Dataset Information

Derivation of background mortality by smoking and obesity in cancer simulation models.

ABSTRACT: Simulation models designed to evaluate cancer prevention strategies make assumptions on background mortality-the competing risk of death from causes other than the cancer being studied. Researchers often use the U.S. life tables and assume homogeneous other-cause mortality rates. However, this can lead to bias because common risk factors such as smoking and obesity also predispose individuals for deaths from other causes such as cardiovascular disease.We obtained calendar year-, age-, and sex-specific other-cause mortality rates by removing deaths due to a specific cancer from U.S. all-cause life tables. Prevalence across 12 risk factor groups (3 smoking [never, past, and current smoker] and 4 body mass index [BMI] categories [<25, 25-30, 30-35, 35+ kg/m(2)]) were estimated from national surveys (National Health and Nutrition Examination Surveys [NHANES] 1971-2004). Using NHANES linked mortality data, we estimated hazard ratios for death by BMI/smoking using a Poisson regression model. Finally, we combined these results to create 12 sets of BMI and smoking-specific other-cause life tables for U.S. adults aged 40 years and older that can be used in simulation models of lung, colorectal, or breast cancer.We found substantial differences in background mortality when accounting for BMI and smoking. Ignoring the heterogeneity in background mortality in cancer simulation models can lead to underestimation of competing risk of deaths for higher-risk individuals (e.g., male, 60-year old, white obese smokers) by as high as 45%.Not properly accounting for competing risks of death may introduce bias when using simulation modeling to evaluate population health strategies for prevention, screening, or treatment. Further research is warranted on how these biases may affect cancer-screening strategies targeted at high-risk individuals.

SUBMITTER: Wang YC

PROVIDER: S-EPMC3663442 | biostudies-literature | 2013 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Derivation of background mortality by smoking and obesity in cancer simulation models.

Wang Y Claire YC Graubard Barry I BI Rosenberg Marjorie A MA Kuntz Karen M KM Zauber Ann G AG Kahle Lisa L Schechter Clyde B CB Feuer Eric J EJ

Medical decision making : an international journal of the Society for Medical Decision Making 20121106 2

<h4>Background</h4>Simulation models designed to evaluate cancer prevention strategies make assumptions on background mortality-the competing risk of death from causes other than the cancer being studied. Researchers often use the U.S. life tables and assume homogeneous other-cause mortality rates. However, this can lead to bias because common risk factors such as smoking and obesity also predispose individuals for deaths from other causes such as cardiovascular disease.<h4>Methods</h4>We obtain ...[more]

PMID: 23132901

Similar Datasets

Project description:Aims/hypothesisHospital admissions for hypoglycaemia represent a significant burden on individuals with diabetes and have a substantial economic impact on healthcare systems. To date, no prognostic models have been developed to predict outcomes following admission for hypoglycaemia. We aimed to develop and validate prediction models to estimate risk of inpatient death, 24 h discharge and one month readmission in people admitted to hospital for hypoglycaemia.MethodsWe used the Hospital Episode Statistics database, which includes data on all hospital admission to National Health Service hospital trusts in England, to extract admissions for hypoglycaemia between 2010 and 2014. We developed, internally and temporally validated, and compared two prognostic risk models for each outcome. The first model included age, sex, ethnicity, region, social deprivation and Charlson score ('base' model). In the second model, we added to the 'base' model the 20 most common medical conditions and applied a stepwise backward selection of variables ('disease' model). We used C-index and calibration plots to assess model performance and developed a calculator to estimate probabilities of outcomes according to individual characteristics.ResultsIn derivation samples, 296 out of 11,136 admissions resulted in inpatient death, 1789/33,825 in one month readmission and 8396/33,803 in 24 h discharge. Corresponding values for validation samples were: 296/10,976, 1207/22,112 and 5363/22,107. The two models had similar discrimination. In derivation samples, C-indices for the base and disease models, respectively, were: 0.77 (95% CI 0.75, 0.80) and 0.78 (0.75, 0.80) for death, 0.57 (0.56, 0.59) and 0.57 (0.56, 0.58) for one month readmission, and 0.68 (0.67, 0.69) and 0.69 (0.68, 0.69) for 24 h discharge. Corresponding values in validation samples were: 0.74 (0.71, 0.76) and 0.74 (0.72, 0.77), 0.55 (0.54, 0.57) and 0.55 (0.53, 0.56), and 0.66 (0.65, 0.67) and 0.67 (0.66, 0.68). In both derivation and validation samples, calibration plots showed good agreement for the three outcomes. We developed a calculator of probabilities for inpatient death and 24 h discharge given the low performance of one month readmission models.Conclusions/interpretationThis simple and pragmatic tool to predict in-hospital death and 24 h discharge has the potential to reduce mortality and improve discharge in people admitted for hypoglycaemia.

Project description:BACKGROUND:Life-style interventions, including smoking cessation and weight control are of importance for managing future escalating prevalence of obesity. Smoking habits and obesity have jointly great impact on mortality, however mechanisms behind the effect and variables involved in the obesity paradox is still unknown. OBJECTIVES:This study examines risk factors for all-cause, cardiovascular, and cancer mortality in males and females with high cardiovascular risk, mediated by smoking habits, body mass index (BMI, kg/m2), and serum phosphate (S-P) levels. METHODS:Patients were admitted to the Vindeln Patient Education Center in groups of 30 for a four-week residential comprehensive program (114 hours) focusing on smoking cessation, stress reduction, food preferences and selections, and physical exercise. The follow-up, in years from 1984 to 2014 corresponds to 30 years. This study included 2,504 patients (1,408 females and 1,096 males). Cox regression analysis was used to assess mortality risk associated with smoking habits, low and high BMI, and low and high S-P levels. RESULTS:High BMI (>34,2 kg/m2), current smoking, type 2 diabetes mellitus (T2DM), high serum calcium (S-Ca), mmol/L and high systolic blood pressure (SBP, mmHg) were associated with all-cause mortality irrespective of sex. Former and current smoking females had a high all-cause mortality (adjusted hazard ratio [HR] 1.581; 95% CI 1.108-2.256, adjusted hazard ratio [HR] 1.935; 95% CI 1.461-2.562, respectively) while current smoking and high BMI increased risk for cardiovascular mortality (adjusted hazard ratio [HR] 3.505; 95% CI 2.140-5.740 and [HR] 1.536; 95% CI 1.058-2.231, respectively). Neither low nor high levels of S-P predicted all-cause, cardiovascular disease (CVD) and cancer mortality in males or females while low levels of S-P predicted all-cause mortality in smokers (adjusted hazard ratio [HR] 1.713; 95% CI 1.211-2.424). In non-smokers, low BMI (<27.6 kg/m2) was protecting and high BMI a risk for all-cause mortality. In males, ischemic heart disease (IHD), and low serum albumin (S-Alb) were associated with all-cause mortality. In females, an interaction between high BMI and smoking (HbmiSM) decreased the cardiovascular mortality (adjusted hazard ratio [HR] 0.410; 95% CI 0.179-0.937, respectively). CONCLUSIONS:High BMI and current smoking were associated with all-cause mortality in both males and females in the present high cardiovascular-risk cohort. In current smokers and non-smokers, T2DM and high S-Ca were associated with an increase in all-cause mortality, while low S-P was associated with all-cause mortality in smokers. Interaction between high BMI and smoking contribute to the obesity paradox by being protective for cardiovascular mortality in females.

Project description:Smoking is associated with an increased risk of total and cause-specific death, but the rate of mortality risk reduction after quitting compared with continuing to smoke is uncertain. There is inadequate or insufficient evidence to infer the presence or absence of a causal relationship between smoking and ovarian cancer and colorectal cancer.To assess the relationship between cigarette smoking and smoking cessation on total and cause-specific mortality in women.Prospective observational study of 104,519 female participants in the Nurses' Health Study with follow-up from 1980 to 2004.Hazard ratios (HRs) for total mortality, further categorized into vascular and respiratory diseases, lung cancer, other cancers, and other causes.A total of 12,483 deaths occurred in this cohort, 4485 (35.9%) among never smokers, 3602 (28.9%) among current smokers, and 4396 (35.2%) among past smokers. Compared with never smokers, current smokers had an increased risk of total mortality (HR, 2.81; 95% confidence interval [CI], 2.68-2.95) and all major cause-specific mortality. The HR for cancers classified by the 2004 surgeon general's report to be smoking-related was 7.25 (95% CI, 6.43-8.18) and 1.58 (95% CI, 1.45-1.73) for other cancers. Compared with never smokers, the HR for colorectal cancer was 1.63 (95% CI, 1.29-2.05) for current smokers and 1.23 (95% CI, 1.02-1.49) for former smokers. A significant association was not observed for ovarian cancer. Significant trends were observed for earlier age at initiation of smoking for total mortality (P = .003), respiratory disease mortality (P = .001), and all smoking-related cancer mortality (P = .001). The excess risk for all-cause mortality decreases to the level of a never smoker 20 years after quitting, with different time frames for risk reduction observed across outcomes. Approximately 64% of deaths among current smokers and 28% of deaths among former smokers were attributable to cigarette smoking.Most of the excess risk of vascular mortality due to smoking in women may be eliminated rapidly upon cessation and within 20 years for lung diseases. Postponing the age of smoking initiation reduces the risk of respiratory disease, lung cancer, and other smoking-related cancer deaths but has little effect on other cause-specific mortality. These data suggest that smoking is associated with an increased risk of colorectal cancer mortality but not ovarian cancer mortality.

Dataset Information

Derivation of background mortality by smoking and obesity in cancer simulation models.

Publications

Derivation of background mortality by smoking and obesity in cancer simulation models.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets