Cancer incidence and mortality in 23 000 patients with type 1 diabetes in the UK: Long-term follow-up.

Type 2 diabetes is associated with raised risk of several cancers, but for type 1 diabetes risk data are fewer and inconsistent We assembled a cohort of 23 473 UK patients with insulin-treated diabetes diagnosed at ages <30, almost all of whom will have had type 1 diabetes, and for comparison 5058 diagnosed at ages 30 to 49, of whom we estimate two-thirds will have had type 2, and followed them for an average of 30 years for cancer incidence and mortality compared with general population rates. Patients aged <30 at diabetes diagnosis had significantly raised risks only for ovarian (standardised incidence ratio = 1.58; 95% confidence interval 1.16-2.11; P < .01) and vulval (3.55; 1.94-5.96; P < .001) cancers, with greatest risk when diabetes was diagnosed at ages 10-14. Risks of cancer overall (0.89; 0.84-0.95; P < .001) and sites including lung and larynx were significantly diminished. Patients diagnosed with diabetes at ages 30 to 49 had significantly raised risks of liver (1.76;1.08-2.72) and kidney (1.46;1.03-2.00) cancers, and reduced risk of cancer overall (0.89; 0.84-0.95). The raised ovarian and vulval cancer risks in patients with type 1 diabetes, especially with diabetes diagnosed around pubertal ages, suggest possible susceptibility of these organs at puberty to metabolic disruption at diabetes onset. Reduced risk of cancer overall, particularly smoking and alcohol-related sites, might reflect adoption of a healthy lifestyle.

Patients' help-seeking experiences and delay in cancer presentation: a qualitative synthesis.

BACKGROUND: The reduction of delay in cancer diagnosis has been targeted as a way to improve survival. We undertook a qualitative synthesis of international research evidence to provide insight into patients' experiences of recognising symptoms of cancer and seeking help. METHODS: We searched international publications (1985-2004) for delay in cancer diagnosis to identify the relevant qualitative research, and used meta-ethnography to identify the common themes across the studies. Our synthesis interpreted individual studies by identification of second-order constructs (interpretations offered by the original researchers) and third-order constructs (development of new interpretations beyond those offered in individual studies). FINDINGS: We identified 32 papers (>775 patients and carers) reporting help-seeking experiences for at least 20 different types of cancer. The analysis showed strong similarities in patients with different cancer types. Key concepts were recognition and interpretation of symptoms, and fear of consultation. Fear manifested as a fear of embarrassment (the feeling that symptoms were trivial or that symptoms affected a sensitive body area), or a fear of cancer (pain, suffering, and death), or both. Such analyses allowed exploration of third-order constructs. The patient's gender and the sanctioning of help-seeking were important factors in prompt consultation. INTERPRETATION: Strategies to understand and reduce patients' delay in cancer presentation can help symptom recognition but need to address patients' anxieties. The effect of the patient's sex in help-seeking also needs to be recognised, as does the important role of friends, family, and health-care professionals in the sanctioning of consultation. This meta-ethnography provides an international overview through the systematic synthesis of a diverse group of small-scale qualitative studies.

Mortality from cerebrovascular disease in a cohort of 23 000 patients with insulin-treated diabetes.

BACKGROUND AND PURPOSE: Disease of the cardiovascular system is the main cause of long-term complications and mortality in patients with type I (insulin-dependent) and type II (non-insulin-dependent) diabetes. Cerebrovascular mortality rates have been shown to be raised in patients with type II diabetes but have not previously been reported by age and sex in patients with type I diabetes. METHODS: A cohort of 23 751 patients with insulin-treated diabetes, diagnosed under the age of 30 years from throughout the United Kingdom, was identified during 1972 to 1993 and followed up for mortality until the end of December 2000. Age- and sex-specific mortality rates and standardized mortality ratios (SMRs) were calculated. RESULTS: There were 1437 deaths during the follow-up, 80 due to cerebrovascular disease. Overall, the cerebrovascular mortality rates in the cohort were higher than the corresponding rates in the general population, and the SMRs were 3.1 (95% CI, 2.2 to 4.3) for men and 4.4 (95% CI, 3.1 to 6.0) for women. When stratified by age, the SMRs were highest in the 20- to 39-year age group. After subdivision of cause of death into hemorrhagic and nonhemorrhagic origins, there remained a significant increase in mortality from stroke of nonhemorrhagic origin. CONCLUSIONS: Analyses of mortality from this cohort, essentially one of patients with type I diabetes, has shown for the first time that cerebrovascular mortality is raised at all ages in these patients. Type I diabetes is at least as great a risk factor for cerebrovascular mortality as type II diabetes.

Providing more up-to-date estimates of patient survival: a comparison of standard survival analysis with period analysis using life-table methods and proportional hazards models.

OBJECTIVE: Standard survival methods can yield out-of-date estimates of long-term survival. Period analysis, based on life-table methodology, provides more up-to-date survival estimates by exploring survival during a restricted recent period of interest. It excludes the short-term survival of patients recruited at the start of the study. We use statistical models to further develop the method of period analysis, providing more up-to-date estimates of survival and the ability to explore differences in survival by covariates and adjust for case mix. METHODS: We use cancer registry data for colorectal cancer in Leicestershire, UK, to illustrate the use of Cox proportional hazards (CPH) models to estimate period and standard survival. We compare these estimates with those obtained using life-table methodology. RESULTS: Period estimates were slightly higher than the standard estimates as they reflect recent improvements in short-term survival. The results for period analysis using the life-table approach and using CPH models were similar. However, CPH models allowed further investigation of other risk factors and the ability to control for potential confounding variables. CONCLUSION: Using period survival estimates, more up-to-date information is available to clinicians and others with an interest in monitoring survival. Period CHP models offer all the advantages of statistical modeling, and are straightforward to fit in standard statistical packages.

Additive and multiplicative covariate regression models for relative survival incorporating fractional polynomials for time-dependent effects.

Relative survival is used to estimate patient survival excluding causes of death not related to the disease of interest. Rather than using cause of death information from death certificates, which is often poorly recorded, relative survival compares the observed survival to that expected in a matched group from the general population. Models for relative survival can be expressed on the hazard (mortality) rate scale as the sum of two components where the total mortality rate is the sum of the underlying baseline mortality rate and the excess mortality rate due to the disease of interest. Previous models for relative survival have assumed that covariate effects act multiplicatively and have thus provided relative effects of differences between groups using excess mortality rate ratios. In this paper we consider (i) the use of an additive covariate model, which provides estimates of the absolute difference in the excess mortality rate; and (ii) the use of fractional polynomials in relative survival models for the baseline excess mortality rate and time-dependent effects. The approaches are illustrated using data on 115 331 female breast cancer patients diagnosed between 1 January 1986 and 31 December 1990. The use of additive covariate relative survival models can be useful in situations when the excess mortality rate is zero or slightly less than zero and can provide useful information from a public health perspective. The use of fractional polynomials has advantages over the usual piecewise estimation by providing smooth estimates of the baseline excess mortality rate and time-dependent effects for both the multiplicative and additive covariate models. All models presented in this paper can be estimated within a generalized linear models framework and thus can be implemented using standard software.