Dataset Information

Improved estimates of cancer-specific survival rates from population-based data.

ABSTRACT:

Background

Accurate estimates of cancer survival are important for assessing optimal patient care and prognosis. Evaluation of these estimates via relative survival (a ratio of observed and expected survival rates) requires a population life table that is matched to the cancer population by age, sex, race and/or ethnicity, socioeconomic status, and ideally risk factors for the cancer under examination. Because life tables for all subgroups in a study may be unavailable, we investigated whether cause-specific survival could be used as an alternative for relative survival.

Methods

We used data from the Surveillance, Epidemiology, and End Results Program for 2,330,905 cancer patients from January 1, 1992, through December 31, 2004. We defined cancer-specific deaths according to the following variables: cause of death, only one tumor or the first of multiple tumors, site of the original cancer diagnosis, and comorbidities. Estimates of relative survival and cause-specific survival that were derived by use of an actuarial method were compared.

Results

Among breast cancer patients who were white, black, or of Asian or Pacific Islander descent and who were older than 65 years, estimates of 5-year relative survival (107.5%, 106.6%, and 103.0%, respectively) were higher than estimates of 5-year cause-specific survival (98.6%, 95% confidence interval [CI] = 98.4% to 98.8%; 97.4%, 95% CI = 96.2% to 98.2%; and 99.2%, 95% CI = 98.4%, 99.6%, respectively). Relative survival methods likely underestimated rates for cancers of the oral cavity and pharynx (eg, for white cancer patients aged ?65 years, relative survival = 54.2%, 95% CI = 53.1% to 55.3%, and cause-specific survival = 60.1%, 95% CI = 59.1% to 60.9%) and the lung and bronchus (eg, for black cancer patients aged ?65 years, relative survival = 10.5%, 95% CI = 9.9% to 11.2%, and cause-specific survival = 11.9%, 95% CI = 11.2 % to 12.6%), largely because of mismatches between the population with these diseases and the population used to derive the life table. Socioeconomic differences between groups with low and high status in relative survival estimates appeared to be inflated (eg, corpus and uterus socioeconomic status gradient was 13.3% by relative survival methods and 8.8% by cause-specific survival methods).

Conclusion

Although accuracy of the cause of death on a death certificate can be problematic for cause-specific survival estimates, cause-specific survival methods may be an alternative to relative survival methods when suitable life tables are not available.

SUBMITTER: Howlader N

PROVIDER: S-EPMC2957430 | biostudies-literature | 2010 Oct

REPOSITORIES: biostudies-literature

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Publications

Improved estimates of cancer-specific survival rates from population-based data.

Howlader Nadia N Ries Lynn A G LA Mariotto Angela B AB Reichman Marsha E ME Ruhl Jennifer J Cronin Kathleen A KA

Journal of the National Cancer Institute 20101011 20

<h4>Background</h4>Accurate estimates of cancer survival are important for assessing optimal patient care and prognosis. Evaluation of these estimates via relative survival (a ratio of observed and expected survival rates) requires a population life table that is matched to the cancer population by age, sex, race and/or ethnicity, socioeconomic status, and ideally risk factors for the cancer under examination. Because life tables for all subgroups in a study may be unavailable, we investigated w ...[more]

PMID: 20937991

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Project description:Cause-specific (CS) and net survival in a relative survival framework (RS) are two of the most common methods for estimating cancer survival. In this paper, we assess the differences in results produced by two permutations of cause-specific and relative survival applied to estimating cancer survival and disparities in cancer survival, using data from First Nations and non-Aboriginal populations in Canada.Subjects were members of the 1991 Canadian Census Mortality Cohort, a population-based cohort of adult respondents to the 1991 Long Form Census who have been followed up for incident cancers and death through linkage to administrative databases. We compared four methods: relative survival analyses with ethnicity-specific life tables (RS-ELT); relative survival with general population life tables (RS-GLT); cause-specific survival with a broad definition of cancer death (CS-Broad); and cause-specific survival with a narrow definition of cause of death (CS-Narrow) and applied these to the nine most common cancers among First Nations.Apart from breast and prostate cancers, RS-ELT, RS-GLT, and CS-Broad tended to produce similar estimates of age-standardized five-year survival, whereas CS-Narrow yielded higher estimates of survival. CS-Narrow estimates were particularly unlike those based on the other methods for cancers of the digestive and respiratory tracts. Estimates of disparities in survival were generally comparable across the four methods except for breast and prostate cancers.Cancer surveillance efforts in sub-populations defined by race, ethnicity, geography, socioeconomic status, or similar factors are necessary for identifying disparities and monitoring progress toward reducing them. In the absence of routine monitoring of cancer survival and cancer survival disparities in these populations, estimates generated by different methods will inevitably be compared over time and across populations. In this study, we demonstrate that caution should be exercised in making these comparisons, particularly in interpreting cause-specific survival rates with an unknown or narrow definition of cancer death and in estimates of breast and prostate cancer survival and/or disparities in survival generated by different methods.

Project description:ObjectivesAntiepileptic and antiarrhythmic drugs inhibit voltage-gated sodium (Na+) channels (VGSCs), and preclinical studies show that these medications reduce tumour growth, invasion and metastasis. We investigated the association between VGSC inhibitor use and survival in patients with breast, bowel and prostate cancer.DesignRetrospective cohort study.SettingIndividual electronic primary healthcare records extracted from the Clinical Practice Research Datalink.ParticipantsRecords for 132 996 patients with a diagnosis of breast, bowel or prostate cancer.Outcome measuresAdjusted Cox proportional hazards regression was used to analyse cancer-specific survival associated with exposure to VGSC inhibitors. Exposure to non-VGSC-inhibiting antiepileptic medication and other non-VGSC blockers were also considered. Drug exposure was treated as a time-varying covariate to account for immortal time bias.ResultsDuring 1 002 225 person-years of follow-up, there were 42 037 cancer-specific deaths. 53 724 (40.4%) patients with cancer had at least one prescription for a VGSC inhibitor of interest. Increased risk of cancer-specific mortality was associated with exposure to this group of drugs (HR 1.59, 95% CI 1.56 to 1.63, p<0.001). This applied to VGSC-inhibiting tricyclic antidepressants (HR 1.61, 95% CI 1.50 to 1.65, p<0.001), local anaesthetics (HR 1.49, 95% CI 1.43 to 1.55, p<0.001) and anticonvulsants (HR 1.40, 95% CI 1.34 to 1.48, p<0.001) and persisted in sensitivity analyses. In contrast, exposure to VGSC-inhibiting class 1c and 1d antiarrhythmics was associated with significantly improved cancer-specific survival (HR 0.75, 95% CI 0.64 to 0.88, p<0.001 and HR 0.54, 95% CI 0.33 to 0.88, p=0.01, respectively).ConclusionsAssociation between VGSC inhibitor use and mortality in patients with cancer varies according to indication. Exposure to VGSC-inhibiting antiarrhythmics, but not anticonvulsants, supports findings from preclinical data, with improved survival. However, additional confounding factors may underlie these associations, highlighting the need for further study.

Dataset Information

Improved estimates of cancer-specific survival rates from population-based data.

Background

Methods

Results

Conclusion

Publications

Improved estimates of cancer-specific survival rates from population-based data.

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