How long do dolphins live? Survival rates and life expectancies for bottlenose dolphins in zoological facilities vs. wild populations

Survival rates and life expectancies are commonly agreed upon indicators of well‐being for animals in zoological facilities, but even the most recent survival statistics for bottlenose dolphins (Tursiops truncatus) in marine mammal parks and aquariums use data that are now more than 25 yr old. The current study provides a comprehensive assessment of life expectancy and survival rates for bottlenose dolphins in U.S. zoological facilities from 1974 to 2012, utilizing three different analyses (annual survival rate, age‐at‐death, and Kaplan‐Meier), examining historical trends, and comparing to comparable data from wild populations. Both survival rate and life expectancy for dolphins in zoological facilities increased significantly over the past few decades, with a modern ASR of 0.972, and mean and median life expectancies calculated via Kaplan‐Meier of 28.2 and 29.2 yr, respectively. Survival rates and life expectancies for dolphins in U.S. zoological facilities today are at least as high as those for the wild dolphin populations for which there are comparable data.     

Evaluating the Clinical Impact of a Genomic Classifier in Prostate Cancer Using Individualized Decision Analysis

Background

Currently there is controversy surrounding the optimal way to treat patients with prostate cancer in the post-prostatectomy setting. Adjuvant therapies carry possible benefits of improved curative results, but there is uncertainty in which patients should receive adjuvant therapy. There are concerns about giving toxicity to a whole population for the benefit of only a subset. We hypothesized that making post-prostatectomy treatment decisions using genomics-based risk prediction estimates would improve cancer and quality of life outcomes.

Methods

We developed a state-transition model to simulate outcomes over a 10 year horizon for a cohort of post-prostatectomy patients. Outcomes included cancer progression rates at 5 and 10 years, overall survival, and quality-adjusted survival with reductions for treatment, side effects, and cancer stage. We compared outcomes using population-level versus individual-level risk of cancer progression, and for genomics-based care versus usual care treatment recommendations.

Results

Cancer progression outcomes, expected life-years (LYs), and expected quality-adjusted life-years (QALYs) were significantly different when individual genomics-based cancer progression risk estimates were used in place of population-level risk estimates. Use of the genomic classifier to guide treatment decisions provided small, but statistically significant, improvements in model outcomes. We observed an additional 0.03 LYs and 0.07 QALYs, a 12% relative increase in the 5-year recurrence-free survival probability, and a 4% relative reduction in the 5-year probability of metastatic disease or death.

Conclusions

The use of genomics-based risk prediction to guide treatment decisions may improve outcomes for prostate cancer patients. This study offers a framework for individualized decision analysis, and can be extended to incorporate a wide range of personal attributes to enable delivery of patient-centered tools for informed decision-making.     

Assessing the contribution of age-sex differentials in causes of death due to infectious and parasitic diseases to the trends in age-sex differentials in life expectancy in Mauritius

This study applies two methodologies to Mauritian life tables and cause-of-death data: (1) the decomposition of sex differentials in life expectancy using Arriaga's approach and (2) the estimation of the effect of marginal reduction in deaths from infectious and parasitic diseases on life expectancy using Keyfitz's methodology on cause-specific entropy and that of Nanjo. The findings in this paper support earlier findings about the importance of the period 1969-1976 in the mortality transition in Mauritius, a period in which sex differentials in life expectancies reached a peak level. The results suggest that the driving force behind those sex differentials in life expectancy was the sex differential in mortality in infectious and parasitic diseases, first among the young (ages below 10 years) and second among the older population (ages above 50 years). If the decline in mortality due to infectious and parasitic diseases was differentially greater in the older ages compared to the younger ages, that difference would have gone a long way toward reducing the magnitude of the historic peak sex differential in life expectancy achieved in 1976.     

Life Expectancy and Treatment Patterns in Elderly Patients With Low-Risk Papillary Thyroid Cancer: A Population-Based Analysis

ObjectiveGuidelines endorse active surveillance for low-risk papillary thyroid carcinoma (PTC), but this is not commonly utilized. Those with limited life expectancy due to age and comorbidity may be best suited for active surveillance given their higher likelihood of other-cause mortality compared to disease-specific mortality.MethodsSurveillance, epidemiology, and end results-Medicare was queried for patients >65 years with T1, N0, M0 PTC who received surgery. We evaluated the overall survival, disease-specific survival (DSS), and survival based on tumor size and extent of surgery (hemi- vs total thyroidectomy). We created a competing risk model to identify the cumulative incidence of other-cause mortality to define patient groups with life expectancies of less than 10 and 15 years.ResultsA total of 3280 patients were included. The 20-year overall survival and DSS were 38.2% and 98.5%, respectively. DSS was comparable between patients based on tumor size and surgery. The cancer cohort had better survival compared to matched controls (P < .001). Life expectancy was less than 15 years for any patient aged >80 years regardless of Charlson comorbidity score (CCS ≥ 0) and any patient aged >70 years with CCS ≥ 1. Life expectancy was less than 10 years for any patient a >80 years with CCS ≥ 1 and aged >70 years with CCS ≥ 3.ConclusionOlder patients with comorbidities have limited life expectancies but excellent DSS from low-risk PTC. Incorporating life expectancy into management decisions and guidelines would likely promote selection of less aggressive management for populations that are most suited for this approach.     

Relationship of income and childlessness

ABSTRACT

We investigated the impact of diabetes on US life expectancy by sex and race/ethnicity using a prospective cohort study design. Cohorts were drawn from 1997 to 2009 waves of the National Health Interview Survey and linked to death records through December 31, 2011. We combined data on the prevalence of diabetes among decedents with estimates of the hazard ratios of individuals diagnosed with diabetes to calculate population attributable fractions (PAFs) by age, sex, and race/ethnicity at ages 30 and above. These estimates were then applied to deaths in the official US life table for 2010 to estimate effects of diabetes on life expectancy.

Diabetes was responsible for a reduction of 0.83 years of life expectancy for men at age 30 and 0.89 years for 30-year-old women. The impact was greatest among Black women at 1.05 years. Estimates based on traditional demographic and actuarial methods using the frequency with which a disease appears as an underlying cause of death on death certificates produced a reduction in life expectancy at age 30 of only 0.33 years.

We conclude that diabetes is substantially reducing US longevity and that its effect is seriously underestimated when using data on underlying causes of death.     

Life expectancy in less developed countries: socioeconomic development or public health?

Various studies have enquired into the influence of socioeconomic development or public health measures on life expectancies in less developed countries. Analysis of the effect of these two groups of factors upon life expectancy, using data for 95 less developed countries, indicates that mortality is primarily influenced by such socioeconomic development measures as urbanization, industrialization, and education, and secondarily by such public health measures as access to safe water, physicians, and adequate nutrition.     

Years of potential life lost caused by prostate cancer deaths in the United States—Projection from 2004 through 2050

BackgroundThe purpose of this study is to estimate and project the number of years of potential life lost (YPLL) among males who die of prostate cancer in the United States from 2004 through 2050 and compare the projections by race/ethnicity and age, accounting for demographic changes and population growth.MethodsWe applied the life expectancy method to estimate YPLL caused by deaths of prostate cancer and all cancers in men by using 1999–2004 national mortality data, 2008 census population demographic projections, and 2004 U.S. life tables. We performed sensitivity analyses by varying death rate and population projections, and examined increase in YPLL from population growth, changes in demographics, and death rates.ResultsThe number of YPLL caused by prostate cancer deaths was projected to increase by 226.1%, from 291,853 in 2004 to 951,753 in 2050. Hispanics were projected to have the fastest growth in YPLL (977.1% from 2004 to 2050) caused by prostate cancer, followed by non-Hispanic blacks (543.1%), and non-Hispanic others (269.7%). People aged 75 or older was projected to account for 62.0% of YPLL from prostate cancer in 2050 compared with 50.8% in 2004. Of the projected increase in YPLL caused by prostate cancer deaths by 2050, 9.8% were due to changes in demographic composition, 26.8% because of mortality change, and 63.4% because of population growth.ConclusionsYPLL due to prostate cancer deaths are projected to increase dramatically, and become a greater burden in the future. The projections highlight the importance of comprehensive cancer control and research on cancers including prostate cancer and racial/ethnic-specific estimates.     

Changes in Jewish mortality and survival, 1963–1987

Abstract

From information on mortality of Jews obtained from individual death certificates and population data from surveys of the Jewish population undertaken in 1963 and 1987, age‐specific death rates and life expectancy of the Jewish population of Rhode Island are compared with those of the total white population for 1963 and 1987 to assess changing differentials. The Jewish mortality experience continues to differ from that of the larger population even while both groups have experienced noticeable improvements. For males, the age standardized rates have widened in favor of Jews as have the life expectancies at birth and the percentage surviving to old age. By contrast, for females, the standardized death rate has widened considerably in favor of whites, while life expectancy has improved almost identically for both groups and therefore remained about equal, as it was in 1963. Reasons for these patterns are explored through attention to differences between Jews and the general white population in death rates at particular stages of the life cycle. Jews tend to be more advantaged at all but the most advanced ages, age groups in which proportionally more of the Jewish population and Jewish deaths are concentrated.     

Experiences and attitudes towards end-of-life decisions amongst Danish physicians

In this survey we have investigated the experiences and attitudes of Danish physicians regarding end-of-life decisions. Most respondents have made decisions that involve hastening the death of a patient, and almost all find it acceptable to do so. Such decisions are made more often, and considered ethically more acceptable, with the informed consent of the patient than without. But both non-resuscitation decisions, and decisions to provide pain relief in doses that will shorten the patient's life, have been made and found acceptable by at least 50% of the respondents, even when there is no informed consent. Furthermore, 12% have doubled morphine dosages with fixed intervals, thus providing doses substantially higher than that necessary to control pain, without the informed consent of the patient. Two per cent have helped in assisted suicide, and 5% have administered a lethal injection at the patient's request. Respectively 37% and 34% find these last two practices ethically acceptable. Amongst those that do not find them acceptable, the most important reasons to be opposed are, the doctrine of double effect, the doctrine of doing and allowing, and the view that human life is sacred. Amongst supporters, the most important reasons mentioned are, that the patient's right to self-determination should be respected, the view that a patient should not be forced to suffer, and the view that the patient has a right to be helped to a dignified death.     

The Promise of Prevention: The Effects of Four Preventable Risk Factors on National Life Expectancy and Life Expectancy Disparities by Race and County in the United States

Background

There has been substantial research on psychosocial and health care determinants of health disparities in the United States (US) but less on the role of modifiable risk factors. We estimated the effects of smoking, high blood pressure, elevated blood glucose, and adiposity on national life expectancy and on disparities in life expectancy and disease-specific mortality among eight subgroups of the US population (the “Eight Americas”) defined on the basis of race and the location and socioeconomic characteristics of county of residence, in 2005.

Methods and Findings

We combined data from the National Health and Nutrition Examination Survey and the Behavioral Risk Factor Surveillance System to estimate unbiased risk factor levels for the Eight Americas. We used data from the National Center for Health Statistics to estimate age–sex–disease-specific number of deaths in 2005. We used systematic reviews and meta-analyses of epidemiologic studies to obtain risk factor effect sizes for disease-specific mortality. We used epidemiologic methods for multiple risk factors to estimate the effects of current exposure to these risk factors on death rates, and life table methods to estimate effects on life expectancy. Asians had the lowest mean body mass index, fasting plasma glucose, and smoking; whites had the lowest systolic blood pressure (SBP). SBP was highest in blacks, especially in the rural South—5–7 mmHg higher than whites. The other three risk factors were highest in Western Native Americans, Southern low-income rural blacks, and/or low-income whites in Appalachia and the Mississippi Valley. Nationally, these four risk factors reduced life expectancy at birth in 2005 by an estimated 4.9 y in men and 4.1 y in women. Life expectancy effects were smallest in Asians (M, 4.1 y; F, 3.6 y) and largest in Southern rural blacks (M, 6.7 y; F, 5.7 y). Standard deviation of life expectancies in the Eight Americas would decline by 0.50 y (18%) in men and 0.45 y (21%) in women if these risks had been reduced to optimal levels. Disparities in the probabilities of dying from cardiovascular diseases and diabetes at different ages would decline by 69%–80%; the corresponding reduction for probabilities of dying from cancers would be 29%–50%. Individually, smoking and high blood pressure had the largest effect on life expectancy disparities.

Conclusions

Disparities in smoking, blood pressure, blood glucose, and adiposity explain a significant proportion of disparities in mortality from cardiovascular diseases and cancers, and some of the life expectancy disparities in the US. Please see later in the article for the Editors'' Summary     

Life Expectancies of South African Adults Starting Antiretroviral Treatment: Collaborative Analysis of Cohort Studies

Background

Few estimates exist of the life expectancy of HIV-positive adults receiving antiretroviral treatment (ART) in low- and middle-income countries. We aimed to estimate the life expectancy of patients starting ART in South Africa and compare it with that of HIV-negative adults.

Methods and Findings

Data were collected from six South African ART cohorts. Analysis was restricted to 37,740 HIV-positive adults starting ART for the first time. Estimates of mortality were obtained by linking patient records to the national population register. Relative survival models were used to estimate the excess mortality attributable to HIV by age, for different baseline CD4 categories and different durations. Non-HIV mortality was estimated using a South African demographic model. The average life expectancy of men starting ART varied between 27.6 y (95% CI: 25.2–30.2) at age 20 y and 10.1 y (95% CI: 9.3–10.8) at age 60 y, while estimates for women at the same ages were substantially higher, at 36.8 y (95% CI: 34.0–39.7) and 14.4 y (95% CI: 13.3–15.3), respectively. The life expectancy of a 20-y-old woman was 43.1 y (95% CI: 40.1–46.0) if her baseline CD4 count was ≥200 cells/µl, compared to 29.5 y (95% CI: 26.2–33.0) if her baseline CD4 count was <50 cells/µl. Life expectancies of patients with baseline CD4 counts ≥200 cells/µl were between 70% and 86% of those in HIV-negative adults of the same age and sex, and life expectancies were increased by 15%–20% in patients who had survived 2 y after starting ART. However, the analysis was limited by a lack of mortality data at longer durations.

Conclusions

South African HIV-positive adults can have a near-normal life expectancy, provided that they start ART before their CD4 count drops below 200 cells/µl. These findings demonstrate that the near-normal life expectancies of HIV-positive individuals receiving ART in high-income countries can apply to low- and middle-income countries as well. Please see later in the article for the Editors'' Summary     

Adjusting for the proportion of cancer deaths in the general population when using relative survival: a sensitivity analysis

Background: Relative survival is an extensively used method in population based cancer studies as it provides a measure of survival without the need for accurate cause of death information. It gives an estimate for the probability of dying from cancer in the absence of other causes by estimating the excess mortality in the study population when compared to an external group. The external group is usually the general population within a country or state and mortality estimates are taken from national life tables that are broken down by age, sex, calendar year and, where applicable, race/ethnicity. One potential bias when using relative survival that is most often overlooked occurs when there are a high proportion of deaths due to a specific cancer in the external group. Methods: This paper uses data from the Finnish Cancer Registry to illustrate, through the use of a simple sensitivity analysis, the impact that specific cancer deaths in the population mortality figures can have on the estimate of relative survival. Results: We found that when examining specific diseases such as breast cancer and colon cancer, the proportion of deaths due to these specific cancers in the general population is so small in comparison to the total mortality that they make little difference to the relative survival estimates. However, prostate cancer proved to be an exception to this. For all cancer sites combined the sensitivity analysis illustrates a major limitation for this type of analysis, particularly with the older age groups. Conclusion: We recommend that, with a classification of diseases as wide as all cancer sites, relative survival should not be used without appropriate adjustment.     

Stochastic dynamics of cancer initiation

Most human cancer types result from the accumulation of multiple genetic and epigenetic alterations in a single cell. Once the first change (or changes) have arisen, tumorigenesis is initiated and the subsequent emergence of additional alterations drives progression to more aggressive and ultimately invasive phenotypes. Elucidation of the dynamics of cancer initiation is of importance for an understanding of tumor evolution and cancer incidence data. In this paper, we develop a novel mathematical framework to study the processes of cancer initiation. Cells at risk of accumulating oncogenic mutations are organized into small compartments of cells and proliferate according to a stochastic process. During each cell division, an (epi)genetic alteration may arise which leads to a random fitness change, drawn from a probability distribution. Cancer is initiated when a cell gains a fitness sufficiently high to escape from the homeostatic mechanisms of the cell compartment. To investigate cancer initiation during a human lifetime, a 'race' between this fitness process and the aging process of the patient is considered; the latter is modeled as a second stochastic Markov process in an aging dimension. This model allows us to investigate the dynamics of cancer initiation and its dependence on the mutational fitness distribution. Our framework also provides a methodology to assess the effects of different life expectancy distributions on lifetime cancer incidence. We apply this methodology to colorectal tumorigenesis while considering life expectancy data of the US population to inform the dynamics of the aging process. We study how the probability of cancer initiation prior to death, the time until cancer initiation, and the mutational profile of the cancer-initiating cell depends on the shape of the mutational fitness distribution and life expectancy of the population.     

Personalized Prediction of Lifetime Benefits with Statin Therapy for Asymptomatic Individuals: A Modeling Study

Background

Physicians need to inform asymptomatic individuals about personalized outcomes of statin therapy for primary prevention of cardiovascular disease (CVD). However, current prediction models focus on short-term outcomes and ignore the competing risk of death due to other causes. We aimed to predict the potential lifetime benefits with statin therapy, taking into account competing risks.

Methods and Findings

A microsimulation model based on 5-y follow-up data from the Rotterdam Study, a population-based cohort of individuals aged 55 y and older living in the Ommoord district of Rotterdam, the Netherlands, was used to estimate lifetime outcomes with and without statin therapy. The model was validated in-sample using 10-y follow-up data. We used baseline variables and model output to construct (1) a web-based calculator for gains in total and CVD-free life expectancy and (2) color charts for comparing these gains to the Systematic Coronary Risk Evaluation (SCORE) charts. In 2,428 participants (mean age 67.7 y, 35.5% men), statin therapy increased total life expectancy by 0.3 y (SD 0.2) and CVD-free life expectancy by 0.7 y (SD 0.4). Age, sex, smoking, blood pressure, hypertension, lipids, diabetes, glucose, body mass index, waist-to-hip ratio, and creatinine were included in the calculator. Gains in total and CVD-free life expectancy increased with blood pressure, unfavorable lipid levels, and body mass index after multivariable adjustment. Gains decreased considerably with advancing age, while SCORE 10-y CVD mortality risk increased with age. Twenty-five percent of participants with a low SCORE risk achieved equal or larger gains in CVD-free life expectancy than the median gain in participants with a high SCORE risk.

Conclusions

We developed tools to predict personalized increases in total and CVD-free life expectancy with statin therapy. The predicted gains we found are small. If the underlying model is validated in an independent cohort, the tools may be useful in discussing with patients their individual outcomes with statin therapy. Please see later in the article for the Editors'' Summary     

Using Lifetime Risk Estimates in Personal Genomic Profiles: Estimation of Uncertainty

Personal genome tests are now offered direct-to-consumer (DTC) via genetic variants identified by genome-wide association studies (GWAS) for common diseases. Tests report risk estimates (age-specific and lifetime) for various diseases based on genotypes at multiple loci. However, uncertainty surrounding such risk estimates has not been systematically investigated. With breast cancer as an example, we examined the combined effect of uncertainties in population incidence rates, genotype frequency, effect sizes, and models of joint effects among genetic variants on lifetime risk estimates. We performed simulations to estimate lifetime breast cancer risk for carriers and noncarriers of genetic variants. We derived population-based cancer incidence rates from Surveillance, Epidemiology, and End Results (SEER) Program and comparative international data. We used data for non-Hispanic white women from 2003 to 2005. We derived genotype frequencies and effect sizes from published GWAS and meta-analyses. For a single genetic variant in FGFR2 gene (rs2981582), combination of uncertainty in these parameters produced risk estimates where upper and lower 95% simulation intervals differed by more than 3-fold. Difference in population incidence rates was the largest contributor to variation in risk estimates. For a panel of five genetic variants, estimated lifetime risk of developing breast cancer before age 80 for a woman that carried all risk variants ranged from 6.1% to 21%, depending on assumptions of additive or multiplicative joint effects and breast cancer incidence rates. Epidemiologic parameters involved in computation of disease risk have substantial uncertainty, and cumulative uncertainty should be properly recognized. Reliance on point estimates alone could be seriously misleading.     

Threshold Levels of Infant and Under-Five Mortality for Crossover between Life Expectancies at Ages Zero,One and Five in India: A Decomposition Analysis

Objectives

Under the prevailing conditions of imbalanced life table and historic gender discrimination in India, our study examines crossover between life expectancies at ages zero, one and five years for India and quantifies the relative share of infant and under-five mortality towards this crossover.

Methods

We estimate threshold levels of infant and under-five mortality required for crossover using age specific death rates during 1981–2009 for 16 Indian states by sex (comprising of India’s 90% population in 2011). Kitagawa decomposition equations were used to analyse relative share of infant and under-five mortality towards crossover.

Findings

India experienced crossover between life expectancies at ages zero and five in 2004 for menand in 2009 for women; eleven and nine Indian states have experienced this crossover for men and women, respectively. Men usually experienced crossover four years earlier than the women. Improvements in mortality below ages five have mostly contributed towards this crossover. Life expectancy at age one exceeds that at age zero for both men and women in India except for Kerala (the only state to experience this crossover in 2000 for men and 1999 for women).

Conclusions

For India, using life expectancy at age zero and under-five mortality rate together may be more meaningful to measure overall health of its people until the crossover. Delayed crossover for women, despite higher life expectancy at birth than for men reiterates that Indian women are still disadvantaged and hence use of life expectancies at ages zero, one and five become important for India. Greater programmatic efforts to control leading causes of death during the first month and 1–59 months in high child mortality areas can help India to attain this crossover early.     

Years of Potential Life Lost Due to Occupational Fatal Injury in the United States

Fatal injury surveillance data coupled with life expectancy data may be used to assess the impact of occupational fatal injuries on years of potential life lost (YPLL). We compare three definitions of YPLL and trends over time in YPLL. Two definitions determine YPLL as expected life lost to fixed life expectancies of 65 or 85 years. The third definition uses actuarial adjustments of life expectancy given survival to a given age stratified by gender and race. Fatalities from the National Traumatic Occupational Fatality (NTOF) database are used to illustrate the three definitions of YPLL. The three YPLL measures were similar in magnitude and direction of the trend in YPLL over 1980-1992. Proper interpretation of these trends can only be made in conjunction with other measures (e.g., rates). Almost all YPLL trends are declining, implying that over time fatal injuries are shifting to older workers. The exception is the increasing trend in YPLL for the retail trade industry, injury rates have also been increasing over time for this industry. Mining and construction have the highest YPLL among all industries. This analysis suggests efforts to prevent the occupational fatalities of younger workers should focus on the retail trade, mining, and construction industries.     

Risk-adjusted female breast cancer incidence rates in the United States

A method has been previously proposed for estimating risk-adjusted incidence rates (RAIRs) from cancer data from the Surveillance, Epidemiology, and End Results (SEER) program. Unlike conventionally reported SEER-based cancer incidence rates in the United States, but similar to the approach taken by the International Association of Cancer Registries and the International Agency for Research on Cancer, the method uses only the first primary cancer of the given site. In addition, it also adjusts for population-based cancer prevalence in order to obtain a better population-based measure of cancer risk. For most cancers multiple cancer primaries are rare and the prevalence of the disease is low. However, female breast cancer has a comparatively high risk of subsequent breast cancers and is the most prevalent cancer in women. Hence, in white women RAIRs are 3.0% lower in ages 30-39, 4.2% lower in ages 40-49, 4.0% lower in ages 50-59, 4.1% lower in ages 60-69, 3.8% lower in ages 70-79, and 4.3% lower in ages 80 years and older compared with conventional rates. Corresponding lower percentages for black women are 3.9%, 6.9%, 5.1%, 7.8%, 6.0%, and 2.2%, respectively. Age-group specific trends in breast cancer incidence rates differed between RAIRs and conventional incidence rates, increasingly so with older age. The number of cancer cases in the United States is estimated from conventional incidence rates and population estimates. In 2007, the estimated number of malignant breast cancer cases was 181,665 for white women and 20,203 for black women. The estimated number of breast cancer cases decreased by 4.8% for whites and 6.5% for blacks when based on RAIRs. RAIRs are a better measure of breast cancer risk and trends in RAIRs are better for monitoring the effect of risk factors.     

Measuring Burden of Unhealthy Behaviours Using a Multivariable Predictive Approach: Life Expectancy Lost in Canada Attributable to Smoking,Alcohol, Physical Inactivity,and Diet

BackgroundBehaviours such as smoking, poor diet, physical inactivity, and unhealthy alcohol consumption are leading risk factors for death. We assessed the Canadian burden attributable to these behaviours by developing, validating, and applying a multivariable predictive model for risk of all-cause death.MethodsA predictive algorithm for 5 y risk of death—the Mortality Population Risk Tool (MPoRT)—was developed and validated using the 2001 to 2008 Canadian Community Health Surveys. There were approximately 1 million person-years of follow-up and 9,900 deaths in the development and validation datasets. After validation, MPoRT was used to predict future mortality and estimate the burden of smoking, alcohol, physical inactivity, and poor diet in the presence of sociodemographic and other risk factors using the 2010 national survey (approximately 90,000 respondents). Canadian period life tables were generated using predicted risk of death from MPoRT. The burden of behavioural risk factors attributable to life expectancy was estimated using hazard ratios from the MPoRT risk model.FindingsThe MPoRT 5 y mortality risk algorithms were discriminating (C-statistic: males 0.874 [95% CI: 0.867–0.881]; females 0.875 [0.868–0.882]) and well calibrated in all 58 predefined subgroups. Discrimination was maintained or improved in the validation cohorts. For the 2010 Canadian population, unhealthy behaviour attributable life expectancy lost was 6.0 years for both men and women (for men 95% CI: 5.8 to 6.3 for women 5.8 to 6.2). The Canadian life expectancy associated with health behaviour recommendations was 17.9 years (95% CI: 17.7 to 18.1) greater for people with the most favourable risk profile compared to those with the least favourable risk profile (88.2 years versus 70.3 years). Smoking, by itself, was associated with 32% to 39% of the difference in life expectancy across social groups (by education achieved or neighbourhood deprivation).ConclusionsMultivariable predictive algorithms such as MPoRT can be used to assess health burdens for sociodemographic groups or for small changes in population exposure to risks, thereby addressing some limitations of more commonly used measurement approaches. Unhealthy behaviours have a substantial collective burden on the life expectancy of the Canadian population.     

Survival and life expectancy of the tree Protea roupelliae subsp. roupelliae in a montane grassland savanna: Effects of fire regime and plant structure

Survival and life expectancy are key demographic determinants of population dynamics. Using data collected in a field experiment monitored over 14 years in montane grassland of the Ukhahlamba‐Drakensberg Park, South Africa, we determined the effects of components of fire regime and plant structure on the survival and life expectancy of the tree Protea roupelliae subsp. roupelliae (Proteaceae). The field experiment comprised six plots (0.2–0.5 ha in area) from which the survival and life expectancies of 1567 juveniles (non‐reproductives) and 329 adults (reproductives) were estimated in response to differences in fire frequency, biennial seasonal fire, flame height, juvenile height, adult height, basal area and canopy vigour. Juvenile survival and life expectancies were highest when fires were excluded for 8 years. However, a fire after 12 years of fire exclusion and another fire 2 years later eliminated all juveniles. Over the same 14‐year period of biennial fires, juvenile survival was 5%. Juvenile survival and life expectancy were higher after biennial, winter fires than after annual, winter fires. Flame height had no effect on juvenile survival and life expectancy. Both survival and life expectancy of juveniles increased as plants got older and grew taller. Adult survival was unaffected by fire frequency, flame height or tree size, but the survival of adults in response to fire seasonality was inconclusive. Adults with low canopy vigour (<25%) were negatively affected by fire. Juvenile survival and life expectancy are critical bottlenecks in the demography of P. roupelliae. This species is neither a reseeder nor a resprouter. It avoids lethal fire damage by being restricted to rocky habitats with low fire intensities. Biennial winter fires least threaten the survival and life expectancy of P. roupelliae and impact least on its role in the summer feeding and breeding of Gurney's sugarbird.