become a major area of research and con- cern over the past 10-15 years. With the publication by Wertheimer and Leeper of the seminal paper investigating the associa- tion between residential exposure to electric and magnetic fields and the incidence of childhood cancers in Denver, Colorado (1), there has been an explosion of concern by the press and the public and expanding study by the scientific community (2). Studies designed to replicate this seminal study in Denver and elsewhere (3-10) have had mixed results, generally finding associa- tions between indicators of magnetic field exposures and cancer more often than not. Overall, in residential studies, elevated mag- netic fields but not electric fields have been associated with excess cancer. A complementary approach used to investigate the association between electric and magnetic fields and cancer has been the study of mortality patterns of workers with high occupational exposure to electric and magnetic fields. The excess rates of leukemia and brain cancer deaths observed in these studies [e.g., Theriault (11)] have substanti- ated concern about exposure to electric and magnetic fields. But because the actual mag- nitude of electric and magnetic field expo- sures has not been documented in these occupational studies (exposure estimates for most such studies are based on job titles) and because these workers may be exposed to other hazardous substances while on the job (leading to confounding), these studies are not considered conclusive. One important observation about the residential studies is that, due to epidemio- logic design characteristics, most exposures cited have been fairly low, nearly all below 1 pT (= 10 mG) and most below 0.5 pT (= 5 mG). However, numerous homes have exposures as high as 2 pT or more, and the principal source of this exposure is most often ascribed to proximity to high-voltage transmission lines. Further, prediction of the magnetic fields attributable to proximity to high-voltage transmission lines is far easi- er and more accurate than prediction of fields generated from other sources. The goal of this study was to develop methodology to identify populations that are exposed to electric and magnetic fields from overhead high-voltage transmission lines. This will enable us to determine the number of exposed people and characterize their demographic attributes for risk assess- ment and public policy considerations. In addition, if sufficiently accurate, this method could be used to determine the incremental exposure to electric and mag- netic fields that populations incur from high-voltage transmission lines, a possible exposure metric for use in epidemiologic investigations of excess cancer. By focusing on these highly exposed populations, we believe we would increase both the statisti- cal power and precision of epidemiologic investigations. We know of only two studies that previ- ously attempted to identify and/or charac- terize populations residing near high-volt- age transmission lines. Florig and Morgan (14 assessed the density of housing along transmission lines by reviewing aerial pho- tographs. They found that the population density close to the lines was lower than elsewhere in the region and that the differ- ence in density decreased as the distance of the residences from the line increased up to 200 m, the maximum distance they report. Salzberg et al. (13), in Melbourne, Aus- tralia, investigated the association of ambi- ent magnetic fields with various indices of socioeconomic status. Using an arbitrary sampling grid in which 77% of the sam- pling locations were under overhead trans- mission or distribution lines, they found only weak associations between the strength of the magnetic field and specific aspects of socioeconomic status, and none with com- bined indices of socioeconomic status vari- ables. They concluded that there was no overall association (13). While other epi- demiologic investigators have considered transmission lines as confounders (i.e., fac- tors associated with both exposure and dis- ease, although not of primary interest), they generally have not analyzed demographic data with respect to these lines (1,3-J1). Address correspondence to D. Wartenberg, Department of Environmental and Community Medicine, UMDNJ-Robert Wood Johnson Med- ical School, 675 Hoes Lane, Piscataway, NJ 08854 USA. We gratefully acknowledge the assistance of J. E. Flynn, manager of Overhead Transmission Engin- eering, and Frank Blahuta, both of Public Service Electric and Gas, for providing maps and informa- tion regarding the S-2219 Sewaren-Brunswick 230 kV transmission line. George Rhoads provided useful discussions and advice on epidemiologic concerns regarding the exposure characterization. John Bukowski provided useful discussions on epi- demiologic considerations and assisted in the preparation of the maps for data input. John Bognar, Richard Oster, and Richard Bochkay pro- vided assistance in the GIS processing. This research was partially funded by a seed money grant from NIEHS Center grant no. 05022. Received 1 February 1993; accepted 7 September 1993. Environmental Health Perspectives 626 A - *- 3 f I 3 9. * M I In Scandinavia, it is possible to identify populations living near high-voltage trans- mission lines using public records. These records, which are far more detailed and/or accessible than those available in the United States, include cancer registries, population registries (which contain com- plete residence histories), and utility trans- mission line databases. Using these data to derive separate exposure estimates based on distance from the transmission line, cur- rent magnetic field strength, and recon- structed historical magnetic fields strengths (using historical annual average load data for each year of residence), epidemiologic studies have been conducted in Sweden (14), Denmark (15), and Finland (16). Although these studies evaluated the asso- ciation of estimated magnetic field expo- sure with disease incidence, they did not look at indicators of socioeconomic status. To demonstrate the feasibility of con- ducting an epidemiologic study in the United States using highly exposed popula- tions, we chose to identify populations liv- ing within a few hundred meters of high- voltage transmission lines. To get a suffi- cient number of such individuals, it may be necessary to include hundreds of miles of such lines. Although high exposure pop- ulations could be identified using aerial photographs, it would be extremely time consuming and labor intensive. Our ap- proach is to use a computer-based geo- graphic information system (GIS) to com- bine independently developed transmission line location data with the most recent (1990) U.S. Census data to identify and characterize such populations. In addition, address ranges can be extracted for record matching with disease registries and for contacting individuals. To demonstrate our approach, we have undertaken the pilot study reported here. To demonstrate that this approach will generate sufficient number of subjects at the appropriate scale of geographic resolu- tion, a larger study would be needed. We plan to begin such a study to identify all populations living near 230-kV and higher voltage lines in New York State in fall 1993 in cooperation the Empire State Electric Energy Corporation. Background: Electric and Magnetic Fields and Cancer In general, measuring and assessing expo- sure to electric and magnetic fields have been problematic in epidemiologic studies. Growing concern about a relationship between electric and magnetic fields and cancer is driving research to improve expo- sure assessment. Investigators have estimat- ed residential exposure in a variety of ways, basing exposure on proximity to high-volt- age transmission lines, the configuration of electrical wiring outside each residence (i.e., the so-called wire codes), spot mea- surements of magnetic fields, 24-hr mea- surements of magnetic fields, and historical reconstruction of cumulative magnetic fields based on line load data. However, the consistency among these measures has been less than desired for epidemiologic studies. The goal of most exposure studies has been to capture relevant aspects of the hourly, daily, seasonal, and secular patterns of variation while accommodating histori- cal changes in electric power delivery. However, because there is no known mechanism of disease causation from expo- sure to nonionizing radiation, it is not clear what aspects of exposure are biologi- cally relevant. In occupational studies, job titles have been used to classify exposures, which likely results in much imprecision and substantial confounding. Kaune (17), in a recent review, notes that there are also many limitations to the spot measurement technique used in some residential studies, including short-term variability, spatial variability, and selection of a metric for time averaging. One noteworthy observation is that homes near high-voltage transmission lines often receive a substantial but variable por- tion of their magnetic field exposure from those lines (Table 1). For example, Caola et al. (18) measured electric and magnetic fields in three New Jersey homes and found that electric fields produced by the house wiring were similar to those pro- duced by the transmission lines, with shielding of external fields provided by walls without windows, while magnetic fields inside the houses were not affected by the walls (i.e., there was little shielding) and were about 0.25 pT. Maddock et al. (19) discuss the magnitude of electric and magnetic fields under high-voltage trans- mission lines in the United Kingdom and state that for a 400 kV line, electric fields at 25 m from the center line are less than 1 kV/m, whereas magnetic fields at 25 m rarely exceed 10 pT. Stuchly (20) reports calculated maximum magnetic fields of 13 pT at the center line for a 230-kV line, 33 PT for a 500-kV line, and 29 PT for a 765-kV line. Residential measurements, she reports, range from typical back- grounds of less than 0.1 pT to levels over 0.5 pT for houses with electric heaters. Levels for homes in Germany were sub- stantially higher (20). Heroux (21) investigated ambient, urban electric, and magnetic fields result- ing from electric distribution lines between 49 kV and 735 kV and found magnetic fields generally below 1 pT and electric fields generally below 0.3 kV/m. Dlugosz et al. (22), reporting magnetic field mea- surements made at 33 street corners in Buffalo, New York, found flux densities as high as 1.6 pT. Three street corners had transmission lines within 46 m, and these were the three highest mean flux densities (1.08-1.44 pT). Bracken (23) summarized exposures in public access areas by noting that both electric and magnetic field exposures are related to the proximity of transmission and distribution systems and, while gener- ally similar to residential exposures, can be as high as 180 V/m and 10 pT. Com- mercial buildings, however, likely have dif- ferent electric shielding properties. Kavet et al. (24) studied 45 adult residents in Maine and found that, for the 30 who lived near high-voltage transmission lines, the trans- mission lines were a significant source of exposure (more than 50% of the total exposure) and that in-home measurements were a reliable index of total exposure ranging from about 0.5 pT to 6 pT. Proximity to Transmission Lines as an Exposure Metric As homes with unusually high magnetic fields (e.g., greater than 1 pT) generally are close to high-voltage transmission lines, if there is an association between high mag- netic fields and cancer incidence, residents of these homes should be at greatest risk. Yet, only a few epidemiological studies have emphasized the role of transmission lines in elevating exposures. Table 1. Exposures near high-voltage transmission lines Source of exposure Magnetic field (pT) Electric field (kV/m) Reference Homes near transmission lines 0.25 (18) 400-kV line (19) Center line <40 <5 25 m <10 <1 Center line (20) 230-kV line 13 500-kV line 33 765-kV line 29 Residence 0.1-0.5 49-kV-735-kV urban line <1 <0.3 (21) 33 Buffalo, NY street corners <1.6 (22) Residences <10 <0.18 (23) Homes 79 m-465 m from 345-kV line 0.08-0.58 (24) Volume 101, Number 7, December 1993 627 Myers et al. (6,7) studied children liv- ing near overhead electric lines in York- shire, England, and did not find a signifi- cant association between distance of resi- dence from overhead line or calculated magnetic field (based on maximum load during year of birth) and the incidence of childhood cancer. However, critics have pointed out that only 5 out of 962 subjects had exposures above 0.1 pT, suggesting unusually low exposures overall. McDowall (25) investigated the mortality experienced by persons living near electric transmission facilities in East Anglia, England, and found lower than expected mortality in the study population, with only female lung cancer being statistically significantly ele- vated. (The lung cancer observation was hard to interpret because the investigators did not have data on smoking habits.) Tomenius (8), in a case-control study in Sweden, found that those living in prox- imity (within 150 m) to a 200-kV electric transmission line were at excess risk of can- cer [relative risk (RR) of 2.1]. Coleman et al. (26) investigated the association be- tween leukemia incidence and proximity to electricity transmission equipment in Southeast England and found elevated but not statistically significant effects (RR of 1.5 for residence within 100 m of an over- head transmission line, RR of 2.0 within 50 m). Johnson et al. (27) conducted a spatial analysis of leukemia and brain can- cer incidences and transmission line loca- tion. Although the disease incidence pat- terns exhibited a nonrandom pattern, they found no association with transmission line location. Schreiber et al. (28) studied the mortality experience of the population living near two 150-kV lines and one transformer substation. They also did not find significant elevations of cancer mortal- ity rates. Most recently, a series of nested case- control studies has been conducted in Scandinavia. These studies used a variety of exposure metrics including measured distance to transmission lines, measured magnetic fields, and computed exposures attributable to electrical transmission con- nections and substations based on histori- cal line loads and tower configurations. Feychting and Ahlbom (14) conducted a residential study among people living near high-voltage transmission lines. Elevated cancer rates were observed within 300 m of these lines [odds ratios (ORs) generally from 2 to 5 for leukemia for calculated magnetic fields]. When analyzed similarly, these data gave relative risks comparable to those of Savitz et al. (9) as reported by Wartenberg and Savitz (29). Olsen et al. (15) compared exposures of all children diagnosed with leukemia, tumors of the central nervous system, or malignant lym- phoma from 1968 to 1986. They found a nonsignificant, elevated relative risk for all Figure 1. A route map of a 230-kV electric transmission line from Woodbridge, New Jersey to South Brunswick, New Jersey. Note that a USGS quadrangle is used as a base map. cancers studied using an a priori cutpoint (OR = 1.5 for cutpoint of 0.25 pT) and a statistically significant excess for a higher, a posteriori cutpoint (OR = 5.6 for cutpoint of 0.4 pT). Verkasalo et al. (16), studying children living within 500 m of a high- voltage transmission line in Finland, found elevated leukemia, nervous system cancers, and overall cancers, with the rate of ner- vous system cancers being statistically sig- nificantly elevated [standardized incidence ratios = 2.3), especially for gliomas (SIR) = 6.5. Methods The basic methodology we used was: 1) select a transmission line, 2) digitize it, 3) superimpose it on the U.S. Census TIGER files, 4) construct a buffer around the line, 5) identify all census blocks contained within or intersecting the buffer, and 6) extract the relevant demographics for these census blocks from the U.S. Census demo- graphic files. We describe this process in more detail below. To begin our pilot study, we wanted to use a high-voltage transmission line in the vicinity convenient to our research team. After consultation with the local utility, Public Service Electric and Gas, we select- ed a 29-km segment of a 230-kV line that runs from the Sewaren Switching Station in Woodbridge, New Jersey, to the Deans Switching Station in South Brunswick, New Jersey, circuit S-2219. We note that while this line may not be typical of the United States, it is not atypical of lines in eastern New Jersey, a very densely populat- ed area. The first step in this procedure was specification of the exact location of each transmission tower. Using a series of maps developed by the utility company (Fig. 1), we digitized the geographic coordinates of each transmission tower in a Universal Transverse Mercator coordinate system and stored the resulting data in a vector digital line graph format. To locate the line and retrieve demo- graphic data for the populations living near the line, we used the 1990 U.S. Census data. The Census Bureau has released a computerized set of detailed geographic map files known as TIGER (topologically integrated geographic encoding and refer- encing) files. These files contain details on the physical features and census tract (and block) boundaries for every county in the United States. These data are relatively fine-scaled, particularly in more densely populated areas, and enable researchers to reference these geographic locations to cen- sus tract (and block)-level demographic data (30,31). We related the location of the trans- mission line to the U.S. Census data using Environmental Health Perspectives - ----- ---- . I - NEEM: IM-1,11,011-Oz -? Z I -- 1. I 628 M -1-~~~~~ e * -ee the Arc/Info GIS package (Figs. 2 and 3). Then, we specified an arbitrary 100-m buffer zone on either side of the transmis- sion line as the region of concern. The width of this buffer corresponds to a mag- netic field exposure of approximately 0.2 pT (See appendix). Some studies have con- sidered even wider buffers [e.g., 200 m (8), 300 m (14), 500 m (16)], but we believed a smaller buffer would provide a more rig- orous test of the methodology. We extracted the block numbers of all intersecting blocks, the total area contained within each census block, and the area of each census block contained within the buffer to enable us to calculate the percent- age of each intersecting census block inside the buffer. To obtain the demographic data for the identified census blocks, we matched the ID numbers for the blocks intersecting or contained within our buffer with the attribute data and extracted the relevant information. For comparison purposes, we also extracted summary data for each town (municipality) in our study. These data were further processed and summarized using our own software. Results Overall, we found 201 census blocks that intersected or were contained within a buffer of 100 m on either side of the center line, containing a population of 18,040 individuals and 7,154 housing units. Of these blocks, 21% (42 of 201) had no housing units and hence no population, as reported by the U.S. Census, and 30 blocks had no data reported because the population sizes within these individual blocks were so small that release of block data would have jeopardized individual confidentiality. These blocks represented a total of 2,865 (16%) individuals and 1,161 (16%) housing units. The remaining 129 blocks contain 15,175 individuals and 5,993 housing units. Two of the six towns along the path of the transmission line, New Brunswick and Milltown, did not have any blocks with enumerated popula- tions. They are shown on the figures but omitted from the tables. All further calcu- lations and tabulations in this paper are based on those blocks with fully enumerat- ed data only. The demographics in these blocks were characterized and are summa- rized in Tables 2 and 3. Table 2 categorizes the census block data by the proportion of the area of the block contained within the 100-m buffer we defined. The majority of the popula- tion identified lives in blocks in which only a small proportion of their area lies within the buffer. It is likely that most of the indi- viduals residing in these blocks live outside the buffer. However, some of the blocks Figure 2. A map of the electric transmission line shown in Figure 1 digitized and superimposed on town- ship boundaries generated using U.S. Census TIGER files. Figure 3. The map and line shown in Figure 1 digitized and superimposed on U.S. Census block bound- aries generated from the U.S. Census TIGER files, with a 100-m buffer on either side of the line. Table 2. Population and housing units with 100-m buffer % of area of census blocka 100 95 90 70 50 30 10 o b No. of census blocks included (%) 7 (5.4) 11 (8.5) 12 (9.3) 15 (11.6) 37 (28.7) 62(48.1) 91 (70.5) 129 (100.0) No. of people in census blocks included (%) 358 (2.4) 530 (3.5) 535 (3.5) 678 (4.5) 2,724 (18.0) 6,681 (44.0) 11,001 (72.5) 15,175 (100.0) No. of houses in census blocks included (%) 183(3.1) 255 (4.3) 257 (4.3) 311 (5.2) 1,128 (18.8) 2,977 (49.7) 4,447 (74.2) 5,993 (100.0) aWithin buffer required for inclusion. bAll census blocks intersection buffer included. Volume 101, Number 7, December 1993 629 are wholly contained, or mostly contained, within the buffer. Many of the individuals living in these blocks live within the buffer. Refinement of these data would require field evaluation or analysis of aerial pho- tography to locate individual housing units with respect to the buffer border. We did not undertake such analyses in this study. Table 3 compares some of the demo- graphic and perceived housing value char- acteristics of the populations living within the buffer, or near the buffer, with the sim- ilar characteristics of the town in which the buffer is contained. In general, demo- graphic values are similar among towns and inside and outside the buffer. For example, for the percentage of the popula- tion under age 18, the entire towns show between 19% and 26%, while blocks inter- secting or contained within the buffer show between 13% and 25%. Similarly, the percentage of,the population over 65 years of age is generally between 6% and 16%, the percent white is generally be- tween 79% and 95%, and the percent black is between 0% and 12%. For these four demographic variables, the average differences among the towns are similar to the average differences between each town and that part of the town contained within the buffer except perhaps for percent white, which shows slightly larger variation within than between towns. Interestingly, the blocks within the buffers tend to have fewer people under 18 years of age, more whites, and fewer blacks. Variables reflective of perceived hous- ing value, however, differ more greatly within towns than between, as shown by the differences at the bottom of Table 3. Percent owner-occupied varies between 61% and 82% for towns as a whole, while it varies between 60% and 98% for blocks within the buffer. Average housing price varies between $163,400 and $204,500 among towns, while it varies between $127,062 and $274,979 for blocks within the buffer. Average rent varies between $644 and $725 among towns and between $520 and $1175 for blocks within the buffer. One association between the vari- ables is noted: if the percent owner-occu- pied is greater for blocks within the buffer, so is the cost. In general, except for North Brunswick, rents tend to be lower for blocks inside the buffer. Only one town, Woodbridge, has a sufficient number of blocks nearly wholly contained within the buffer for evaluation (Table 4). They are listed as those blocks with 90% of their area contained within the buffer. The patterns for these blocks are similar to those described above, with more white and fewer black people, and the average rent being even lower than in all the blocks intersected by the buffer. Discussion Previous epidemiologic studies of the asso- ciation between exposure to magnetic fields and the incidence of cancer sought to quantify a relatively small risk for rare dis- eases. As such, epidemiologists used a case-control design to identify a popula- tion of individuals with the disease of con- cern and a control population and to char- acterize their exposures. Because the study subjects were selected on the basis of dis- ease status rather than exposure status, their exposures reflected the most common levels of exposure, mainly those below 0.5 pT. Generally, individual studies com- pared populations whose mean exposures differed by only a few tenths of a microtes- la. Taken as a whole, results of these stud- ies are uncertain, show numerous inconsis- tencies, and conclusions tend to be contro- versial. Given the widespread distribution of electrical distribution systems, there is a substantial number of people with expo- sures markedly higher than 0.5 pT. Al- though these individuals represent a small proportion of the entire U.S. population, we believe that they are common enough to represent a useful cohort for epidemio- logic study. If there is an association between residential exposure to magnetic fields and cancer, and if the dose-response relationship is monotonic, then studies comparing populations with mean expo- sures that differ by 1-3 pT should have substantially more statistical power and precision than those comparing popula- tions with mean exposures that differ by 0.1-0.5 pT. Toward this end, we developed a meth- od for identifying and characterizing these highly exposed individuals. We used a computerized procedure so that large regions can be assessed rapidly and easily and so that populations of sufficient size for epidemiologic study can be readily identified. In our pilot study in New Jersey, we examined the demographics of the popula- tions living near a single high-voltage transmission line in five towns and com- pared these data to comparable data for each town as a whole. We found that the Table 3. Population characteristics by town: overall and within 100-m buffer No. of Housing % under % over % Owner Mean Mean Township blocks Population units 18 65 % White % Black occupied cost ($) rent ($) East Brunswick - 43,548 15,395 24.0 8.7 88.1 2.2 81.7 203,700 725 Blocks intersecting buffer 10 731 248 24.6 10.7 94.1 0.4 90.3 274,979 535 Edison 88,680 32,832 21.7 10.7 79.5 5.6 64.7 204,500 659 Blocks intersecting buffer 50 6,436 2678 19.5 9.4 86.1 4.1 62.7 177,405 588 North Brunswick - 31,287 12,186 20.4 9.2 80.1 11.1 61.2 199,300 681 Blocks intersecting buffer 15 2,003 369 13.3 6.6 86.9 3.9 97.3 268,968 1175 South Brunswick 25,792 9,962 25.2 6.5 84.1 6.2 70.5 201,600 724 Blocks intersecting buffer 1 152 55 20.4 15.1 94.7 0.7 81.8 233,400 520 Woodbridge 93,086 34,498 19.3 13.0 86.6 6.5 70.7 163,400 644 Blocks intersecting buffer 53 5,853 2643 18.0 13.4 85.8 4.5 60.4 127,062 602 Mean difference 3.1 3.0 4.7 3.7 9.4 17,320 45 among towns Mean difference between 3.2 3.0 6.2 3.6 13.7 47,236 200 town and buffer Table 4. Population characteristics of Woodbridge: overall and within 100-m buffer No. of % under % over Housing % Owner % Renter Mean Mean blocks Population 18 65 %White % Black units occupied Occupied cost ($) rent ($) Town 93,086 19.3 13.0 86.6 6.5 34,498 70.7 26.3 163,400 644 Blocks intersecting buffer 53 5,853 18.0 13.4 85.8 4.5 2643 60.4 36.1 127,062 602 Blocks 90% within buffer 9 442 19.5 10.0 96.4 2.9 212 54.3 44.3 146,246 593 Environmental Health Perspectives 1.--- - .- -- . .. 630 - population characteristics (e.g., age, ethnic- ity) did not differ markedly between those close to the lines and those far away, although the perceived housing value vari- ables (e.g., house value, rent, proportion owner-occupied) varied more so. Further, the perceived housing value variables dif- fered not only within a town but also between towns. We note, however, that these observations are likely to be highly unstable due to the very small sample size. To explain these variations, we visited the area in question. The reasons for the differences, we believe, are town specific. Although the towns are similar in terms of overall demographics and perceived hous- ing value, the areas of the town through which the transmission line runs are dif- ferent. For example, in one portion of Edison, the line runs along a major local road and borders on a low-income hous- ing project. Thus, it is not surprising that the census blocks within the buffer are more frequently renter occupied and that housing and rental costs are relatively low. In North Brunswick, on the other hand, the transmission line runs through a fairly upscale region, as is reflected in the high owner-occupancy rate and the high rental and housing costs. This suggests that it is probably not possible to generalize about populations that live near high-voltage transmission lines but rather to note that, since all people need electricity, lines run through all towns and through all kinds of neighborhoods. One interesting observation is that, based on this small and arbitrary sample of data, there is no evidence of environmen- tal disparity with respect to ethnicity or socioeconomic status. That is, in spite of the possible undesirability of proximity to high-voltage transmission lines (for health or aesthetic reasons), we do not see them preferentially located in nonwhite or less affluent regions. Rather, their locations are town dependent. In most of the towns we studied, the populations living closest to the line were more white and had a wider age distribution than the towns that sur- rounded them. Housing values varied markedly by town, although values within the buffer were lower than the town as a whole more often than not, possibly sug- gesting a perception of lower value. Conclusions Our pilot study had two objectives: to demonstrate the feasibility of identifying populations living near high-voltage trans- mission lines for epidemiologic study and to characterize these populations. We have shown that we can identify these popula- tions readily using a GIS and the 1990 U.S. Census databases. Although we can- not estimate the distance from the center line for each individual or housing unit, we can provide grouped estimates based on reasonable buffer sizes. Since we as- sessed the population along only a few miles of a single line in New Jersey and found hundreds of people living within 100 m, we believe that this methodology could be used, at least in New Jersey, to identify a cohort of sufficient size for epi- demiologic study. Further, these popula- tions are not different socio-demographi- cally from the rest of the population, mak- ing them attractive for epidemiologic study. In terms of the characteristics of these individuals, our pilot study demonstrates that for a single, arbitrarily chosen 230-kV line in New Jersey, the populations living close to the line have fewer people under age 18, are more white, and have less expensive rents. Housing costs depend more on the communities we examined than on the houses' proximity to the power line. These data support the notion of environmental equity for this potential health hazard in this pilot study area, although further study is warranted. Appendix Determining the Magnetic Field Strength at the Edge of the Right-of-Way To determine the relevance of our arbi- trary buffer width, we calculated a sample magnetic field strength at the edge of the buffer. To do so, one needs to know the geometric configuration of the three con- ductors on the tower, the distance be- tween the conductors, and the current flowing through the line (17). Often, along a transmission line, the tower con- figurations will vary. For these calculations we selected an arbitrary tower to use in our calculations. The three conductors for this line were configured vertically (that is, one was directly above the other, which was directly above the third), and each pair was separated by 21 feet, or 6.4 m (F. Blahuta, personal communication). The normal current load on this line was 953 amps U. Flynn, personal communication). This is not the maximal load, but rather a typical load used for rough calculations. To calculate the ambient magnetic field attributable to this line, we used the following formula (17): I 2 +S2 +S B = 12 = S13 23 5R 2 2 where B is the field's total flux density in microtesla, I is the current in amperes car- ried by each of the three phase conductors, R is the distance in meters from the line to the point where the field is being calculat- ed, and S,i, is the transverse distance in meters between the ith and jth conduc- tors. Therefore, B = 953 (6.4) + (12.8)2 + (6.4)2 5(100)2 2 = 0.21luT Thus, the magnetic field attributable to the transmission line at the edge of the buffer was 0.2 pT. At 50 m from the center line, the field would be 0.5 pT. At 25 m from the center line, the field would be 2.1 pT. And, at the center line, the field would be 1315 J. REFERENCES 1. Wertheimer N, Leeper E. Electric wiring con- figurations and childhood cancer. Am J Epi- demiol 109:273-284(1979). 2. Wartenberg D, Greenberg M. Epidemiology, the press and the EMF Controversy. Public Understanding of Science 1:383-394(1992). 3. Wertheimer N, Leeper E. Re "Electrical wiring configurations and childhood leukemia in Rhode Island." Am J Epidemiol 111:461- 462 (1980). 4. Wertheimer N, Leeper E. Adult cancer related to electric wires near the house. Int J Epi- demiol 11:345-355(1982). 5. Fulton JP, Cobb S, Preble L, Leone L, Forman E. Electrical wiring configurations and childhood leukemia in Rhode Island. Am J Epidemiol 109:273-284(1980). 6. Myers A, Cartwright RA, Bonnell JA, Male JC, Cartwright SC. Overhead power lines and childhood cancer. In: International confer- ence on electric and magnetic fields in medi- cine and biology. Conference publication 257. New York:Institute of Electrical En- gineers, 1985;126-130. 7. Myers A, Cartwright RA, Bonnell JA, Male JC, Cartwright SC. Childhood cancer and overhead powerlines: a case-control study. Br J Cancer 62:1008-1014(1990). 8. Tomenius, L. 50-Hz electromagnetic environ- ment and the incidence of childhood tumors in Stockholm County. Bioelectromagnetics 7:191-207(1986). 9. Savitz DA, Wachtel H, Barnes FA, John EM, Tvrdik JG. Case-control study of childhood cancer and exposure to 60-Hertz magnetic fields. Am J Epidemiol 128:21-38(1988). 10. London SJ, Thomas DC, Bowman JD, Sobel E, Cheng T-C, Peters JM. Exposure to resi- dential electric and magnetic fields and risk of childhood leukemia. Am J Epidemiol 134: 923-937(1991). 11. Theriault GP. Health effects of electromag- netic radiation on workers: epidemiologic studies. In: Proceedings of the scientific work- shop on the health effects of electric and mag- netic fields on workers. DHHS publication number 91-111. Washington, DC:National Institute for Occupational Safety and Health, 1991;93-124. 12. Florig HK, Morgan MG. Measurements of housing density along transmission lines. Bioelectromagnetics 9:87-93(1988). 13. Salzberg MR, Farish SJ, Delpizzo V. An analysis of associations between social class and ambient magnetic fields in metropolitan Melbourne. Bioelectromagnetics 13:163-167 (1992). Volume 101, Number 7, December 1993 631 A - 14. Feychting M, Ahlbom A. Magnetic fields and cancer in people residing near Swedish high voltage power lines. Stockholm:Karolinska Institutet, Institutet for Miljomedicin, 1992. 15. Olsen JH, Nielsen A, Schulgen G. Residence near high-voltage facilities and the risk of can- cer in children. Br Med J (in press). 16. Verkasalo PH, Pukkala El, Hongisto MY, Valjus HE, Jarvinen PJ, Koskenvuo M. Cancer risk of Finnish children living close to power lines-national follow-up study. Presented at the Fifth International Con- ference of the International Society for Environmental Epidemiology, Stockholm, Sweden, August 1993. 17. Kaune WT. Assessing human exposure to power-frequency electric and magnetic fields. Environ Health Perspect Suppl 101(4):in press. 18. Caola RJ Jr, Deno DW, Dymek VSW. Meas- urement of electric and magnetic fields in and around homes near a 500 kV transmission line. IEEE Transactions on Power Apparatus and Systems 102:3338-3347(1983). 19. Maddock BJ, Male JC, Norris WT. 50 Hz electric and magnetic fields near power trans- mission circuits and some associated exposure and health studies. In: International Con- ference on Electric and Magnetic Fields in Medicine and Biology. Conference publica- tion 257. 1985;122-125 20. Stuchly MA. Human exposure to static and time-varying magnetic fields. Health Phys 51:215-225(1986). 21. Heroux P. 60-Hz electric and magnetic fields generated by a distribution network. Bio- electromagnetics 8:135-148(1987). 22. Dlugosz LJ, Byers T, Vena J, Zielenzny M. Ambient 60-Hz magnetic flux density in an urban neighborhood. Bioelectromagnetics 10:187-196(1989). 23. Bracken TD. The 60-Hz electric and magnet- ic field environment. EPRI utility seminar on epidemiologic studies of electromagnetic field exposure, Minneapolis, MN, 1987. 24. Kavet R, Silva JM, Thornton D. Magnetic fields exposure assessment for adult residents of Maine who live near and far away from overhead transmission lines. Bioelectro- magnetics 13:33-55(1992). 25. McDowall ME. Mortality of persons resident in the vicinity of electricity transmission facil- ities. Br J Cancer 53:271-279(1986). 26. Coleman MP, Bell CMJ, Taylor H-L, Primic- Zakelj M. Leukemia and residence near elec- tricity transmission equipment: a case-control study. Br J Cancer 60:793-798(1989). 27. Johnson J, Kung H-T, Kirsch S. Spatial analysis of childhood cancer incidence and electric power line location in Memphis and Shelby County, Tennessee. Southeastern Geographer 32:148-162(1992). 28. Schreiber GH, Swaen GMH, Meijers JMM, Slangen JJM, Sturmans F. Cancer mortality and residence near electricity transmission equipment: a retrospective cohort study. Int J Epidemiol 22:9-15(1993). 29. Wartenberg D, Savitz DA. Exposure cutpoint selection in epidemiologic studies of electric and magnetic fields. Bioelectromagnetics 14:237-245(1993). 30. Marx RW, ed. The Census Bureau's TIGER system. Cartography and Geographic Infor- mation Systems 17, vol 1. Washington, DC:Bureau of the Census, 1990. 31. U.S. Bureau of the Census. TIGER/line cen- sus files. Technical documentation. Washing- ton DC:Bureau of the Census, 1990. 632 Environmental Health Perspectives