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Choice of relative or cause-specific approach to cancer survival analysis impacts estimates differentially by cancer type, population, and application: evidence from a Canadian population-based cohort study.

ABSTRACT: 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.

SUBMITTER: Withrow DR

PROVIDER: S-EPMC5496357 | biostudies-other | 2017 Jul

REPOSITORIES: biostudies-other

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Choice of relative or cause-specific approach to cancer survival analysis impacts estimates differentially by cancer type, population, and application: evidence from a Canadian population-based cohort study.

Withrow Diana R DR Pole Jason D JD Nishri E Diane ED Tjepkema Michael M Marrett Loraine D LD

Population health metrics 20170703 1

<h4>Background</h4>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.<h4>Methods</h4>Subjects were members of the 1991 Canadian Census Mortali ...[more]

PMID: 28673318

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Project description:BackgroundHIV eradication strategies are now being evaluated in vitro and in vivo. A cornerstone of such approaches is maximal suppression of viral replication with combination antiretroviral therapy (ART). Since many antiretroviral agents have off target effects, and different classes target different components of the viral life cycle, we questioned whether different classes of ART might differentially affect the survival and persistence of productively HIV-infected CD4 T cells.MethodsIn vitro infections of primary CD4 T cells using clinical isolates of HIV-1 that were either protease inhibitor susceptible (HIV PI-S), or resistant (HIV PI-R) were treated with nothing, lopinavir, efavirenz or raltegravir. Cell viability, apoptosis, and the proportion of surviving cells that were P24 positive was assessed by flow cytometry.ResultsIn HIV PI-S infected primary cultures, all three antiretroviral agents decreased viral replication, and reduced the total number of cells that were undergoing apoptosis (P?<?0.01) similarly. Similarly, in the HIV PI-R infected cultures, both efavirenz and raltegravir reduced viral replication and reduced apoptosis compared to untreated control (P?<?0.01), while lopinavir did not, suggesting that HIV replication drives T cell apoptosis, which was confirmed by association by linear regression (P?<?0.0001) . However since HIV protease has been suggested to directly induce apoptosis of infected CD4 T cells, and HIV PI are intrinsically antiapoptotic, we evaluated apoptosis in productively infected (HIV P24+) cells. More HIV p24 positive cells were apoptotic in the Efavirenz or raltegravir treated cultures than the lopinavir treated cultures (P?=?0.0008 for HIV PI-R and P?=?0.06 for the HIV PI-S), indicating that drug class impacts survival of productively infected CD4 T cells.ConclusionsInhibiting HIV replication with a PI, NNRTI or INSTI reduces total HIV-induced T cell apoptosis. However, blocking HIV replication with PI but not with NNRTI or INSTI promotes survival of productively HIV-infected cells. Thus, selection of antiretroviral agents may impact the success of HIV eradication strategies.

Project description:BackgroundHIV eradication strategies are now being evaluated in vitro and in vivo. A cornerstone of such approaches is maximal suppression of viral replication with combination antiretroviral therapy (ART). Since many antiretroviral agents have off target effects, and different classes target different components of the viral life cycle, we questioned whether different classes of ART might differentially affect the survival and persistence of productively HIV-infected CD4 T cells.MethodsIn vitro infections of primary CD4 T cells using clinical isolates of HIV-1 that were either protease inhibitor susceptible (HIV PI-S), or resistant (HIV PI-R) were treated with nothing, lopinavir, efavirenz or raltegravir. Cell viability, apoptosis, and the proportion of surviving cells that were P24 positive was assessed by flow cytometry.ResultsIn HIV PI-S infected primary cultures, all three antiretroviral agents decreased viral replication, and reduced the total number of cells that were undergoing apoptosis (P < 0.01) similarly. Similarly, in the HIV PI-R infected cultures, both efavirenz and raltegravir reduced viral replication and reduced apoptosis compared to untreated control (P < 0.01), while lopinavir did not, suggesting that HIV replication drives T cell apoptosis, which was confirmed by association by linear regression (P < 0.0001) . However since HIV protease has been suggested to directly induce apoptosis of infected CD4 T cells, and HIV PI are intrinsically antiapoptotic, we evaluated apoptosis in productively infected (HIV P24+) cells. More HIV p24 positive cells were apoptotic in the Efavirenz or raltegravir treated cultures than the lopinavir treated cultures (P = 0.0008 for HIV PI-R and P = 0.06 for the HIV PI-S), indicating that drug class impacts survival of productively infected CD4 T cells.ConclusionsInhibiting HIV replication with a PI, NNRTI or INSTI reduces total HIV-induced T cell apoptosis. However, blocking HIV replication with PI but not with NNRTI or INSTI promotes survival of productively HIV-infected cells. Thus, selection of antiretroviral agents may impact the success of HIV eradication strategies.

Project description:BackgroundThe spatial modeling of infectious disease distributions and dynamics is increasingly being undertaken for health services planning and disease control monitoring, implementation, and evaluation. Where risks are heterogeneous in space or dependent on person-to-person transmission, spatial data on human population distributions are required to estimate infectious disease risks, burdens, and dynamics. Several different modeled human population distribution datasets are available and widely used, but the disparities among them and the implications for enumerating disease burdens and populations at risk have not been considered systematically. Here, we quantify some of these effects using global estimates of populations at risk (PAR) of P. falciparum malaria as an example.MethodsThe recent construction of a global map of P. falciparum malaria endemicity enabled the testing of different gridded population datasets for providing estimates of PAR by endemicity class. The estimated population numbers within each class were calculated for each country using four different global gridded human population datasets: GRUMP (~1 km spatial resolution), LandScan (~1 km), UNEP Global Population Databases (~5 km), and GPW3 (~5 km). More detailed assessments of PAR variation and accuracy were conducted for three African countries where census data were available at a higher administrative-unit level than used by any of the four gridded population datasets.ResultsThe estimates of PAR based on the datasets varied by more than 10 million people for some countries, even accounting for the fact that estimates of population totals made by different agencies are used to correct national totals in these datasets and can vary by more than 5% for many low-income countries. In many cases, these variations in PAR estimates comprised more than 10% of the total national population. The detailed country-level assessments suggested that none of the datasets was consistently more accurate than the others in estimating PAR. The sizes of such differences among modeled human populations were related to variations in the methods, input resolution, and date of the census data underlying each dataset. Data quality varied from country to country within the spatial population datasets.ConclusionsDetailed, highly spatially resolved human population data are an essential resource for planning health service delivery for disease control, for the spatial modeling of epidemics, and for decision-making processes related to public health. However, our results highlight that for the low-income regions of the world where disease burden is greatest, existing datasets display substantial variations in estimated population distributions, resulting in uncertainty in disease assessments that utilize them. Increased efforts are required to gather contemporary and spatially detailed demographic data to reduce this uncertainty, particularly in Africa, and to develop population distribution modeling methods that match the rigor, sophistication, and ability to handle uncertainty of contemporary disease mapping and spread modeling. In the meantime, studies that utilize a particular spatial population dataset need to acknowledge the uncertainties inherent within them and consider how the methods and data that comprise each will affect conclusions.

Project description:BackgroundUnderstanding rates of mortality in kidney transplant recipients relative to other common diseases can enhance our understanding of the mortality burden in kidney transplant recipients.ObjectiveTo compare the survival probability in Canadian female and male kidney transplant recipients with patients with common cancers (female: breast, colorectal, lung, or pancreas; male: prostate, colorectal, lung, or pancreas) in a contemporary population.DesignPopulation-based cohort study using linked administrative health care databases.SettingOntario, Canada.PatientsA total of 6888 incident kidney transplant recipients (median age was 50 and 51 years in females and males, respectively) and a total of 532 452 incident patients with cancer (median age range 60 to 72 years across cancer types) from 1997 to 2015.MeasurementsAll-cause mortality.MethodsThe survival of study participants was described using the Kaplan-Meier product limit estimator. The rate of survival was compared between kidney transplant recipients and patients with cancer using extended Cox regression with a Heaviside function.ResultsKidney transplant recipients had a higher survival probability compared with all cancer types. For example, male kidney transplant recipients had a 5-year survival probability of 89.6% (95% confidence interval [CI]: 88.6%-90.5%) compared with 83.3% (95% CI: 83.1%-83.5%) in patients with prostate cancer, and 14.0% (95% CI: 13.7%-14.3%), 56.1% (95% CI: 55.7%-56.5%), and 9.1% (95% CI: 8.5%-9.7%) in patients with lung, colorectal, and pancreas cancer, respectively. After presenting survival probabilities by age at cohort entry and after adjusting for clinical characteristics, similar results were found with a few exceptions. Unlike the unadjusted analysis, in the adjusted analysis males with prostate cancer had a significantly higher survival compared with kidney transplant recipients and females with breast cancer had higher survival compared with kidney transplant recipients at 2+ years of follow-up. In a subpopulation of the cohort who had information available on cancer stage (ie, stages 1-4), we generally found similar results to our primary analysis with kidney transplant recipients having a higher survival probability compared with each cancer stage. However, female kidney transplant recipients had a lower survival probability compared with females with stage 1 breast cancer, whereas male kidney transplant recipients had a lower survival probability compared with males with stage 1 to 3 prostate cancer.LimitationsExternal generalizability, residual confounding, and cancer stage could only be provided for a subpopulation.ConclusionMortality in kidney transplant recipients is lower than in patients with several cancer types. These results improve our understanding of the mortality burden in this population and reaffirm kidney transplantation as a good treatment option for end-stage kidney disease but also highlight the continuing need to improve posttransplant survival.Trial registrationThis is not applicable as this is a population-based cohort study and not a clinical trial.

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Dataset Information

Choice of relative or cause-specific approach to cancer survival analysis impacts estimates differentially by cancer type, population, and application: evidence from a Canadian population-based cohort study.

Publications

Choice of relative or cause-specific approach to cancer survival analysis impacts estimates differentially by cancer type, population, and application: evidence from a Canadian population-based cohort study.

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