N E West
email: j.west@see.leeds.ac.uk.n.odling@see.leeds.ac.uk

S H Odling

Bottrell

Alison H Parker
email: a.parker@see.leeds.ac.uk

L J West

N E Odling

S H Bottrell
email: s.bottrell@see.leeds.ac.uk

Measuring groundwater parameters to improve modeling and regulation

Keywords: water resources, contaminants, pollution, modeling, quality

Measuring groundwater parameters to improve modeling and regulation

INTRODUCTION

In the UK, groundwater provides about 40% of the drinking water supplies (UK Groundwater Forum, 1998). By 2002, 10% of aquifers had urbanized land directly above them. Whilst there is an increasing trend for abstracting water in rural areas and pumping it to the cities, [START_REF] Morris | Using linked process models to improve urban groundwater management: an example from Doncaster England[END_REF], many public water drinking supplies are still pumped from aquifers in urban areas. In addition many industries are based in the cities, and their private abstraction wells put additional pressure on groundwater. Groundwater is usually considered to be much cleaner than surface water, and often requires little or no treatment before it can be used for drinking. However, overabstraction can lead to deterioration in quality, as low quality water is drawn in from neighboring sources, including other aquifers, rivers or the sea. Urbanization brings additional threats to groundwater quality from sources like petrol filling stations, waste disposal, leaking septic tanks, leaking sewers and the removal of any protective confining aquifer layer during construction works [START_REF] Aldrick | Urban groundwater and environment management: Hull, East Yorkshire[END_REF]. Urban drainage can also decrease soil infiltration rates, so rainfall never enters the aquifer. In the UK, land use and groundwater abstraction rates are now regulated by the Environment Agency in order to maintain high groundwater quality.

In order to be effective regulators, the Environment Agency maintain a series of groundwater flow models which can predict the impact of changes in abstraction rates, and track potential contaminants. Whilst many of the input parameters for these models are well known, some are much harder to measure, including hydraulic conductivity. Hydraulic conductivity is defined as "the rate at which water flows through a rock", and together with the pressure gradient, controls the speed of groundwater flows, which ultimately controls how much water can be abstracted and the speed and dilution rates of any potential contaminant plumes. Whilst the methods for measuring an average value of hydraulic conductivity over the depth of the aquifer are standard, methods for establishing the variations in hydraulic conductivity with depth are still being developed. It is important to understand these variations. For example, where the horizons with high hydraulic conductivity are all situated near the top of the aquifer, drilling expensive, deep wells is unnecessary. In addition, contaminants can travel faster through a few thin zones of high hydraulic conductivity than a thick zone of low hydraulic conductivity, and are hence the contaminants will be less diluted. This paper presents several different methods of measuring the vertical distribution of hydraulic conductivity including impeller flow logging and dilution testing under both pumped and ambient conditions.

METHODS

Pumped well methods

The principal of these methods is illustrated in figure 1 and is best described by [START_REF] Molz | The impeller meter for measuring aquifer permeability variations: Evaluation and comparison with other tests[END_REF]. A pump is set up at the top of the borehole, so that the water flows radially towards the borehole, and then up the borehole towards the pump inlet.

As depth down the borehole increases, fluid speed decreases as each producing zone is passed. If there is no ambient flow, the derivative of this log is proportional to hydraulic conductivity. the water, plus the speed of the flowmeter (line speed). Line speed is subsequently subtracted during calibration.

Pumped dilution testing. This is described by [START_REF] West | Characterisation of a multi-layer aquifer using open well dilution tests[END_REF]. A hosepipe is lowered down the borehole, and filled with salt solution. The hosepipe is then removed from the borehole, so the borehole is filled with water of a greater conductivity than the surrounding aquifer water. As this water is drawn into the borehole by a pump, the resulting pattern of dilution is observed by measuring the electrical conductivity of the borehole fluid. From this the vertical fluid flow profile can be found.

Ambient methods

Sometimes, it is impossible to pump water from a borehole, perhaps due to time and cost constraints, or very large lifts (i.e. the depth to the rest water level is large). In this case, the ambient well flows can be measured. Ambient flows occur up or down a borehole due to pressure differences between two different conductive horizons intersected by the borehole. The vertical flow velocity between the layers depends on both the pressure difference and the hydraulic conductivity of each layer. As the pressure difference between layers is unknown, a quantitative hydraulic conductivity profile cannot be obtained, but the ambient flows indicate where the layers of high hydraulic conductivity are located and the direction of flow (upwards or downwards) in that part of the aquifer.

RESULTS

The results here are examples from the Chalk aquifer of East Yorkshire, which is a fracture flow aquifer. Fractures are subsequently enlarged by the dissolution of the carbonate rock matrix. Both of these factors mean that hydraulic conductivity varies considerably with depth. The aquifer has both confined and unconfined zones, with glacial clay till acting as the confining layer. Most boreholes in the aquifer are open to the Chalk, but are cased through its uppermost layers, and the till where present. All depths are in metres below ground level. Ordnance Survey National Grid references are given for location.

Pumped methods

Impeller flow logging. A pumped impeller flow log from Wilfholme Landing (TA 062 472) on the confined Chalk is shown in figure 2. The position of the base of the well casing is indicated. The measured data are noisy due principally to turbulence in the borehole set off from rugosities in the borehole wall, and variations in borehole diameter.

A regression technique was developed which fits straight lines to the noisy plot. This is described in detail in Parker et al (under review). The modeled flow shows two zones where flow decreases rapidly, an upper zone between 24 and 32m, and a lower zone between 41 and 43m. The hydraulic conductivity profile found from this flow log is shown in figure 3, which also shows the magnitude of the hydraulic conductivity for the two zones described above. The regression technique gives an indication of the confidence of the model fit; 95% confidence limits are shown on this profile.

Ambient methods

Impeller flow logging. Figure 5 shows an ambient impeller flow log from a borehole at Henpit Hole (TA 025 658), on the unconfined Chalk. The resting water level in this borehole is below the casing. This borehole has a blockage at about 30m, below which the impeller cannot pass. However, the flow log indicates that flow is coming from beneath this blockage and leaving the borehole between 21 and 14m depth. It leaves most rapidly at 18m, suggesting this is the location of a high hydraulically conductive layer, possibly even a single conductive fracture. ilution testing. Figure 6 shows an ambient dilution test from a well at Weaverthorpe (SE 981 D 702) on the unconfined Chalk. Salt concentration profiles at several different times are plotted, and the concentration can be seen decreasing with time after injection. The profiles show there is an inflow at the top of the water column; this water flows down the borehole and leaves at 35m depth. There is another inflow at 40m. Water from here flows up the borehole and leaves at the same depth of 35m.

The salt concentration in the bottom of the borehole remains constant throughout the duration of this test, suggesting that below 40m there are no ambient flows.

Analysis of the salt concentration curves suggest that the upper ambient flows are 4 times faster than the lower flows. This suggests there are highly conductive horizons at the level of the water table (22m), and also at 35 and 40m depth.

DISCUSSION

ethods are presented here for establishing the variation of hydraulic conductivity peller flow logging under pumped conditions gives the most comprehensive results, giving umped dilution testing provides exact depths of layers of high hydraulic conductivity, with a Four different m with depth are presented here. Each has its advantages and disadvantages.

Im both the depths and magnitudes of hydraulic conductivity, and also an indication of the certainty of those values from the confidence limits. However, it requires the most complicated and expensive equipment -not only the logging equipment but also a high capacity pump. P more approximate estimate of their magnitude. The conductivity can be measured using an inexpensive handheld conductivity probe. A much lower capacity pump can be used (for example a swimming pool pump running from a generator), although as the distance of the resting water level below the ground surface increases, the disturbance induced by pumping will decrease. If large ambient flows are present, they will swamp the efforts of a low capacity pump meaning the data is very hard to interpret; in this situation it would be better to carry out an ambient dilution test.

Ambient tests give a clear indication of the location of the layers of high conductivity, but cannot give any information of their magnitude. Dilution tests are the cheapest to carry out as the only specialist equipment required is the conductivity probe. Their only limitation is in boreholes with high ambient flow, where it becomes necessary to use the impeller to measure flow speed as it can measure much faster flows, but again, the equipment is expensive.

CONCLUSION

Four different methods of characterizing the vertical hydraulic conductivity distribution within an aquifer have been presented. In general as cost decreases, data quality is compromised, although there are some other physical constraints (the magnitude of ambient flows and the distance below the ground surface of the resting water level), and even the lowest cost method provides some very useful information about the aquifer.

The methods presented here can be used to better define groundwater flow model input parameters. The more accurate the model, the better the regulator will be able to predict the effects of changes to the aquifer, for example, the effect of installing a new abstraction well, or the contamination risk presented by a change to industrial land use, as well as the long term effects of increasing urbanization and climate change.

Figure 1 :

 1 Figure 1: The basic prinicples of a pumped test

8th

  World Wide Workshop for Young Environmental Scientists WWW-YES-2008: Urban waters: resource or risks? 13-16 May 2008 4 / 7

Figure 2 :

 2 Figure 2: An impeller flow log from Wilfholme Landing, showing the measured data and the regression model fit. The depth of the base of the casing is indicated.

Figure 3 :

 3 Figure 3: The hydraulic conductivity profile produced from the impeller flow log from Wilfholme Landing. The depth of the base of the casing is indicated.

Figure 4 :

 4 Figure 4: Salt concentration profiles through the duration of a pumped dilution test at Wilfholme Landing. The pump inlet was at the bottom. The base of the casing is indicated.

Figure 5 :

 5 Figure 5: An ambient flow log from Henpit Hole, showing the measured data and the regression model fit.

Figure 6 :

 6 Figure 6: Salt concentration profiles through the duration of an ambient dilution test at Weaverthorpe.


Prof Elisabeth Cardis
email: ecardis@creal.cat

Australia : Armstrong

Canada -Montreal : Siemiatycki

Canada -Ottawa / Vancouver

McBride Krewski D 10

: Denmark

Johansen

Collatz Christensen

Finland : Auvinen

Kurttio P 13

France : Hours

Germany : Schüz

Berg-Beckhoff

: Israel

Sadetzki

Confidential

Japan : Takebayashi

New Zealand

: Woodward

Norway : Tynes

Blaasaas Kg

Sweden : Feychting

Mckinney Pa

Hepworth Sj

Muir Kr

Swerdlow Aj

Schoemaker Mj

Brain tumour risk in relation to mobile telephone use: results of the INTERPHONE international case-control study The INTERPHONE Study Group*

Keywords: mobile phones, radiofrequency fields, brain tumours brain tumours, mobile phones, radiofrequency fields

Background:

The rapid increase in mobile telephone use has generated concern about possible health risks related to radiofrequency electromagnetic fields from this technology.

Methods: An interview-based case-control study with 2708 glioma and 2409 meningioma cases and matched controls was conducted in 13 countries using a common protocol.

Results: A reduced odds ratio (OR) related to ever having been a regular mobile phone user was seen for glioma (OR 0.81, 95% confidence interval (CI): 0.70, 0.94) and meningioma (OR 0.79; 95% CI 0.68, 0.91), possibly reflecting participation bias or other methodological limitations. No elevated OR was observed 10 or more years after first phone use (glioma: OR 0.98, 95% CI 0.76, 1.26; meningioma: OR 0.83, 95% CI 0.61, 1.14).

Odds ratios were below 1.0 for all deciles of lifetime number of phone calls and nine deciles of cumulative call time. In the tenth decile of recalled cumulative call time, 1640 hours or longer, the odds ratio was 1.40 (95% CI 1.03, 1.89) for glioma, and 1.15 (95% CI 0.81, 1.62) for meningioma; but there are implausible values of reported use in this group. Odds ratios for glioma tended to be greater in the temporal lobe than in other lobes of the brain, but the confidence intervals around the lobe-specific estimates were wide. Odds ratios for glioma tended to be greater in subjects who reported usual phone use on the same side of the head as their tumour than on the opposite side.

Conclusions: Overall, no increase in risk of glioma or meningioma was observed with use of mobile phones. There were suggestions of an increased risk of glioma at the highest exposure levels, but biases and error prevent a causal interpretation. The possible effects of long-term heavy use of mobile phones require further investigation.

INTRODUCTION

Mobile phone use has increased dramatically in many countries since its introduction in the early to-mid 1980's. The expanding use of this technology has been accompanied by concerns about health and safety. In the late 1990s, several expert groups critically reviewed the evidence on health effects of low-level exposure to radiofrequency (RF) electromagnetic fields, and recommended research into the possible adverse health effects of mobile telephony (1-4). As a result, the International Agency for Research on Cancer (IARC) coordinated a feasibility study in 1998 and 1999, which concluded that an international study of the relationship between mobile phone use and brain tumour risk would be feasible and informative (5;6). INTERPHONE was therefore initiated as an international set of case-control studies focusing on four types of tumours in tissues that most absorb RF energy emitted by mobile phones: tumours of the brain (glioma and meningioma), acoustic nerve (schwannoma), and parotid gland. The objective was to determine whether mobile phone use increases the risk of these tumours and, specifically, whether RF energy emitted by mobile phones is tumourigenic. This paper presents the results of analyses of brain tumour risk in relation to mobile phone use in all INTERPHONE study centres combined. Analyses of brain tumours in relation to mobile phone use have been reported from a number of cohort [START_REF] Dreyer | Cause-specific mortality in cellular telephone users[END_REF][START_REF] Johansen | Cellular telephones and cancer--a nationwide cohort study in Denmark[END_REF][START_REF] Schuz | Cellular telephone use and cancer risk: update of a nationwide Danish cohort[END_REF] and case-control studies, including several of the national components of INTERPHONE [START_REF] Auvinen | Brain tumors and salivary gland cancers among cellular telephone users[END_REF][START_REF] Christensen | Cellular telephones and risk for brain tumors: a population-based, incident casecontrol study[END_REF][START_REF] Hardell | Use of cellular telephones and the risk for brain tumours: A case-control study[END_REF][START_REF] Hardell | Pooled analysis of two case-control studies on the use of cellular and cordless telephones and the risk of benign brain tumours diagnosed during 1997-2003[END_REF][START_REF] Hardell | Pooled analysis of two case-control studies on use of cellular and cordless telephones and the risk for malignant brain tumours diagnosed in 1997-2003[END_REF][START_REF] Hardell | Tumour risk associated with use of cellular telephones or cordless desktop telephones[END_REF][START_REF] Hepworth | Mobile phone use and risk of glioma in adults: case-control study[END_REF][START_REF] Hours | Cell Phones and Risk of brain and acoustic nerve tumours: the French INTERPHONE case-control study[END_REF][START_REF] Inskip | Cellular-telephone use and brain tumors[END_REF][START_REF] Klaeboe | Use of mobile phones in Norway and risk of intracranial tumours[END_REF][START_REF] Lahkola | Mobile phone use and risk of glioma in 5 North European countries[END_REF][START_REF] Lahkola | Meningioma and mobile phone use -a collaborative case-control study in five North European countries[END_REF][START_REF] Lonn | Long-term mobile phone use and brain tumor risk[END_REF][START_REF] Muscat | Handheld cellular telephone use and risk of brain cancer[END_REF][START_REF] Schuz | Cellular phones, cordless phones, and the risks of glioma and meningioma (Interphone study group, Germany)[END_REF][START_REF] Takebayashi | Mobile phone use, exposure to radiofrequency electromagnetic field, and brain tumour: a case-control study[END_REF]. No studies, however, have included as many exposed cases, particularly long-term and heavy users of mobile phones, as this study.

METHODS

Study design

The INTERPHONE study is an international, largely population-based case-control study.

The common core study protocol is described in detail elsewhere (5;26). Sixteen study centres from 13 countries (Australia, Canada, Denmark, Finland, France, Germany, Israel, Italy, Japan, New Zealand, Norway, Sweden, and the UK) were included. To maximize statistical power, the INTERPHONE study focused on tumours in younger people, 30-59 years of age, as they were expected to have had the highest prevalence of mobile phone use in the previous five to 10 years, and on regions likely to have the longest and highest use of mobile phones (mainly large urban areas).

Eligible cases were all patients with a glioma or meningioma of the brain diagnosed in the study regions during study periods of two to four years between 2000 and 2004. Cases were ascertained from all neurological and neurosurgical facilities in the study regions (except in Paris and Tokyo where some did not agree to participate), and in some centres also from cancer registries. All diagnoses were histologically confirmed or based on unequivocal diagnostic imaging. To facilitate interviews soon after diagnosis, cases were ascertained actively within treatment facilities wherever possible. Completeness of ascertainment was checked through secondary sources, such as population-or hospital-based cancer registries, medical archives and hospital discharge or billing files [START_REF] Cardis | The INTERPHONE Study: Design, Epidemiological Methods, and Description of the Study Population[END_REF].

One control was selected for each case from a locally appropriate population-based sampling frame, except in Germany where two controls were chosen. The sampling procedure involved individual matching in seven centres (Canada-Ottawa, Canada-Vancouver, France, Israel, Japan, New Zealand and UK-North) and frequency matching elsewhere. The matching variables were age (within 5 years), sex, and region of residence within each study centre. In Israel, the subjects were also matched on ethnic origin. Where stratified matching had been used, individual matching was conducted post hoc, with cases being assigned one control (two in Germany), interviewed as close as possible in time to the case, from those who fitted the matching criteria.

Detailed information on past mobile phone use was collected during face-to-face interviews with the study subject, or a proxy, if the subject had ever been a regular user of a mobile phone (had an average of at least one call per week for a period of 6 months or more) [START_REF] Cardis | The INTERPHONE Study: Design, Epidemiological Methods, and Description of the Study Population[END_REF]. A proxy was sought when the study subject had died or was too ill to be interviewed. The interviews were conducted by a trained interviewer using a computer-assisted questionnaire, except in Finland where a paper version was used. The questionnaire also included sections on socio-demographic factors, occupational exposure to electromagnetic fields and ionizing radiation, medical history (subject's and family), medical ionizing and non-ionizing radiation exposure and smoking. For cases, information was also collected on the anatomic location and histologic type of the tumours. Where possible, location data were obtained from MRI reports or images; they were otherwise obtained from surgical records or clinical notes. Details of the specific source for each case were not recorded in the INTERPHONE database. Those collecting the data did not know the reported side of mobile phone use of individual cases.

Statistical methods

Data from countries with multiple centres were combined for the analyses, except in the UK where the UK South and UK North, each with large numbers of subjects, were kept separate.

The word "centre" in the remainder of this paper is used to refer to the 14 analytic entities (12 countries, UK North and UK South). All analyses were carried out for all centres combined and for each centre separately. Formal tests for heterogeneity of risk across centres were conducted by allowing for an interaction between centre and the exposure variables.

The analyses presented here focus on past mobile phone use as reported by or for the study subjects. The main analyses were based on conditional logistic regression for matched sets [START_REF] Breslow | Statistical methods in cancer research. Volume I -The analysis of case-control studies[END_REF]. The date of diagnosis of the case was used as the reference date for cases and controls in each matched set. For the main analyses, the reference category for odds ratios was the set of subjects who reported that they had never been regular users. Exposure variables included: ever having been a regular user (as defined above), time (years) since first regular use, cumulative number of calls, and cumulative duration of calls. To allow for a latency period of one year, the year before the reference date was included in the reference category for time since first regular use and all other exposure variables were censored at 1 year before the reference date. Cumulative number and duration of calls were analyzed as categorical variables, based on deciles of the distribution of these variables among all controls who were regular users, including those matched to patients with an acoustic neuroma or a parotid gland tumour, so that the same cut-off points are used in all analyses [START_REF] Cardis | The INTERPHONE Study: Design, Epidemiological Methods, and Description of the Study Population[END_REF]. Cumulative use excluded use of mobile phones with hands-free devices: for all time periods for which the subject reported the use of hands-free devices the amount of use was reduced by 100%, 75%, 50% or 25% depending on whether hands-free devices were used always or almost always, more than half, about half, or less than half of the time. For ease of presentation, some results are shown for the following grouping of deciles: 1, 2-5, 6-7, 8-9 and 10, chosen post hoc to reflect the spread of the highly skewed distribution of these variables. For convenience we will systematically use the term "regular user" in text and tables to refer to ever having been a regular user.

The reference group for these analyses, never regular users, included people who had some mobile phone use but never as much as one call a week on average for six months or more (about 32% of meningioma and 26% of glioma cases and 30% of meningioma and 26% of glioma controls) and people who had never used a mobile phone (about 11% of meningioma and 9% of glioma cases and 8% of meningioma and 6% of glioma controls). These percentages are approximate because never use and never regular use were defined at different dates; the reference date and the date of interview respectively. We are not able to determine whether inclusion of subjects with some occasional mobile phone use in the reference group had a material effect on our results because this difference in definition dates prevented us from distinguishing participants with only occasional use from those with no use at all at their reference dates. Moreover, because numbers of never users at the date of interview were small, particularly in certain age-and gender-specific sub-groups (such as young men), never users were not a suitable reference group for this analysis.

All analyses were adjusted for educational level; an a priori decision had been made to adjust for it as a surrogate for socio-economic status. Creation of consistent educational levels across the 13 countries is described elsewhere [START_REF] Cardis | The INTERPHONE Study: Design, Epidemiological Methods, and Description of the Study Population[END_REF]. In practice this adjustment had little impact on odds ratio estimates, changing their values by 2% or less in most instances and in all cases by less than 5%. Using a 10% change-in-estimate criterion for confounding [START_REF] Greenland | The impact of confounder selection criteria on effect estimation[END_REF], no other covariate among those collected (see list above) was included in the main analyses. The interval between the start date of interviews in the study centre and the date of each subject's interview was modelled by fitting the interaction of this interval with study centre.

A common protocol was applied to impute missing data for cases and controls [START_REF] Cardis | The INTERPHONE Study: Design, Epidemiological Methods, and Description of the Study Population[END_REF]. The study questionnaire allowed ranges to be given instead of exact answers to a number of questions, including number and duration of calls and dates of start and end of mobile phone use; in such instances, the main analyses in this paper were based on the mid-point of the reported range.

Because absorption of RF energy from mobile phones is highly localized [START_REF] Cardis | Distribution of RF energy emitted by mobile phones in anatomical structures of the brain[END_REF], three different types of analyses were conducted to account for tumour location. First, analyses were conducted by the anatomical lobe of the brain in which the tumour occurred. Second, separate analyses were conducted for the subjects who reported using the mobile phone mainly on one or the other side of the head, and the preferred side was compared with the side on which the tumor occurred. For this, each control was assigned the location of the tumour of his or her matched case. Exposure was considered to be ipsilateral if the phone was used predominantly on the same side as the tumour or on both sides of the head, and contralateral if used mainly on the side of the head opposite to the tumour. Laterality was not assigned if the tumour was reported to be centrally located (i.e. it crossed the midline of the brain); these cases were excluded from laterality analyses. Third, case-case analyses were carried out on the concordance between tumour side and laterality of phone use using the method proposed by Inskip and collaborators [START_REF] Inskip | Cellular-telephone use and brain tumors[END_REF].

Sensitivity analyses

To complement these primary analyses, we undertook sensitivity analyses to try to determine The prevalence of regular mobile phone use 1 year before the reference date was 52% for meningioma cases (ranging from 34% to 73% across study centres) and 56% in matched controls (35% to 78%). It was higher for glioma cases (62% overall, range: 42% to 80%) and controls (64% overall, range: 45% to 84%), reflecting the different sex distributions of this tumour.

The majority of subjects in the study were not heavy mobile phone users; the median lifetime cumulative call time among meningioma controls using mobile phones was around 75 hours, with a median call time of about 2 hours per month and a median lifetime number of calls about 1500. Corresponding values for glioma controls were approximately 100 hours lifetime, 2.5 hours per month and about 2000 calls. The distributions of time since start of mobile phone use and cumulative call time were highly skewed, with few long-term and heavy users, and varied across study centres and by age and sex (not shown).

Relation between mobile phone use and risk of brain tumours

Meningioma

A reduced OR of meningioma was found for regular mobile phone use one year or more in the past, OR 0.79 (95% CI 0.68, 0.91) (Table 2). There was some suggestion of heterogeneity There was no appreciable effect modification by age or sex on any of these results (not shown).

In analyses by anatomical location of the meningioma, the OR for temporal lobe tumours with regular use was 0.55 (95% CI 0.36, 0.82) and the ORs were less than 1.0 in all categories of time since start of use, cumulative call time, and cumulative number of calls. Odds ratios for other lobes were also mostly below 1.0 (Table 4).

The odds ratio for mainly ipsilateral use among regular users was 0.86 (95% CI 0.69, 1.08), and that for contralateral use was 0.59 (95% CI 0.46, 0.76) (Table 5). The ORs by time since start of use were below 1.0 in all categories of ipsilateral and contralateral use. When analysing by any of the exposure metrics in Table 5, the ratios of the ORs for ipsilateral use to contralateral use were always one or above one regardless of level of exposure and they were highest (around 2 or 3) for the two highest categories of cumulative call time and the second highest category of cumulative number of calls. A case-case analysis, based on Inskip's method, showed an OR of 1.07 ((95% CI 1.00, 1.16; Appendix Table 2) for ipsilateral use.

The OR for those who reported regular use of only an analogue phone was 0.81 (95% CI 0.65, 1.03) and for only a digital phone it was 0.79 (95% CI 0.68, 0.92). Focusing on the highest decile of cumulative call time, the OR among those who used only an analogue phone was 0.50 (95% CI 0.25, 0.99); among those who used only a digital phone it was 1.84 (95% CI 1.17, 2.88) and among those using both 4.43 (95% CI 1.42-13.9) (Appendix Table 3).

Glioma

A reduced risk of glioma was seen for regular mobile phone use one year or more in the past (OR 0.81, 95% CI 0.70, 0.94, Table 2) relative to never regular users. There was little evidence of heterogeneity in results across centres (p=0.68). ORs were below 1.0 in all study centres except Australia, France and New Zealand.

Analyses by time since start of use showed a reduced OR in all categories of use; the OR for ≥10 years since start of use was 0.98 (95% CI 0.76, 1.26) (Table 2). The pattern of results by duration of mobile phone use was similar (not shown).

Analyses by categories of cumulative call time showed decreased ORs in eight of the first nine deciles (five of which had upper confidence bounds below 1.0) and an increased OR of 1.40 (95% CI 1.03, 1.89) in the highest exposure category, 1640+ hours. Analyses by cumulative number of calls showed ORs below 1.0 in all categories, with the OR in the highest decile approaching unity.

Analyses of cumulative call time stratified by short-, medium-and long-term use (Table 3)

showed reduced risks in the lower call time categories in all strata of time since start of use and ORs above 1.0 in the highest category (1640+ hours cumulative call time) in each of the three strata. The highest OR was in the short-term users but its confidence interval was very wide.

The lobe of the brain in which the tumour was located was known for 96% of cases. The OR for temporal lobe tumours with regular use was 0.86 (95% CI 0.66, 1.13, Table 4). The point estimates for the highest categories of cumulative call time, cumulative number of calls and time since start of use were higher for tumours in the temporal lobe than in other locations, but their 95% confidence intervals were wide. For meningioma, some point estimates differed from the OR of 1.15 from the main analysis, but the estimates were imprecise and, with one exception based on 9 cases and 4 controls, fell well within the confidence interval of this "benchmark" value.

For glioma, results from the various sensitivity analyses were generally similar to those from the primary analysis. All the OR estimates, except one based on 9 cases and 3 controls, are well within the 95% CI of the OR from the main analysis. When subjects with high reported use were included, but with use truncated at 5 hours per day, the OR was hardly affected.

When subjects who reported over 5 hours call time per day [START_REF] Birkett | Effect of nondifferential misclassification on estimates of odds ratios with multiple levels of exposure[END_REF]

DISCUSSION

The INTERPHONE study is the largest case-control study of mobile phones and brain tumours conducted to date, including the largest numbers of users with at least 10 years of exposure and the greatest cumulative hours of use of any study. An exhaustive analysis of this large data set involved estimation of hundreds of odds ratios; rather than focus on the most extreme values, the interpretation should rest on the overall balance of evidence. The null hypothesis of no association would be expected to produce an approximately symmetric pattern of negative and positive log odds ratios. A skewed distribution could be due to a bias or to a true effect. Our results include a disproportionately high number of ORs below one, but also a small number of elevated ORs. This could be taken to indicate an underlying lack of association with mobile phone use, systematic bias from one or more sources, a few random but essentially meaningless increased ORs, or a small effect detectable only in a subset of the data.

For meningioma, there is little evidence to counter a global null hypothesis, and we conclude that INTERPHONE finds no signs of an increased risk of meningioma among users of mobile telephones.

For glioma, an increased OR was seen in analyses in the highest decile of cumulative call time, including tumours in the temporal lobe and subjects who reported having used the mobile phone mainly on the same side as where the tumour occurred. Still, the evidence for an increased risk of glioma among the highest users was inconclusive, as the increase could be due to one or more of the possible sources of error discussed below.

In the following sections, we explore possible explanations for the apparently decreased risk of meningioma and glioma for regular users compared with never regular users, and the apparently increased risk of glioma in a subset of users.

Decreased risk with ever regular use of a mobile phone

An apparently decreased risk of brain tumours with ever regular use of a mobile phone (relative to never regular use) has been seen in other studies (18;23). Putting aside a genuine protective effect as implausible, we have considered other reasons for these observations.

Sampling bias

In all but two centres, a population-based design was used. This requires that the cases in the study were representative of all cases in the respective population and that the controls represented all non-cases, within matching strata. In practice it is difficult to demonstrate that these conditions have been fulfilled in any case-control study. Cases may be missed due to lack of detection, misdiagnosis or incomplete registration (such problems may be more likely for meningioma than for glioma). It is uncertain whether the sampling frames used to select controls represented the study base in some countries. To the extent possible, we conducted sensitivity analyses that examined the effects of different recruitment strategies between centres; they did not show substantial changes in the results (Table 6).

Levels of participation

Constrained by the requirements of ethical review committees and facing the population's increasing reluctance to participate in interview studies, we attained participation rates of 78% among meningioma cases, 64% among glioma cases, and 53% among controls [START_REF] Cardis | The INTERPHONE Study: Design, Epidemiological Methods, and Description of the Study Population[END_REF]. While such proportions are not unusually low, they raise the possibility of selection bias with respect to mobile phone use. Controls in 11 centres and cases in 9 centres who refused the full interview were asked to respond to a brief non-respondent questionnaire on mobile phone use. The cases and controls who complied with this short inquiry reported a lower lifetime prevalence of ever regular use of a mobile phone than did respondents to the full interview, implying that information from those who participated in the full interview may overestimate prevalence among all eligible subjects. Because participation and refusal differed between cases and controls, such nonrepresentativeness may have distorted the OR estimates [START_REF] Vrijheid | Quantifying the impact of selection bias caused by nonparticipation in a case-control study of mobile phone use[END_REF]. While caution is required in extrapolating from the findings of the sub-study, we estimated, in the more plausible scenarios, that non-participation bias may have led to a reduction in the odds ratios for regular use of 5% to 15% [START_REF] Vrijheid | Quantifying the impact of selection bias caused by nonparticipation in a case-control study of mobile phone use[END_REF], which is less than the observed reductions below the null in the odds ratios in ever regular mobile phone users for meningioma (21%, 95% CI 32%, 9%) and glioma (19%, 95% CI 30%, 6%) (Table 2).

Prodromal symptoms

Prodromal symptoms of a brain tumour could dissuade subjects from becoming phone users or reduce their use before diagnosis (reverse causation). Glioma is typically diagnosed quite soon after first symptoms. While prodromal symptoms might result in lowered ORs among very recent users (eg <2 years since starting use), these are unlikely to explain the reduction in ORs observed among the vast majority of the users in our study population who started using mobile phones 2-10 years before disease onset.

Timing of interviews

As the use of mobile phones has become more common over time, the later interview dates of controls could have spuriously increased the prevalence of exposure in the control group. However, restricting analyses to matched sets in which the cases and controls were interviewed within one month of each other resulted in very little change in the OR for regular use one or more years in the past (Table 6) and hence seems unlikely to explain the low ORs overall. Further, the use of a common reference date for each case and its matched control should have minimised any bias induced by differential timing of interviews.

Confounding

Higher socioeconomic status has been associated with a higher risk of brain cancer in some but not all relevant studies (31;32), and with mobile phone use, particularly when the technology was new [START_REF] Schuz | Cellular telephone use and cancer risk: update of a nationwide Danish cohort[END_REF]. We adjusted for education level in all analyses, but acknowledge this is an imperfect indicator of SES. Otherwise, there are few well-established risk factors for brain tumours; analyses adjusting for measured potential confounders had little impact on the ORs (Appendix Table 4).

Low overall risks among mobile phone users

The reduced OR for regular users compared with never regular users seems unlikely to reflect a genuine protective effect and makes our results difficult to interpret [START_REF] Saracci | Commentary: Observational studies may conceal a weakly elevated risk under the appearance of consistently reduced risks[END_REF]. It could result from the sources of error discussed above, although based on the evidence we have regarding their magnitude and effects (30;34) they may not account fully for the observed reduction in risk.

It might be possible to correct, at least crudely, for assumed downwards bias in the odds ratios for mobile phone use by undertaking a series of analyses using the lowest category of users as the reference category for OR estimates in higher categories. Results of such an analysis of the mobile phone use variables in by a discussion of the strengths and weaknesses of this approach. We have also done some work to characterize possible sources of bias (30;34) and are currently exploring the possibility of correcting the odds ratio estimates mathematically for their effects.

Elevated risks of glioma among heavy users

There was some evidence of an elevated risk of glioma in the highest decile of cumulative call time, with the highest point estimates seen for tumours in the temporal lobe and for subjects who reported having used their mobile phone mainly on the same side as that on which the tumour occurred. We explore here possible interpretations of these findings.

Biases related to possible differential quality of exposure data

When compared with controls, glioma cases had a higher proportion of proxy respondents, a higher number of imputations for missing values, and a higher proportion of subjects judged by their interviewer to be non-responsive or having poor memory (data not shown). However, sensitivity analyses showed that these differences, on their own, did not explain the results seen in the highest decile of cumulative call time (Table 6).

Differential error between cases and controls in reporting of mobile phone use could substantially affect our results; such information bias could arise from several sources. First, a brain tumour, particularly in the frontal or temporal lobes, may adversely affect cognition and memory [START_REF] Kolb | Fundamentals of Neuropsychology[END_REF]. Second, cases may be more motivated to recall and report a publicised potential risk factor for their disease.

To investigate the accuracy of self-reported phone use, two validation sub-studies were conducted in some of the INTERPHONE centres. Amongst healthy volunteers using software-modified phones (recording number and times of calls), phone use in the past year was reported with substantial random error; with over-and under-estimation both frequent [START_REF] Vrijheid | Validation of short-term recall of mobile phone use for the Interphone Study[END_REF]. Errors were larger for duration of calls than for number of calls, and phone use was under-estimated by light users and over-estimated by heavy users. In another sub-study, records of mobile phone use up to 6 years previously were obtained for some participants in three INTERPHONE centres, allowing us to compare the interview responses with the records [START_REF] Vrijheid | Recall bias in the assessment of exposure to mobile phones[END_REF]. Overall, there was little evidence that recall quality differed between cases and controls, but there was some indication of greater over-reporting by cases than by controls for periods 3-5 years before interview. These sub-studies provide no information regarding differential reporting error for periods more distant than 5 years before interview.

Some subjects reported very high daily average call times and this was more common among cases than controls. Thirty-eight cases and 22 controls reported more than 5 hours use per day and 10 cases and no controls reported 12 hours or more per day. There is reasonable doubt about the credibility of such reports. Excluding all subjects who reported more than 5 hours use per day reduced the ORs in the highest decile of cumulative time from 1.40 to 1.27 (95% CI 0.92-1.74). By contrast, truncating the average call time to 5 hours per day had little effect on the OR. It is not clear which of these two approaches (if either) is the more appropriate.

However, the key question is whether these cases with unreasonably high values reflect a general tendency for cases to overestimate more than controls, which could contribute to the apparent excess risk in the highest decile. As noted above, there is evidence that cases tended to overestimate their past exposure more than controls did [START_REF] Vrijheid | Recall bias in the assessment of exposure to mobile phones[END_REF].

Non-differential error (random variability or uncertainty in the exposure estimates) may also affect the findings. With dichotomous exposure indicators such bias is towards the null, but for polytomous variables the effect is difficult to predict [START_REF] Birkett | Effect of nondifferential misclassification on estimates of odds ratios with multiple levels of exposure[END_REF][START_REF] Brenner | Varied forms of bias due to nondifferential error in measuring exposure[END_REF][START_REF] Dosemeci | Does nondifferential misclassification of exposure always bias a true effect toward the null value?[END_REF].

Location of tumours and laterality of use of phones

Absorption of RF energy from mobile phones is highly localized [START_REF] Cardis | Distribution of RF energy emitted by mobile phones in anatomical structures of the brain[END_REF]. Thus, an association of phone use with tumours occurring near the location of the phone would constitute stronger evidence for aetiology than an association with more distant tumours.

Ipsilateral ORs were almost always greater than contralateral ORs. There was no consistent pattern with regard to level of exposure, although a trend towards a stronger effect of ipsilateral use relative to contralateral use with increasing exposure was observed for cumulative number of calls. Results of case-case analyses (using Inskip's method ( 18)) also suggested higher risks of gliomas with ipsilateral phone use, but again no consistent trend with increasing exposure. The observation of an unlikely ipsilateral effect in low exposure categories suggests that cases might have over-reported use on the side of the tumour.

There is, though, evidence of lack of such reporting bias from a sub-study. In three centres (Australia, Canada, Japan), participants (172 glioma and 160 meningioma cases and 340 controls who were regular users) were asked at the end of their interview to put a mobile phone to their ear as if answering a call. The concordance between the reported side of use of the phone and the side where it was held was lower for cases (72% glioma cases, 66% meningioma) than controls (95%). The greater degree of concordance among controls suggests differential reporting quality. Among cases, however, there was as much discrepancy in the contralateral direction (52 instances) as in the ipsilateral direction (48 instances). Thus, it is possible that the ipsilateral effect is a true effect, is due to reporting bias or is a mixture of both.

Few studies have related field strength to anatomic structures, but a recent investigation of 110 phone models found that exposure is generally highest in the temporal lobe [START_REF] Cardis | Distribution of RF energy emitted by mobile phones in anatomical structures of the brain[END_REF]. While laterality analyses may be biased by the respondent's knowledge of the side of the tumour, results for tumours in different lobes are probably less susceptible to reporting bias. ORs for glioma in the highest exposure categories were higher for tumours in the temporal lobe than in other lobes, but the confidence intervals around the lobe-specific estimates for each measure were wide.

Coherence and consistency

The strongest evidence of an increased risk of glioma was found for cumulative call time, which is a function of the number and duration of calls. Conceptually, cumulative call time might be the most relevant measure of exposure. However, in validation studies, the number of calls was recalled more accurately than the duration of calls (36;37). For the cumulative number of calls, the ORs, while highest in the highest deciles, were consistently below one. In the absence of a known biological mechanism, it is hard to know whether more weight should be put on results from the more accurate or the conceptually preferred exposure measure.

The apparently increased risk of glioma for cumulative call time was restricted to the top decile, more than 1640 hours. There was no upward trend across the first nine deciles of cumulative call time. By contrast with the excess risk seen on the scale of cumulative call time, risk did not appear to be increased by length of time since first exposure or by duration of exposure. The pattern of point estimates of ORs in the high call time categories in three strata of time since exposure started -3.8 in the most recent and 1.3 in the more distant ones ( weigh against cause and effect. Nevertheless given the uncertainty surrounding possible effects of RF on the brain, no strong case can be made for the plausibility or implausibility of any observed exposure response pattern.

Comparison of meningioma and glioma results

While the ORs for meningioma were lower than for glioma in high exposure subgroups, there were some similar patterns. First the OR for all regular users compared with never regular users was very similar. Second, there was no trend in relation to cumulative call time except for an elevated OR in the highest decile. Third, the increase in the last decile was more pronounced for cumulative call time than number of calls. Fourth, the highest OR for cumulative call time was seen among subjects who had recently started regular use. Fifth, the ORs were greater for ipsilateral than contralateral use and the ratios of ipsilateral ORs divided by their corresponding contralateral ORs were of a similar magnitude. However, while there was evidence of a higher risk of gliomas in the temporal lobe than elsewhere with several different exposure metrics, there was no such evidence for meningioma. Although ORs for meningioma were generally lower than for glioma, the otherwise similar patterns of associations of mobile phone use with meningioma and glioma could indicate shared aetiology or shared bias.

Interpretation of these findings

We have no certain explanation for the overall reduced risk of brain cancer among mobile phone users in this study, although selection bias is almost certainly a contributor. There is some evidence that very high users experienced excess risk of glioma, but that evidence is inconclusive because of possible bias. Further light may be shed on dose-response relations by work now being undertaken with the INTERPHONE data using precise coordinate The possibility of raised risk in heavy users of mobile phones is an important issue because of their ever-increasing use. Moreover, few subjects in our study had used mobile phones for more than 12 years; therefore our results are uninformative with respect to lag periods longer than this.

Consistency with previous research

Our results are consistent with most of the research published to date. A large Danish cohort study of mobile telephone subscribers (8;9), with an average follow up time of 8.5 years, found no increased risk of brain tumours in subscribers of 10 years or more. The first casecontrol studies conducted included cases diagnosed in the mid to late 1990s and therefore could only address possible risks among short-term mobile phone users (10;12;18;23). In addition, the highest cumulative call times in these studies were much less than in ours.

Generally, these studies reported "negative" results. In contrast, increased risks of malignant brain tumours at higher levels of accumulated use of analogue and digital mobile phones and cordless desktop phones were reported from a sequence of three case-control studies from the same authors with cases in the last diagnosed as late as 2003 [START_REF] Hardell | Pooled analysis of two case-control studies on the use of cellular and cordless telephones and the risk of benign brain tumours diagnosed during 1997-2003[END_REF][START_REF] Hardell | Pooled analysis of two case-control studies on use of cellular and cordless telephones and the risk for malignant brain tumours diagnosed in 1997-2003[END_REF][START_REF] Hardell | Tumour risk associated with use of cellular telephones or cordless desktop telephones[END_REF]. The methods of these studies have been questioned, however [START_REF] Ahlbom | Epidemiologic evidence on mobile phones and tumor risk: a review[END_REF]. for glioma, for tumours in the temporal lobe. However, biases and errors limit the strength of the conclusions we can draw from these analyses and prevent a causal interpretation.

Some of the INTERPHONE centres

KEY MESSAGES

INTERPHONE is the largest case-control study of mobile phone use and brain tumours yet and includes the largest numbers of users with at least 10 years of exposure. A reduced odds ratio for glioma and meningioma related to ever having been a regular mobile phone user possibly reflects participation bias or other methodological limitations. No elevated odds ratio for glioma or meningioma was observed 10 or more years after first phone use. There were suggestions of an increased risk of glioma, and much less so meningioma, in the highest decile of cumulative call time, in subjects who reported usual phone use on the same side of the head as their tumour and, for glioma, for tumours in the temporal lobe. Biases and errors limit the strength of the conclusions that can be drawn from these analyses and prevent a causal interpretation.

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7 category and the fact that the number of ipsilateral and contralateral regular user cases (and controls) does not add up to the total number of regular users in the previous table. 2 ORs adjusted for sex, age, study centre, ethnicity in Israel, and education. 3 Ratio of OR for ipsilateral tumours to OR for contralateral tumours 4 Deciles of exposure used in Table 2 have been collapsed into six categories for these analyses: deciles 1, 2-5, 6-7, 8-9 and 10.

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9 5 Restricted to study subjects who the interviewers judged to be fairly or very cooperative and responsive and who were judged to remember fairly well, well or very well both their current and past mobile phone use history. 6 Truncation and exclusion were based on all use of a mobile phone; that is, including when using hands-free devices 7 Working days were assumed to be 5.5 days a week for 50 weeks of the year and new deciles were created to reflect the changed total hours.

  of risk across centres (p=0.08) with ORs below 1.0 except in Canada, Denmark, Germany and Italy (not shown). ORs were below 1.0 for regular users in all categories of time since start of use and cumulative number of calls. Analyses by cumulative call time showed ORs less than 1.0 in the first 9 deciles and an OR of 1.15 (95% CI 0.81-1.62) in the highest decile. Analyses of cumulative call time among recent-, medium-and long-term users (Table3) showed no indication of excess risk except in the highest call time category in those who started phone before the reference date: OR 4.80 (95% CI 1.49-15.4).

  within the brain in relation to estimates of absorbed RF energy.

  . The Canada-Montreal team acknowledges the diligent work of fieldwork staff including Marie-Claire Goulet, Sylvie Plante, Sally Campbell and the interviewer team. We are grateful to Dr. Rafael Glikstein and Dr. Geneviève Matte who contributed to the tumour localisation efforts. The following hospitals and physicians in Montreal permitted access to their patients: Hôpital Charles-Lemoyne (Dr C. Chaâlala, Dr J. Demers, Dr N. Gauthier, Dr A. Roux). Centre hospitalier de l'Université de Montréal (Dr M.W. Bojanowski, Dr A. Bouthillier, Dr J.-J. Dufour, Dr R.A. Moumdjian). Hôpital Maisonneuve-Rosemont (Dr L.N. Poirier, Dr M. Séguin). Hôpital du Sacré-Coeur (Dr J.-F. Giguère, Dr M.F. Giroux). Jewish General Hospital (Dr M. J. Black, Dr. E. Marmor, Dr G. Mohr), Montreal Neurological Institute (Dr R. Del Maestro, Dr A. Olivier, Dr A. Sadikot). The Canada-Ottawa centre gratefully acknowledges the work of the interview team, particularly Lynn Pratt and Daniel Bédard for their leading roles in study coordination; participating clinicians at the Ottawa Hospital included Drs. Charles B. Agbi, Brien Benoit, Martin J. Corsten, Vasco F. DaSilva, André Lamothe, Howard J. Lesiuk, William Miller, Paul F. Odell, and David Schramm. The Canada-Vancouver centre wishes to acknowledge the work carried out by Dr. Alison Pope, Patricia Nelson, Nelson Ha, Dr. Kaushik Bhagat and the interviewer team. We are grateful to Dr. Rafael Glikstein and Dr. Geneviève Matte who contributed to the tumour localisation efforts. The Finnish centre thank Dr J J Jääskeläinen (Helsinki University Hospital), Dr S Valtonen (Turku University Hospital), Professor J Koivukangas (Oulu University Hospital), Professor M Vapalahti (Kuopio University Hospital), Dr T Kuurne (Tampere University Hospital) and Professor R Sankila (Finnish Cancer Registry). We would like to thank the French fieldwork team, Mary-Pierre Herrscher, Fatima Lamri, Agnès Boidart, Hélène Gire, Juliette Krassilchik, Judith Lenti, Delphine Maillac, Frédérique Sonnet, Flore Taguiev, Julie Frantz, France Castay, Florian Gay, for their excellent work; Prof. Doyon (Paris) and Dr. Marc Hermier (Lyon) who were actively

Table 5 )

 5 For regular use one year or more in the past, the odds ratio for ipsilateral mobile phone use was 0.84 (95% CI 0.69, 1.04) and that for contralateral use was 0.67 (95% CI 0.52, 0.87, . The ORs by time since start of use were below 1.0 in all categories, except for ipsilateral use beginning 10 or more years in the past (OR 1.21, 95% CI 0.82, 1.80). The ORs by cumulative number of calls were below 1.0 irrespective of laterality and exposure level, except for ipsilateral use in the two highest categories. The results by cumulative call time showed a similar pattern, but the OR for ipsilateral use in the highest category was appreciably elevated (OR 1.96, 95% CI 1.22, 3.16) and that for contralateral use was slightly

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Only for the highest decile of cumulative call time was the OR for temporal lobe tumours appreciably elevated (1.87, 95% CI 1.09,

3.22)

. elevated (OR 1.25, 95% CI 0.64, 2.42). The ratios of the ipsilateral ORs to the contralateral ORs were all above one with one exception (0.99 for 2-4 years since start of use) and the highest (around 2) were in 1-1.9 and 10+ years since start of use, the lowest category of cumulative call time, and the highest category of cumulative number of calls. For cumulative number of calls there was a consistent trend towards increasing ratios with increasing exposure.

Case-case analyses of the concordance between tumour side and preferred side of phone use using the Inskip method showed an elevated risk for ipsilateral use among regular users (OR 1.27, 95% CI 1.19, 1.37) and among those in the highest decile of cumulative call time (OR 1.55, 95% CI 1.24, 1.99) (Appendix Table

2

). When stratified on time since first use, the point estimate of the OR using Inskip's method in the highest decile was higher among short-

The OR for ever use of an analogue phone was 1.00 (95% CI 0.83, 1.21) and for ever use of a digital phone 0.76 (95% CI 0.66, 0.88). Increased ORs were seen in the highest decile of

Sensitivity analyses

Selected findings of sensitivity analyses are shown in Table

6

and Appendix Table

4

. Because of a hint of a possible excess risk in subjects in the highest decile of mobile phone cumulative call time, for glioma (OR 1.40) and to a lesser extent for meningioma (OR 1.15), we focus presentation of sensitivity analyses on the findings in this highest decile, corresponding to 1640 or more cumulative hours of use.

  Results of sensitivity analyses focusing on the OR for regular use one year or more in the past are shown in Appendix Table5. All the OR estimates, except two ORs for meningioma relating to the presentation of the study, are well within the 95% CI of the OR from the main analysis.

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	cases and 22 controls) were
	excluded altogether, on the premise that such responses were unreliable, the OR decreased to
	1.27 (95% CI 0.92-1.75).
	December 2009 December 2009	20 21

  Table 2 are shown in the Table of the Annex, accompanied

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Table 3 )

 3 -is not what one would expect if there were a causal association; although the confidence interval in the newest users was wide and encompassed the point estimates for heavy use in the two longer use groups. By analogy with known carcinogens, the lack of a consistently increasing risk with dose, duration of exposure and time since first exposure

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  Much biological research has been done in recent years on possible biological effects of RF fields. This work covers in vitro and in vivo exposure, alone and in combination with other physical or chemical agents, and has found no evidence that RF fields are carcinogenic in laboratory rodents or cause DNA damage in cells in culture[START_REF]Emerging and Newly Identified Health Risks (SCENIHR)[END_REF]. Possible effects of RF fields on other biological endpoints are still being explored.The possible effects of long-term heavy use of mobile phones on risk of brain tumours require further investigation, given increasing mobile phone use, its extension to children and its penetration worldwide. The problems presented by selection and information bias in this and probably other studies suggest that new studies should, in general, only be done if they can substantially reduce or eliminate selection bias, obtain detailed and high quality exposure information over the full period of use and offer sufficient statistical power to detect comparatively small effects in people with heavy or long continued exposure. Monitoring of age-and gender-specific incidence rates may also be valuable, particularly if informed by good longitudinal data on mobile phone use by age and sex, and having regard to features such as brain tumour location that may allow more specific inferences about possible mobile phone use effects.CONCLUSIONThis is the largest study of the risk of brain tumours in relation to mobile phone use conducted to date and it included substantial numbers of subjects who had used mobile phones for 10 or more years. Overall, no increase in risk of either glioma or meningioma was observed in association with use of mobile phones. There were suggestions of an increased risk of glioma, and much less so meningioma, at the highest exposure levels, for ipsilateral exposures and,

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	have published their results for brain tumours
	(11;16;17;19;22;24;25) and two pooled analyses from Northern European centres have also
	been published (20;21). Most cases in these reports are included in the present analyses and
	constitute 69% of gliomas and 57% of meningiomas. The centre-specific analyses are
	consistent with our all-centre results.
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Table 1 -Selected characteristics of meningioma and glioma cases included in the main analyses 1

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Table 2 -ORs between mobile phone use and brain tumours (meningioma and glioma separately) by regular use, time since start of use, cumulative call time and cumulative number of calls -excludes use with hands-free devices

 2

			Meningioma				Glioma
		Cases Controls OR 1	95 % CI	Cases Controls OR 1	95 % CI
	Regular use 1 or more years in the past
	No	1147	1174 1.00			1042	1078 1.00
	Yes	1262	1488 0.79	0.68	0.91	1666	1894 0.81	0.70	0.94
	Time since start of use (years)
	Never regular user	1147	1174 1.00			1042	1078 1.00
	1-1.9 years	178	214 0.90	0.68	1.18	156	247 0.62	0.46	0.81
	2-4	557	675 0.77	0.65	0.92	644	725 0.84	0.70	1.00
	5-9	417	487 0.76	0.63	0.93	614	690 0.81	0.67	0.97
	10+	110	112 0.83	0.61	1.14	252	232 0.98	0.76	1.26
	Cumulative call time with no hands-free devices (hours) 2
	Never regular user	1147	1174 1.00			1042	1078 1.00
	<5 hours	160	197 0.90	0.69	1.18	141	197 0.70	0.52	0.94
	5.0-12.9	142	159 0.82	0.61	1.10	145	198 0.71	0.53	0.94
	13-30.9	144	194 0.69	0.52	0.91	189	179 1.05	0.79	1.38
	31-60.9	122	145 0.69	0.51	0.94	144	196 0.74	0.55	0.98
	61-114.9	129	162 0.75	0.55	1.00	171	193 0.81	0.61	1.08
	115-199.9	96	155 0.69	0.50	0.96	160	194 0.73	0.54	0.98
	200-359.9	108	133 0.71	0.51	0.98	158	194 0.76	0.57	1.01
	360-734.9	123	133 0.90	0.66	1.23	189	205 0.82	0.62	1.08
	735-1639.9	108	103 0.76	0.54	1.08	159	184 0.71	0.53	0.96
	1640+	130	107 1.15	0.81	1.62	210	154 1.40	1.03	1.89
	Cumulative number of calls with no hands-free devices (in hundreds) 2
	Never regular user	1147	1174 1.00			1042	1078 1.00
	<1.5 x 100 calls	159	180 0.95	0.72	1.27	147	182 0.74	0.55	0.99
	1.5-3.4	136	182 0.62	0.46	0.83	141	200 0.71	0.54	0.95
	3.5-7.4	148	176 0.90	0.68	1.19	161	201 0.76	0.58	1.00
	7.5-13.9	143	173 0.80	0.61	1.07	174	179 0.90	0.68	1.20
	14-25.4	122	181 0.60	0.45	0.81	180	206 0.78	0.59	1.02
	25.5-41.4	111	126 0.81	0.58	1.13	156	190 0.83	0.62	1.10
	41.5-67.9	129	146 0.79	0.58	1.09	163	194 0.71	0.53	0.94
	68-127.9	134	126 0.92	0.67	1.26	186	200 0.93	0.70	1.23
	128-269.9	100	100 0.81	0.57	1.16	193	180 0.96	0.72	1.28
	270+	80	98 0.80	0.55	1.17	165	162 0.96	0.71	1.31

Table 3 -ORs between mobile phone use and brain tumours (meningioma and glioma separately) by cumulative call time, stratified by recency of starting regular use -excludes use with hands-free devices

 3 115 gliomas and 357 meningiomas with unknown anatomical location are not included2 ORs adjusted for sex, age, study centre, ethnicity in Israel, and education3 Deciles of exposure used in Table2have been collapsed into six categories for these analyses: deciles 1, 2-5, 6-7, 8-9 and 10.

				Meningioma					Glioma
	Cumulative	Cases Controls	OR 1	95% CI	Cases Controls	OR 1	95% CI
	Call time (hours)					Non-regular users
		1147	1174	1.00			1042	1078	1.00
				Short-term users: start of phone use 1-4 years before reference date
	<5 hours	150	186	0.92	0.69	1.22	127	182	0.68	0.50	0.93
	5 -114.9	401	500	0.74	0.61	0.90	449	533	0.82	0.67	0.99
	115-359.9	95	126	0.79	0.55	1.12	121	154	0.74	0.52	1.03
	360-1639.9	67	72	0.77	0.49	1.20	80	95	0.75	0.50	1.13
	1640+	22	5	4.80	1.49	15.4	23	8	3.77	1.25	11.4
				Medium-term users: start of phone use 5-9 years before reference date
	<5 hours	7	9	0.67	0.23	1.96	10	13	0.86	0.32	2.28
	5 -114.9	122	145	0.73	0.54	0.98	180	208	0.86	0.66	1.12
	115-359.9	95	140	0.67	0.48	0.93	156	192	0.71	0.53	0.95
	360-1639.9	129	131	0.83	0.60	1.14	174	204	0.72	0.54	0.95
	1640+	64	62	1.03	0.65	1.65	94	73	1.28	0.84	1.95
				Long-term users: start of phone use 10+ years before reference date
	<5 hours	3	2	1.31	0.21	8.07	4	2	1.13	0.16	7.79
	5 -114.9	14	15	0.79	0.36	1.73	20	25	0.63	0.32	1.25
	115-359.9	14	22	0.49	0.24	1.01	41	42	0.89	0.53	1.50
	360-1639.9	35	33	1.00	0.58	1.72	94	90	0.91	0.63	1.31
	1640+	44	40	0.95	0.56	1.63	93	73	1.34	0.90	2.01

1

Table 6 -Results of sensitivity analyses on ORs between mobile phone use and brain tumours (meningioma and glioma separately) for the highest decile of cumulative call time, covering possible indicators of sample representativeness and response quality

 6 ORs adjusted for sex, age, study centre, ethnicity in Israel, and education.2 Japan and France-Paris excluded3 Included only interviewers who conducted at least 20 interviews4 Included only interviewers' whose case/control interview ratio was between 1/4 and 3/4 (between 1/6 and 5/6 in Germany where 2 controls were matched to each case)

	Meningioma	Glioma

December 2009

CONFIDENTIAL December 2009

The controls for each case series have the same distributions of characteristics as the cases to which they are matched.

Excluded are cases for whom no controls could be found (55 for glioma and 15 for meningioma) and cases in matched sets (2 for glioma and 1 for meningioma) where the regular use status of the case or the control was unknown.

ORs adjusted for sex, age, study centre, ethnicity in Israel, and education.

Categories are based on the deciles of the distribution among all eligible regular user controls (see text).

ORs adjusted for sex, age, study centre, ethnicity in Israel, and education.

72 glioma matched sets and 43 meningioma matched sets including subjects with unknown laterality of use or with cases with unknown side of tumour are excluded from these analyses. The reference category consists of subjects who were not regular users 1 year before the reference date. Because the main analyses in this paper use matched conditional logistic regression, all matched sets in which the case and/or the control was a regular contralateral user are excluded from the ipsilateral analyses; similarly, sets in which the case and/or the control was a regular ipsilateral user are excluded from the contralateral analyses. This explains the differences in the numbers of cases and controls in the reference

ACKNOWLEDGEMENTS

The authors are grateful to Chris Wild (Director, IARC, Lyon, France) for guidance provided to the INTERPHONE Study Group in coming to closure on the final draft of this paper.

Special thanks are also due to Peter Inskip (NCI, Bethesda, Md, USA) and Jorn Olsen (Danish Epidemiology Science Centre, Aarhus, Denmark and UCLA, Los Angeles, Ca, USA) for their advice to the Study Group in revising working drafts of the paper. The authors would like to thank Lesley Richardson (Montreal, Canada -formerly at IARC) for her major role in the coordination of the study and assistance in drafting the present manuscript and Dr Baruch Modan (Israel -deceased) for his assistance and enthusiasm in the design and setting up of this study. James Doughty performed miracles implementing the CAPI in several languages and several versions, assisted by Roger Parslow. We would also like to thank Jan Ivar Martinsen for additional programming work. Liz Findlay contributed a great deal to the development of materials and training of interviewers. We would also like to thank all the research assistants and interviewers in the different study centres for their efforts to ensure that the study was carried out with care and with due consideration for the participants. The Australian team would like to acknowledge the support given to study design and implementation by Associate Professor Michael Besser and Professor Andrew Kaye and the substantial contributions neurosurgery, neuropathology and other clinical staff made to conduct of the study; and to thank the fieldwork staff in Melbourne-Monique Kilkenny, Georgina Marr, Tracey McPhail, Fiona Phillips, Hayley Shaw, Yvonne Torn-Broers; and Sydney-Matthew Carroll, Sally Dunlop, Virginia MacDonald and Elizabeth Willows-and the many interviewers for their hard work, and the NSW and Victorian Cancer Registries for

FUNDING

This work was supported by funding from the European Fifth Framework Program, ''Quality of Life and Management of Living Resources'' [contract QLK4-CT-1999901563] and the International Union against Cancer (UICC). The UICC received funds for this purpose from the Mobile Manufacturers' Forum and GSM Association. Provision of funds to the INTERPHONE study investigators via the UICC was governed by agreements that guaranteed INTERPHONE's complete scientific independence. The terms of these agreements are publicly available at http://www.iarc.fr/en/research-groups/RAD/RCAd.html. The Australian centre was supported by the Australian National Health and Medical Research Council [EME Grant 219129] with funds originally derived from mobile phone service licence fees; Bruce Armstrong was supported by a University of Sydney Medical Foundation Foundation and the Academy of Finland. Additional funding for the study in France was provided by l'Association pour la Recherche sur le Cancer (ARC) [Contrat N°5142] and three network operators (Orange, SFR, Bouygues Télécom). The funds provided by the operators represented 5 % of the total cost of the French study and were governed by contracts guaranteeing the complete scientific independence of the investigators. In Germany additional funds were received from the German Mobile Phone Research Program (Deutsches Mobilfunkforschungsprogramm) of the German Federal Ministry for the Environment, Nuclear Safety, and Nature Protection; the Ministry for the Environment and Traffic of the state of Baden-Württemberg; the Ministry for the Environment of the state of North Rhine-Westphalia; the MAIFOR Program (Mainzer Forschungsforderungsprogramm) of the University of Mainz. The study conducted in Japan was fully funded by the Ministry of Internal Affairs and Communications of Japan. In New Zealand, funding was provided by the Health Research Council, Hawkes Bay Medical Research Foundation, the Wellington Medical Research Foundation, the Waikato Medical Research Foundation and the Cancer Society of New Zealand. The Swedish centre was also supported by the Swedish Research Council and the Swedish Cancer Society. Additional funding for the UK-North & UK-South studies was received from the Mobile Telecommunications, Health and Research (MTHR) program and the UK-North study received funding from the Health and Safety Executive, the Department of Health, the UK Network Operators (O2, Orange, T-Mobile, Vodafone, '3') and the Scottish Executive. The Institute of Cancer Research acknowledges NHS funding to the NIHR Biomedical Research Centre. The views expressed in the publication are those of the authors and not necessarily of the funders.