Socioeconomic inequalities in stage-specific breast cancer incidence: a nationwide registry study of 1.1 million young women in Norway, 2000-2015

BACKGROUND

Women with high socioeconomic status (SES) have the highest incidence rates of breast cancer. We wanted to determine if high SES women only have higher rates of localized disease, or whether they also have higher rates of non-localized disease. To study this, we used data on a young population with universal health care, but not offered screening.

MATERIAL AND METHODS

Using individually linked registry data, we compared stage-specific breast cancer incidence, by education level and income quintile, in a Norwegian cohort of 1,106,863 women aged 30-48 years during 2000-2015 (N = 7531 breast cancer cases). We calculated stage-specific age-standardized rates and incidence rate ratios and rate differences using Poisson models adjusted for age, period and immigration history.

RESULTS

Incidence of localized and regional disease increased significantly with increasing education and income level. Incidence of distant stage disease did not vary significantly by education level but was significantly reduced in the four highest compared to the lowest income quintile. The age-standardized rates for tertiary versus compulsory educated women were: localized 28.2 vs 19.8, regional 50.8 vs 40.4 and distant 2.3 vs 2.6 per 100,000 person-years. The adjusted incidence rate ratios (tertiary versus compulsory) were: localized 1.40 (95% CI 1.25-1.56), regional 1.25 (1.15-1.35), distant 0.90 (0.64-1.26). The age-standardized rates for women in the highest versus lowest income quintile were: localized 28.9 vs 17.7, regional 52.8 vs 41.5 and distant 2.3 vs 3.2 per 100,000 person-years. The adjusted incidence rate ratios (highest versus lowest quintile) were: localized 1.63 (1.42-1.87), regional 1.27 (1.09-1.32), distant 0.64 (0.43-0.94).

CONCLUSION

Increased breast cancer rates among young high SES women is not just increased detection of small localized tumors, but also increased incidence of tumors with regional spread. The higher incidence of young high SES women is therefore real and not only because of excessive screening.

Stage-specific survival has improved for young breast cancer patients since 2000: but not equally

Purpose

The stage-specific survival of young breast cancer patients has improved, likely due to diagnostic and treatment advances. We addressed whether survival improvements have reached all socioeconomic groups in a country with universal health care and national treatment guidelines.

Methods

Using Norwegian registry data, we assessed stage-specific breast cancer survival by education and income level of 7501 patients (2317 localized, 4457 regional, 233 distant and 494 unknown stage) aged 30–48 years at diagnosis during 2000–2015. Using flexible parametric models and national life tables, we compared excess mortality up to 12 years from diagnosis and 5-year relative survival trends, by education and income as measures of socioeconomic status (SES).

Results

Throughout 2000–2015, regional and distant stage 5-year relative survival improved steadily for patients with high education and high income (high SES), but not for patients with low education and low income (low SES). Regional stage 5-year relative survival improved from 85 to 94% for high SES patients (9% change; 95% confidence interval: 6, 13%), but remained at 84% for low SES patients (0% change; − 12, 12%). Distant stage 5-year relative survival improved from 22 to 58% for high SES patients (36% change; 24, 49%), but remained at 11% for low SES patients (0% change; − 19, 19%).

Conclusions

Regional and distant stage breast cancer survival has improved markedly for high SES patients, but there has been little survival gain for low SES patients. Socioeconomic status matters for the stage-specific survival of young breast cancer patients, even with universal health care.

Electronic supplementary material

The online version of this article (10.1007/s10549-020-05698-z) contains supplementary material, which is available to authorized users.

Adjusting for BMI in analyses of volumetric mammographic density and breast cancer risk

BACKGROUND

Fully automated assessment of mammographic density (MD), a biomarker of breast cancer risk, is being increasingly performed in screening settings. However, data on body mass index (BMI), a confounder of the MD-risk association, are not routinely collected at screening. We investigated whether the amount of fat in the breast, as captured by the amount of mammographic non-dense tissue seen on the mammographic image, can be used as a proxy for BMI when data on the latter are unavailable.

METHODS

Data from a UK case control study (numbers of cases/controls: 414/685) and a Norwegian cohort study (numbers of cases/non-cases: 657/61059), both with volumetric MD measurements (dense volume (DV), non-dense volume (NDV) and percent density (%MD)) from screening-age women, were analysed. BMI (self-reported) and NDV were taken as measures of adiposity. Correlations between BMI and NDV, %MD and DV were examined after log-transformation and adjustment for age, menopausal status and parity. Logistic regression models were fitted to the UK study, and Cox regression models to the Norwegian study, to assess associations between MD and breast cancer risk, expressed as odds/hazard ratios per adjusted standard deviation (OPERA). Adjustments were first made for standard risk factors except BMI (minimally adjusted models) and then also for BMI or NDV. OPERA pooled relative risks (RRs) were estimated by fixed-effect models, and between-study heterogeneity was assessed by the I2 statistics.

RESULTS

BMI was positively correlated with NDV (adjusted r = 0.74 in the UK study and r = 0.72 in the Norwegian study) and with DV (r = 0.33 and r = 0.25, respectively). Both %MD and DV were positively associated with breast cancer risk in minimally adjusted models (pooled OPERA RR (95% confidence interval): 1.34 (1.25, 1.43) and 1.46 (1.36, 1.56), respectively; I2 = 0%, P >0.48 for both). Further adjustment for BMI or NDV strengthened the %MD-risk association (1.51 (1.41, 1.61); I2 = 0%, P = 0.33 and 1.51 (1.41, 1.61); I2 = 0%, P = 0.32, respectively). Adjusting for BMI or NDV marginally affected the magnitude of the DV-risk association (1.44 (1.34, 1.54); I2 = 0%, P = 0.87 and 1.49 (1.40, 1.60); I2 = 0%, P = 0.36, respectively).

CONCLUSIONS

When volumetric MD-breast cancer risk associations are investigated, NDV can be used as a measure of adiposity when BMI data are unavailable.

Number of Risky Lifestyle Behaviors and Breast Cancer Risk

Background

Lifestyle factors are associated with overall breast cancer risk, but less is known about their associations, alone or jointly, with risk of specific breast cancer subtypes.

Methods

We conducted a case-control subjects study nested within a cohort of women who participated in the Norwegian Breast Cancer Screening Program during 2006-2014 to examine associations between risky lifestyle factors and breast cancer risk. In all, 4402 breast cancer cases subjects with information on risk factors and hormone receptor status were identified. Conditional logistic regression was used to estimate odds ratios (ORs), with 95% confidence intervals (CIs), in relation to five risky lifestyle factors: body mass index (BMI) of 25 kg/m² or greater, three or more glasses of alcoholic beverages per week, ever smoking, fewer than four hours of physical activity per week, and ever use of menopausal hormone therapy. Analyses were adjusted for education, age at menarche, number of pregnancies, and menopausal status. All statistical tests were two-sided.

Results

Compared with women with no risky lifestyle behaviors, those with five had 85% (OR = 1.85, 95% CI = 1.42 to 2.42, P _trend < .0001) increased risk of breast cancer overall. This association was limited to luminal A-like (OR = 2.20, 95% CI = 1.55 to 3.12, P _trend < .0001) and luminal B-like human epidermal growth factor receptor 2 (HER2)-positive (OR = 1.66, 95% CI = 0.61 to 4.54, P _trend < .004) subtypes. Number of risky lifestyle factors was not associated with increased risk of luminal B-like HER2-negative, HER2-positive, or triple-negative subtypes (P _trend > .18 for all).

Conclusions

Number of risky lifestyle factors was positively associated with increased risk for luminal A-like and luminal B-like HER2-positive breast cancer.

Alcohol, Physical Activity, Smoking, and Breast Cancer Subtypes in a Large, Nested Case-Control Study from the Norwegian Breast Cancer Screening Program

Background: To what extent alcohol, smoking, and physical activity are associated with the various subtypes of breast cancer is not clear. We took advantage of a large population-based screening cohort to determine whether these risk factors also increase the risk of the poor prognosis subtypes.Methods: We conducted a matched case-control study nested within the Norwegian Breast Cancer Screening Program during 2006-2014. A total of 4,402 breast cancer cases with risk factor and receptor data were identified. Five controls were matched to each case on year of birth and year of screening. Conditional logistic regression was used to estimate ORs of breast cancer subtypes adjusted for potential confounders.Results: There were 2,761 luminal A-like, 709 luminal B-like HER2-negative, 367 luminal B-like HER2-positive, 204 HER2-positive, and 361 triple-negative cancers. Current alcohol consumption was associated with breast cancer risk overall [OR 1.26; 95% confidence interval (CI), 1.09-1.45] comparing 6+ glasses a week to never drinkers. However, this risk increase was found only for luminal A-like breast cancer. Smoking 20+ cigarettes a day was associated with an OR of 1.41 (95% CI, 1.06-1.89) overall, with significant trends for luminal A-like and luminal B-like HER2-negative cancer. Current physical activity (4+ hours/week compared with none) was associated with 15% decreased risk of luminal A-like cancer, but not clearly with other subtypes.Conclusions: In this large study, alcohol, smoking, and physical activity were predominantly associated with luminal A-like breast cancer.Impact: Alcohol, smoking, and physical activity were associated with luminal A-like breast cancer subtype. Cancer Epidemiol Biomarkers Prev; 26(12); 1736-44. ©2017 AACR.

Adiposity, physical activity and risk of diabetes mellitus: prospective data from the population-based HUNT study, Norway

BACKGROUND

Physical activity may counteract the adverse effects of adiposity on cardiovascular mortality; however, the evidence of a similar effect on diabetes is sparse. This study examines whether physical activity may compensate for the adverse effect of adiposity on diabetes risk.

METHODS

The study population consisted of 38 231 individuals aged 20 years or more who participated in two consecutive waves of the prospective longitudinal Nord-Trøndelag Health Study in Norway: in 1984-1986 and in 1995-1997. A Poisson regression model with SEs derived from robust variance was used to estimate adjusted risk ratios of diabetes between categories of body mass index and physical activity.

RESULTS

Risk of diabetes increased both with increasing body mass (P_trend <0.001) and with decreasing physical activity level (P_trend <0.001 in men and 0.01 in women). Combined analyses showed that men who were both obese and had low activity levels had a risk ratio of 17 (95% CI 9.52 to 30) compared to men who were normal weight and highly active, whereas obese men who reported high activity had a risk ratio of 13 (95% CI 6.92 to 26). Corresponding analysis in obese women produced risk ratios of 15 (95% CI 9.18 to 25) and 13 (95% CI 7.42 to 21) among women reporting low and high activity levels, respectively.

CONCLUSIONS

This study shows that overweight and obesity are associated with a substantially increased risk of diabetes, particularly among those who also reported being physically inactive. High levels of physical activity were associated with a lower risk of diabetes within all categories of body mass index, but there was no clear evidence that being physically active could entirely compensate for the adverse effect of adiposity on diabetes risk.