June 29th, 2012 by Hasham

Breast cancer – UK incidence statistics

Incidence statistics for invasive breast cancer are presented here by country in the UK, sex, age, trends over time and prevalence. For females, there are also data on lifetime risk, geographic and socio-economic variation and in situ breast carcinoma (ICD-10 code D05). Data on invasive breast cancer incidence in males is also shown. The ICD code for invasive breast cancer is ICD-10 C50.

The latest cancer incidence statistics available for the UK are for 2009, and for mortality the latest statistics are for 2010. We are currently working to update all the incidence and mortality pages on this site

By country in the UK and sex

Breast cancer has been the most common cancer in the UK since 1997, despite the fact that it is rare in men. It is by far the most common cancer among women in the UK, accounting for 31% all new cases of cancer in females.1-4

In 2009, there were 48,788 new cases of breast cancer in the UK (Table 1.1): 48,417 in women and 371 (less than 1%) in men, giving a female:male ratio of around 131:1.1-4

European age-standardised incidence rates (AS rates) do not differ significantly between the constituent countries of the UK (Table 1.1); however, the lowest rates are recorded in Northern Ireland, and this has been a consistent pattern for almost two decades.5 Scotland is the only nation in the UK where breast cancer is not the most common cancer overall; there lung cancer is more common.1-4 An analysis of female breast cancer incidence rates throughout the UK in 2005 reported only modest variation between cancer networks.

By age (females)

Female breast cancer incidence is strongly related to age, with the highest incidence rates overall being in older women, supporting a link with hormonal status. In the UK between 2007 and 2009, 45% of cases were diagnosed in women aged 65 and over, and 81% were diagnosed in the 50s and over (Figure 1.1).1-4 Age-specific incidence rates rise steeply from around age 35-39, level off for women in their 50s, then rise further to age 65-69, drop slightly for women aged 70-74, then increase steadily to reach an overall peak in the 85+ age group.1-4 The peaks and troughs of incidence for women aged 50 and over may reflect the impact of the national screening programme.

Introduction

The aim of this study was to describe breast tumor subtypes by common breast cancer risk factors and to determine correlates of subtypes using baseline data from two pooled prospective breast cancer studies within a large health maintenance organization.
Methods

Tumor data on 2544 invasive breast cancer cases subtyped by estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 (Her2) status were obtained (1868 luminal A tumors, 294 luminal B tumors, 288 triple-negative tumors and 94 Her2-overexpressing tumors). Demographic, reproductive and lifestyle information was collected either in person or by mailed questionnaires. Case-only odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using logistic regression, adjusting for age at diagnosis, race/ethnicity, and study origin.
Results

Compared with luminal A cases, luminal B cases were more likely to be younger at diagnosis (P = 0.0001) and were less likely to consume alcohol (OR = 0.74, 95% CI = 0.56 to 0.98), use hormone replacement therapy (HRT) (OR = 0.66, 95% CI = 0.46 to 0.94), and oral contraceptives (OR = 0.73, 95% CI = 0.55 to 0.96). Compared with luminal A cases, triple-negative cases tended to be younger at diagnosis (P ≤ 0.0001) and African American (OR = 3.14, 95% CI = 2.12 to 4.16), were more likely to have not breastfed if they had parity greater than or equal to three (OR = 1.68, 95% CI = 1.00 to 2.81), and were more likely to be overweight (OR = 1.82, 95% CI = 1.03 to 3.24) or obese (OR = 1.97, 95% CI = 1.03 to 3.77) if premenopausal. Her2-overexpressing cases were more likely to be younger at diagnosis (P = 0.03) and Hispanic (OR = 2.19, 95% CI = 1.16 to 4.13) or Asian (OR = 2.02, 95% CI = 1.05 to 3.88), and less likely to use HRT (OR = 0.45, 95% CI = 0.26 to 0.79).
Conclusions

These observations suggest that investigators should consider tumor heterogeneity in associations with traditional breast cancer risk factors. Important modifiable lifestyle factors that may be related to the development of a specific tumor subtype, but not all subtypes, include obesity, breastfeeding, and alcohol consumption. Future work that will further categorize triple-negative cases into basal and non-basal tumors may help to elucidate these associations further.

Abstract
Cancer incidence and mortality rates are increasing rapidly. Specifically breast cancer is the most common malignancy among women in all the countries wherever it has been studied. The trends in occurrence of breast cancer might be related to various social, cultural, environmental, life-style related habits and many other factors. The variations in the density and diversity of these factors among areas demand separate study for each geographical area. There has been research work on the medical aspect of breast cancer in Pakistan but it is deficient in epidemiological studies on breast cancer. This epidemiological research work on breast cancer has been presented with the application of advanced statistical methodology.

There are basically two choices for an epidemiological study, namely, prospective and retrospective or more technically cohort and case-control studies. For both types of studies, the statistical methodology used for analysis is logistic regression. Case-control studies are of two kinds; unmatched and matched case-control studies. For matched studies the controls are matched to the cases for some confounding variable. Age is a confounding variable for studies on cancer. Unconditional logistic regression analysis is applied to unmatched case-control studies and conditional logistic regression for analyzing matched case-control studies. The latter technique is quite costly therefore limited in application.

A matched case-control study on breast cancer has been carried out to determine the risk factors of breast cancer in Punjab, Pakistan. The data for breast cancer patients were collected from the two leading cancer hospitals Shaukat Khanum Memorial Cancer Hospital (SKMCH) and Institute of Nuclear Medicines and Oncology Lahore (lNMOL). Population-based controls were matched for age at diagnosis of the patients within two years in the ratio 1:2. The interview schedule designed for the study included questions regarding socio-economic status, monthly income, history of smoking, family marriage, family history of cancer and breast cancer, menstrual and reproductive history and anthropometric variables. The data set comprised of 1166 breast cancer patients and 2506 controls in all. For this study, the controls were selected as follows. Three villages were selected to represent the rural population (Shah De KIlUi, Manga Mandi and Ghandran) and two cities were selected to represent the urban population (Lahore being metropolitan city and Gujranwala being an industrial city). These areas were randomly selected and individual houses were selected according to convenience. One control from one house was interviewed for the study. Continuous variables of the study were examined for the assumption of linearity. For the variables with non-linear trend appropriate transformations for linearity were applied to include them in linear logistic models. All the continuous variables were finally modeled as categorical variables. Univariate analysis was used to identify variables to be included in multivariate analysis using p<0.25. All the variables of statistical or biological importance were included in the models for conditional multiple logistic regression analysis.

As a first step main effects model was developed. Later on various multivariate models were developed by including potential confounders. Some of the models (odds ratios and the significance level) developed for the study were presented in different Tables. The decision regarding inclusion or exclusion of the confounders or interaction tem1s was made as guided by the significance of the change in likelihood. Of the statistically significant interaction terms only those of biological importance were finally retained in the model.

Multiple logistic regression models were developed for this data set also by using unconditional approach. Various multivariate models were attempted by including different potential confounders. Some of these models were presented in the tables. These models were then compared to arrive at a final statistical model. Comparison between the two approaches of analysis was also shown. The risk of breast cancer increases after menopause. The risk factors of breast cancer before menopause may be different from those after menopause. Therefore a separate analysis was carried out for postmenopausal women. It was observed that women with late age at menopause were at significantly higher risk of breast cancer.

During the analysis some of the results (odds ratios or their confidence intervals) were observed to be inconsistent. Therefore an attempt was made to base analysis of the study on cases with complete infom1ation on all variables. It was termed as complete case analysis. All the cases with missing information for any covariate were deleted along with the corresponding matched controls. Logistic regression was applied to the revised dataset by using; both conditional and unconditional approach. The two types of models developed for the earlier revised data set were compared. The same analysis was carried out for postmenopausal women of the study also.

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