The Prognosis in Palliative care Study II (PiPS2): A prospective observational validation study of a prognostic tool with an embedded qualitative evaluation.

BACKGROUND: Prognosis in Palliative care Study (PiPS) models predict survival probabilities in advanced cancer. PiPS-A (clinical observations only) and PiPS-B (additionally requiring blood results) consist of 14- and 56-day models (PiPS-A14; PiPS-A56; PiPS-B14; PiPS-B56) to create survival risk categories: days, weeks, months. The primary aim was to compare PIPS-B risk categories against agreed multi-professional estimates of survival (AMPES) and to validate PiPS-A and PiPS-B. Secondary aims were to assess acceptability of PiPS to patients, caregivers and health professionals (HPs). METHODS AND FINDINGS: A national, multi-centre, prospective, observational, cohort study with nested qualitative sub-study using interviews with patients, caregivers and HPs. Validation study participants were adults with incurable cancer; with or without capacity; recently referred to community, hospital and hospice palliative care services across England and Wales. Sub-study participants were patients, caregivers and HPs. 1833 participants were recruited. PiPS-B risk categories were as accurate as AMPES [PiPS-B accuracy (910/1484; 61%); AMPES (914/1484; 61%); p = 0.851]. PiPS-B14 discrimination (C-statistic 0.837) and PiPS-B56 (0.810) were excellent. PiPS-B14 predictions were too high in the 57-74% risk group (Calibration-in-the-large [CiL] -0.202; Calibration slope [CS] 0.840). PiPS-B56 was well-calibrated (CiL 0.152; CS 0.914). PiPS-A risk categories were less accurate than AMPES (p<0.001). PiPS-A14 (C-statistic 0.825; CiL -0.037; CS 0.981) and PiPS-A56 (C-statistic 0.776; CiL 0.109; CS 0.946) had excellent or reasonably good discrimination and calibration. Interviewed patients (n = 29) and caregivers (n = 20) wanted prognostic information and considered that PiPS may aid communication. HPs (n = 32) found PiPS user-friendly and considered risk categories potentially helpful for decision-making. The need for a blood test for PiPS-B was considered a limitation. CONCLUSIONS: PiPS-B risk categories are as accurate as AMPES made by experienced doctors and nurses. PiPS-A categories are less accurate. Patients, carers and HPs regard PiPS as potentially helpful in clinical practice. STUDY REGISTRATION: ISRCTN13688211.

Prognostic tools or clinical predictions: Which are better in palliative care?

PURPOSE: The Palliative Prognostic (PaP) score; Palliative Prognostic Index (PPI); Feliu Prognostic Nomogram (FPN) and Palliative Performance Scale (PPS) have all been proposed as prognostic tools for palliative cancer care. However, clinical judgement remains the principal way by which palliative care professionals determine prognoses and it is important that the performance of prognostic tools is compared against clinical predictions of survival (CPS). METHODS: This was a multi-centre, cohort validation study of prognostic tools. Study participants were adults with advanced cancer receiving palliative care, with or without capacity to consent. Key prognostic data were collected at baseline, shortly after referral to palliative care services. CPS were obtained independently from a doctor and a nurse. RESULTS: Prognostic data were collected on 1833 participants. All prognostic tools showed acceptable discrimination and calibration, but none showed superiority to CPS. Both PaP and CPS were equally able to accurately categorise patients according to their risk of dying within 30 days. There was no difference in performance between CPS and FPN at stratifying patients according to their risk of dying at 15, 30 or 60 days. PPI was significantly (p<0.001) worse than CPS at predicting which patients would survive for 3 or 6 weeks. PPS and CPS were both able to discriminate palliative care patients into multiple iso-prognostic groups. CONCLUSIONS: Although four commonly used prognostic algorithms for palliative care generally showed good discrimination and calibration, none of them demonstrated superiority to CPS. Prognostic tools which are less accurate than CPS are of no clinical use. However, prognostic tools which perform similarly to CPS may have other advantages to recommend them for use in clinical practice (e.g. being more objective, more reproducible, acting as a second opinion or as an educational tool). Future studies should therefore assess the impact of prognostic tools on clinical practice and decision-making.

A brief intervention for weight control based on habit-formation theory delivered through primary care: results from a randomised controlled trial.

BACKGROUND: Primary care is the 'first port of call' for weight control advice, creating a need for simple, effective interventions that can be delivered without specialist skills. Ten Top Tips (10TT) is a leaflet based on habit-formation theory that could fill this gap. The aim of the current study was to test the hypothesis that 10TT can achieve significantly greater weight loss over 3 months than 'usual care'. METHODS: A two-arm, individually randomised, controlled trial in primary care. Adults with obesity were identified from 14 primary care providers across England. Patients were randomised to either 10TT or 'usual care' and followed up at 3, 6, 12, 18 and 24 months. The primary outcome was weight loss at 3 months, assessed by a health professional blinded to group allocation. Difference between arms was assessed using a mixed-effect linear model taking into account the health professionals delivering 10TT, and adjusted for baseline weight. Secondary outcomes included body mass index, waist circumference, the number achieving a 5% weight reduction, clinical markers for potential comorbidities, weight loss over 24 months and basic costs. RESULTS: Five-hundred and thirty-seven participants were randomised to 10TT (n=267) or to 'usual care' (n=270). Data were available for 389 (72%) participants at 3 months and for 312 (58%) at 24 months. Participants receiving 10TT lost significantly more weight over 3 months than those receiving usual care (mean difference =-0.87kg; 95% confidence interval: -1.47 to -0.27; P=0.004). At 24 months, the 10TT group had maintained their weight loss, but the 'usual care' group had lost a similar amount. The basic cost of 10TT was low, that is, around £23 ($32) per participant. CONCLUSIONS: The 10TT leaflet delivered through primary care is effective in the short-term and a low-cost option over the longer term. It is the first habit-based intervention to be used in a health service setting and offers a low-intensity alternative to 'usual care'.

Predicting dementia risk in primary care: development and validation of the Dementia Risk Score using routinely collected data.

BACKGROUND: Existing dementia risk scores require collection of additional data from patients, limiting their use in practice. Routinely collected healthcare data have the potential to assess dementia risk without the need to collect further information. Our objective was to develop and validate a 5-year dementia risk score derived from primary healthcare data. METHODS: We used data from general practices in The Health Improvement Network (THIN) database from across the UK, randomly selecting 377 practices for a development cohort and identifying 930,395 patients aged 60-95 years without a recording of dementia, cognitive impairment or memory symptoms at baseline. We developed risk algorithm models for two age groups (60-79 and 80-95 years). An external validation was conducted by validating the model on a separate cohort of 264,224 patients from 95 randomly chosen THIN practices that did not contribute to the development cohort. Our main outcome was 5-year risk of first recorded dementia diagnosis. Potential predictors included sociodemographic, cardiovascular, lifestyle and mental health variables. RESULTS: Dementia incidence was 1.88 (95% CI, 1.83-1.93) and 16.53 (95% CI, 16.15-16.92) per 1000 PYAR for those aged 60-79 (n = 6017) and 80-95 years (n = 7104), respectively. Predictors for those aged 60-79 included age, sex, social deprivation, smoking, BMI, heavy alcohol use, anti-hypertensive drugs, diabetes, stroke/TIA, atrial fibrillation, aspirin, depression. The discrimination and calibration of the risk algorithm were good for the 60-79 years model; D statistic 2.03 (95% CI, 1.95-2.11), C index 0.84 (95% CI, 0.81-0.87), and calibration slope 0.98 (95% CI, 0.93-1.02). The algorithm had a high negative predictive value, but lower positive predictive value at most risk thresholds. Discrimination and calibration were poor for the 80-95 years model. CONCLUSIONS: Routinely collected data predicts 5-year risk of recorded diagnosis of dementia for those aged 60-79, but not those aged 80+. This algorithm can identify higher risk populations for dementia in primary care. The risk score has a high negative predictive value and may be most helpful in 'ruling out' those at very low risk from further testing or intensive preventative activities.

An evaluation of penalised survival methods for developing prognostic models with rare events.

Prognostic models for survival outcomes are often developed by fitting standard survival regression models, such as the Cox proportional hazards model, to representative datasets. However, these models can be unreliable if the datasets contain few events, which may be the case if either the disease or the event of interest is rare. Specific problems include predictions that are too extreme, and poor discrimination between low-risk and high-risk patients. The objective of this paper is to evaluate three existing penalised methods that have been proposed to improve predictive accuracy. In particular, ridge, lasso and the garotte, which use penalised maximum likelihood to shrink coefficient estimates and in some cases omit predictors entirely, are assessed using simulated data derived from two clinical datasets. The predictions obtained using these methods are compared with those from Cox models fitted using standard maximum likelihood. The simulation results suggest that Cox models fitted using maximum likelihood can perform poorly when there are few events, and that significant improvements are possible by taking a penalised modelling approach. The ridge method generally performed the best, although lasso is recommended if variable selection is required.

Factors affecting self-efficacy and pain intensity in patients with chronic musculoskeletal pain seen in a specialist rheumatology pain clinic.

OBJECTIVE: Chronic musculoskeletal pain is a very common and costly health problem. Patients presenting to rheumatology clinics with chronic pain can be difficult to manage. We studied 354 patients referred to a rheumatology chronic pain clinic over 5 yrs to identify factors affecting their self-efficacy and intensity of pain. METHODS: We collected data for each patient, covering demographic and psychosocial factors, characteristics of their pain and previous treatment. We measured self-efficacy using a validated questionnaire, and pain intensity (PI) on an NRS. We performed multiple regression analysis to determine as to which factors were independently associated with these outcomes. RESULTS: Despite extensive previous investigations and treatment, these patients had low self-efficacy [median = 26.5, interquartile range (IQR) 15-38, best possible = 60] and high PI scores (median = 7, worst possible = 10, IQR 5-9). Low self-efficacy was most clearly associated with depressive symptoms and not being employed. PI was most clearly associated with depressive symptoms, extensive pain and lower level of education. CONCLUSION: Community-based studies suggest psychosocial factors are very important in determining outcomes in patients with chronic pain. This study suggests that the same is true in patients referred to rheumatologists due to chronic musculoskeletal pain and that these factors-particularly depressive symptoms and not being employed-are more important than site or duration of pain in those patients.

An evaluation of existing risk stratification models as a tool for comparison of surgical performances for coronary artery bypass grafting between institutions.

OBJECTIVE: Risk stratification systems are used in cardiac surgery to estimate mortality risk for individual patients and to compare surgical performance between institutions or surgeons. This study investigates the suitability of six existing risk stratification systems for these purposes. METHODS: Data on 5471 patients who underwent isolated coronary artery bypass grafting at two UK cardiac centres between 1993 and 1999 were extracted from a prospective computerised clinical data base. Of these patients, 184 (3.3%) died in hospital. In-hospital mortality risk scores were calculated for each patient using the Parsonnet score, the EuroSCORE, the ACC/AHA score and three UK Bayes models (old, new complex and new simple). The accuracy for predicting mortality at an institutional level was assessed by comparing total observed and predicted mortality. The accuracy of the risk scores for predicting mortality for a patient was assessed by the Hosmer-Lemeshow test. The receiver operating characteristic (ROC) curve was used to evaluate how well a system ranks the patient with respect to their risk of mortality and can be useful for patient management. RESULTS: Both EuroSCORE and the simple Bayes model were reasonably accurate at predicting overall mortality. However predictive accuracy at the patient level was poor for all systems, although EuroSCORE was accurate for low to medium risk patients. Discrimination was fair with the following ROC areas: Parsonnet 0.73, EuroSCORE 0.76, ACC/AHA system 0.76, old Bayes 0.77, complex Bayes 0.76, simple Bayes 0.76. CONCLUSIONS: This study suggests that two of the scores may be useful in comparing institutions. None of the risk scores provide accurate risk estimates for individual patients in the two hospitals studied although EuroSCORE may have some utility for certain patients. All six systems perform moderately at ranking the patients and so may be useful for patient management. More results are needed from other institutions to confirm that the EuroSCORE and the simple Bayes model are suitable for institutional risk-adjusted comparisons.

Importance of emergency identification schemes.

BACKGROUND: Millions of people worldwide may have a hidden medical condition that could endanger their life in an emergency. These conditions may include cardiac conditions, severe allergies, or diabetes. Emergency identification schemes such as Medic Alert produce emblems that alert health care professionals to potential problems and can ensure appropriate and prompt treatment. This paper uses mechanical failure of the Björk-Shiley convexo-concave (BSCC) heart valve as an example of a hidden medical condition. These patients have been encouraged to carry information to alert staff in an emergency that they have a BSCC patient in their care and to be alert to the signs and symptoms of acute valve malfunction. OBJECTIVE: To establish awareness and credibility of emergency identification schemes among emergency personnel and to assess if information on specific medical conditions would influence ambulance personnel regarding destination hospitals. METHODS: Questionnaires were sent to senior staff (n=380) of accident and emergency (A&E) departments and operational directors of ambulance headquarters (n=39) throughout the United Kingdom. Hospitals were divided into regional divisions to assess differences in responses across regions. RESULTS: The majority of respondents (99%) had heard of emergency identification schemes and felt that it was important for patients with special conditions to carry some form of identification. Nearly all ambulance respondents (97%) indicated it was routine to search for body worn emblems in contrast with only 71% of A & E staff. However, more than half of ambulance respondents (53.9%) stated information on emblems/cards would not influence their choice of destination hospital. CONCLUSIONS: The importance of how information on pre-existing medical conditions can influence care, is highlighted by the BSCC valve issue, where immediate diagnosis is essential for patient survival. It is vital that all staff routinely search patients for this information and if necessary act upon the information provided.

Meta-analysis of continuous outcome data from individual patients.

Meta-analyses using individual patient data are becoming increasingly common and have several advantages over meta-analyses of summary statistics. We explore the use of multilevel or hierarchical models for the meta-analysis of continuous individual patient outcome data from clinical trials. A general framework is developed which encompasses traditional meta-analysis, as well as meta-regression and the inclusion of patient-level covariates for investigation of heterogeneity. Unexplained variation in treatment differences between trials is considered as random. We focus on models with fixed trial effects, although an extension to a random effect for trial is described. The methods are illustrated on an example in Alzheimer's disease in a classical framework using SAS PROC MIXED and MLwiN, and in a Bayesian framework using BUGS. Relative merits of the three software packages for such meta-analyses are discussed, as are the assessment of model assumptions and extensions to incorporate more than two treatments.

Meta-analysis of ordinal outcomes using individual patient data.

Meta-analyses are being undertaken in an increasing diversity of diseases and conditions, some of which involve outcomes measured on an ordered categorical scale. We consider methodology for undertaking a meta-analysis on individual patient data for an ordinal response. The approach is based on the proportional odds model, in which the treatment effect is represented by the log-odds ratio. A general framework is proposed for fixed and random effect models. Tests of the validity of the various assumptions made in the meta-analysis models, such as a global test of the assumption of proportional odds between treatments, are presented. The combination of studies with different definitions or numbers of response categories is discussed. The methods are illustrated on two data sets, in a classical framework using SAS and MLn and in a Bayesian framework using BUGS. The relative merits of the three software packages for such meta-analyses are discussed.

Outlet strut fracture of Björk-Shiley convexo-concave valves: can valve-manufacturing characteristics explain the risk?

BACKGROUND: Björk-Shiley 60 degrees convexo-concave prosthetic heart valves (Shiley, Inc, Irvine, Calif, a subsidiary of Pfizer, Inc) continue to be a concern for approximately 35,000 nonexplanted patients worldwide, with approximately 600 events reported to the manufacturer to date. Fractures of the outlet struts of the valves began to appear in the early 1980s and have continued to the present, but their causes are only partially understood. METHODS: A matched case-control study was conducted evaluating manufacturing records for 52 valves with outlet strut fractures and 248 control subjects matched for age at implantation, valve size, and valve position. RESULTS: In addition to the risk factors recognized as determinants of outlet strut fracture, the United Kingdom case-control study has observed 7- to 9-fold increased risk with performance of multiple hook deflection tests. This test was performed more than once, usually after rework on the valve. Six valves in this study underwent multiple hook deflection tests, of which 4 experienced an outlet strut fracture. Cracks and further rework were noted for these valves. Significant associations were also observed between outlet strut fracture and disc-to-strut gap measurements taken before the attachment of the sewing ring. CONCLUSIONS: It is our view that a combination of factors related to valve design, manufacturing process, and patient characteristics are responsible for outlet strut fractures of Björk-Shiley convexo-concave valves. Multiple hook deflection tests have emerged as a potential new risk factor for outlet strut fracture in both The Netherlands and the United Kingdom. This factor appears to be correlated with the presence of other abnormalities. A further study is needed to investigate the factors correlated with multiple hook deflection tests. On confirmation of risk, the presence of multiple hook deflection tests may be added to equations, quantifying the risk of outlet strut fracture for comparison against risk of mortality and serious morbidity from explant operations.

Outlet strut fracture of Björk-Shiley convexo concave heart valves: the UK cohort study.

OBJECTIVE: To investigate the risk of outlet strut fracture (OSF) in Björk-Shiley convexo concave (BSCC) valves in relation to patients' clinical and valve characteristics. DESIGN: A cohort of 2977 patients with 3325 valves with a follow up of 18 years. SETTING: 38 cardiac implantation centres in the UK. RESULTS: 56 OSF events were reported with 43 occurring in mitral and 13 in aortic valves. The overall OSF rate was 0.17%/year. No dominant clinical factor of risk was found, but multiple regression analysis identified age, body surface area, valve size, shop order fracture rate, and manufacturing period as risk factors for OSF. A 4% (95% confidence interval (CI) 2% to 6%) decrease in the risk of OSF was observed for each advancing year of age and a fivefold (95% CI 2 to 13) increase in risk for a 0.5 m(2) increase in body surface area. The association between the risk of OSF and valve size was not constant over time. Excess risks among 31 mm and 33 mm sizes (mainly mitral valves) decreased over time while that for 23 mm (almost all aortic valves) increased. The risk of OSF increased by 40% (95% CI 20% to 50%) for a unit increase in the fracture rate of other valves in the same batch. For valves manufactured during 1981 to 1984 the risk of OSF was 4 (95% CI 2 to 12) times greater than for valves manufactured before 1981. CONCLUSIONS: The OSF rates for 60 degrees BSCC valves observed in the UK are the highest among all monitored populations. The changing patterns of mitral and aortic valve OSF rates over time observed in this study have not been identified previously and highlight the need for continued monitoring of patients with the BSCC valve.

Risks of fracture of Björk-Shiley 60 degree convexo-concave prosthetic heart valves: long-term cohort follow up in the UK, Netherlands and USA.

BACKGROUND AND AIM OF THE STUDY: Approximately 82,000 Björk-Shiley convexo-concave (BSCC) 60 degree prosthetic heart valves were implanted in patients worldwide between 1979 and 1986. Outlet strut fractures (OSF) of some of the valves were first reported shortly after their introduction. Here, the determinants of OSF are examined, and the between-country variation and long-term risk are assessed. METHODS: Cohorts of patients in the UK, Netherlands and USA with 15,770 BSCC 60 degree heart valves were followed up to 18 years for the occurrence of OSF. RESULTS: Crude rates of OSF were highest in the UK (0.18% per year), intermediate in the Netherlands (0.13%), and lowest in the USA (0.06%), although risk factor adjustment reduced the inter-country differences. Furthermore, in the UK and Netherlands, OSF rates (particularly for mitral valves) declined with time since implantation, and between-country differences were considerably diminished 10 or more years post implantation. The risk of OSF decreased steadily with advancing patient age. Fracture rates were lower among women than men, and also varied significantly with valve size and position and OSF status of other valves in the same shoporder. CONCLUSION: This long-term follow up of BSCC 60 degree heart valve patients indicates that risk factors for valve fracture are generally similar in the UK, Netherlands and USA. It also identifies a strong association between fracture risk and age, newly reveals gender-related differences, and shows that the risk of valve fracture persisted, albeit at a reduced rate, into the 1990s.

Bayesian methods of analysis for cluster randomized trials with binary outcome data.

We explore the potential of Bayesian hierarchical modelling for the analysis of cluster randomized trials with binary outcome data, and apply the methods to a trial randomized by general practice. An approximate relationship is derived between the intracluster correlation coefficient (ICC) and the between-cluster variance used in a hierarchical logistic regression model. By constructing an informative prior for the ICC on the basis of available information, we are thus able implicitly to specify an informative prior for the between-cluster variance. The approach also provides us with a credible interval for the ICC for binary outcome data. Several approaches to constructing informative priors from empirical ICC values are described. We investigate the sensitivity of results to the prior specified and find that the estimate of intervention effect changes very little in this data set, while its interval estimate is more sensitive. The Bayesian approach allows us to assume distributions other than normality for the random effects used to model the clustering. This enables us to gain insight into the robustness of our parameter estimates to the classical normality assumption. In a model with a more complex variance structure, Bayesian methods can provide credible intervals for a difference between two variance components, in order for example to investigate whether the effect of intervention varies across clusters. We compare our results with those obtained from classical estimation, discuss the relative merits of the Bayesian framework, and conclude that the flexibility of the Bayesian approach offers some substantial advantages, although selection of prior distributions is not straightforward.

A multilevel model framework for meta-analysis of clinical trials with binary outcomes.

In this paper we explore the potential of multilevel models for meta-analysis of trials with binary outcomes for both summary data, such as log-odds ratios, and individual patient data. Conventional fixed effect and random effects models are put into a multilevel model framework, which provides maximum likelihood or restricted maximum likelihood estimation. To exemplify the methods, we use the results from 22 trials to prevent respiratory tract infections; we also make comparisons with a second example data set comprising fewer trials. Within summary data methods, confidence intervals for the overall treatment effect and for the between-trial variance may be derived from likelihood based methods or a parametric bootstrap as well as from Wald methods; the bootstrap intervals are preferred because they relax the assumptions required by the other two methods. When modelling individual patient data, a bias corrected bootstrap may be used to provide unbiased estimation and correctly located confidence intervals; this method is particularly valuable for the between-trial variance. The trial effects may be modelled as either fixed or random within individual data models, and we discuss the corresponding assumptions and implications. If random trial effects are used, the covariance between these and the random treatment effects should be included; the resulting model is equivalent to a bivariate approach to meta-analysis. Having implemented these techniques, the flexibility of multilevel modelling may be exploited in facilitating extensions to standard meta-analysis methods.

Analysis of a cluster randomized trial with binary outcome data using a multi-level model.

The use of multi-level logistic regression models was explored for the analysis of data from a cluster randomized trial investigating whether a training programme for general practitioners' reception staff could improve women's attendance at breast screening. Twenty-six general practices were randomized with women nested within them, requiring a two-level model which allowed for between-practice variability. Comparisons were made with fixed effect (FE) and random effects (RE) cluster summary statistic methods, ordinary logistic regression and a marginal model based on generalized estimating equations with robust variance estimates. An FE summary statistic method and ordinary logistic regression considerably understated the variance of the intervention effect, thus overstating its statistical significance. The marginal model produced a higher statistical significance for the intervention effect compared to that obtained from the RE summary statistic method and the multi-level model. Because there was only a moderate number of practices and these had unbalanced cluster sizes, reliable asymptotic properties for the robust standard errors used in the marginal model may not have been achieved. While the RE summary statistic method cannot handle multiple covariates easily, marginal and multi-level models can do so. In contrast to multi-level models however, marginal models do not provide direct estimates of variance components, but treat these as nuisance parameters. Estimates of the variance components were of particular interest in this example. Additionally, parametric bootstrap methods within the multi-level model framework provide confidence intervals for these variance components, as well as a confidence interval for the effect of intervention which allows for the imprecision in the estimated variance components. The assumption of normality of the random effects can be checked, and the models extended to investigate multiple sources of variability.

Analysing repeated measurements data: a practical comparison of methods.

A variety of methods are available for analysing repeated measurements data where the outcome is continuous. However, there is little information on how established methods, such as summary statistics and repeated measures analysis of variance (RMAOV), compare in practice with methods that have become available to applied statisticians more recently, such as marginal models (based on generalized estimating equation methodology) and multilevel models (that is, hierarchical random effects models). The aim of this paper is to exemplify the use of these methods, and directly compare their results by application to a clinical trial data set. The focus is on practical aspects rather than technical issues. The data considered were taken from a clinical trial of treatments for asthma in 240 children, in which a baseline and four post-randomization measurements of outcomes were taken. The simplicity of the method of summary statistics using the post-randomization mean of observations provided a useful initial analysis. However, fixed time effects or treatment-time interactions cannot be included in such an analysis, and choice of appropriate weighting when there is substantial missing data is problematic. RMAOV, marginal models and multilevel models generally provided similar estimates and standard errors for the treatment effects, although in one example with a relatively complex variance structure the marginal model produced less efficient estimates. Two advantages of multilevel models are that they provide direct estimates of variance components which are often of interest in their own right, and that they can be naturally extended to handle multivariate outcomes.

Cancer mortality and morbidity among plutonium workers at the Sellafield plant of British Nuclear Fuels.

The mortality of all 14 319 workers employed at the Sellafield plant of British Nuclear Fuels between 1947 and 1975 was studied up to the end of 1992, and cancer incidence was examined from 1971 to 1986, in relation to their exposures to plutonium and to external radiation. The cancer mortality rate was 5% lower than that of England and Wales and 3% less than that of Cumbria. The significant excesses of deaths from cancer of the pleura and thyroid found in an earlier study persist with further follow-up (14 observed, 4.0 expected for pleura; 6 observed, 2.2 expected for thyroid). All of the deaths from pleural cancer were among radiation workers. For neither site was there a significant association between the risk of the cancer and accumulated radiation dose. There were significant deficits of deaths from cancers of mouth and pharynx, liver and gall bladder, and larynx and leukaemia when compared with the national rates. Among all radiation workers, there was a significant positive association between accumulated external radiation dose and mortality from cancers of ill-defined and secondary sites (10-year lag, P = 0.04), leukaemia (no lag, P = 0.03; 2-year lag, P = 0.05), multiple myeloma (20-year lag, P = 0.02), all lymphatic and haematopoietic cancers (20-year lag, P= 0.03) and all causes of death combined (20-year lag, P= 0.008). Among plutonium workers, there were significant excesses of deaths from cancer of the breast (6 observed, 2.6 expected) and ill-defined and secondary cancers (29 observed, 20.1 expected). No significant positive trends were observed between the risk of deaths from cancers of any specific site, or all cancers combined, and cumulative plutonium and external radiation doses. For no cancer site was there a significant excess of cancer registrations compared with rates for England and Wales. Analysis of trends in cancer incidence showed significant increases in risk with cumulative plutonium plus external radiation doses for all lymphatic and haematopoietic neoplasms for 0-, 10- and 20-year lag periods. Taken as a whole, our findings do not suggest that workers at Sellafield who have been exposed to plutonium are at an overall significantly increased risk of cancer compared with other radiation workers.

Cancer mortality in relation to monitoring for radionuclide exposure in three UK nuclear industry workforces.

Cancer mortality in 40,761 employees of three UK nuclear industry facilities who had been monitored for external radiation exposure was examined according to whether they had also been monitored for possible internal exposure to tritium, plutonium or other radionuclides (uranium, polonium, actinium or other unspecified). Death rates from cancer were compared both with national rates and with rates in radiation workers not monitored for exposure to any radionuclides. Among workers monitored for tritium exposure, overall cancer mortality was significantly below national rates [standardized mortality ratio (SMR) = 83, 165 deaths; 2P = 0.02] and none of the cancer-specific death rates was significantly above either the national average or rates in non-monitored workers. Although the overall death rate from cancer in workers monitored for plutonium exposure was also significantly low relative to national rates (SMR = 89, 581 deaths; 2P = 0.005), mortality from pleural cancer was significantly raised (SMR = 357, nine deaths; 2P = 0.002); none of the rates differed significantly from those of non-monitored workers. Workers monitored for radionuclides other than tritium or plutonium also had a death rate from all cancers combined that was below the national average (SMR = 86, 418 deaths; 2P = 0.002) but prostatic cancer mortality was raised both in relation to death rates in the general population (SMR = 153, 37 deaths; 2P = 0.02) and to death rates in radiation workers who had not been monitored for exposure to any radionuclide [rate ratio (RR) = 1.65; 2P = 0.03]. Mortality from cancer of the lung was also significantly increased in workers monitored for other radionuclides compared with those of radiation workers not monitored for exposure to radionuclides (RR = 1.31, 164 deaths; 2P = 0.01). For cancers of the lung, prostate and all cancers combined, death rates in monitored workers were examined according to the timing and duration of monitoring for radionuclide exposure, with rates of radiation workers not monitored for any radionuclide forming the comparison group. In tritium-monitored workers, RRs for prostatic cancer varied significantly according to the number of years in which they were monitored (2P = 0.03). In workers monitored for plutonium exposure, RRs for all cancers combined increased with the number of years in which they were monitored (2P = 0.04) and with the number of years since first monitoring (2P = 0.0003). There was little suggestion of systematic variation in RRs for workers monitored for other radionuclides in relation to the timing or duration of monitoring, nor did it appear that their raised rates of cancer of the lung and prostate were explained by external radiation dose. These analyses of cancer mortality in relation to monitoring for radionuclide exposure reported in a large cohort of nuclear industry workers suggest that certain patterns of monitoring for some radionuclides may be associated with higher death rates from cancers of the lung, pleura, prostate and all cancers combined. Some of these findings may be due to chance. Moreover, because of the paucity of related data and lack of information about other possible exposures, such as whether plutonium workers are more likely to be exposed to asbestos, firm conclusions cannot be drawn at this stage. Further investigations of the relationship between radionuclide exposure and cancer in nuclear industry workers are needed.