Public Concerns Regarding the Effect of Nantygwyddon Landfill Site

Home , Maerdy, Pentre, Tonypandy, Trealaw, Tylorstown, Ystrad

Public concerns regarding the effect of Nant-y-Gwyddon Landfill Site (NYG) on the incidence of Non Hodgkin’s Lymphoma (NHL) in the South Wales Rhondda Valley

Steward JA, Wright M, White C, Wade R Welsh Cancer Intelligence & Surveillance Unit, Cardiff (WCISU), Wales, UK

Background

In response to public concern about the health effects of living close to Nant-y- Gwyddon Landfill site (referred to in this document as NYG), WCISU were asked by the local resident campaign group, RANT (Rhondda Against Nant-y-Gwyddon Tip) and also the Welsh Assembly, to investigate a possible link between residence close to the site and the incidence of Non-Hodgkin’s Lymphoma (NHL). The landfill site opened in January 1988 and was closed due to public pressure in March 2002.

Emissions from the Nant-y-Gwyddon landfill site

Landfill gas is produced by the decomposition of organic material contained in the waste. Environmental monitoring at the site commissioned by the Environment Agency identified 117 different compounds in the raw landfill gas at NYG1. Very high levels of hydrogen sulphide were reported and several other compounds – styrene, dimethyl styrene, ethylbenzene and C4 alkyl benzenes – were found at concentrations exceeding those reported at other UK landfill sites.

On four occasions between July and September 1997 community air samples were collected for detailed analysis. The results showed that with the exception of benzene, the carcinogenic agents present in the raw landfill gas sampled were not found at measurable concentrations in the community.

Benzene is a colourless liquid with a sweet odour. It evaporates into the air very quickly and dissolves slightly in water. Outdoor air contains low levels of benzene from tobacco smoke, automobile service stations, exhaust from motor vehicles and industrial emissions. Indoor air quality contains higher levels of benzene from products that contain it, such as glues, paints, furniture wax and detergents. A major source of benzene exposure is tobacco smoke. Long term 2,3 exposure to high levels of benzene in the air can cause leukaemia

Average ambient air concentration is between 5-20µg/m3(WHO). The air quality standard accepted by the Government recommended by the Expert Panel on Air Quality Standards is 16.25µg/m3 [5 parts per billion (ppb)]. However, they also recommend that the Government should work towards a running annual average of 1ppb. The current long term Environmental Assessment Level (EAL) is set at 3.24µg/m3 (1ppb).

Benzene was observed in concentrations exceeding its long term EAL in three of the air samples collected in areas away from the landfill (500m-1km) – measurements of 17.9µg/m3, 12µg/m3 and 5.8µg/m3 were recorded.

Modelling suggests that the contribution of benzene to the community air from landfill gas is in the order of 1.1 ppb (3.24µg/m3) - less than benzene derived from other sources (e.g. vehicle emissions).

Air around the site was monitored again following the closure of the site. Between January and June 2002 monitoring took place weekly4 Three areas around the site were compared to two control locations in Pontypridd. The mean (maximum) concentrations of benzene found at Jones Street (Blaenclydach), Berw Road (Tonypandy) and Primrose Hill (Gelli) were 0.22ppb(1.71ppb), 0.4ppb(4.65ppb) and 0.0ppb(0.00ppb) respectively. In the Sardis Road and Llanfair Road control sites levels were 0.58ppb(2.24ppb) and 0.06ppb(0.8ppb) respectively. Comparison of levels will only be sensible if the study and control sites are comparable (with respect to traffic volume for example). Toulene and Xylene levels were also monitored. Levels of these were no different in the NYG area to those observed in Pontypridd. All were within the current WHO guidelines.

A toxicological review by the Institute for Environment and Health (IEH) for the independent investigator5 considers all possible pathways of exposure including gasses, contamination of water sources and dispersal of dust and concludes that apart from benzene and hydrogen sulphide there is little evidence of exposure. The independent investigator’s report6 (Purchon report) itself does not provide specific evidence of exposure although it appears to raise some doubts about the quality of environmental monitoring in the past.

Clinical terminology - Non Hodgkin’s lymphomas – NHL

Malignant lymphomas are malignant neoplasms arising in the lymphatic tissues mainly from B-cells. Lymph nodes are an integral part of the body’s immune system and it seems paradoxical that malignancy should arise in it. Some types of lymphoma are indistinguishable from the lymphoid leukaemias which originate in bone marrow. Hodgkin’s disease (C81) is distinguished by clinical features suggestive of chronic infection, an epidemiology with links to Epstein-Barr virus (EBV) and a characteristic histology which includes the Reed-Sternberg cells. The Non-Hodgkin’s lymphomas (NHL) are basically all the others, a heterogeneous group of neoplasms with a complex classification7. The usual clinical presentation of NHL is enlargement of one or more lymph nodes with progressive general malaise, weight loss, intermittent fever and night sweats.

The categorisation of NHL (using WHO International Classification of Disease (ICD108)) includes follicular NHL (C82), diffuse NHL (C83), T cell lymphomas

2 (C84), other unspecified NHL (C85) and other unspecified lymphoid and haematopoietic neoplasms (C96). Many pathologists and clinicians now use the more detailed Revised European American classification of lymphoid neoplasms (REAL). Most epidemiological studies of NHL are necessarily “broad brush” as they group diverse diseases together. In the 10 year period, 1989-98, NHL was the 8th most common cancer in males and 10th in females in Wales. The corresponding crude rates were 15.9 and 12.9 per 100,000 population per annum, respectively.

Brief review of the research literature

The literature shows unexplained worldwide increases in the incidence of NHL. Most studies suggest that this increase is related to calendar period effects (cross-sectional) rather than birth cohort effects (longitudinal). Some workers consider that this is an artefact of classification changes but most agree that the increase is a real effect, presumably of some unknown environmental factor9,10.

Various potential risk factors have been investigated. By far the strongest association with NHL is with immunodeficiency, whether that be amongst patients with the rare inherited primary disorder, or those with a secondary disorder such as that consequent on HIV infection or taking immunosuppressive drugs e.g. to facilitate transplant. Virus infection has been considered but the association with EBV infection is not so strong as with Hodgkin’s disease, with the exception of lymphomas associated with immunosuppression. The rare adult T-cell lymphoma has been linked to infection by the human retrovirus HTLV-1 that is prevalent in certain tropical areas.

Other types of medical treatment may be associated with NHL. Some studies have suggested links with BCG vaccination, blood transfusion, certain anti-epileptic drugs and cancer chemotherapy. There is weak evidence for a link with medical radiation from the well known Court-Brown and Doll study of ankylosing spondylitis.

Various cohort and case-control studies have highlighted a number of occupations, which are identified as being at increased risk of NHL. These include painters, carpenters, brick and stone masons, plumbers and roofers, rubber workers, chemists, chemical workers, dry cleaners, petroleum refinery workers, printing workers, abattoir workers, beauticians, farmers and wood workers. Amongst the specific risk factors identified in such studies are exposure to chlorophenols/ phenoxyacetic acids, wood dust, thinner, white spirits, solvents, mineral/cutting/lubricating oil, benzene, arsenic, hair dye and pesticides. The association with farming may not be an effect of increased exposure to agri-chemicals although organophosphates, as for example in sheep dips, are immuno-suppressive. Some studies show veterinary surgeons also have a high incidence of NHL. Perhaps animal exposure per se could be a risk factor e.g. by some unknown zoonotic infection? In fact the strongest dietary association with NHL is with the consumption of milk, which is puzzling but could be linked with the latter hypothesis.

High dietary intake of fats and certain meats and low intake of vegetables (particularly cruciferous) have also been found to be risk factors as with some other cancers. Cigarette smoking, tricyclic antidepressants, aspirin, amphetamine or cocaine use, adult-onset diabetes and low birthweight have all been reported in the literature as being associated with excess NHL risk. Other suspected risk factors such

3 as exposure to ethylene oxide, residence proximal to nuclear installations, exposure to sunlight and consumption of tea and coffee have all been found to have no association with NHL after repeated epidemiological studies.

Across the literature on aetiology, many studies present contradictory conclusions however. For example, one study showed no NHL association with exposure to benzene and another stated chorophenols were not a risk factor. Nitrate levels in drinking water, phenoxy-herbicides and alcohol consumption have also caused debate. One problem may be the lack of diagnostic precision available to epidemiological studies and difference in case classifications. 10

Methods

The statistical analysis of putative sources poses several well-known problems 11,12. There is no universally approved method. Thus a variety of methods were employed in an attempt to acquire a robust overview of the situation.

All NHL cases (1983-2001) were extracted from the WCISU database as frozen at May 2002. Using postcodes, the incidence was allocated to defined geographical boundaries, the 1991 census wards in Wales. Numbers of cases, expected numbers, and age-standardised incidence rates (using Wales as the standard population) were examined for wards whose centroid was within 2.5km of NYG. A distance of 2.5km was chosen on the basis of a literature review of distances used in similar studies, and the perceived area of risk identified by RANT. These 5 proximal study wards also corresponded to those used in a previous study13 examining health effects in the area, chosen on the basis of wards from which residents had complained of odours. The observed counts and expected counts (based on all Wales rates) were employed to calculate several useful measures of risk:-

(i) risk difference – the absolute difference between observed and expected counts, (ii) relative risk - the ratio of observed to expected counts, (iii) scaled risk difference (or approximate z score) which is the risk difference divided by the square root of the expected count (hence by an approximation to the Poisson sd).

This latter measure allows for heterogeneity of population size between areas. These measures were also calculated for all Welsh wards and then mapped in order to highlight the areas of higher incidence.

In addition, we obtained co-ordinates of the other 31 main landfill sites in Wales from the Environment Agency (thus we ignored all minor landfill sites unlikely to be of health significance as thus would confuse the picture and attenuate any effect). These sites all opened between 1963 and 1997.

Negative binomial regression 14 (Poisson regression which allows for unstructured random effects and overdispersion) was used to examine these two hypotheses:-

4 (a) did the wards in a 2.5km radius of the main landfill sites in Wales experience higher rates than the other wards in Wales outside this area?

(b) did the area specifically around NYG have a higher incidence rate than the areas within 2.5km of the other main landfill sites in Wales?

If a site was operating at some time during the study period in question, it was included in the analysis. The analysis was then repeated only including sites that had been open for at least 5 years by the study period (1983-2001), to make some allowance for a latency period. For part (b), the opening date was included as a variable to see if this influenced the results. As a crude adjustment for differing age distributions between wards, a variable was added to the models containing the percentage of residents over the age of 50. A variable containing Townsend deprivation score was also added to the model to reflect socio-economic factors.

Next, spatial analysis was carried out using the Kulldorf scan statistic15,16,17,18,19 . This technique effectively “scans” for clusters of wards with high rates of NHL. The spatial scan statistic imposes a circular window on the map. The window is in turn centred around each of several possible centroids positioned throughout the study region (centroids of the electoral ward census areas in our case). For each centroid, the radius of the window varies continuously in size from zero to some upper limit. For each location and size of the scanning window, the alternative hypothesis is that there is an elevated rate within the window as compared to outside. The advantage of the test is that it examines a potentially infinite range of zone sites and does not rely on a formal model of null and alternative hypotheses. As opposed to this “generalised” cluster testing, it is also possible to fix analysis at the co-ordinates of the putative source rather than use the centroids of all census areas (focused testing). The analysis was therefore repeated fixing the co-ordinates at NYG.

Models based on both Poisson and Bernoulli distributions can be analysed. Using a Poisson based model the number of cases in an area (in this instance, census wards) is assumed to be Poisson distributed under the null hypothesis. Thus when there are no covariates, the expected number of cases in each area is proportional to its population size. If there are covariates then an adjusted expected number of cases is used. The Bernoulli model uses event data. Controls are selected for each individual case. We used two sets of controls (i) population controls - anonymised controls were selected at random from the NHS administrative register in a ratio of one case to four controls, (ii) a control disease - lower body cancers (cervix, uterus, prostate, testes) were selected from the cancer registry.

Secondary analysis was carried out using spatial-time analysis, enumeration districts as the areas of interest, and adjusting for Townsend quintile of deprivation.

Throughout all analyses, four distinct time periods were examined:- (i) 1983-87 (pre- opening of site), (ii)1988-92, (iii) 1993-7 and (iv)1998-2001 (most recent data available).

The 1991 Census ward population figures were used to calculate population at risk and expected counts for the 1988-1997 analyses. For the 1983-1987 analysis,

5 approximate 1985 based population estimates were used. These were obtained by taking the relevant UA mid-year estimated from ONS and applying the proportional population change to all wards within that UA. The age-distribution in wards was assumed not to have changed. Similarly 1998-2001 population figures were 2000 based and obtained by the same method.

The following software were used for the analyses:- Stata version 7 and SatScan version 2.1.

Results

Summary statistics for NHL in Wales for the time periods examined are shown below.

Table 1 Summary of data 1983-1987 1988-1992 1993-1997 1998-2001

Number of wards 908 908 908 908 Number of cases 1443 1950 2129 1713 Cases per 100,000 population per year 10.3 13.5 14.6 14.6

The five wards surrounding NYG that have their centroid within a 2.5km radius are Cwm Clydach, Llywn-y-Pia, Pentre, Tonypandy and Ystrad (figure 1).

Figure 1

Maerdy

N N Ferndale Treorchy

N Ystrad Tylorstown N Pentre N

# Llwyn-y-pia N

N N Cwm Clydach N Trealaw Nant-y-Moel N

Tonypandy

N Pen-y-graig Ogmore Vale Blackmill N

6 Summary statistics for these 5 wards are given in table 2.

Table 2 SUMMARY OF DATA 1983-1987 1988-1992 1993-1997 1998-2001 Observed 8 20 11 22 Expected 11.6 15.3 16.1 12.8 Observed minus Expected -3.6 4.7 -5.1 9.2 Age standardised RR (95% 0.69 1.27 0.61 1.70 confidence interval) (0.35,1.40) (0.82,2.00) (0.34,1.11) (1.12,2.60) Age standardised incidence 7.3 17.2 9.0 24.0 rate/100,000 pop per annum (2.2,12.4) (9.6,24.8) (3.7,14.4) (13.9,34.0)

The incidence rate of NHL in these five wards has fluctuated compared to the all Wales rate throughout the time periods examined. Since the local population is much smaller than Wales as a whole this small sample fluctuation is to be expected. In the first and third time periods, the rate of NHL was lower than that observed in Wales as a whole and in the second and fourth time periods it was higher than Wales.

During the latest time period 1998-2001 the excess incidence was statistically significant at the non-simultaneous level (i.e. ignoring the multiple testing at four time periods) at 1.7 times increased risk (95% CI:1.12 to 2.60). The reader should note that this last period is 4 years duration rather than 5 years, thus accounting for the reduced expected count.

Year by year estimates of risk are shown in figure 2. Under the null hypothesis of no difference in risk between the 5 wards and Wales as a whole, the ‘observed-expected statistic’ is expected to be zero. The risk fluctuates around zero. There are some ‘freak’ single years with around 3 cases less or more than expected. Taking 3 year averages ‘smoothes’ out these unusual occurrences and we can see that for the most recent 4 years there have been between 1 and 2 excess cases per year. Caution is required in the interpretation of these results as numbers are small.

7 Figure 2

Observed - Expected for Nantygwyddon

5.00

4.00 3.00

2.00 1.00

0.00

-1.00 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 98 8 98 8 98 98 98 98 98 99 99 99 99 99 99 99 99 99 99 00 00 1 19 1 19 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 -2.00 -3.00 -4.00 Year

Obs -Exp 3 yr moving average

Geographical Mapping

The map below shows scaled estimates of observed minus expected for all Welsh wards for the period 1988-2001. Five categories of equal interval were created. Three of the five wards in question fall into the middle category, approximately equating to observed risk equalling expected risk. Pentre and Tonypandy are areas highlighted as having observed cases exceeding expected, but none of the wards in question fall into the fifth category indicating the highest departure from observed equal to expected.

This map is intended to put the five NYG wards in an overall context. No attempt has been made to smooth these data or reduce small number fluctuations by Bayesian methods. Thus some sparsely populated wards in rural Wales may exhibit extreme values purely by chance and caution is needed over interpretation. However these NYG wards do not appear to be the focus of a cluster of NHL.

8 Observed − Expected By Ward In Wales 1988-2001 Expected

Maerdy Ferndale

Treorchy

Tylorstown Ys trad Pentre

Ll wy n-y-pia Nant-y-Moel Yny shir Cwm Clydach Trealaw

Tonypandy Porth Pen-y-graig

Cymmer

-5 - (-3) -3 - (-1) -1 - 1 1 - 3 3 - 5

Negative Binomial Regression

Tables 3 and 4 present the results of negative binomial regression. The incidence rate ratio (IRR) measures how much higher (or lower) incidence rates in the study wards are compared to the reference wards. A value of 1.00 means rates in the study wards are the same as rates in the reference wards, a value larger than 1.00 that they are higher, and a value smaller than 1.00 that they are lower.

There were approximately 70 out of the 908 Welsh wards within 2.5km of a main landfill site. When comparing these wards with those not proximal to a landfill site (i.e. further than 2.5km) (table 3), there were no significant differences in the incidence rate of NHL. Adjustment was made for population differences and Townsend deprivation score. In the 1988-92 period for example, wards within a 2.5km radius of a landfill site experienced incidence rates of NHL 2% higher than seen in wards further away (95% CI: 23% lower to 34% higher) after adjustment for age and deprivation. This result was not statistically significant however (p=0.9). Allowing for a 5-year lag time made no difference to interpretation of results.

Table 3 Variable IRR (95% confidence P value interval) 1988-92 Ward classification not within 2.5km landfill 1 within 2.5km landfill 1.02 (0.77 , 1.34) 0.90

1993-97 Ward classification not within 2.5km landfill 1 within 2.5km landfill 1.11 (0.88 , 1.41) 0.38

1998-01 Ward classification not within 2.5km landfill 1 within 2.5km landfill 1.23 (0.97 , 1.57) 0.09

Table 4 shows that incidence of NHL in the wards within 2.5km of NYG (i.e. 5 wards) was found to be no different to incidence in those wards within 2.5km of the other 31 main landfill sites in Wales (i.e. approximately 65 wards). Adjusting for age, deprivation and opening date did not alter the findings, neither did allowing for a 5- year lag.

10 Table 4 Variable IRR (95% confidence P value interval) 1988-92 Landfill other landfill 1 classification NYG 1.23 (0.59 , 2.56) 0.59

1993-97 Landfill other landfill 1 classification NYG 0.53 (0.22 , 1.25) 0.15

1998-01 Landfill other landfill 1 classification NYG 1.28 (0.64 , 2.57) 0.48

**********

Cluster Analysis

Scanning a map of Wales using each of the 908 ward centroids (generalised cluster testing) did not highlight the area around NYG as having a significant cluster of NHL in any of the time periods.

When the co-ordinates of NYG were used as the centroid for the spatial analysis (focused cluster testing), no NHL cluster was found for the years up to 1997. In the 1998-2001 time period however, the 5 wards surrounding NYG were included in those identified as the most likely cluster, for the Poisson and both Bernoulli models (i.e. population controls and control disease). Six contiguous wards were in fact identified in this cluster:-Cwm Clydach, Llywn-y-Pia, Pentre, Tonypandy, Trealaw and Ystrad (N.B. Trealaw is the additional ward highlighted). The p values obtained were p=0.03, p=0.04 and p=0.04 respectively for the models used.

Using enumeration districts rather than wards for the 1998-2001 focused analysis (the Bernoulli model only was applied owing to the problem of obtaining updated population estimates prior to the publication of the 2001 census) led to 48 enumeration districts (out of a possible 68) across 7 wards (Pen-y-Graig being the additional ward) being highlighted as a cluster. This result was also of borderline statistical significance (p=0.05). Adjusting for possible socio-economic confounding using the Townsend deprivation quintile did not alter the interpretation of these results.

Therefore, for the years up to 1997, neither the spatial analysis, nor the space-time analysis indicated any significant evidence of elevated risk of NHL in the area surrounding NYG. However, in the most recent period studied, 1998-2001, there is some weak indication of a cluster.

Discussion

There appears to be some evidence, albeit weak evidence, that rates in 1998-2001 are elevated around NYG. There are many problems and limitations in the interpretation of studies of this kind however.

Epidemiological Issues

Studies of geographical variation always need to be interpreted with caution since many factors apart from environmental exposures can contribute to any variation in the recorded frequency of disease – for example, unmeasured confounders (i.e. not taking into account other factors that are related to the disease), quality of diagnosis and classification of disease; accuracy of population estimates; variations due to chance. Problems can also arise due to the fact location of residence at registration is the only location taken into account. Recent migration and day-to-day movement are not considered here as it is impractical to measure these without a very costly study.

Even if elevated risk is observed around a putative source, clusters can appear and disappear depending on choice of time span studied, population denominators and geographical boundaries etc.. For example, the ward population estimates for years prior to and beyond 1991 are very crude. This could seriously bias the results. New housing projects since 1991 could totally alter the denominator for a particular ward and therefore lead to an inflated estimate of risk. The release of 2001 Census data will improve estimates.

In addition, it is not easy to determine whether the putative source is the cause of any increased incidence or there is another explanation. Several criteria have to be met for a cause and effect relationship to be established in epidemiological theory20. These include in this context of suspected clusters:- the strength of the association (e.g. size of the relative risk), the consistency with existing knowledge (literature review), the biological plausibility (mechanism for exposure to known carcinogens), the time sequence (e.g. effects do not precede the cause), specificity (is there a mixture of cancers in the alleged cluster or a specific type?), and existence of biological gradient (e.g. effect of distance as proxy for exposure). In fact in many real life situations, it appears that rather than one cause there is a “web of causation” and it is difficult to pick out the “signal” from the “noise”.

Considering the suggested risk factors for NHL in the literature, there may be confounding factors. For example if carpenters and painters are at higher risk of developing NHL, and the proportion of people around NYG with these occupations is higher than in the rest of Wales, the estimated relative risk in this area may be spuriously high as a result of confounding for which we have not been able to adjust for.

The literature does not provide any evidence that landfill sites in general are associated with NHL incidence. In support of this, we examined all main landfill sites in Wales due to our reservations about the amount of data at a single site. We found

12 no evidence that wards proximal to landfills had different NHL rates to wards further away.

The association with cancer incidence in general and landfill sites is in fact weak. In August 2001 a large national study examined risk of adverse birth outcomes and selected cancers near landfill sites using data on all known sites in Great Britain22. The cancers chosen for study were those considered to have a possible association with landfills. These were bladder, brain, hepatobiliary, childhood and adult leukaemia (very weak borderline evidence was found for bladder and hepatobiliary cancers). NHL was not considered. We looked at NHL as RANT had concerns about this particular cancer. In the past we have previously made available to RANT and other interested parties, cancer incidence rates around NYG for several other possible sites they have also had concerns over. No positive findings have been found.

The residents around NYG are clearly at a heightened state of alertness. Moreover, in our experience, traditional “valley communities” are “closer knit” and tend to have a lower threshold for reporting clusters than affluent rural communities for example.

In the case of NYG, adequate exposure modelling has not been carried out (to our knowledge). The importance of identifying an exposed area with reference to the possible effects of the local topography, prevailing winds and any other meteorological peculiarities that may exist is well documented in spatial epidemiology. Even after considering all the relevant reports and also consulting with RANT, it is still not clear to us what the postulated exposure risk actually comprises of, nor the exposure pathway or mechanism which would explain how this risk could pass from the landfill site to local residents.

One theory would involve discharge of gases whilst other theories relate to contamination of the water supply. Benzene could be a potential causal factor in the sense that it is recognised by IARC and other agencies as a carcinogen. However the literature provides contradictory evidence regarding the association between NHL and benzene - the studies that have been carried out are occupation based, so the subjects are exposed to much higher levels than those recorded in ambient air around the community living near the tip. Benzene is an established risk factor for leukaemia, yet incidence rates of leukaemia in the five wards surrounding NYG were found by us to be no different to those observed in Wales as a whole.

It should be noted that the independent Purchon report commissioned by the Assembly did not find any evidence for exposure of the local population although it implied that monitoring arrangements in the early years were sub-optimal.

Statistical issues

The main inferential problems arising in putative-source studies are (i) ‘post-hoc’ analysis and (ii) multiple comparisons. By ‘post-hoc’ analysis we mean that a supposed effect has been reported or claimed to exist at a location and subsequent analysis is carried out with the knowledge of this effect. Hypotheses should be generated before data collection otherwise results can be biased and p values

13 invalidated. Multiple statistical testing (e.g., considering multiple disease, years, geographical boundaries) gives a high probability of finding a significant difference just by chance. This probability of rejecting the null hypothesis when it is true (known as an alpha level), is often set at a more stringent level than the usual 5% level in the presence of multiple testing.

Expected rates and relative risks (RR) are commonly used to examine the possibility of clusters. There are various problems with this approach. With rare diseases, it is common to see very large RRs based on just one or two actual cases. Just one observed case against an expected value of 0.2, will indicate a 5-fold increase in risk for example. A change of just one or two cases can dramatically alter the results. The associated wide confidence intervals demonstrate the inherent uncertainty of the point estimate and its lack of robustness.

For much of the analysis, it is assumed counts in the regions are independent and Poisson distributed. The usual assumptions for this sampling distribution are often not met. In general there is extra variation in small area health data due to unobserved confounding variables. This extra variability affects the precision of the parameter estimates and can lead to misleading results. The analysis including all Welsh landfill sites attempts to address this issue by using negative binomial regression which allows for overdispersion in Poisson data.

In the national landfill report cited above22, 99% confidence intervals were used rather than the conventional 95% intervals to allow for data anomalies, unmeasured confounding and sampling variability in the rates. If we adopted this approach the 1998-2001 statistically significant relative risk around NYG (table 2) would no longer be significant – the lower CI would become 0.97 – i.e. a 3% lower risk of NHL in the area around NYG. – and the CI would contain unity, RR = 1.0 (non significant).

The results from the 1998-2001 spatial analysis did not achieve a strong level of significance. In addition, the cluster was not identified when the “generalised” method was used. If the risk around NYG was sufficiently elevated, the cluster would have been detected by this “generalised” method. Although the scan statistic addresses the issue of multiple testing with regards to the number of circles drawn, multiple testing issues still apply as several time-periods were considered as well as the two statistics, purely spatial and space-time. Applying the Bonferroni correction for multiple testing would lead to the observed, conventional significance being lost.

We have used expected rates, controls drawn from the population and the incidence of a control disease to carry out the spatial analysis. The control disease shouldn’t be related to the effect of interest. Lower body cancers are thought to be cancers least associated with air pollution sources23. There is much debate around use of controls as opposed to expected rates from external sources. However, despite the existence of a control disease being subject to epidemiological debate, according to Lawson if such data are available, the statistical basis of the methods is sound 24. Reassuringly, our results from Poisson and both Bernoulli methods are internally consistent suggesting the results are robust. Results for population controls supported the use of disease controls.

Conclusion

On the basis of the current evidence, it is not clear to us whether NHL rates are truly elevated in the study area. If there is a ‘true’ raised incidence in more recent years, it is only slight and there is no evidence to attribute it to NYG. It is most likely due to chance.

The independent Purchon report has provided no evidence of exposure to the local residence living near NYG. The only evidence we have found relates to benzene and levels of that were very low.

Owing to statistical reservations about the amount of data at a single site, we examined all main landfill sites in Wales and found no evidence that wards proximal to landfills had different NHL rates to wards further away.

Moreover there is no evidence in the literature to link NHL with landfill sites.

If this were to be investigated further, adequate exposure modelling would need to be carried out and all potential confounders explored (e.g. certain occupations) using the methods of analytical epidemiology. This is likely to be a very expensive exercise and on the basis of the evidence available to date probably not justified.

WCISU intend to continue to monitor incidence of NHL around NYG. In addition, we plan to extend our study to include cancer sites that have been mentioned in the literature as having possible links to landfill sites.

Acknowledgements

We are very grateful for the expert advice, and comments on an earlier draft of this report, from Professor FDJ Dunstan, Professor of Medical Statistics at University of Wales College of Medicine.

References

1. Scott PE, Crozier F, Birch C, Leach A. Investigation into odour problems at Nant-y-Gwyddon landfill, South East Wales: final report. Cardiff:Environment Agency, 1998.

2. Agency for Toxic Substances and Disease Registry (ATSDR). Toxicological profile for benzene. Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service. 1997.

3. Vainio H, Hemminki K, Wilbourn J. Data on the carcinogenicity of chemicals in the IARC Monographs Programme. Carcinogenisis 1985;6:1653-1665

4. David G. Jones and Gareth Purnell. Air quality monitoring in the communities around Nant-y- Gwyddon: Interim report for the period 1 January to 12 June 2002. 2002.

5. IEH: Institute for Environment and Health. Nantygwyddon landfill site, Gelli. Rhondda – Exposure and toxicological review. An IEH client report to the independent investigator, October 2001 (available from http://www.wales.gov.uk/keypubassemeplantrans/content/nant/home-e.htm )

6. Purchon DW. Investigator’s Report. Independent Investigation of Nantygwyddon Landfill Site. Environment, Planning & Transport Committee. National Assembly for Wales, 12th December 2001. (see http://www.wales.gov.uk/keypubassemeplantrans/content/nant/nant-report.pdf)

7. MacGrath IT, Introduction: concepts and controversies in lymphoid neoplasia. Chapter 1 in: MacGrath IT (ed). The Non-Hodgkin Lymphomas (2nd edition). Arnold, London 1997.

8. WHO, ICD10: International Classification of Diseases and Related Problems (tenth revision). World Health Organization, Geneva 1992.

9. Rabkin CS, Ward MH, Manns A, Blattner WA. Epidemiology of non-Hodgkin’s lymphomas. Chapter 6 in: MacGrath IT (ed). The Non-Hodgkin Lymphomas (2nd edition). Arnold, London 1997.

10. Scherr PA, Mueller NE. Non-Hodgkin’s Lymphomas. Chapter 42 in: Schottenfeld D and Fraumeni JF. Cancer Epidemiology and Prevention. Oxford University Press, New York 1996.

11. Lawson AB, Biggeri A, Williams FLR. A review of modelling approaches in risk assessment around putative sources. Chapter 17 in; Lawson AB, Biggeri A, Bohning D et al (eds.) Disease Mapping and Risk Assessment for Public Health. John Wiley & Sons, Chichester 1999.

12. Morris SE, Wakefield LC. Assessment of disease risk in relation to a predefined source. Chapter 9 in: Elliott P, Wakefield J, Best N and Briggs D (eds). Spatial Epidemiology Methods and Applications. Oxford University Press, Oxford 2001.

13. Fielder HMP, Poon-King CM, Palmer SR, N Moss N, Coleman G. Assessment of impact on health of residents living near the Nant-y-Gwyddon landfill site: retrospective analysis. BMJ 2000; 320:19-23.

14. Wakefield JC, Best NG, Waller L. Bayesian approaches to disease mapping. Chapter 7 in Elliott P, Wakefield J, Best N and Briggs D (eds). Spatial Epidemiology Methods and Applications. Oxford University Press, Oxford 2001.

15. Kulldorf M and Nargawalla N. Spatial disease clusters: detection and inference. Statistics in Medicine 1995;14:799-810.

16, Kulldorff M. A spatial scan statistic. Communications in Statistics: Theory and Methods, 1997;26:1481-1496.

16 17. Kulldorff M, Feuer EJ, Miller BA, Freedman LS. Breast cancer in northeastern United States: A geographical analysis. American Journal of Epidemiology, 1997;146:161-170.

18. Kulldorff M, Athas WF, Feuer EJ, Miller BA, Key CR. Evaluating Cluster Alarms: A space-time scan statistic and brain cancer in Los Alamos. American Journal of Public Health, 1998; 88:1377- 1380.

19. Martin Kulldorff, Katherine Rand, Greg Gherman, Gray Williams, and David DeFrancesco: SaTScan v2.1: Software for the spatial and space-time scan statistics. Bethesda, MD: National Cancer Institute, 1998

20. MacMahon B, Trichopoulos D. Epidemiology, Principles and Methods. Little, Brown and Company, Boston 1996. (chapter 2: concepts of cause refers)

21 ATSDR. HazDat database. Agency for Toxic Substances and Disease Registry. (available from: http://www.atsdr.cdc.gov/hazdat.html)

22. Elliot P et al. Birth outcomes and selected cancers in populations living near landfill sites. The Small Area Health Statistics Unit. 2001

23 Lawson AB (personal communication)

24. Lawson AB. Statistical Methods in Spatial Epidemiology. Wiley series in probability and statistics. John Wiley & Sons, Chichester 2001 (chapter 7 Small scale: putative sources of hazard – 7.2 study design refers)