Mortality due to hematological tumors in towns near Spain’s seven nuclear power plants and five nuclear fuel facilities during the period 1975–1993 was ascertained. The study was based on 610 leukemia-, 198 lymphoma-, and 122 myeloma-induced deaths in 489 towns situated within a 30-km radius of such installations. As control areas, we used 477 towns lying within a 50- to 100-km radius of each installation, matched by population size and a series of sociodemographic characteristics (income level, proportion of active population engaged in farming, proportion of unemployed, percentage of illiteracy, and province). Relative risk (RR) for each area and the trends in risk with increasing proximity to an installation were analyzed using log-linear models. None of the nuclear power plants registered an excess risk of leukemia-induced mortality in any of the surrounding areas. Excess risk of leukemia mortality was, however, observed in the vicinity of the uranium-processing facilities in Andújar [RR, 1.30; 95% confidence interval, 1.03–1.64] and Ciudad Rodrigo (RR, 1.68; 95% confidence interval, 0.92–3.08). Excess risk of multiple-myeloma mortality was found in the area surrounding the Zorita nuclear power plant. Statistical testing revealed that, with the single exception of multiple myeloma, none of the tumors studied showed evidence of a rise in risk with proximity to an installation. No study area yielded evidence of a raised risk of leukemia mortality among persons under the age of 25 years. More specific studies are called for in areas near installations that have been fully operational for longer periods. In this connection, stress should be laid on the importance of using dosimetric information in all future studies.

Because of the contradictory nature of their results, studies into cancer incidence and mortality in areas near nuclear installations have not served to dispel existing doubts as to the possible adverse population effects of radioactive discharges emanating from the routine running of such installations. The report that appeared in late 1983 concerning a cluster of leukemias in young residents living in the vicinity of a nuclear fuel reprocessing plant in Sellafield (England) resulted in a considerable amount of investigation. In the main, this research has tended to focus on leukemias in persons under the age of 25. There has been confirmation of excess risk of childhood leukemias in areas near the Sellafield plant (1, 2), the Dounreay and Hunterston facilities in Scotland (3, 4, 5, 6), and the Aldermaston Atomic Weapons Establishment (7, 8) in England. Reports have also been received of excess risk of leukemia- and lymphoma-related mortality in the proximity of some NPPs3(9, 10, 11). Etiological studies have in turn been conducted in an effort to ascertain the role played by radiation from such installations in disease (12, 13, 14, 15). A considerable number of studies carried out in different countries have reported an absence of risk in areas around NFFs and NPPs (16, 17, 18, 19, 20, 21, 22, 23, 24). Radioactive discharges in the vicinity of these kinds of installations involve very low doses, far below the level of natural background radiation. It has, therefore, been argued that these doses are unable to account for the excess risk of the incidence of certain malignancies, thus giving rise to a number of alternative hypotheses (10, 13, 25).

Whatever the case, the role played by exposure to low levels of ionizing radiation in the etiology of cancer continues to stimulate debate and study. The epidemiological designs most favored for the purpose of investigating this possible relationship are: (a) follow-up of worker cohorts with dosimetric controls (26, 27, 28, 29, 30); (b) case-control studies, basically of childhood leukemias (8, 12, 13, 31, 32); and (c) cancer incidence and mortality studies in populations residing near and around nuclear installations (9, 17, 33, 34, 35).

Spain currently has 7 NPPs, with a total of 10 reactors (9 fully operational and 1 in the process of being dismantled) and nine nuclear fuel facilities (three fully operational, one shut down and five in the process of being dismantled). Although the country’s first NPP came into operation in 1968, no study has ever been conducted to assess the specific risk faced by populations residing near these kinds of installations. Mortality drawn from death certificates was the only nation-wide source of information in Spain, on which a first analysis of this nature could be based. We, therefore, proceeded to carry out a cancer mortality study covering towns situated in the proximity of NPPs and NFF. This paper reports on the results of that study with respect to leukemia, lymphomas, and multiple-myeloma mortality. The analysis presented here sought: (a) to quantify the RR of death in the vicinity of such installations; (b) to ascertain said risk before and after the date on which these installations first came into operation; (c) to study changes in risk in accordance with the subjects’ relative proximity to the respective installations; and (d) given the descriptive and exploratory nature of this study, to provide additional pointers for new research.

We studied cancer mortality in towns situated near 7 NPPs with 10 reactors that had been operational in the period 1975–1993 and 5 NFFs that had been operational in the same period. With the exception of El Cabril, which is a NWSF built on the site of an abandoned uranium mine, the NFFs are uranium-concentrate processing facilities located in mining areas where the ore is extracted. Of the remaining installations, three were excluded because they were experimental research reactors and a fourth because it came into service in 1993. Fig. 1 shows the site of these installations. This was a retrospective cohort study whose population base was made up of the inhabitants of towns neighboring the nuclear installations under review. For the purposes of description and analysis, the area falling within a 30-km radius of any such installation was called the “exposed zone”; and towns (selected as outlined below) lying within a 50–100-km radius of said installation were called the “reference zone.” Although the choice of a 30-km radius was arbitrary, it roughly coincides with the area used in other studies.

UTM-format (Universal Transversa Mercator projection) centroid coordinates for municipal population centers were furnished by the National Geographical Institute (Instituto Geográfico Nacional). With the aid of a Geographic Information System, these coordinates were used to measure the distance from the population centroids to the nuclear installations. Distances so calculated were then subjected to quality control, involving manual checking of the accuracy of measurement against a random sample of 50 population centroids plotted on Army Geographical Unit maps, drawn to a scale of 1:50,000 and showing the precise position of the NPPs. The measurements obtained proved accurate in all cases.

Follow-up covered the period January 1, 1975, to December 31, 1993. For all of the NPPs as a whole, 316 towns within a 30-km radius and 303 within a 50 to 100-km radius were included in the study, matched by income level, number of inhabitants, proportion of the active population engaged in farming, proportion of unemployed, percentage of illiteracy, and province. These towns were selected at random from among all of those that met the matching conditions. For all of the nuclear fuel facilities as a whole, 173 (there were originally 177, but four towns merged with another two) and 174 towns in the exposed and reference zones, respectively, were included in the study, matched as above. The small disparities in the number of towns were attributable to changes in municipal boundary lines between 1981 and 1991 or to the impossibility of matching. The study covered a total of 644,044 persons in the exposed zone for all types of installations. Sociodemographic data were taken from the 1991 census (36) and information on income levels was taken from the Spanish Market Yearbook (Anuario del Mercado Español; Ref. 37). The distance chosen excluded all of the towns lying between 30 and 50 kms of any nuclear facility.

This paper presents the results on mortality due to leukemias (ICD-8 204–207, ICD-9 204–208), Hodgkin’s disease (ICD 201), non-Hodgkin’s lymphomas (ICD 200, 202) and multiple myeloma (ICD 203). The latency periods used were 1 year for leukemias (38) and 10 years for all other tumors. A latency period of 10 years rules out the possibility of studying cancer mortality other than that due to leukemias for the areas surrounding the Ascó, Cofrentes, Trillo and Juzbado facilities, because all these plants were inaugurated relatively recently. Data specific to this study were supplied on computer files by the National Statistics Institute (Instituto Nacional de Estadística). Individual records were broken down by cause, sex, age group, year of death and town of residence. Town-of-residence data for deceased persons are treated as confidential in Spain in the case of towns having fewer than 10,000 inhabitants, thus calling for a special agreement with the National Statistics Institute for the purposes of this study.

To obtain a population breakdown by sex, age, and year for towns included in the study, recourse was had to the 1981 population census, 1986 municipal roll, and 1991 census, as furnished by the National Statistics Institute. 1981-census data were restricted to towns of over 5000 inhabitants, so that the 1981 age-based distribution of populations in towns of under 5000 inhabitants had to be estimated on the basis of the 1986 and 1991 distributions, by categorizing age into 5-year age groups (18 groups) and using a procedure appropriate for small localities (39). Relying on a log-linear polynomial regression model (39), interpolation was used to estimate annual municipal population figures for the period 1981–1991. Pre-1981 and post-1991 populations were extrapolated by adopting a linear procedure, allocating more weight to the nearest census year. With the annual population estimates for each town, person-years for each age band (0–4, 5–14, 15–24, 25–34, 35–44, 45–54, 55–64, 65–74, and 75+), sex, and period (1975–1978, 1979–1983, 1984–1988, and 1989–1993) were then calculated, taking into account those variables that had changed over time, such as operational start-up of reactors and installations.

For analysis purposes, log-linear models were fitted on the assumption that the number of deaths per stratum followed a Poisson distribution. In these models, observed cases were the dependent variable and, as an external standard (40), concurrent Spanish cause-specific mortality rates were used, with expected cases being computed by age, sex, and time period for each town in the exposed and reference (control) zones. Expected cases were included as offset in the models. A term that we called “exposure” (a radius of 30 km or less from the facility), was included as the independent variable. The regression coefficient of this exposure term provided us with the logarithm of the ratio between the respective SMRs for the exposed and reference zones, something that we, in a departure from the traditional use of the term, called “relative risk” (RR). This estimator was adjusted for age, sex, time period, and matching variables. In the case of leukemia-related mortality, results specific to the under-25 age group are shown.

Similar models were fitted to study the effect of distance on mortality. This variable was constructed by categorizing distances in the 0–30-km belt into 5 levels (consisting of circular sectors having equal surface areas) and using towns situated at a distance of 50–100 km as the reference level. Expressed in kilometers, the cutoff points for the intervals were as follows: 0-, 13.4-, 19.6-, 23.2-, 26.8–30, and 50–100. This was included in all of the models both as a categorical and as a continuous variable (in kilometers), which rendered it possible: (a) in the former case, to estimate the effect for the respective distances; and (b) in the latter case, to ascertain the existence of radial effects (rise in RR with increasing proximity to an installation) and, by applying the likelihood ratio test, the statistical significance of such distance-induced effects. Matching variables were included in this analysis as continuous covariates centered around their mean to ensure control of possible gradients in these variables with proximity to the installation. In view of the heterogeneity of the installations, we ran specific analyses on individual installations and a joint analysis on all the installations.

We studied changes in risk by comparing the position before and after the date on which NPPs and fuel facilities first came into operation (start-up), taking latency periods into account. These periods were included in the assessment of risk before start-up. The statistical significance of this change was obtained by means of the likelihood ratio test, which evaluates the interaction term, exposure × plant operation, in regression models.

RR CIs were calculated using the SEs of the parameters yielded by the model. Model results were checked and corrected for over-dispersion problems (41) using the robust methods recommended by Breslow (42) for the purpose because these methods are insensitive to the form adopted by variance.

Table 1 sets out the socioeconomic characteristics of populations residing near nuclear installations. According to the 1991 census, the study populations in 30-km belts totaled 302,861 and 341,203 for NPPs and NFFs, respectively. Furthermore, Table 1 shows these contributions to the study in terms of person-years.

Tables 2 and 3 show the number of observed deaths, SMRs, for the reference zones and areas in a radius of 0–15 and 0–30 km of each installation, and the RRs and CIs yielded by comparison with the reference zones for both sexes and across all of the age groups, for the different causes studied. Table 4 shows RR by reference to the distance from the respective installations, for tumors causing a minimum of 10 deaths. The results of the pre-and post-start-up analyses are quoted in Table 5.

NPPs.

In essence, the results (Table 2) revealed excess risk of multiple myeloma mortality in the Zorita power plant area. This was the sole tumor to register a mortality higher than expected, and one that was statistically significant in the context of this installation’s surroundings. The RR for leukemias in the area nearest the plant (0–15 km) was 1.58 (95% CI, 0.81–3.67). Analysis of the distance variable (Table 4) showed a statistically significant increase in myeloma with proximity to the installation and a RR rising to a maximum in the 13–19-km sector.

In the exposed zone around the Vandellós power plant (Table 2), the RR for leukemias was 1.19 (95% CI, 0.82–1.73). Within a 15-km radius, the RR for leukemias was 1.59. Yet on categorization and application of the statistical test, none of these diseases appeared to evince (distance-related) radial effects (Table 4).

Because of the fact that Ascó, Cofrentes, and Trillo came into operation relatively recently, only the results for leukemias are shown (Tables 2 and 4). In no case did the pattern of risk prove remarkable.

The results for leukemias among the under-25 age group are shown in Table 2. The number of cases was very low overall, Garoña being the only installation where the RR exceeded 1, though even here, the excess risk was not statistically significant.

The last column in Table 4 shows the P for trend of the effect according to distance. The only malignancy to register a clear and statistically significant distance-induced gradient (radial effect) was multiple myeloma in Zorita.

With respect to leukemias, the pre-and post-start-up mortality levels proved very similar for the three areas in which the study was feasible. In the case of multiple myeloma, the post-start-up risk proved higher for the Zorita and Garoña catchment areas, yet the increase was not statistically significant.

NFFs.

Table 3 shows the RRs for the nuclear fuel facilities. The Andújar area registered excess leukemia mortality (RR 1.30; 95% CI 1.03–1.64). In the 15-km sector surrounding the Ciudad Rodrigo facility, the RR was 1.68 (95% CI, 0.92–3.08). In all, 14 of the 30 deaths took place in the Ciudad Rodrigo metropolitan area.

Table 4 reports the results of the analysis of risk in accordance with the distance to the uranium-concentrate processing plants and the El Cabril NWSF. None of the causes studied registered a pattern that indicated a rise in risk with proximity and at the same time proved statistically significant with the test used. The El Cabril storage facility is located in a relatively deserted area, only 9 towns lying within a 30-km radius; the nearest is over 16 kms away. For distances under 20 kms, the of leukemias was in excess of 2.

We were able to study the start-up effect (Table 5) in the case of the La Haba, Ciudad Rodrigo, and Juzbado facilities. Available data failed to show evidence of a statistically significant rise in mortality.

Overall, the results point to a rise in risk of death from multiple myeloma in the area surrounding the Zorita NPP. Similarly, the possible existence of excess risk of leukemias in the proximity of the Andújar and Ciudad Rodrigo facilities argues in favor of more specific studies in areas adjacent to nuclear fuel facilities.

The validity of death-certificate diagnoses for investigating cancer is generally accepted (17, 19, 35, 43). Mortality data, although showing a high degree of accuracy in cancers of the lymphatic tissue and hematopoietic system (43, 44), are nevertheless not the most ideal means for studying diseases such as childhood leukemia because of therapeutic improvements that have succeeded in lowering mortality without altering incidence. Yet, with the single exception of Tarragona, none of the provinces studied are equipped with population-based cancer registries that would otherwise enable cancer incidence to be studied in these areas.

In the calculation of person-years, interpolation and extrapolation techniques had to be used. These techniques were applied in the same way to all of the provinces and towns included in the various studies. Hence, any possible deviations that are inherent in the estimates will be equally present in all of the areas compared.

Specific methodological problems are posed by investigation into relatively rare diseases in areas adjacent to sources of contamination. Stress has been laid on the importance of ascertaining disease-frequency and -distribution in other areas similar in size to those being studied (45), a suggestion that was followed in our design. Indeed, a great part of the SMRs obtained from comparison with overall Spanish mortality were under 1 for the exposed and reference zones (Tables 2 and 3). Diagnostic verification of all of the cases is essential, because findings yielded by small areas are more sensitive to errors of classification, diagnosis, and reporting than those obtained for larger areas (45). Here however, case-diagnosis verification was ruled out by the very nature of our study, yet possible errors of classification would necessarily affect exposed and nonexposed towns in each region alike. In general, the areas compared in this study were rural. Reference towns were matched to exposed towns by sociodemographic variables and would, thus, indirectly maintain their comparability insofar as diagnostic accuracy and accessibility to the healthcare system are concerned. Sociodemographic information for the entire study period was not available. However, bearing in mind the characteristics of the Spanish National Health System, we would have no reason to suspect that there might be differential access to health care and diagnosis between exposed and reference areas.

In theoretical terms, comparison of SMRs are open to criticism on the basis that, internally, the SMRs use different standard populations. In this study, thanks to the matching procedure followed in the design, differences in structure between the populations compared were not very marked. The method of comparison of SMRs adopted in the presentation of these results was chosen because: (a) it was the same as that used in the references (9, 17); (b) it included the comparison with Spanish mortality, and (c) it allowed all of the age strata to be collapsed, thereby reducing sensitivity to instability in age-specific rates (40). Moreover, analysis based on comparison of mortality rates (rate ratios) via models that use person-years as offset and include age, was found to yield equivalent results.

The study of the distance variable seeks to associate mortality with the nuclear installation as the putative source of contamination. Distance to the installation tends to be used as a surrogate variable for exposure in cases in which dosimetric information or the radiological history of an installation’s environs is not forthcoming (46, 47), with reconstruction of such data constituting a field of investigation in itself (48). Indeed, in this respect, the study is “ecological,” in that individual levels of exposure are unknown and the inhabitants of any given town are thus implicitly assumed to have received similar exposures. There will inevitably be persons who have resided for part of their lives in exposed towns and then moved to nonexposed areas and vice versa.

The Zorita power plant’s area in the Province of Guadalajara showed evidence of an excess risk of multiple myeloma cases. Although not statistically significant, this excess risk remained in evidence even when the whole country was taken as reference. Yet, analysis adjusted for geographical location is nevertheless, to be preferred. Excess risk of myeloma connected with ionizing radiation has been documented in follow-up studies of Japanese atomic-bomb survivors (49) and nuclear industry workers (50, 51, 52, 53). A raised risk has also been found in populations residing near English-based nuclear installations that were commissioned before 1955 (9). Guadalajara is a province with a myeloma mortality risk verging on the average for Spain (54), yet its municipal pattern of distribution is marked by a cluster in the southwest of the province where the NPP is situated. There are no grounds for thinking that different biological and/or genetic determinants may have a nonhomogeneous distribution in the natural agrarian region to which the Zorita area belongs. Apart from ionizing radiation, known risk factors for myeloma include certain specific occupational exposures, such as pesticides, benzene, paints and solvents, and engine exhausts (55). Increased risk of myeloma has been reported for farm workers and workers in the petroleum refining, rubber and plastics manufacturing, and wood products industries. There are no petroleum refineries or plastics-related industries in this area. The exposed and reference towns were matched according to the proportion of farm workers. Moreover, based on the information available, no differences in crop cultivation were identified as between the areas compared. The data (in conjunction with the presence of a radial pattern peaking at a distance of 14 km from the plant), though not technically attributable to the nuclear installation by virtue of the nature of this study, nevertheless, lend heightened interest to using case-by-case diagnostic verification to ascertain the true incidence of this disease in the province.

With respect to leukemia-related mortality, mention should be made of: (a) excess risks observed in the environs of the Andújar facility; and (b) figures on the borderline of statistical significance in the Vandellós, Zorita, and Ciudad Rodrigo catchment areas. In this analysis, no distinction was drawn between types of leukemia because of the importance of the number of deaths certified as leukemia of unspecified cell type. Nevertheless, exploratory exclusion of specified chronic lymphatic leukemia produced very similar results.

No excess risk of leukemia-related mortality was detected in the population aged 0–24 years residing within a 30-km radius of any NPP. The only area to register a high RR was that of Garoña, but here, the excess risk failed to attain statistical significance. Joint analysis registered: an RR of 0.70 for areas surrounding the different NPPs and 1.02 for areas surrounding the different NFFs. In view of the length of the follow-up period, the statistical power of the studies on the respective installations is low with respect to the under-25 age group. With a total of 40 expected cases for all of the NPP catchment areas, the probability (power) of detecting a 50% rise in risk (RR = 1.5) is 86% (with a type I error of 5%).

Exposure to environmental radon is the greatest single source of human exposure to ionizing radiation (56). The influence of natural radiation on mortality could not be incorporated into this analysis. All of the towns selected (exposed and reference zones) in the Zorita and Vandellós areas lie in localities that have extremely low levels of natural radiation. Rates of exposure are below 10 μRoentgen/h, equivalent to an average effective dose of 1.09 μSievert/year (109 mrem/year; Ref. 57). We, therefore, think that adjusting the effect estimates for levels of natural radiation in these two areas would have no influence whatsoever on the results. However, such an adjustment may well be of greater interest in the case of uranium-concentrate and NFF catchment areas located in parts of the country with high levels of natural radiation (Ciudad Rodrigo, Juzbado, and Andújar).

The results reported here have to be interpreted with a great deal of caution, because of the nature of the study and the limitations described above. Being an ecological and exploratory study, any possible deduction linking the presence of nuclear installations to cancer mortality in their environs, must be viewed as largely speculative. More specific studies are called for in the Vandellós, Zorita, and Garoña areas, sites of the first NPPs to be built in Spain. In this connection, stress should be laid on the importance of using dosimetric information in all future studies.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

        
1

Supported in part by Grant 96/300 from Spain’s Fondo de Investigación Sanitaria (Health Research Fund).

                
3

The abbreviations used are: NPP, nuclear power plant; NFF, nuclear fuel facility; NWSF, nuclear waste storage facility; SMR, standardized mortality ratio; ICD, International Classification of Diseases; RR, relative risk; CI, confidence interval.

Fig. 1.

Sites of NPPs and NFFs in Spain.

Fig. 1.

Sites of NPPs and NFFs in Spain.

Close modal
Table 1

General characteristics of populations studied in areas adjacent to nuclear power plants and fuel facilities

PopulationaPerson-years (thousands)PercentAverage incomeAverage population
Total<25 yrLatency 1ybLatency 10ycIlliteracyUnemployedFarming
NPPsd          
 Zorita (1968)e          
  0–30 km 25,816 7,305 538.9 415.8 5.6 10.4 22.7 6.2 461.0 
  50–100 km 29,914 9,293 558.5 440.5 4.5 10.9 18.8 6.5 564.4 
 Garoña (1971)          
  0–30 km 57,625 20,236 1,328.2 897.5 1.3 13.4 12.7 6.7 992.3 
  50–100 km 50,060 15,475 987.9 670.2 1.1 14.7 23.4 7.0 725.7 
 Vandellós (1972)          
  0–30 km 73,594 26,161 1,474.3 930.4 3.1 13.5 16.8 6.2 2,628.4 
  50–100 km 43,373 14,675 801.0 513.8 2.6 12.9 11.3 6.6 1,606.4 
 Almaraz (1981)          
  0–30 km 47,637 17,672 571.9 143.2 5.4 30.3 32.7 5.6 1,488.7 
  50–100 km 45,946 16,390 561.8 138.0 5.2 27.5 31.0 5.8 1,584.3 
 Ascó (1983)          
  0–30 km 49,049 13,410 499.9 1.9 10.7 27.1 6.5 876.7 
  50–100 km 61,594 19,275 603.3 2.1 9.7 23.5 6.6 1,162.2 
 Cofrentes (1984)          
  0–30 km 35,881 11,733 331.9 4.0 17.8 16.8 6.8 1,888.5 
  50–100 km 71,975 27,159 649.3 4.0 19.0 10.6 6.1 4,498.4 
 Trillo (1988)          
  0–30 km 13,259 3,312 68.0 3.2 11.2 25.5 5.4 232.6 
  50–100 km 12,976 3,392 66.3 2.4 11.4 26.2 5.7 231.7 
 Total          
  0–30 km 302,861 100,075 4,813.1 2,386.9      
  50–100 km 315,838 105,702 4,228.0 1,762.5      
NFFsf          
 Andújar (1959)          
  0–30 km 126,063 50,411 2,386.2 2,386.2 8.4 22.4 30.7 5.1 6,003.0 
  50–100 km 152,673 58,224 2,918.8 2,918.8 8.8 21.6 31.8 5.3 7,270.1 
 El Cabril (1961)          
  0–30 km 38,781 13,545 764.7 764.7 9.7 34.8 25.2 5.1 4,309.0 
  50–100 km 44,373 18,114 814.7 814.7 10.2 35.6 39.0 4.5 5,546.6 
 La Haba (1977)          
  0–30 km 111,456 41,790 1,825.5 791.4 6.2 27.5 26.5 5.5 4,458.2 
  50–100 km 151,289 59,682 2,407.5 1,079.6 6.0 26.5 21.0 5.6 6,051.6 
 Ciudad Rodrigo (1978)          
  0–30 km 32,276 9,393 484.0 163.7 2.7 18.7 24.2 5.8 733.6 
  50–100 km 35,848 10,556 525.4 180.5 2.1 19.9 19.8 5.7 833.7 
 Juzbado (1985)          
  0–30 km 32,627 11,151 261.6 0.8 16.2 26.9 5.6 429.2 
  50–100 km 36,713 10,832 302.4 1.2 16.6 30.1 5.8 476.8 
 Total          
  0–30 km 341,203 126,290 4,029.4 2,911.6      
  50–100 km 420,896 157,408 4,889.6 2,386.2      
PopulationaPerson-years (thousands)PercentAverage incomeAverage population
Total<25 yrLatency 1ybLatency 10ycIlliteracyUnemployedFarming
NPPsd          
 Zorita (1968)e          
  0–30 km 25,816 7,305 538.9 415.8 5.6 10.4 22.7 6.2 461.0 
  50–100 km 29,914 9,293 558.5 440.5 4.5 10.9 18.8 6.5 564.4 
 Garoña (1971)          
  0–30 km 57,625 20,236 1,328.2 897.5 1.3 13.4 12.7 6.7 992.3 
  50–100 km 50,060 15,475 987.9 670.2 1.1 14.7 23.4 7.0 725.7 
 Vandellós (1972)          
  0–30 km 73,594 26,161 1,474.3 930.4 3.1 13.5 16.8 6.2 2,628.4 
  50–100 km 43,373 14,675 801.0 513.8 2.6 12.9 11.3 6.6 1,606.4 
 Almaraz (1981)          
  0–30 km 47,637 17,672 571.9 143.2 5.4 30.3 32.7 5.6 1,488.7 
  50–100 km 45,946 16,390 561.8 138.0 5.2 27.5 31.0 5.8 1,584.3 
 Ascó (1983)          
  0–30 km 49,049 13,410 499.9 1.9 10.7 27.1 6.5 876.7 
  50–100 km 61,594 19,275 603.3 2.1 9.7 23.5 6.6 1,162.2 
 Cofrentes (1984)          
  0–30 km 35,881 11,733 331.9 4.0 17.8 16.8 6.8 1,888.5 
  50–100 km 71,975 27,159 649.3 4.0 19.0 10.6 6.1 4,498.4 
 Trillo (1988)          
  0–30 km 13,259 3,312 68.0 3.2 11.2 25.5 5.4 232.6 
  50–100 km 12,976 3,392 66.3 2.4 11.4 26.2 5.7 231.7 
 Total          
  0–30 km 302,861 100,075 4,813.1 2,386.9      
  50–100 km 315,838 105,702 4,228.0 1,762.5      
NFFsf          
 Andújar (1959)          
  0–30 km 126,063 50,411 2,386.2 2,386.2 8.4 22.4 30.7 5.1 6,003.0 
  50–100 km 152,673 58,224 2,918.8 2,918.8 8.8 21.6 31.8 5.3 7,270.1 
 El Cabril (1961)          
  0–30 km 38,781 13,545 764.7 764.7 9.7 34.8 25.2 5.1 4,309.0 
  50–100 km 44,373 18,114 814.7 814.7 10.2 35.6 39.0 4.5 5,546.6 
 La Haba (1977)          
  0–30 km 111,456 41,790 1,825.5 791.4 6.2 27.5 26.5 5.5 4,458.2 
  50–100 km 151,289 59,682 2,407.5 1,079.6 6.0 26.5 21.0 5.6 6,051.6 
 Ciudad Rodrigo (1978)          
  0–30 km 32,276 9,393 484.0 163.7 2.7 18.7 24.2 5.8 733.6 
  50–100 km 35,848 10,556 525.4 180.5 2.1 19.9 19.8 5.7 833.7 
 Juzbado (1985)          
  0–30 km 32,627 11,151 261.6 0.8 16.2 26.9 5.6 429.2 
  50–100 km 36,713 10,832 302.4 1.2 16.6 30.1 5.8 476.8 
 Total          
  0–30 km 341,203 126,290 4,029.4 2,911.6      
  50–100 km 420,896 157,408 4,889.6 2,386.2      
a

1991 census.

b

Person-years assuming a latency period of 1 year.

c

Person-years assuming a latency period of 10 years.

d

The study covered 316 towns in the exposed zone (0–30 km) and 303 in the reference zone (50–100 km).

e

In parentheses, year of start-up.

f

The study covered 173 towns in the exposed zone (0–30 km) and 174 in the reference zone (50–100 km).

Table 2

Mortality by cause in areas within a 15- and 30-km radius of NPPs, taking as reference (control) towns lying within a radius of 50–100 kilometersa

Installation/CauseControl0–15 km0–30 km0–15 km0–30 km
ObsbSMRcObsSMRObsSMRRRd95% CIRR95% CI
All NPPs           
 Non-Hodgkin’s lymphomas 55 0.795 0.729 63 0.686 0.920 0.453 –1.868 0.835 0.581 –1.201 
 Hodgkin’s disease 11 0.672 0.691 19 0.865 1.037 0.228 –4.714 1.239 0.589 –2.608 
 Myeloma 26 0.540 0.904 51 0.801 1.616 0.728 –3.584 1.472 0.918 –2.360 
 Leukemias 251 0.892 57 1.027 273 0.834 1.127 0.844–1.506 0.956 0.804 –1.136 
 Leukemias <25e 34 1.128 1.470 27 0.776 1.214 0.529–2.787 0.703 0.418 –1.181 
Zorita           
 Non-Hodgkin’s lymphomas 13 0.727 1.075 13 0.689 1.479 0.527–4.148 0.949 0.440 –2.046 
 Hodgkin’s disease 0.452 1.754 1.100 1.715 0.801–3.670 2.432 0.475 –12.453 
 Myeloma 0.308 1.744 19 1.343 5.653 1.610–19.851 4.354 1.497 –12.663 
 Leukemias 29 0.708 12 1.117 34 0.767 1.578 0.809–3.076 1.083 0.663 –1.770 
 Leukemias <25 2.002 2.050 1.096 1.024 0.218–4.821 0.547 0.165 –1.814 
Garoña           
 Non-Hodgkin’s lymphomas 24 0.942 0.618 20 0.571 0.656 0.156–2.765 0.606 0.335 –1.096 
 Hodgkin’s disease 0.800 0.000 0.352   0.440 0.105–1.840 
 Myeloma 0.340 0.406 15 0.614 1.195 0.144–9.899 1.808 0.702 –4.658 
 Leukemias 45 0.709 0.971 54 0.620 1.370 0.646–2.906 0.874 0.590 –1.296 
 Leukemias <25 0.669 2.222 10 1.022 3.321 0.388–28.425 1.528 0.523 –4.470 
Vandellós           
 Non-Hodgkin’s lymphomas 15 0.773 0.557 30 0.947 0.720 0.165–3.144 1.225 0.662 –2.268 
 Hodgkin’s disease 0.656 0.000 11 1.411   2.150 0.604–7.647 
 Myeloma 14 1.048 0.421 16 0.755 0.402 0.053–3.056 0.721 0.354 –1.469 
 Leukemias 41 0.810 12 1.291 82 0.965 1.593 0.837–3.029 1.191 0.819 –1.732 
 Leukemias <25 1.448 2.264 0.740 1.563 0.423–5.771 0.511 0.203 –1.283 
Almaraz           
 Non-Hodgkin’s lymphomas 0.467 0.000 0.000     
 Hodgkin’s disease 0.893 0.000 0.000     
 Myeloma 0.486 0.000 0.255   0.525 0.049–5.672 
 Leukemias 34 0.930 0.585 35 0.995 0.630 0.196–2.022 1.07 0.668 –1.714 
 Leukemias <25 0.496 0.000 0.000     
Ascó           
 Leukemias 54 1.205 14 0.917 41 0.953 0.761 0.423–1.369 0.791 0.528 –1.187 
 Leukemias <25 0.844 0.882 0.770 1.045 0.109–10.041 0.913 0.153 –5.449 
Cofrentes           
 Leukemias 44 1.164 1.023 21 0.818 0.879 0.349–2.214 0.703 0.418 –1.181 
 Leukemias <25 1.530 0.000 0.975   0.637 0.132–3.064 
Trillo           
 Leukemias 0.56 1.554 0.838 2.777 0.622–12.394 1.497 0.423 –5.298 
 Leukemias <25 0.00 0.000 0.000     
Installation/CauseControl0–15 km0–30 km0–15 km0–30 km
ObsbSMRcObsSMRObsSMRRRd95% CIRR95% CI
All NPPs           
 Non-Hodgkin’s lymphomas 55 0.795 0.729 63 0.686 0.920 0.453 –1.868 0.835 0.581 –1.201 
 Hodgkin’s disease 11 0.672 0.691 19 0.865 1.037 0.228 –4.714 1.239 0.589 –2.608 
 Myeloma 26 0.540 0.904 51 0.801 1.616 0.728 –3.584 1.472 0.918 –2.360 
 Leukemias 251 0.892 57 1.027 273 0.834 1.127 0.844–1.506 0.956 0.804 –1.136 
 Leukemias <25e 34 1.128 1.470 27 0.776 1.214 0.529–2.787 0.703 0.418 –1.181 
Zorita           
 Non-Hodgkin’s lymphomas 13 0.727 1.075 13 0.689 1.479 0.527–4.148 0.949 0.440 –2.046 
 Hodgkin’s disease 0.452 1.754 1.100 1.715 0.801–3.670 2.432 0.475 –12.453 
 Myeloma 0.308 1.744 19 1.343 5.653 1.610–19.851 4.354 1.497 –12.663 
 Leukemias 29 0.708 12 1.117 34 0.767 1.578 0.809–3.076 1.083 0.663 –1.770 
 Leukemias <25 2.002 2.050 1.096 1.024 0.218–4.821 0.547 0.165 –1.814 
Garoña           
 Non-Hodgkin’s lymphomas 24 0.942 0.618 20 0.571 0.656 0.156–2.765 0.606 0.335 –1.096 
 Hodgkin’s disease 0.800 0.000 0.352   0.440 0.105–1.840 
 Myeloma 0.340 0.406 15 0.614 1.195 0.144–9.899 1.808 0.702 –4.658 
 Leukemias 45 0.709 0.971 54 0.620 1.370 0.646–2.906 0.874 0.590 –1.296 
 Leukemias <25 0.669 2.222 10 1.022 3.321 0.388–28.425 1.528 0.523 –4.470 
Vandellós           
 Non-Hodgkin’s lymphomas 15 0.773 0.557 30 0.947 0.720 0.165–3.144 1.225 0.662 –2.268 
 Hodgkin’s disease 0.656 0.000 11 1.411   2.150 0.604–7.647 
 Myeloma 14 1.048 0.421 16 0.755 0.402 0.053–3.056 0.721 0.354 –1.469 
 Leukemias 41 0.810 12 1.291 82 0.965 1.593 0.837–3.029 1.191 0.819 –1.732 
 Leukemias <25 1.448 2.264 0.740 1.563 0.423–5.771 0.511 0.203 –1.283 
Almaraz           
 Non-Hodgkin’s lymphomas 0.467 0.000 0.000     
 Hodgkin’s disease 0.893 0.000 0.000     
 Myeloma 0.486 0.000 0.255   0.525 0.049–5.672 
 Leukemias 34 0.930 0.585 35 0.995 0.630 0.196–2.022 1.07 0.668 –1.714 
 Leukemias <25 0.496 0.000 0.000     
Ascó           
 Leukemias 54 1.205 14 0.917 41 0.953 0.761 0.423–1.369 0.791 0.528 –1.187 
 Leukemias <25 0.844 0.882 0.770 1.045 0.109–10.041 0.913 0.153 –5.449 
Cofrentes           
 Leukemias 44 1.164 1.023 21 0.818 0.879 0.349–2.214 0.703 0.418 –1.181 
 Leukemias <25 1.530 0.000 0.975   0.637 0.132–3.064 
Trillo           
 Leukemias 0.56 1.554 0.838 2.777 0.622–12.394 1.497 0.423 –5.298 
 Leukemias <25 0.00 0.000 0.000     
a

It was assumed that there was a latency period of 1 year for leukemias and 10 years for all other tumors.

b

Obs, observed cases.

c

SMR is the ratio of the number of observed:expected deaths at concurrent Spanish mortality rates.

d

RR compares risk in study versus reference areas. The RR for combined facilities was obtained from a regression model, including the facilities as a factor, and differs from the simple ratio of the SMRs.

e

Leukemias <25, deaths from leukemia at ages under 25 years.

Table 3

Mortality by cause in areas within a 15- and 30-km radius of NFFs, taking as reference (control) towns lying within a radius of 50–100 kma

Installation/CauseControl0–15 km0–30 km0–15 km0–30 km
ObsbSMRcObsSMRObsSMRRRd95% CIRR95% CI
All NFFs           
 Non-Hodgkin’s lymphomas 89 0.577 22 0.555 85 0.643 1.045 0.646 –1.690 1.101 0.817 –1.482 
 Hodgkin’s disease 40 0.960 11 1.006 31 0.873 0.991 0.502–1.956 0.931 0.582 –1.488 
 Myeloma 74 0.727 22 0.844 71 0.799 1.133 0.695–1.846 1.089 0.785 –1.509 
 Leukemias 372 0.902 101 0.980 337 0.957 1.137 0.907–1.425 1.062 0.916 –1.231 
 Leukemias <25e 70 1.181 18 1.217 57 1.191 1.040 0.608–1.780 1.015 0.716 –1.440 
Andújar           
 Non-Hodgkin’s lymphomas 41 0.499 13 0.447 34 0.524 0.896 0.481–1.669 1.050 0.667 –1.653 
 Hodgkin’s disease 26 1.035 0.886 16 0.800 0.856 0.389–1.883 0.772 0.415 –1.436 
 Myeloma 38 0.691 18 0.950 34 0.792 1.373 0.784–2.405 1.146 0.722 –1.818 
 Leukemias 138 0.829 61 1.030 142 1.074 1.242 0.919–1.679 1.296 1.025 –1.638 
 Leukemias <25 33 1.255 13 1.231 30 1.344 0.981 0.517–1.863 1.071 0.654 –1.754 
El Cabrilf           
 Non-Hodgkin’s lymphomas 15 0.695   19 0.741   1.067 0.543–2.097 
 Hodgkin’s disease 0.450   0.795   1.768 0.442–7.065 
 Myeloma 11 0.779   16 0.872   1.120 0.521–2.408 
 Leukemias 40 0.913   55 1.058   1.159 0.289–4.646 
 Leukemias <25 0.794   1.149   1.446 0.490–4.268 
La Haba           
 Non-Hodgkin’s lymphomas 28 0.705 0.663 25 0.788 0.941 0.334–2.653 1.117 0.652 –1.915 
 Hodgkin’s disease 11 1.354 0.876 1.103 0.647 0.084–4.998 0.814 0.316 –2.101 
 Myeloma 16 0.629 0.484 16 0.772 0.770 0.177–3.345 1.226 0.615 –2.445 
 Leukemias 142 1.057 19 0.923 97 0.890 0.874 0.541–1.410 0.842 0.651 –1.090 
 Leukemias <25 29 1.421 0.944 16 1.085 0.664 0.160–2.756 0.763 0.416 –1.402 
Ciudad Rodrigo           
 Non-Hodgkin’s lymphomas 0.461 1.105 0.697 2.397 0.697–8.238 1.511 0.483 –4.733 
 Hodgkin’s disease 0.000 2.626 1.231     
 Myeloma 1.222 0.667 0.728 0.546 0.118–2.525 0.596 0.201 –1.763 
 Leukemias 25 0.598 18 1.006 30 0.758 1.683 0.919–3.082 1.268 0.746 –2.153 
 Leukemias <25 0.000 1.738 0.986     
Juzbado           
 Leukemias 27 1.044 0.556 13 0.670 0.533 0.165–1.721 0.642 0.333 –1.237 
 Leukemias<25 11 1.293 1.301 0.616 1.006 0.130–7.770 0.477 0.167 –1.363 
Installation/CauseControl0–15 km0–30 km0–15 km0–30 km
ObsbSMRcObsSMRObsSMRRRd95% CIRR95% CI
All NFFs           
 Non-Hodgkin’s lymphomas 89 0.577 22 0.555 85 0.643 1.045 0.646 –1.690 1.101 0.817 –1.482 
 Hodgkin’s disease 40 0.960 11 1.006 31 0.873 0.991 0.502–1.956 0.931 0.582 –1.488 
 Myeloma 74 0.727 22 0.844 71 0.799 1.133 0.695–1.846 1.089 0.785 –1.509 
 Leukemias 372 0.902 101 0.980 337 0.957 1.137 0.907–1.425 1.062 0.916 –1.231 
 Leukemias <25e 70 1.181 18 1.217 57 1.191 1.040 0.608–1.780 1.015 0.716 –1.440 
Andújar           
 Non-Hodgkin’s lymphomas 41 0.499 13 0.447 34 0.524 0.896 0.481–1.669 1.050 0.667 –1.653 
 Hodgkin’s disease 26 1.035 0.886 16 0.800 0.856 0.389–1.883 0.772 0.415 –1.436 
 Myeloma 38 0.691 18 0.950 34 0.792 1.373 0.784–2.405 1.146 0.722 –1.818 
 Leukemias 138 0.829 61 1.030 142 1.074 1.242 0.919–1.679 1.296 1.025 –1.638 
 Leukemias <25 33 1.255 13 1.231 30 1.344 0.981 0.517–1.863 1.071 0.654 –1.754 
El Cabrilf           
 Non-Hodgkin’s lymphomas 15 0.695   19 0.741   1.067 0.543–2.097 
 Hodgkin’s disease 0.450   0.795   1.768 0.442–7.065 
 Myeloma 11 0.779   16 0.872   1.120 0.521–2.408 
 Leukemias 40 0.913   55 1.058   1.159 0.289–4.646 
 Leukemias <25 0.794   1.149   1.446 0.490–4.268 
La Haba           
 Non-Hodgkin’s lymphomas 28 0.705 0.663 25 0.788 0.941 0.334–2.653 1.117 0.652 –1.915 
 Hodgkin’s disease 11 1.354 0.876 1.103 0.647 0.084–4.998 0.814 0.316 –2.101 
 Myeloma 16 0.629 0.484 16 0.772 0.770 0.177–3.345 1.226 0.615 –2.445 
 Leukemias 142 1.057 19 0.923 97 0.890 0.874 0.541–1.410 0.842 0.651 –1.090 
 Leukemias <25 29 1.421 0.944 16 1.085 0.664 0.160–2.756 0.763 0.416 –1.402 
Ciudad Rodrigo           
 Non-Hodgkin’s lymphomas 0.461 1.105 0.697 2.397 0.697–8.238 1.511 0.483 –4.733 
 Hodgkin’s disease 0.000 2.626 1.231     
 Myeloma 1.222 0.667 0.728 0.546 0.118–2.525 0.596 0.201 –1.763 
 Leukemias 25 0.598 18 1.006 30 0.758 1.683 0.919–3.082 1.268 0.746 –2.153 
 Leukemias <25 0.000 1.738 0.986     
Juzbado           
 Leukemias 27 1.044 0.556 13 0.670 0.533 0.165–1.721 0.642 0.333 –1.237 
 Leukemias<25 11 1.293 1.301 0.616 1.006 0.130–7.770 0.477 0.167 –1.363 
a

It was assumed that there was a latency period of 1 year for leukemias and 10 years for all other tumors.

b

Obs, observed cases.

c

SMR is the ratio of the number of observed:expected deaths at concurrent Spanish mortality rates.

d

RR compares risk in study versus reference areas. The RR for combined facilities was obtained from a regression model including the facilities as a factor and differs from the simple ratio of the SMRs.

e

Leukemias <25, deaths from leukemia at ages under 25 years.

f

No towns within 15 km of the installation.

Table 4

RR according to distance of population centroids to NPPs and NFFsa

Installationb/CauseDistance, km (reference >50 km)c
26.8–3023.2–26.719–23.113.4–18.90–13.3P for trend
All NPPs       
 Non-Hodgkin’s lymphomas 0.696 2.021 0.800 0.706 0.939 0.2272 
 Hodgkin’s disease 0.754 1.169 0.957 2.178 1.121 0.3526 
 Myeloma 1.070 0.738 1.341 2.128 1.533 0.2403 
 Leukemias 0.778 0.984 1.105 0.965 1.034 0.9852 
 Leukemias <25d 0.285 1.618 0.735 0.413 1.272 0.2702 
Zorita       
 Non-Hodgkin’s lymphomas 0.342 2.334 0.602 2.382 1.163 0.9136 
 Myeloma 1.310 2.712 2.565 8.120 4.917 0.0164 
 Leukemias 0.777 1.351 1.086 0.718 1.495 0.8028 
Garoña       
 Non-Hodgkin’s lymphomas 0.555 2.361 0.655 0.263 1.034 0.1842 
 Myeloma 1.125 0.004 1.575 1.182 2.530 0.3983 
 Leukemias 0.947 0.002 1.128 1.381 0.778 0.4307 
Vandellós       
 Non-Hodgkin’s lymphomas 0.722 2.506 1.582 0.859 0.612 0.9807 
 Myeloma 0.822 0.347 0.273 0.837 0.341 0.0292 
 Leukemias 0.739 0.813 1.541 0.878 1.360 0.5291 
Almaraz       
 Leukemias 1.106 2.071 1.162 0.943 0.782 0.9083 
Ascó       
 Leukemias 0.303 0.951 0.848 0.934 0.637 0.2219 
Cofrentes       
 Leukemias 0.687 1.521 2.953 0.402 1.529 0.7614 
Trillo       
 Leukemias 0.001 1.577 0.953 1.609 6.874 0.3064 
       
All NFFs       
 Non-Hodgkin’s lymphomas 1.022 0.985 1.013 1.216 1.178 0.9147 
 Hodgkin’s disease 0.740 1.251 1.093 0.617 1.080 0.8968 
 Myeloma 1.026 0.636 1.098 1.323 1.188 0.4224 
 Leukemias 0.943 0.924 1.037 1.192 1.125 0.3971 
 Leukemias <25 1.065 0.849 0.707 1.323 0.957 0.7386 
Andújar       
 Non-Hodgkin’s lymphomas 1.127 1.618 0.863 1.449 0.827 0.7260 
 Hodgkin’s disease 0.366 1.354 0.916 0.001 0.940 0.9845 
 Myeloma 0.373 0.747 1.460 0.820 1.343 0.3254 
 Leukemias 1.033 1.087 1.499 1.624 1.137 0.1077 
 Leukemias <25 1.296 0.904 0.979 1.466 0.886 0.9340 
El Cabril       
 Non-Hodgkin’s lymphomas 0.658 0.194 0.288 0.731  0.3179 
 Myeloma 0.679 0.001 0.239 3.293  0.8628 
 Leukemias 1.229 0.593 2.436 2.372  0.4156 
La Haba       
 Non-Hodgkin’s lymphomas 1.125 0.002 0.913 1.074 2.228 0.8382 
 Myeloma 2.142 0.003 1.008 1.177 0.000 0.9397 
 Leukemias 0.800 0.552 0.558 0.987 0.756 0.1231 
 Leukemias <25 1.141 0.001 0.000 1.077 0.000 0.3643 
Ciudad Rodrigo       
 Leukemias 0.648 1.027 0.851 0.600 2.063 0.2084 
Juzbado       
 Leukemias 0.731 0.998 0.001 1.480 0.533 0.5364 
Installationb/CauseDistance, km (reference >50 km)c
26.8–3023.2–26.719–23.113.4–18.90–13.3P for trend
All NPPs       
 Non-Hodgkin’s lymphomas 0.696 2.021 0.800 0.706 0.939 0.2272 
 Hodgkin’s disease 0.754 1.169 0.957 2.178 1.121 0.3526 
 Myeloma 1.070 0.738 1.341 2.128 1.533 0.2403 
 Leukemias 0.778 0.984 1.105 0.965 1.034 0.9852 
 Leukemias <25d 0.285 1.618 0.735 0.413 1.272 0.2702 
Zorita       
 Non-Hodgkin’s lymphomas 0.342 2.334 0.602 2.382 1.163 0.9136 
 Myeloma 1.310 2.712 2.565 8.120 4.917 0.0164 
 Leukemias 0.777 1.351 1.086 0.718 1.495 0.8028 
Garoña       
 Non-Hodgkin’s lymphomas 0.555 2.361 0.655 0.263 1.034 0.1842 
 Myeloma 1.125 0.004 1.575 1.182 2.530 0.3983 
 Leukemias 0.947 0.002 1.128 1.381 0.778 0.4307 
Vandellós       
 Non-Hodgkin’s lymphomas 0.722 2.506 1.582 0.859 0.612 0.9807 
 Myeloma 0.822 0.347 0.273 0.837 0.341 0.0292 
 Leukemias 0.739 0.813 1.541 0.878 1.360 0.5291 
Almaraz       
 Leukemias 1.106 2.071 1.162 0.943 0.782 0.9083 
Ascó       
 Leukemias 0.303 0.951 0.848 0.934 0.637 0.2219 
Cofrentes       
 Leukemias 0.687 1.521 2.953 0.402 1.529 0.7614 
Trillo       
 Leukemias 0.001 1.577 0.953 1.609 6.874 0.3064 
       
All NFFs       
 Non-Hodgkin’s lymphomas 1.022 0.985 1.013 1.216 1.178 0.9147 
 Hodgkin’s disease 0.740 1.251 1.093 0.617 1.080 0.8968 
 Myeloma 1.026 0.636 1.098 1.323 1.188 0.4224 
 Leukemias 0.943 0.924 1.037 1.192 1.125 0.3971 
 Leukemias <25 1.065 0.849 0.707 1.323 0.957 0.7386 
Andújar       
 Non-Hodgkin’s lymphomas 1.127 1.618 0.863 1.449 0.827 0.7260 
 Hodgkin’s disease 0.366 1.354 0.916 0.001 0.940 0.9845 
 Myeloma 0.373 0.747 1.460 0.820 1.343 0.3254 
 Leukemias 1.033 1.087 1.499 1.624 1.137 0.1077 
 Leukemias <25 1.296 0.904 0.979 1.466 0.886 0.9340 
El Cabril       
 Non-Hodgkin’s lymphomas 0.658 0.194 0.288 0.731  0.3179 
 Myeloma 0.679 0.001 0.239 3.293  0.8628 
 Leukemias 1.229 0.593 2.436 2.372  0.4156 
La Haba       
 Non-Hodgkin’s lymphomas 1.125 0.002 0.913 1.074 2.228 0.8382 
 Myeloma 2.142 0.003 1.008 1.177 0.000 0.9397 
 Leukemias 0.800 0.552 0.558 0.987 0.756 0.1231 
 Leukemias <25 1.141 0.001 0.000 1.077 0.000 0.3643 
Ciudad Rodrigo       
 Leukemias 0.648 1.027 0.851 0.600 2.063 0.2084 
Juzbado       
 Leukemias 0.731 0.998 0.001 1.480 0.533 0.5364 
a

It was assumed that there was a latency period of 1 year for leukemias and 10 years for all other tumors.

b

Only sites with 10 or more observed deaths are shown.

c

Estimates have been adjusted for matching variables. The most distant towns (radius, 50–100 km) are taken as reference.

d

Leukemias <25, deaths from leukemia at ages under 25 years.

Table 5

Estimated relative risk for study areas before and after the date on which nuclear facilities first came into operation (pre- and post-start-up)a

Pre-start-upPost-start-upHomogeneity P
Control0–30 km0–30 km
ObsbSMRcObsSMRRRd95% CIRR95% CI
NPPS          
 Zorita     1975–78e  1979–93   
  Non-Hodgkin’s lymphomas 0.397 0.000   0.949 0.440 –2.046 0.2324 
  Hodgkin’s disease 0.670 0.599 0.894 0.056–14.257 2.432 0.475 –12.453 0.5443 
  Myeloma 0.570 0.479 0.840 0.053–13.398 4.354 1.497 –12.663 0.2939 
 Garoña     1975–80  1981–93   
  Non-Hodgkin’s lymphomas 0.315 0.679 2.155 0.439–10.581 0.606 0.335 –1.096 0.1205 
  Hodgkin’s disease 0.880 1.276 1.450 0.364–5.766 0.440 0.105 –1.840 0.2306 
  Myeloma 0.930 0.330 0.355 0.066–1.909 1.808 0.702 –4.658 0.0891 
 Vandellós     1975–81  1982–93   
  Non-Hodgkin’s lymphomas 0.340 0.688 2.022 0.423–9.660 1.225 0.662 –2.268 0.5483 
  Hodgkin’s disease 0.666 0.935 1.403 0.276–7.138 2.150 0.604 –7.647 0.6898 
  Myeloma 0.981 0.895 0.912 0.257–3.229 0.721 0.354 –1.469 0.7510 
 Almaraz     1975–90 (75–80)  1991–93 (81–93)   
  Non-Hodgkin’s lymphomas 15 0.726 0.459 0.632 0.277 –1.443    
  Hodgkin’s disease 1.119 0.725 0.648 0.213 –1.967    
  Myeloma 0.345 0.592 1.713 0.562–5.217 0.525 0.049 –5.672 0.3665 
  Leukemias 15 0.819 16 0.908 1.109 0.550–2.237 1.070 0.668 –1.714 0.9331 
  Leukemias <25 1.462 1.128 0.771 0.208 –2.864    
 Ascó     1975–93 (75–83)  (84–93)   
  Non-Hodgkin’s lymphomas 26 0.673 24 0.632 0.939 0.539 –1.635    
  Hodgkin’s disease 0.707 13 1.181 1.670 0.696 –4.008    
  Myeloma 17 0.625 18 0.648 1.037 0.535 –2.011    
  Leukemias 29 0.922 21 0.638 0.692 0.396–1.211 0.791 0.528 –1.187 0.7052  
  Leukemias <25 0.924 0.547 0.592 0.109–3.226 0.913 0.153 –5.449 0.7308  
 Cofrentes     1975–93 (75–85)  (86–93)   
  Non-Hodgkin’s lymphomas 34 0.948 24 0.969 1.022 0.607 –1.721    
  Hodgkin’s disease 11 0.971 1.090 1.123 0.454 –2.774    
  Myeloma 22 0.950 18 1.023 1.077 0.578 –2.006    
  Leukemias 32 0.918 20 0.807 0.879 0.503–1.535 0.703 0.418 –1.181 0.5655 
  Leukemias <25 1.049 1.875 1.787 0.648–4.927 0.637 0.132 –3.064 0.2611 
 Trillo     1975–93 (75–88)  (89–93)   
  Non-Hodgkin’s lymphomas 0.248 0.169 0.681 0.116 –3.999    
  Hodgkin’s disease 0.301 0.609 2.020 0.185 –22.069    
  Myeloma 0.326 0.672 2.063 0.518 –8.212    
  Leukemias 13 0.751 12 0.710 0.944 0.431 –2.068 1.497 0.423 –5.298 0.5414 
NFFs          
 La Haba     1975–86 (75–78)  1987–93 (79–93)   
  Non-Hodgkin’s lymphomas 27 0.734 13 0.418 0.570 0.295–1.101 1.117 0.652 –1.915 0.1179 
  Hodgkin’s disease 24 1.554 13 0.999 0.643 0.329 –1.256 0.814 0.316 –2.101 0.6902 
  Myeloma 18 0.719 15 0.690 0.960 0.485–1.898 1.226 0.615 –2.445 0.6216 
  Leukemias 20 0.985 17 0.965 0.980 0.518–1.853 0.842 0.651 –1.090 0.6707 
  Leukemias <25 1.006 0.259 0.258 0.030–2.186 0.763 0.416 –1.402 0.2960 
 Ciudad Rodrigo     1975–88 (75–79)  1989–93 (80–93)   
  Non-Hodgkin’s lymphomas 13 0.804 0.458 0.569 0.227–1.424 1.511 0.483 –4.733 0.1874 
  Hodgkin’s disease 0.675 0.538 0.797 0.178 –3.559    
  Myeloma 0.488 0.596 1.222 0.412–3.625 0.596 0.201 –1.763 0.3579 
  Leukemias 0.664 10 0.866 1.304 0.515–3.304 1.268 0.746 –2.153 0.9582 
  Leukemias <25 0.547 0.608 1.110 0.070 –17.694    
 Juzbado     1975–93 (75–86)  (87–93)   
  Non-Hodgkin’s lymphomas 14 0.492 12 0.589 1.198 0.555 –2.585    
  Hodgkin’s disease 0.724 0.839 1.159 0.354 –3.794    
  Myeloma 16 0.772 11 0.759 0.984 0.457 –2.116    
  Leukemias 24 0.761 22 1.003 1.319 0.740–2.352 0.642 0.333 –1.237 0.1050 
  Leukemias <25 0.787 1.389 1.763 0.398–7.811 0.511 0.046 –5.629 0.3784 
Pre-start-upPost-start-upHomogeneity P
Control0–30 km0–30 km
ObsbSMRcObsSMRRRd95% CIRR95% CI
NPPS          
 Zorita     1975–78e  1979–93   
  Non-Hodgkin’s lymphomas 0.397 0.000   0.949 0.440 –2.046 0.2324 
  Hodgkin’s disease 0.670 0.599 0.894 0.056–14.257 2.432 0.475 –12.453 0.5443 
  Myeloma 0.570 0.479 0.840 0.053–13.398 4.354 1.497 –12.663 0.2939 
 Garoña     1975–80  1981–93   
  Non-Hodgkin’s lymphomas 0.315 0.679 2.155 0.439–10.581 0.606 0.335 –1.096 0.1205 
  Hodgkin’s disease 0.880 1.276 1.450 0.364–5.766 0.440 0.105 –1.840 0.2306 
  Myeloma 0.930 0.330 0.355 0.066–1.909 1.808 0.702 –4.658 0.0891 
 Vandellós     1975–81  1982–93   
  Non-Hodgkin’s lymphomas 0.340 0.688 2.022 0.423–9.660 1.225 0.662 –2.268 0.5483 
  Hodgkin’s disease 0.666 0.935 1.403 0.276–7.138 2.150 0.604 –7.647 0.6898 
  Myeloma 0.981 0.895 0.912 0.257–3.229 0.721 0.354 –1.469 0.7510 
 Almaraz     1975–90 (75–80)  1991–93 (81–93)   
  Non-Hodgkin’s lymphomas 15 0.726 0.459 0.632 0.277 –1.443    
  Hodgkin’s disease 1.119 0.725 0.648 0.213 –1.967    
  Myeloma 0.345 0.592 1.713 0.562–5.217 0.525 0.049 –5.672 0.3665 
  Leukemias 15 0.819 16 0.908 1.109 0.550–2.237 1.070 0.668 –1.714 0.9331 
  Leukemias <25 1.462 1.128 0.771 0.208 –2.864    
 Ascó     1975–93 (75–83)  (84–93)   
  Non-Hodgkin’s lymphomas 26 0.673 24 0.632 0.939 0.539 –1.635    
  Hodgkin’s disease 0.707 13 1.181 1.670 0.696 –4.008    
  Myeloma 17 0.625 18 0.648 1.037 0.535 –2.011    
  Leukemias 29 0.922 21 0.638 0.692 0.396–1.211 0.791 0.528 –1.187 0.7052  
  Leukemias <25 0.924 0.547 0.592 0.109–3.226 0.913 0.153 –5.449 0.7308  
 Cofrentes     1975–93 (75–85)  (86–93)   
  Non-Hodgkin’s lymphomas 34 0.948 24 0.969 1.022 0.607 –1.721    
  Hodgkin’s disease 11 0.971 1.090 1.123 0.454 –2.774    
  Myeloma 22 0.950 18 1.023 1.077 0.578 –2.006    
  Leukemias 32 0.918 20 0.807 0.879 0.503–1.535 0.703 0.418 –1.181 0.5655 
  Leukemias <25 1.049 1.875 1.787 0.648–4.927 0.637 0.132 –3.064 0.2611 
 Trillo     1975–93 (75–88)  (89–93)   
  Non-Hodgkin’s lymphomas 0.248 0.169 0.681 0.116 –3.999    
  Hodgkin’s disease 0.301 0.609 2.020 0.185 –22.069    
  Myeloma 0.326 0.672 2.063 0.518 –8.212    
  Leukemias 13 0.751 12 0.710 0.944 0.431 –2.068 1.497 0.423 –5.298 0.5414 
NFFs          
 La Haba     1975–86 (75–78)  1987–93 (79–93)   
  Non-Hodgkin’s lymphomas 27 0.734 13 0.418 0.570 0.295–1.101 1.117 0.652 –1.915 0.1179 
  Hodgkin’s disease 24 1.554 13 0.999 0.643 0.329 –1.256 0.814 0.316 –2.101 0.6902 
  Myeloma 18 0.719 15 0.690 0.960 0.485–1.898 1.226 0.615 –2.445 0.6216 
  Leukemias 20 0.985 17 0.965 0.980 0.518–1.853 0.842 0.651 –1.090 0.6707 
  Leukemias <25 1.006 0.259 0.258 0.030–2.186 0.763 0.416 –1.402 0.2960 
 Ciudad Rodrigo     1975–88 (75–79)  1989–93 (80–93)   
  Non-Hodgkin’s lymphomas 13 0.804 0.458 0.569 0.227–1.424 1.511 0.483 –4.733 0.1874 
  Hodgkin’s disease 0.675 0.538 0.797 0.178 –3.559    
  Myeloma 0.488 0.596 1.222 0.412–3.625 0.596 0.201 –1.763 0.3579 
  Leukemias 0.664 10 0.866 1.304 0.515–3.304 1.268 0.746 –2.153 0.9582 
  Leukemias <25 0.547 0.608 1.110 0.070 –17.694    
 Juzbado     1975–93 (75–86)  (87–93)   
  Non-Hodgkin’s lymphomas 14 0.492 12 0.589 1.198 0.555 –2.585    
  Hodgkin’s disease 0.724 0.839 1.159 0.354 –3.794    
  Myeloma 16 0.772 11 0.759 0.984 0.457 –2.116    
  Leukemias 24 0.761 22 1.003 1.319 0.740–2.352 0.642 0.333 –1.237 0.1050 
  Leukemias <25 0.787 1.389 1.763 0.398–7.811 0.511 0.046 –5.629 0.3784 
a

It was assumed that there was a latency period of 1 year for leukemias and 10 years for all other tumors.

b

Obs, observed cases.

c

SMR is the ratio of the number of observed:the number of expected deaths at concurrent Spanish mortality rates.

d

RR compares risk in study versus reference areas. The RR for combined facilities was obtained from a regression model including the facilities as a factor and differs from the simple ratio of the SMRs.

e

Years included for lymphomas and myeloma. Years included for leukemias are in parentheses.

f

Leukemias <25, deaths from leukemia at ages under 25 years.

We are grateful to Miguel Mata, Nuria Garreta, and the National Statistics Institute for their help in obtaining the mortality and population data. We thank Michael Benedict for his help with the English.

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