What might explain deprivation-specific differences in the excess hazard of breast cancer death amongst screen-detected women? Analysis of patients diagnosed in the West Midlands region of England from 1989 to 2011

Association between socioeconomic status and net survival after primary lung cancer surgery: a tertiary university hospital retrospective observational study in Japan

BACKGROUND

Inequalities in opportunities for primary lung cancer surgery due to socioeconomic status exist. We investigated whether socioeconomic inequalities exist in net survival after curative intent surgery at a tertiary university hospital, in Japan.

METHODS

Data from the hospital-based cancer registry on primary lung cancer patients who received lung resection between 2010 and 2018 were linked to the surgical dataset. An area deprivation index, calculated from small area statistics and ranked into tertiles based on Japan-wide distribution, was linked with the patient's address as a proxy measure for individual socioeconomic status. We estimated net survival of up to 5 years by deprivation tertiles. Socioeconomic inequalities in cancer survival were analyzed using an excess hazard model.

RESULTS

Of the 1039 patient-sample, advanced stage (Stage IIIA+) was more prevalent in the most deprived group (28.1%) than the least deprived group (18.0%). The 5-year net survival rates (95% confidence interval) from the least to the most deprived tertiles were 82.1% (76.2-86.6), 77.6% (70.8-83.0) and 71.4% (62.7-78.4), respectively. The sex- and age-adjusted excess hazard ratio of 5-year death was significantly higher in the most deprived group than the least deprived (excess hazard ratio = 1.64, 95% confidence interval: 1.09-2.47). The hazard ratio reduced toward null after additionally accounting for disease stage, suggesting that the advanced stage may explain the poor prognosis among the deprived group.

CONCLUSION

There was socioeconomic inequality in the net survival of patients who received curative intent surgery for primary lung cancer. The lower socioeconomic status group might be less likely to receive early curative surgery.

Estimating causal effects in the presence of competing events using regression standardisation with the Stata command standsurv

BACKGROUND

When interested in a time-to-event outcome, competing events that prevent the occurrence of the event of interest may be present. In the presence of competing events, various estimands have been suggested for defining the causal effect of treatment on the event of interest. Depending on the estimand, the competing events are either accommodated or eliminated, resulting in causal effects with different interpretations. The former approach captures the total effect of treatment on the event of interest while the latter approach captures the direct effect of treatment on the event of interest that is not mediated by the competing event. Separable effects have also been defined for settings where the treatment can be partitioned into two components that affect the event of interest and the competing event through different causal pathways.

METHODS

We outline various causal effects that may be of interest in the presence of competing events, including total, direct and separable effects, and describe how to obtain estimates using regression standardisation with the Stata command standsurv. Regression standardisation is applied by obtaining the average of individual estimates across all individuals in a study population after fitting a survival model.

RESULTS

With standsurv several contrasts of interest can be calculated including differences, ratios and other user-defined functions. Confidence intervals can also be obtained using the delta method. Throughout we use an example analysing a publicly available dataset on prostate cancer to allow the reader to replicate the analysis and further explore the different effects of interest.

CONCLUSIONS

Several causal effects can be defined in the presence of competing events and, under assumptions, estimates of those can be obtained using regression standardisation with the Stata command standsurv. The choice of which causal effect to define should be given careful consideration based on the research question and the audience to which the findings will be communicated.

Socioeconomic status and its relation with breast cancer recurrence and survival in young women in the Netherlands

BACKGROUND

Associations between socioeconomic status (SES) and breast cancer survival are most pronounced in young patients. We further investigated the relation between SES, subsequent recurrent events and mortality in breast cancer patients < 40 years. Using detailed data on all recurrences that occur between date of diagnosis of the primary tumor and last observation, we provide a unique insight in the prognosis of young breast cancer patients according to SES.

METHODS

All women < 40 years diagnosed with primary operated stage I-III breast cancer in 2005 were selected from the nationwide population-based Netherlands Cancer Registry. Data on all recurrences within 10 years from primary tumor diagnosis were collected directly from patient files. Recurrence patterns and absolute risks of recurrence, contralateral breast cancer (CBC) and mortality - accounting for competing risks - were analysed according to SES. Relationships between SES, recurrence patterns and excess mortality were estimated using a multivariable joint model, wherein the association between recurrent events and excess mortality (expected mortality derived from the general population) was included.

RESULTS

We included 525 patients. The 10-year recurrence risk was lowest in high SES (18.1%), highest in low SES (29.8%). Death and CBC as first events were rare. In high, medium and low SES 13.2%, 15.3% and 19.1% died following a recurrence. Low SES patients had shorter median time intervals between diagnosis, first recurrence and 10-year mortality (2.6 and 2.7 years, respectively) compared to high SES (3.5 and 3.3 years, respectively). In multivariable joint modeling, high SES was significantly related to lower recurrence rates over 10-year follow-up, compared to low SES. A strong association between the recurrent event process and excess mortality was found.

CONCLUSIONS

High SES is associated with lower recurrence risks, less subsequent events and better prognosis after recurrence over 10 years than low SES. Breast cancer risk factors, adjuvant treatment adherence and treatment of recurrence may possibly play a role in this association.

Ethnic, racial and socioeconomic disparities in breast cancer survival in two Brazilian capitals between 1996 and 2012

OBJECTIVE

To study the impact of socio-economic status and ethno-racial strata on excess mortality hazard and net survival of women with breast cancer in two Brazilian state capitals.

METHOD

We conducted a survival analysis with individual data from population-based cancer registries including women with breast cancer diagnosed between 1996 and 2012 in Aracaju and Curitiba. The main outcomes were the excess mortality hazard (EMH) and net survival. The associations of age, year of diagnosis, disease stage, race/skin colour and socioeconomic status (SES) with the excess mortality hazard and net survival were analysed using multi-level spline regression models, modelled as cubic splines with knots at 1 and 5 years of follow-up.

RESULTS

A total of 2045 women in Aracaju and 7872 in Curitiba were included in the analyses. The EMH was higher for women with lower SES and for black and brown women in both municipalities. The greatest difference in excess mortality was seen between the most deprived women and the most affluent women in Curitiba, hazard ratio (HR) 1.93 (95%CI 1.63-2.28). For race/skin colour, the greatest ratio was found in Curitiba (HR 1.35, 95%CI 1.09-1.66) for black women compared with white women. The most important socio-economic difference in net survival was seen in Aracaju. Age-standardised net survival at five years was 55.7% for the most deprived women and 67.2% for the most affluent. Net survival at eight years was 48.3% and 61.0%, respectively. Net survival in Curitiba was higher than in Aracaju in all SES groups."

CONCLUSION

Our findings suggest the presence of contrasting breast cancer survival expectancy in Aracaju and Curitiba, highlighting regional inequalities in access to health care. Lower survival among brown and black women, and those in lower SES groups indicates that early detection, early diagnosis and timely access to treatment must be prioritized to reduce inequalities in outcome among Brazilian women.

Are socio-economic inequalities in breast cancer survival explained by peri-diagnostic factors?

BACKGROUND

Patients living in more deprived localities have lower cancer survival in England, but the role of individual health status at diagnosis and the utilisation of primary health care in explaining these differentials has not been widely considered. We set out to evaluate whether pre-existing individual health status at diagnosis and primary care consultation history (peri-diagnostic factors) could explain socio-economic differentials in survival amongst women diagnosed with breast cancer.

METHODS

We conducted a retrospective cohort study of women aged 15-99 years diagnosed in England using linked routine data. Ecologically-derived measures of income deprivation were combined with individually-linked data from the English National Cancer Registry, Clinical Practice Research Datalink (CPRD) and Hospital Episodes Statistics (HES) databases. Smoking status, alcohol consumption, BMI, comorbidity, and consultation histories were derived for all patients. Time to breast surgery was derived for women diagnosed after 2005. We estimated net survival and modelled the excess hazard ratio of breast cancer death using flexible parametric models. We accounted for missing data using multiple imputation.

RESULTS

Net survival was lower amongst more deprived women, with a single unit increase in deprivation quintile inferring a 4.4% (95% CI 1.4-8.8) increase in excess mortality. Peri-diagnostic co-variables varied by deprivation but did not explain the differentials in multivariable analyses.

CONCLUSIONS

These data show that socio-economic inequalities in survival cannot be explained by consultation history or by pre-existing individual health status, as measured in primary care. Differentials in the effectiveness of treatment, beyond those measuring the inclusion of breast surgery and the timing of surgery, should be considered as part of the wider effort to reduce inequalities in premature mortality.

Factors Explaining Socio-Economic Inequalities in Cancer Survival: A Systematic Review

BACKGROUND

There is strong and well-documented evidence that socio-economic inequality in cancer survival exists within and between countries, but the underlying causes of these differences are not well understood.

METHODS

We systematically searched the Ovid Medline, EMBASE, and CINAHL databases up to 31 May 2020. Observational studies exploring pathways by which socio-economic position (SEP) might causally influence cancer survival were included.

RESULTS

We found 74 eligible articles published between 2005 and 2020. Cancer stage, other tumor characteristics, health-related lifestyle behaviors, co-morbidities and treatment were reported as key contributing factors, although the potential mediating effect of these factors varied across cancer sites. For common cancers such as breast and prostate cancer, stage of disease was generally cited as the primary explanatory factor, while co-morbid conditions and treatment were also reported to contribute to lower survival for more disadvantaged cases. In contrast, for colorectal cancer, most studies found that stage did not explain the observed differences in survival by SEP. For lung cancer, inequalities in survival appear to be partly explained by receipt of treatment and co-morbidities.

CONCLUSIONS

Most studies compared regression models with and without adjusting for potential mediators; this method has several limitations in the presence of multiple mediators that could result in biased estimates of mediating effects and invalid conclusions. It is therefore essential that future studies apply modern methods of causal mediation analysis to accurately estimate the contribution of potential explanatory factors for these inequalities, which may translate into effective interventions to improve survival for disadvantaged cancer patients.

Prediction of cancer survival for cohorts of patients most recently diagnosed using multi-model inference

Despite a large choice of models, functional forms and types of effects, the selection of excess hazard models for prediction of population cancer survival is not widespread in the literature. We propose multi-model inference based on excess hazard model(s) selected using Akaike information criteria or Bayesian information criteria for prediction and projection of cancer survival. We evaluate the properties of this approach using empirical data of patients diagnosed with breast, colon or lung cancer in 1990-2011. We artificially censor the data on 31 December 2010 and predict five-year survival for the 2010 and 2011 cohorts. We compare these predictions to the observed five-year cohort estimates of cancer survival and contrast them to predictions from an a priori selected simple model, and from the period approach. We illustrate the approach by replicating it for cohorts of patients for which stage at diagnosis and other important prognosis factors are available. We find that model-averaged predictions and projections of survival have close to minimal differences with the Pohar-Perme estimation of survival in many instances, particularly in subgroups of the population. Advantages of information-criterion based model selection include (i) transparent model-building strategy, (ii) accounting for model selection uncertainty, (iii) no a priori assumption for effects, and (iv) projections for patients outside of the sample.

Estimating the impact of a cancer diagnosis on life expectancy by socio-economic group for a range of cancer types in England

BACKGROUND

Differences in cancer survival exist across socio-economic groups for many cancer types. Standard metrics fail to show the overall impact for patients and the population.

METHODS

The available data consist of a population of ∼2.5 million patients and include all patients recorded as being diagnosed with melanoma, prostate, bladder, breast, colon, rectum, lung, ovarian and stomach cancers in England between 1998 and 2013. We estimated the average loss in expectation of life per patient in years and the proportion of life lost for a range of cancer types, separately by deprivation group. In addition, estimates for the total number of years lost due to each cancer were also obtained.

RESULTS

Lung and stomach cancers result in the highest overall loss for males and females in all deprivation groups in terms of both absolute life years lost and loss as a proportion of expected life remaining. Female lung cancer patients in the least- and most-deprived group lose 14.4 and 13.8 years on average, respectively, that is translated as 86.1% and 87.3% of their average expected life years remaining. Melanoma, prostate and breast cancers have the lowest overall loss. On the basis of the number of patients diagnosed in 2013, lung cancer results in the most life years lost in total followed by breast cancer. Melanoma and bladder cancer account for the lowest total life years lost.

CONCLUSIONS

There are wide differences in the impact of cancer on life expectancy across deprivation groups, and for most cancers the most affluent lose less years.

Geographical differences in risk of advanced breast cancer: Limited evidence for reductions over time, Queensland, Australia 1997-2014

BACKGROUND

Reducing geographical inequalities in breast cancer stage remains a key focus of public health policy. We explored whether patterns of advanced breast cancer by residential accessibility and disadvantage in Queensland, Australia, have changed over time.

METHODS

Population-based cancer registry study of 38,706 women aged at least 30 years diagnosed with a first primary invasive breast cancer of known stage between 1997 and 2014. Multilevel logistic regression was used to examine temporal changes in associations of area-level factors with odds of advanced disease after adjustment for individual-level factors.

RESULTS

Overall 19,401 (50%) women had advanced breast cancer. Women from the most disadvantaged areas had higher adjusted odds (OR = 1.23 [95%CI 1.13, 1.32]) of advanced disease than those from least disadvantaged areas, with no evidence this association had changed over time (interaction p = 0.197). Living in less accessible areas independently increased the adjusted odds (OR = 1.18 [1.09, 1.28]) of advanced disease, with some evidence that the geographical inequality had reduced over time (p = 0.045). Sensitivity analyses for un-staged cases showed that the original associations remained, regardless of assumptions made about the true stage distribution.

CONCLUSIONS

Both geographical and residential socioeconomic inequalities in advanced stage diagnoses persist, potentially reflecting barriers in accessing diagnostic services. Given the role of screening mammography in early detection of breast cancer, the lack of population-based data on private screening limits our ability to determine overall participation rates by residential characteristics. Without such data, the efficacy of strategies to reduce inequalities in breast cancer stage will remain compromised.

Do pre-diagnosis primary care consultation patterns explain deprivation-specific differences in net survival among women with breast cancer? An examination of individually-linked data from the UK West Midlands cancer registry, national screening programme and Clinical Practice Research Datalink

BACKGROUND

In England and Wales breast cancer survival is higher among more affluent women. Our aim was to investigate the potential of pre-diagnostic factors for explaining deprivation-related differences in survival.

METHODS

Individually-linked data from women aged 50-70 in the West Midlands region of England, diagnosed with breast cancer 1989-2006 and continuously eligible for screening, was retrieved from the cancer registry, screening service and Clinical Practice Research Datalink. Follow-up was to the end of July 2012. Deprivation was measured at small area level, based on the quintiles of the income domain of the English indices of deprivation. Consultation rates per woman per week, time from last breast-related GP consultation to diagnosis, and from diagnosis to first surgery were calculated. We estimated net survival using the non-parametric Pohar-Perme estimator.

RESULTS

The rate of primary care consultations was similar during the 18 months prior to diagnosis in each deprivation group for breast and non-breast symptoms. Survival was lower for more deprived women from 4 years after diagnosis. Lower net survival was associated with more advanced extent of disease and being non-screen-detected. There was a persistent trend of lower net survival for more deprived women, irrespective of the woman's obesity, alcohol, smoking or comorbidity status. There was no significant variation in time from last breast symptom to diagnosis by deprivation. However, women in more deprived categories experienced significantly longer periods between cancer diagnosis and first surgery (mean = 21.5 vs. 28.4 days, p = 0.03). Those whose surgery occurred more than 12 weeks following their cancer diagnosis had substantially lower net survival.

CONCLUSIONS

Our data suggest that although more deprived women with breast cancer display lifestyle factors associated with poorer outcomes, their consultation frequency, comorbidities and the breast cancer symptoms they present with are similar. We found weak evidence of extended times to surgical treatment among most deprived women who were not screen-detected but who presented with symptoms in primary care, which suggests that treatment delay may play a role. Further investigation of interrelationships between these variables within a larger dataset is warranted.