A GEOGRAPHICAL AND STATISTICAL ANALYSIS OF LEUKEMIA DEATHS RELATING TO NUCLEAR POWER PLANTS. Whitney Thompson, Sarah McGinnis, Darius McDaniel,

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1 A GEOGRAPHICAL AD STATISTICAL AALYSIS OF LEUKEMIA DEATHS RELATIG TO UCLEAR POWER PLATS Whtney Thompson, Sarah McGnns, Darus McDanel, Jean Sexton, Rebecca Pettt, Sarah Anderson, Monca Jackson ABSTRACT: Due to an alarmng ncrease n chldhood leukema rates, t has become a prorty to learn what factors contrbute to ths dsease. Whle no drect cause s known, t has been suggested that envronmental factors such as the radaton emtted by nuclear power plants may be to blame. Ths study examnes whether or not there s a geographcal pattern n cancer rates based upon the locatons of nuclear power plants throughout the Unted States. A spatal analyss was conducted to look for global and local clusters wth an ncreased mortalty rate due to chldhood leukema along wth a statstcal analyss to examne whch factors contrbute sgnfcantly to ths rate. We found no evdence to prove that nuclear power plants are responsble for the recent ncrease n chldhood leukema rates. Ths leads us to beleve that a dfferent carcnogen s at fault. Key words: statstcs, chldhood cancer, lnear regresson, Tango s Index, Moran s I, Oden s I*pop I) ITRODUCTIO Chldhood leukema rates have rsen 39.6 percent between 1975 and 000 [9]. Whle drect causes of leukema are unknown, t s beleved that envronmental factors, such as radaton emtted from nuclear power plants, may play a role n the growng number of new cases each year. Ths alarmng ncrease has led to a number of studes examnng the correlaton between these leukema rates and the dstance the sck chld lved from a nuclear power plant. Leukema, a type of cancer found n bone marrow, s one of the types of cancer most commonly affected by radaton [9]. Leukema occurs when a cell begns to dvde rregularly. Ths rregular cell dvson can lead to the formaton of uncharacterstc chromosomes. Exposure to onzng radaton ncreases the lkelhood that transformatons of the chromosomes occur [8]. The sotopes known as strontum-89, strontum-90, and barum-140 commonly target bones and bone-marrow. These cancer causng sotopes, emtted by nuclear power plants, are not part of naturally occurrng forms of radaton.

2 For ths reason, we wll be studyng whether or not leukema rates n countes contanng a nuclear power plant dffer from those wthout one. Safety has always been an ssue concernng nuclear power plants. There are many Amercans who feel that nuclear power plants are not safe, forcng many plants to be shut down over the years. These safety concerns have also led the government to become more nvolved n the process of buldng and updatng the plants and to become very strct about enforcng regulaton rules for nuclear power plants. The uclear Regulatory Commsson (RC) s n charge of overseeng that all plants are workng properly and are up to date. The safety regulatons and rules nvolve operaton, accdent preventon, and emergency plans [3]. RC regulaton helps to make sure that the plants are as safe as they can be n order to prevent accdents. The fsson process that nuclear power plants use creates nuclear waste whch s extremely toxc and harmful to humans. There are specfc rules and regulatons n place for storng nuclear waste so that the toxns are not emtted nto the ar and cause danger for the people lvng near a nuclear plant. The fsson process must be desgned properly and montored whle n use so that the waste s contaned safely f somethng were to go wrong [5]. Lcensng s crucal n makng sure every nuclear power plant s runnng properly and up to date wth the regulatons. Chldren are more susceptble to the radaton emtted from power plants than adults. If a chld and an adult are exposed to the same amount of radonucldes, the effect wll be greater on the chld due to hs or her smaller sze. Chldren s cells are also changng at a faster pace and undergong more dvsons because they are n the growth process. A chld s developng tssue can be harmed by exposure to radaton. Therefore, we wll be lookng at chldhood leukema rates as we expect those to be the best ndcator of the effect of nuclear power plants [9]. Varous types of leukema wll be focused on n our study.

3 Due to the ncreased number of leukema dagnoses n chldren over the past several years, other studes have begun to look at the effects nuclear power plants may have on these numbers. In partcular, a German study known as KKK, looked at the leukema rates n chldren under the age of fve near 16 nuclear plant stes from The study found a.-fold ncreased cancer rate n chldren lvng 5 km away from these stes. The study has been accepted by the German government, whch has concluded that there s an ncreased rsk assocated wth lvng n close proxmty to a nuclear power plant [4]. However, not all studes have shown such a connecton. A study conducted by the atonal Cancer Insttute (CI) concluded that nuclear power plants pose no addtonal rsk to those lvng near them. Ths study looked at deaths from 16 dfferent types of cancer n 107 countes n the Unted States whch contan or are closely located to nuclear power plants. Each county was compared to three other countes not contanng power plants that were consdered to be socoeconomcally smlar [11]. Whle some rates ncreased others decreased, and no clear connecton could be made to the power plants. The study calculated the relatve rsk of developng leukema before and after the plants became operatonal. For most cancers, these rsk numbers decreased after operaton began [11]. The CI study was desgned to examne cancer rates n general, and whle chldhood leukema was looked at ndvdually, our study wll focus on these cancers solely, as t provdes the most benefcal data. The CI study also compared selected countes contanng power plants to a number of control countes. Our study wll look at all countes n the contnental Unted States whch contan a power plant and wll look at ther change n mortalty rates to determne f nuclear power plants are posng a threat. Members of the department of Bology, Bostatstcs, and Epdemology at the Medcal Unversty of South Carolna conducted a meta-analyss n 007. The study found an ncrease of chldhood leukema near power plants but was unable to confrm that ths result was drectly correlated wth the radaton

4 emtted from the power plants themselves. The study ponted out that a populaton densty bas may exst, meanng that results from hghly populated areas wll overshadow those areas of lesser populaton [1]. Our study wll be usng mortalty rates nstead of counts n an effort to correct ths problem. II) METHODS 1) DATA In order to examne whether or not a correlaton exsts between cancer rates and the dstance lved from a nuclear power plant, we needed data coverng a varety of varables. These varables were then compared to determne whch one played the most sgnfcant role n the change n cancer rates. Data from the Centers for Dsease Control and Preventon (CDC) provded the number of leukema related chldhood deaths (transform) n 1988 per county n the Unted States. Our analyss s based upon mortalty rather than the number of ncdences reported. It s possble that one case of leukema can be recorded multple tmes, whereas mortalty rates are much more accurate. Due to ths unrelablty of ncdence data, we chose to use the mortalty data assocated wth chldhood leukema n each county. We converted the mortalty counts provded by the CDC to rates usng the county s populaton for those under 18 (poptotalunder18 and %TotalUnder18) accordng to the Bureau of the Census. We were unable to obtan data on Lovngton County n Texas and Yellowstone Park County n Montana, so these countes were omtted from our study. Los Angeles County, Calforna was also omtted due to ts heterogeneous populaton. The county was an outler for multple varables and tends to complcate many spatal studes [17]. Usng the nformaton provded by the CDC, we separated the data for populaton nto categores based on race and sex []. Ths was done because cancer rates are known to vary amongst males and females as well as varous races [8]. We used three dfferent race categores: Caucasans (pwhmaleu0,

5 pwhfemaleu0), Afrcan Amercans (pblmaleu0, pblfemaleu0), and Other (potmaleu0, potfemaleu0). We defned other as beng all races not ncluded n the Caucasan or Afrcan Amercan categores. Data were only used for those under the age of 0 snce ths age range was more approprate for our study. All data were based upon the year 1988 as t s the most recent data that ncorporates the dates that the nuclear power plants we are consderng were actve. It s much more dffcult to acqure data from recent years due to ncreasngly strct prvacy laws. Data from 1988, however, are readly avalable. The U.S. Census Bureau s poverty estmates from 1989 (popunder18inpov, %Under18InPov) were used due to the lack of avalablty of 1988 data [14]. We are assumng that these estmates are relatvely smlar. Ths data were also only avalable for those under the age of 18; however, we made the assumpton that the rate of poverty would be smlar for the rest of our age group and adusted the data accordngly. Data on the locaton of nuclear power plants n 1988 across the contnental Unted States were used from nformaton collected by the uclear Regulatory Commsson (RC). Plants located n Alaska and Hawa were omtted from our study snce the data from these states could not be used to look at spatal clusterng. Ths data provded the locaton of all power plants (#OfucPlants) usng ther lattude and longtude coordnates. We reverse geocoded the locaton of each plant to nclude the Federal Informaton Processng Standard (FIPS) code n order to make the locaton dentfable by county. The RC data also ncluded the number of days the plant had been operatng (avgdaysoperatng). We used the startng date of operaton untl the mddle of the year n 1988 to determne our average number of days operatng varable. If multple plants were located wthn the same county, the average number of operatng days was used [15]. ) SPATIAL AALYSIS

6 Spatal analyss looks for patterns based on locaton and measures how smlar each locaton s to ts surroundngs [19]. Statstcal methods for testng spatal clusterng can be classfed nto two groups: methods for determnng global clusterng and methods for fndng a local cluster [19]. In our study we use the populaton densty adusted exponental weght functon n our calculatons for global clusterng. Defne weght functon s defned as: w as the th and th element n the weght functon. The w e d k where d s the Eucldean dstance between and. A parameter, k, ncreases the senstvty of the test to large or small clusters, correspondng to large or small values of k. For our study we used values of 0.005, 0.1, 0.5, 1, 0, 50, and 100 for k. In global clusterng, we use varous ndces to examne an area as a whole and determne f there s any relatonshp between adacent locatons. For example, we looked at the Unted States as a whole to see f there were any smlartes amongst neghborng countes. Our null hypothess n performng these tests was that the data are not spatally correlated. The alternatve hypothess was that the data are spatally correlated. We used three of the most common methods to evaluate global clusterng n our study: Tango s Index, Moran s I, and Oden s I*pop. Values near zero ndcate there s no spatal clusterng and values larger than zero show spatal clusterng. It was mportant to use multple ndces and compare ther values snce there s no generally accepted method. The frst ndex we used was Moran s I whch s defned as I 1 S w ( y y)( y w y)

7 where y s the rate of death n geographc unt and s the number of geographc unts. ormally, the number of cases would be used, but snce the populaton vares greatly amongst countes, rates of death were used nstead. We defned the mean and varance of the number of cases as y 1 y and S 1 y y, respectvely. To calculate the p-value, we standardzed the ndex as z [ I E( I)]/ Var( I) so z wll have a standard normal dstrbuton. We defne E ( I) 1/( 1) and 0 w, 1 1 Var ( I ) ( S 1 S 3S 0 ) [( 1)( 1) S 0 ] 1 1 wth S S 1 1 ( w w ) 1 1, and S ( w w ), wth w w and 1 1 w w 1 [10]. Tango's Index was also consdered. Aspects of ths test allow us to measure goodness of ft whch makes ths a satsfactory test for determnng spatal clusterng. Unlke Moran s I, Tango s Index takes nto account the county s populaton. Therefore y s the number of cases rather than a rate. Tango s Index was calculated as T w 1 1 y n y n y n y where y = sum of y for y = number of deaths n geographc unt and populaton under 0 at rsk for geographc unt. n n = sum of n for n = To fnd the p-value for Tango s Index, we appled a ch-squared approxmaton. Let W be a weght matrx and n p n 1 n, n n,..., n, a vector of proportons. The standardzed Tango s Index s represented by

8 T * T ET V T, and V T trace W 1 y where E T tracew * V P * V p Tango s ndex, respectvely, wth V p dagp p p represent the mean and varance of y * where dag(p) denotes the dagonal matrx of the elements n p. Defne trace W * Vp 3 trace W * V 1. 5 skt p and dft 8. A ch- skt squared approxmaton for Tango s Index s dft Tstar * skt ~ dft The fnal ndex we calculated was Oden s I * pop. Ths ndex s derved from Moran s I but ntegrates the populaton s sze of the countes. We calculated Oden s I * pop as I * pop n w b * e v e v n1 b * * 1 b y vv w y vw * [13]. w e nb w v * where b y n, v n n, e y y *, and w w vv [1]. In addton to these ndces whch measure global clusterng, we used Local Moran's I whch s an ndex of spatal correlaton whch detects outlyng countes. It s calculated for each regon usng I r 1 w y rn rn 1 w y rn rn, where r y 1 s the overall rate [6]. 1 n 3) STATISTICAL AALYSIS

9 In order to create a general lnear model, we studed the correlaton between the varables durng our prelmnary analyss. We let the chldhood leukema mortalty rate be our dependent varable whle the varables relatng to race, sex, poverty, number of nuclear power plants, and the average number of days operatng were ndependent varables. We created a correlaton matrx to determne whch ndependent varables were correlated wth each other n order to mnmze the effects of multcollnearty and whch of the ndependent varables were most hghly correlated wth the dependent varable. To create the best model possble, we used a technque known as backwards elmnaton. We ran the regresson usng all of the varables and examned the p-value gven for each one. If ths value was above 0.05, that varable was elmnated and the regresson was re-run. Ths process contnued untl all p-values were n the desred range. III) RESULTS 1) PRELIMIARY AALYSIS In order to use standard regresson technques, we needed to examne our data and perform several tests for normalty. Hstograms of the data revealed that our dependent varables dd not have a normal dstrbuton. A scatter plot of the resduals from our general lnear model showed a pattern whch also verfed that our data were not normally dstrbuted. There were a large number of zeros for the number of deaths whch made the data dffcult to work wth. In order to get the data n a usable format, we attempted to use the Freedman-Tukey transformaton but ths dd not make our data appear normal. We were able to transform our data usng the followng transformaton, transform ln[ 1000*( deaths 1500) popunder0]. Ths alteraton transformed the response varable to gve t an approxmate normal dstrbuton.

10 We looked at a correlaton matrx and scatter plots of all the varables to determne whch varables were most approprate for our model. These revealed that a few of the varables were hghly correlated wth each other, and therefore, dd not all need to be present n our model. In general, men and women of each race were hghly correlated wth each other. Snce men are more lkely to develop leukema, we used only the data for men [8]. Men were also more correlated wth the response varable than women, makng them more useful to the model. The followng table shows the correlaton between all of the varables on the top half of the dagonal. The bottom half of the table below the dagonal gves the p- value for the test of the null hypothess whch was that the correlaton s zero. The alternatve hypothess was that the correlaton s not zero. These p-values were computed usng Pearson s correlaton test.

11 transform pwhmaleu0 pwhfemaleu0 pblmaleu0 pblfemaleu0 potmaleu0 potfemaleu0 poptotalinpov %TotalInPov popunder18inpov %Under18InPov #OfucPlants avgdaysoperatng transform pwhmaleu0 < pwhfemaleu0 < < pblmaleu0 < < < pblfemaleu0 < < < < potmaleu < < < < potfemaleu < < < < < poptotalinpov < < < < < %TotalInPov < < < < < < < popunder18inpov < < < < < < %Under18InPov < < < < < < < < < #OfucPlants < < < < < avgdaysoperatng < < < Table 1. Correlaton table

12 ) SPATIAL STATISTICS To determne f our data were spatally correlated we analyzed our calculatons of Moran s I, Tango s Index, and Oden s I*pop. When calculatng these ndces, we used a weght functon wth varyng kappa values. The results from each of these computatons were very smlar. We determned that there was no statstcal sgnfcance between them and decded that a kappa value of 0.5 was approprate for our analyss. Of the three global ndces, Moran s I was the only ndex that ndcated a correlaton exsted and yelded a p-value less than The p-value calculated usng Tango s Index, however, was whch mples that the data were not spatally correlated. The varous ndces provded by Oden s I*pop were all near zero. Ths would lead to an nsgnfcant p-value. Usng Local Moran s I, we determned that there are no outlyng countes. The results of ths test were nsgnfcant for each county wth all ndces beng near zero, leadng us to beleve that there are no sgnfcant local clusters. 3) REGRESSIO Our fnal model was created usng the followng varables wth ther correspondng values. All of these varables gave a p-value less than 0.05 and therefore showed sgnfcance. Varable Estmate Standard Error Intercept Percent Caucasan Male Under Percent Afrcan Amercan Male Under Percent Other Male Under

13 Total Percentage n Poverty umber of uclear Plants Table. Coeffcents for general lnear model All of these varables proved to be sgnfcant wth p-values under The number of nuclear power plants yelded a negatve coeffcent. Ths mples that the number of plants and a countes mortalty rate are negatvely correlated. All of the other coeffcents are postve, meanng that there s a postve correlaton between all of the varables and the mortalty rates. One of the assumptons that nsures the accuracy of the model s that the resduals are approxmately normal. We examned the Q-Q plot of ths model s resduals and observed that they fell n a relatvely straght lne. Ths s close enough to normal for us to beleve that our model s accurate. We also plotted the resduals versus the predcted values to determne f there were any systematc patterns or evdence of non-constant varance. The plot showed that the assumptons were met. 4) GEOGRAPHICAL AALYSIS The graph was produced usng ArcGIS mappng software. We mapped the death rates per 100,000 people n each county of the contnental USA n 1989 wth Los Angeles County removed and the locaton of the nuclear plants actve n Ths fgure uses a technque known as smoothng. Smoothng the data sums the death rates of the county and ts neghbors and then dvdes them by the number of ts neghbors to produce a weghted average for that county [7].

14 Fgure 1. The smoothed rates of death per 100,000 people for each U.S. county n 1989 From ths map we can begn to draw conclusons. It s obvous that there are no global patterns. Ths graph confrms that no correlaton between power plants and mortalty rates exsts. There appear to be outlers n Mnnesota, Mssour, Texas, Montana, and on the Utah-Arzona border. Further tests were carred out to deny these conclusons. IV) DISCUSSIO At the begnnng of ths study, we expected to fnd that our data were spatally correlated. We ran multple tests to check for global clusterng. Two of the three ndces concluded that no spatal relatonshp exsted. There are more nuclear power plants located n the eastern half of the Unted States, thus we were antcpatng some sort of pattern n ths secton of the country. However, snce our data dd not reveal such a correlaton, we opted for a lnear model rather than usng spatal regresson technques. Our general lnear model provded a negatve coeffcent for the varable representng the number of nuclear power plants. Ths ndcates that there s an nverse relatonshp between chldhood leukema mortalty rates and the number of nuclear power plants n each county whch led us to conclude that lvng n the vcnty of a nuclear power plant does not ncrease a person s lkelhood of developng cancer. Our model also provded a hgher coeffcent for Caucasans than Afrcan Amercans. Ths s n agreement wth the known statstc that Caucasans are more lkely to develop cancer than other races [8]. Our fndngs matched those of the atonal Cancer Insttute s whch looked at 107 countes and the 6 nuclear stes n or near those countes. Ths study compared those countes to 9 other countes whch dd not contan a nuclear plant. The CI looked at many dfferent cancers and found that there

15 was a large range between the rates dependng upon the type of cancer. They concluded that nuclear power plants could not be blamed for any ncrease n rates [11]. However, many European studes contrast our fndngs. The prevously mentoned KKK study whch was accepted by the German government clams that lvng wthn fve klometers of a power plant ncreases the rsk of cancer [4]. Another study done n France whch used very smlar methods to our study found a cluster of chldhood leukema cases near the La Hague power plant n ormandy [16]. A study conducted n Sweden n 1995 looked for clusters throughout ther study area and then tested the sgnfcance of those areas. They specfcally checked for hgher ncdence levels near power plant locatons. The study concluded that none of the detected clusters were sgnfcant [18]. A study done n the Unted States reported that an ncreased rsk does exst for those lvng near a nuclear power plant. The area assocated wth ths rsk s much larger than the KKK study s proposed area due to arborne radaton and the chance of dgestng partcles found n varous food sources. Ths study looked prmarly at ncdence rates whch would explan ther dfferng conclusons. When they examned mortalty rates solely, they dd not fnd any sgnfcance ndcatng that power plants posed a threat [9]. We dd not fnd ncdence data to be relable enough for our study and chose, nstead, to use mortalty rates. Several problems occurred whle tryng to ft our data to a model. Low counts n several countes made varous statstcal methods dffcult to use. For example, many countes reported zero leukema related deaths whch made the data very skewed. Some of the technques that would normally be appled are not robust enough to handle ths problem. We were also unable to obtan current mortalty data due to ncreased regulatons. The fact that we have old data may nfluence the sgnfcance of our results. We also faled to account for other types

16 of cancer and used only mortalty rates assocated wth chldhood leukema. It s possble that other cancers are mpacted by the radaton emtted by nuclear power plants. Our model only takes nto account a small number of varables. It s not known what drectly causes leukema; so, we ncluded varables that we assumed to be nfluental. There are many other factors that may or may not be responsble for the current ncrease n cancer rates. ot all of these are easly measured or obtanable. Our gven data made t dffcult to quantfy the varous types of power plants. Snce only those that emt radaton are necessary for our study, we only wanted to nclude those that may actually be harmng the surroundng nhabtants. In order to do a more conclusve study, we would lke to run smlar technques on more recent data. Current mortalty rates would make the study more relable, but we would also lke to obtan more accurate ncdence data to better account for all cases of the dsease, not ust the fatal ones. It would also be benefcal to look at a wder varety of varables and try to create a model that better explans a countes death rate. In order to check the valdty of our model, we would lke to perform the same procedures usng European data. Our study s n agreement wth the CI s study whch looked at smlar locatons. We would lke to perform our analyss on a dataset n whch a study has found nuclear power plants to be a cause of leukema to see f we obtan comparable results.

17 Works Cted 1. Baker, P., & Hoel, D. (007). Meta analyss of standardzed ncdence and mortalty rates of chldhood leukaema n proxmty to nuclear facltes. European Journal of Cancer Care.. Centers for Dsease Control and Preventon. (011, May 16). Compressed Mortalty Fle. Retreved June 9, 011, from 3. Fensten, J. S. (1989). The Safety Regulaton of U.S. uclear Power Plants: Volatons, Inspectons, and Abnormal Occurences. The Journal of Poltcal Economy, Ghrga, G. (010). Rsk of cancer n chldren lvng near nuclear power plants. Italan Journal of Pedatrcs. 5. Green, H. P. (1973). Publc Partcpaton n uclear Power Plant Lcensng: The Great Deluson. Wllam and Mary Law Revew, Jackson, M. C., Huang, L., Luo, J., Hachey, M., & Feuer, E. (009). Comparson of tests for spatal herogenety on data wth global clusterng patterns and outlers. Internatonal Journal of Health Geographcs, Lance, W., & Gotway, C. (004). Appled Spatal Statstcs for Publc Health Data. Hoboken: John Wley & Sons, Inc. 8. Lghtfoot, T. (005). Aetology of Chldhood Leukema. Boelectromagnetcs Supplement, S5 S Mangano, J. J., Sherman, J., Chang, C., Dave, A., Fenberg, E., & Frmer, M. (003). Elevated Chldhood Cancer Incdence Proxmate to U.S. uclear Power Plants. Archves of Envronmental Health, Moran, P. (1950). otes on contnuous stochastc phenomena. Bometrka, atonal Cancer Insttute. (011, Aprl 19). o Excess Mortalty Rsk Found n Countes wth uclear Facltes. Retreved June 8, 011, from 1. Oden,. (1995). Adustng Moran's I for populaton densty. Stat Med, Tango, T. (1995). A class of tests for detectng 'general' and 'focused' clusterng of rare dseases. Stat Med, U.S. Census Bureau. (010, December 8). Small Area Income and Poverty Estmates. Retreved June 9, 011, from

18 15. Unted States Government. (009, August). U.S. Commercal uclear Power Reactors. Retreved June 9, 011, from data.gov: and Utltes/U S Commercal uclear Power Reactors/rfn 7hyh 16. Vel, J. F., Pobel, D., & Carrre, A. (1995). Incdence of leukaema n young people around the La hague nuclear waste reprocessng plant: A senstvty analyss. Statstcs n Medcne, Waller, L. A., Hll, E. G., & Rudd, R. A. (006). The geography of power: Statstcal performance of tests of clusters and clusterng n heterogeneous populatons. Statstcs n Medcne, Waller, L. A., Turnbull, B. W., Gustafsson, G., Halmars, U., & Andersson, B. (1995). Detecton and assessment of clusters of dsease: An applcaton to nuclear power plant facltes and chldhood leukaema n Sweden. Statstcs n Medcne, Waller, L., & Gotway, C. A. (004). Appled Statstcs for Publc Health. ew York: Wley.

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