Predicted Life Expectancy Research Articles

Brachytherapy in the Palliation of Oesophageal Cancer: Effective but Impractical?

Dysphagia in people with advanced oesophageal cancer can be treated by oesophageal stents, external beam radiotherapy (EBRT) and intraluminal brachytherapy. Despite guidelines recommending brachytherapy for patients with a predicted life expectancy exceeding 3 months, its uptake in the UK has been limited. Here we examine the strength of the evidence supporting the use of brachytherapy compared with oesophageal stents and EBRT and possible reasons for its limited uptake. Trials and observational studies suggest brachytherapy alone confers a benefit to patients, but its impact is less immediate than oesophageal stents; the evidence on effectiveness and value-for-money is limited. Moreover, stronger evidence will probably be insufficient to increase uptake, due to the extra complexity of delivery compared with stents and EBRT and a lack of experience among specialists.

Diabetes mellitus predicts inferior survival in diffuse large B-cell lymphoma: a propensity score-matched analysis.

Purpose: Diabetes mellitus (DM) is associated with elevated cancer risk and poor survival. The objective of this study was to assess the prognostic value of DM in diffuse large B-cell lymphoma (DLBCL).Methods: Five hundred and fifty-three newly diagnosed DLBCL patients whose treatments included rituximab were recruited. Propensity score-matched method was performed to balance baseline characteristics and eliminate possible bias. Multivariate Cox regression analyses screened the prognostic risk factors in relation to progression-free survival (PFS) and cancer-specific survival (CSS). Receiver-operator characteristic curves and the corresponding areas under the curve (AUC) assessed the predictive accuracy of international prognostic index (IPI) together with DM.Results: One hundred and nine patients (19.71%) had pre-existing DM. In the propensity-matched cohort, DM was associated with unfavorable PFS and CSS in rituximab era, and it was an independent risk factor for both inferior PFS and CSS, especially in patients with age ≤60 years, IPI 0−2, B symptoms and lactate dehydrogenase ≤upper limit of normal. Prediabetics also demonstrated inferior prognostic outcomes compared to patients with no diabetic tendency. DM as one additional point to IPI had larger AUC compared with IPI alone in CSS prediction and could improve the prognostic capacity of IPI.Conclusion: The results indicate that preexisting DM is an important risk factor for survival. It could help predict life expectancy and build refined prognostication models for DLBCL.

Influence of music therapy on the telomere length: a brief review and a pilot study

Relation between music perception and telomere length is discussed. Telomeres are now considered as markers of general health and as possible predictor of life expectancy, while their length correlates with either the risk of age-related pathologies or with higher adaptive performance. The ability to withstand agerelated telomere shortening by means of practicing psychological training is discussed. Problems and prospects of present-day music therapy are reviewed, as well as absence of studies of alteration of telomere length, related to music perception. Sixty-three practically healthy young (aged 24 ± 5 years) randomly selected Russian-speaking students were divided into three subgroups, each of which listened to music of a specific type for 14 days, 90 minutes each day. Before and after the two-week course, the telomere length and telomerase activity in blood samples were measured. A limited but statistically reliable increase in the telomere length and in telomerase activity is demonstrated to have occurred as a result of completing the music course. Classical music tends to bring about less stress, positively affect one’s mood, and physiology, and, finally, such essential aging markers as telomerase activity and the telomere length. Non-classical music conditioned manifestation of some stress, which tended to affect the telomere length in a negative way. In order to cope with this difficulty, telomerase was activated.

External validation of a nomogram for the prediction of 10-year life expectancy in candidates for radical prostatectomy

Accurate life expectancy prediction is essential in decision-making concerning treatment of clinically localized prostate cancer (PCa). Nomogram predictions are more precise and reproducible than clinician's estimations. The most accurate nomogram addressing 10-year life expectancy in PCa patients has not been externally validated to date. Therefore, we aimed to evaluate the performance of this nomogram in a contemporary external cohort. For this, we enrolled all consecutive patients, who underwent radical prostatectomy at a single institution between 2005 and 2007. Age at surgery and Charlson Comorbidity Index (CCI) were assessed. PCa-related deaths and patients under 55years were excluded as indicated by the nomogram. The prediction of 10-year life expectancy was calculated according to the nomogram and compared to actual survival data. Calibration and discrimination were assessed using calibration plots. Overall, 1597 patients were evaluated, with a median age of 64years (range 55-78years) at surgery and a median follow-up of 134.4months (range 0.1-161.7months). Median CCI was 0 (range 0-10). At 10years, 134 patients (8.4%) had died of other causes than PCa. The nomogram showed moderate discrimination capacities on receiver-operator characteristic analysis (c-index: 0.64). On calibration curves, the nomogram underestimated the actual life expectancy. The performance accuracy of this prediction model was moderate and underestimated 10-year life expectancy of contemporary PCa patients. In conclusion, prediction of life expectancy remains challenging with a continued need for more precise tools.

PT377 - Long-term survival rates of localized prostate cancer patients treated by radical prostatectomy are significantly better than predicted life expectancy of the general population

PT377 - Long-term survival rates of localized prostate cancer patients treated by radical prostatectomy are significantly better than predicted life expectancy of the general population

Predicting life expectancy with a long short-term memory recurrent neural network using electronic medical records

BackgroundLife expectancy is one of the most important factors in end-of-life decision making. Good prognostication for example helps to determine the course of treatment and helps to anticipate the procurement of health care services and facilities, or more broadly: facilitates Advance Care Planning. Advance Care Planning improves the quality of the final phase of life by stimulating doctors to explore the preferences for end-of-life care with their patients, and people close to the patients. Physicians, however, tend to overestimate life expectancy, and miss the window of opportunity to initiate Advance Care Planning. This research tests the potential of using machine learning and natural language processing techniques for predicting life expectancy from electronic medical records.MethodsWe approached the task of predicting life expectancy as a supervised machine learning task. We trained and tested a long short-term memory recurrent neural network on the medical records of deceased patients. We developed the model with a ten-fold cross-validation procedure, and evaluated its performance on a held-out set of test data. We compared the performance of a model which does not use text features (baseline model) to the performance of a model which uses features extracted from the free texts of the medical records (keyword model), and to doctors’ performance on a similar task as described in scientific literature.ResultsBoth doctors and the baseline model were correct in 20% of the cases, taking a margin of 33% around the actual life expectancy as the target. The keyword model, in comparison, attained an accuracy of 29% with its prognoses. While doctors overestimated life expectancy in 63% of the incorrect prognoses, which harms anticipation to appropriate end-of-life care, the keyword model overestimated life expectancy in only 31% of the incorrect prognoses.ConclusionsPrognostication of life expectancy is difficult for humans. Our research shows that machine learning and natural language processing techniques offer a feasible and promising approach to predicting life expectancy. The research has potential for real-life applications, such as supporting timely recognition of the right moment to start Advance Care Planning.

An Energy-Based Approach for Fatigue Life Estimation of Welded Joints without Residual Stress through Thermal-Graphic Measurement

The traditional methodologies for fatigue life assessment of welded joints strongly depend on geometries and surface characteristics, as well as time. In this paper, an energy-based approach, independent of structures though thermal-graphic measurement, was presented to predict life expectancy of welded joints, via limited number of tests. In order to eliminate the thermal elastic effect caused by the welding residual stress, annealing was first conducted on welded specimens. Both monotonic and cyclic tests for welded joints were implemented. Then, based on the thermal evolution of welded joints measured by the quantitative thermo-graphic method, an energy-based approach, taking the linear temperature evolution and the intrinsic dissipation into account, was employed on the fatigue life prediction of flat butt-welded joints. The estimated results showed good agreement with the experimental ones, and the energy tolerance to failure E c for different stress amplitudes was found to be constant.

Impact of Accuracy of Survival Predictions on Quality of End-of-Life Care Among Patients With Metastatic Cancer Who Receive Radiation Therapy.

For patients treated with palliative radiation, we examined the association between life expectancy predictions by radiation oncologists and aggressive end-of-life care. We included decedents from a study that assessed the ability of oncologists to predict survival of patients with metastatic cancer who received radiation. We identified patients who died within 12 months of study enrollment to assess accuracy of predictions. Aggressive end-of-life care was defined by the National Quality Forum, ASCO Quality Oncology Practice Initiative metrics, and advanced radiation modalities in the last month of life. Survival predictions were categorized as follows: correct (< 12 months), 12 to 18 months, 18 to 24 months, and more than 24 months. We assessed association between prediction and aggressive end-of-life care using a generalized estimation equation. Of 489 decedents, we identified 467 encounters with survival estimates. Overall, 156 decedents (32%) met at least one metric of aggressive end-of-life care. Factors associated with aggressive end-of-life care included younger age, female sex, primary cancer diagnosis, no brain metastases, and private insurance. In each encounter when an oncologist predicted survival, 363 predictions (78%) were correct (< 12 months), 54 (11%) incorrectly predicted 12 to 18 months, 27 (6%) predicted 18 to 24 months, and 23 (5%) predicted more than 24 months. Compared with patients who had encounters that had correct survival predictions, patients predicted to live more than 24 months were more likely to meet at least one metric of aggressive end-of-life care (odds ratio, 2.55; 95% CI, 1.09 to 5.99; P = .03). Inaccurate survival predictions by oncologists are associated with more aggressive end-of-life care for patients with advanced cancer.

Refining the TEACHH model: Towards improved clinical utility in the modern era.

128 Background: The TEACHH model was previously developed to predict life expectancy in patients with metastatic cancer receiving palliative radiation. However, the model was developed prior to the immunotherapy era and limited in its clinical utility due to broad ranges of survival in each prognostic group. The aim of this project is to refine the TEACHH model to improve its clinical utility in a modern cohort. Methods: We retrospectively reviewed the charts of patients seen in consultation for palliative radiation at two tertiary centers and 4 community satellite practices from 7/1/2015 – 12/31/2016. Cox proportional hazards regression was used to construct a prediction model for overall survival (OS), with a goal of predicting the probability of surviving to the clinically relevant timepoints of 2 months, 6 months, and 1 year. The model was stratified by consult location (tertiary vs. community). Results: A total of 744 patients were included in this analysis. The final model included: 1) Gender (Male HR 1.35, 95% CI 1.11 – 1.64) 2) ECOG PS (ECOG 1 HR 1.63, 95% CI 1.20 – 2.22, ECOG 2 HR 2.59, 95% CI 1.86 – 3.60, ECOG 3/4 HR 4.12, 95% CI 2.84 – 5.98) 3) Consultation type (Inpatient HR 1.87, 95% CI 1.40 – 2.49) 4) Primary histology 5) Number of metastases 6) Number of prior palliative chemotherapy courses. Due to interactions between primary histology and number of metastases as well as primary histology and number of prior palliative chemotherapy courses, hazard ratios for these variables are shown in Table 1. Conclusions: Our final refined TEACHH model includes 6 variables to predict OS. We hypothesize that its increased complexity will increase its sensitivity and we will validate the model in a separate patient cohort. A web-based calculator will then be created to facilitate easy use of the model. Hazard ratios of variables with significant interactions. [Table: see text]

Influence of Age, Health, and Function on Cancer Screening in Older Adults with Limited Life Expectancy.

We examined the relationship between cancer screening and life expectancy predictors, focusing on the influence of age versus health and function, in older adults with limited life expectancy. Longitudinal cohort study SETTING: National Health and Aging Trends Study (NHATS) with linked Medicare claims. Three cohorts of adults 65+ enrolled in fee-for-service Medicare were constructed: women eligible for breast cancer screening (n = 2043); men eligible for prostate cancer screening (n = 1287); men and women eligible for colorectal cancer screening (n = 3759). We assessed 10-year mortality risk using 2011 NHATS data, then used claims data to assess 2-year prostate and breast cancer screening rates and 3-year colorectal cancer screening rates. Among those with limited life expectancy (10-year mortality risk > 50%), we stratified participants at each level of predicted mortality risk and split participants in each risk stratum by the median age. We assembled two sub-groups from these strata that were matched on predicted life expectancy: a "younger sub-group" with relatively poorer health/functional status and an "older sub-group" with relatively better health/functional status. We compared screening rates between sub-groups. For all three cancer screenings, the younger sub-groups (average ages 73.4-76.1) had higher screening rates than the older sub-groups (average ages 83.6-86.9); screening rates were 42.9% versus 34.2% for prostate cancer screening (p = 0.02), 33.6% versus 20.6% for breast cancer screening (p < 0.001), 13.1% versus 6.7% for colorectal cancer screening in women (p =0.006), and 20.5% versus 12.1% for colorectal cancer screening in men (p = 0.002). Among older adults with limited life expectancy, those who are relatively younger with poorer health and functional status are over-screened for cancer at higher rates than those who are older with the same predicted life expectancy.

Older Adults' Preferences for Discussing Long-Term Life Expectancy: Results From a National Survey.

Clinical practice guidelines recommend incorporating long-term life expectancy to inform a number of decisions in primary care. We aimed to examine older adults' preferences for discussing life expectancy in a national sample. We invited 1,272 older adults (aged 65 or older) from a national, probability-based online panel to participate in 2016. We presented a hypothetical patient with limited life expectancy who was not imminently dying. We asked participants if they were that patient, whether they would like to talk with the doctor about how long they may live, whether it was acceptable for the doctor to offer this discussion, whether they want the doctor to discuss life expectancy with family or friends, and when it should be discussed. The 878 participants (69.0% participation rate) had a mean age of 73.4 years. The majority, 59.4%, did not want to discuss how long they might live in the presented scenario. Within this group, 59.9% also did not think that the doctor should offer the discussion, and 87.7% also did not want the doctor to discuss life expectancy with family or friends. Fully 55.8% wanted to discuss life expectancy only if it were less than 2 years. Factors positively associated with wanting to have the discussion included higher educational level, believing that doctors can accurately predict life expectancy, and past experience with either a life-threatening illness or having discussed life expectancy of a loved one. Reporting that religion is important was negatively associated. The majority of older adults did not wish to discuss life expectancy when we depicted a hypothetical patient with limited life expectancy. Many also did not want to be offered discussion, raising a dilemma for how clinicians may identify patients' preferences regarding this sensitive topic.

Overachieving Municipalities in Public Health: A Machine-learning Approach.

Identifying successful public health ideas and practices is a difficult challenge towing to the presence of complex baseline characteristics that can affect health outcomes. We propose the use of machine learning algorithms to predict life expectancy at birth, and then compare health-related characteristics of the under- and overachievers (i.e., municipalities that have a worse and better outcome than predicted, respectively). Our outcome was life expectancy at birth for Brazilian municipalities, and we used as predictors 60 local characteristics that are not directly controlled by public health officials (e.g., socioeconomic factors). The highest predictive performance was achieved by an ensemble of machine learning algorithms (cross-validated mean squared error of 0.168), including a 35% gain in comparison with standard decision trees. Overachievers presented better results regarding primary health care, such as higher coverage of the massive multidisciplinary program Family Health Strategy. On the other hand, underachievers performed more cesarean deliveries and mammographies and had more life-support health equipment. The findings suggest that analyzing the predicted value of a health outcome may bring insights about good public health practices.

Refining the TEACHH Model: Towards Improved Clinical Utility in the Modern Era

128Background: The TEACHH model was previously developed to predict life expectancy in patients with metastatic cancer receiving palliative radiation. However, the model was developed prior to the immunotherapy era and limited in its clinical utility due to broad ranges of survival in each prognostic group. The aim of this project is to refine the TEACHH model to improve its clinical utility in a modern cohort. Methods: We retrospectively reviewed the charts of patients seen in consultation for palliative radiation at two tertiary centers and 4 community satellite practices from 7/1/2015 – 12/31/2016. Cox proportional hazards regression was used to construct a prediction model for overall survival (OS), with a goal of predicting the probability of surviving to the clinically relevant timepoints of 2 months, 6 months, and 1 year. The model was stratified by consult location (tertiary vs. community). Results: A total of 744 patients were included in this analysis. The final model included: 1) Gender (Male H...

A novel scoring system to guide prognosis in patients with pathological fractures

BackgroundThe most appropriate treatment of pathological fractures from metastatic disease depends on several factors, one of the most important being predicted life expectancy. The aim of this study was to identify the variables that influence prognosis and utilise these to develop a novel scoring system to better predict life expectancy post-pathological fracture.MethodsThe records of all patients that presented with metastatic pathological fractures over a 10-year period from the only tertiary orthopaedic departments in Western Australia were retrospectively examined. Variables assessed were primary cancer type, fracture site, fixation method, cement augmentation, pre-morbid level of physical functioning, complication rate, treatment with chemotherapy or radiotherapy and appendicular, spinal and visceral metastatic load.ResultsA total of 233 patients were included. Median survival from fracture to death was 4.1 months. Median time from cancer diagnosis to pathological fracture was 14.2 months. There was a statistically significant association between patient survival and primary cancer type, physical functional score, spinal metastatic burden and use of chemotherapy or radiotherapy.ConclusionA novel scoring system has been developed that offers a survival probability based on patient’s individual circumstances. This can guide specialist management and offer patients a more accurate expectation of functional outcome and survival time.

Systematic Predictive Analysis of Personalized Life Expectancy Using Smart Devices

With the emergence of technologies such as electronic health and mobile health (eHealth/mHealth), cloud computing, big data, and the Internet of Things (IoT), health related data are increasing and many applications such as smartphone apps and wearable devices that provide wellness and fitness tracking are entering the market. Some apps provide health related data such as sleep monitoring, heart rate measuring, and calorie expenditure collected and processed by the devices and servers in the cloud. These requirements can be extended to provide a personalized life expectancy (PLE) for the purpose of wellbeing and encouraging lifestyle improvement. No existing works provide this PLE information that is developed and customized for the individual. This article is based on the concurrent models and methodologies to calculate and predict life expectancy (LE) and proposes an idea of using multi-phased approaches to the solution as the project requires an immense and broad range of work to accomplish. As a result, the current prediction of LE, which was found to be up to a maximum of five years could potentially be extended to a lifetime prediction by utilizing generic health data. In this article, the novel idea of the solution proposing a PLE on an individual basis, which can be extended to lifetime is presented in addition to the existing works.

Prognostic factors in patients with metastatic spine tumors derived from lung cancer\u2014a novel scoring system for predicting life expectancy

BackgroundRecently, molecule-targeting and bone-modifying agents have improved the treatment outcomes of lung cancer-derived metastatic spine tumors. Therefore, the prognostic factors for such tumors were examined, and novel scoring systems for predicting the life expectancy of patients with such tumors were proposed.MethodsIn 207 patients with lung cancer-derived metastatic spine tumors (surgery 49; conservative therapy 158), we retrospectively examined the factors that influenced the post-treatment survival time (age, sex, the affected site, pathology, general condition, the number of extraspinal bone metastases, the number of spinal metastases, the presence/absence of major internal organ metastasis, paralysis state, the total Tokuhashi score, the serum alkaline phosphatase level, the serum carcinoembryonic antigen level, molecule-targeting drug treatment, and bone-modifying agent treatment). Based on the results, we devised novel scoring systems for predicting the prognosis of such patients.ResultsUnivariate analyses showed that the pathology of the primary lung tumor, the patient’s general condition and paralysis state, and the presence/absence of molecule-targeting drug treatment significantly influenced survival. We performed a Cox regression analysis of these four factors and developed criteria for a novel scoring system based on the patient’s general condition and paralysis state, which exhibited significance in the regression analysis. A retrospective review indicated that the consistency rate between predicted life expectancy and actual survival was 67.3%. When criteria based on the four factors that exhibited significance in the univariate analyses were adopted, the consistency rate was 76.2%.ConclusionThe patient’s general condition and paralysis state, the pathology of the primary lung tumor, and molecule-targeting drug treatment influenced survival among patients with lung cancer-derived metastatic spine tumors. Novel scoring systems based on these four factors were proposed.

Use of ANN in Predicting Life Expectancy: The Case of Turkey

Life expectancy is a good measure for comparing parameters such as welfare, health level and development of countries. The high value for this indicator can only be achieved by identifying the positive and negative effects of these determinants and by making initiatives in this direction. In our study, we observed life expectancy estimation by using time series models and Artificial Neural Network (ANN) in terms of social, economic and environmental factors, that affect the life expectancy. Comparison of two estimator models performed using data compiled from OECD and WORLDBANK of Turkey between 1960-2016. In the application performed on social sciences data, meaningful indicators were interpreted together with the success of the ANN method. As a result of the study, a number of suggestions and development recommendations are presented in order to increase the life expectancy from birth, which is a decisive criterion for the country's level of prosperity, in a positive way.

Validation of the Social Security Administration Life Tables (2004–2014) in Localized Prostate Cancer Patients within the Surveillance, Epidemiology, and End Results database

BackroundAccurate life expectancy estimation is crucial in clinical decision-making including management and treatment of clinically localized prostate cancer (PCa). ObjectiveWe hypothesized that Social Security Administration (SSA) life tables’ derived survival estimates closely follow observed survival of PCa patients. To test this relationship, we examined 10-yr overall survival rates in patients with clinically localized PCa and compared it with survival estimates derived from the SSA life tables. Patients and methodsWithin the Surveillance, Epidemiology, and End Results database (2004), we identified patients aged >50–<90yr. Follow-up was at least 10 yr for patients who did not die of disease or other causes. Outcome measurements and statistical analysisMonte Carlo method was used to define individual survival in years, according to the SSA life tables (2004–2014). Subsequently, SSA life tables’ predicted survival was compared with observed survival rates in Kaplan-Meier analyses. Subgroup analyses were stratified according to treatment type and D’Amico risk classification. Results and limitationsOverall, 39191 patients with localized PCa were identified. At 10-yr follow-up, the SSA life tables’ predicted survival was 69.5% versus 73.1% according to the observed rate (p<0.0001). The largest differences between estimated versus observed survival rates were recorded for D’Amico low-risk PCa (8.0%), brachytherapy (9.1%), and radical prostatectomy (8.6%) patients. Conversely, the smallest differences were recorded for external beam radiotherapy (1.7%) and unknown treatment type (1.6%) patients. ConclusionsOverall, SSA life tables’ predicted life expectancy closely approximate observed overall survival rates. However, SSA life tables’ predicted rates underestimate by as much as 9.1% the survival in brachytherapy patients, as well as in D’Amico low-risk and radical prostatectomy patients. In these patient categories, an adjustment for the degree of underestimation might be required when counseling is provided in clinical practice. Patient summarySocial Security Administration (SSA) life tables’ predicted life expectancy closely approximate observed overall survival rates. However, SSA life tables’ predicted rates underestimate by as much as 9.1% the survival in brachytherapy patients, as well as in D’Amico low-risk and radical prostatectomy patients.

Omission of surgery in older women with early breast cancer has an adverse impact on breast cancer-specific survival

Primary endocrine therapy is used as an alternative to surgery in up to 40 per cent of women with early breast cancer aged over 70 years in the UK. This study investigated the impact of surgery versus primary endocrine therapy on breast cancer-specific survival (BCSS) in older women. Cancer registration data for 2002-2010 were obtained from two English regions. A retrospective analysis was performed for women with oestrogen receptor (ER)-positive disease, using statistical modelling to show the effect of treatment (surgery or primary endocrine therapy) and age and health status on BCSS. Missing data were handled using multiple imputation. Cancer registration data on 23 961 women were retrieved. After data preprocessing, 18 730 of 23 849 women (78·5 per cent) were identified as having ER-positive disease; of these, 10 087 (53·9 per cent) had surgery and 8643 (46·1 per cent) had primary endocrine therapy. BCSS was worse in the primary endocrine therapy group than in the surgical group (5-year BCSS rate 69·4 and 89·9 per cent respectively). This was true for all strata considered, although the difference was less in the cohort with the greatest degree of co-morbidity. For older, frailer patients the hazard of breast cancer death had less relative impact on overall survival. BCSS in older women with ER-positive disease is worse if surgery is omitted. This treatment choice may contribute to inferior cancer outcomes. Selection for surgery on the basis of predicted life expectancy may permit choice of women for whom surgery confers little benefit.

VA3 - “It’s Tough to Make Predictions, Especially About the Future”: Comparing Life Expectancy Predictions Based on Period Versus Cohort Life Tables

Many cost-effectiveness models over a patient’s lifetime incorporate background mortality. Analysts often use recently available country life tables for these estimates, for example, those from the US Centers for Disease Control and Prevention (CDC). These period life tables that provide data on mortality rates (i.e., conditional survival probabilities) from a single year. As historical data, they may not take into account future reductions in mortality rates due to technological advancement and other factors. In contrast, the Social Security Administration’s (SSA) actuarial cohort life tables attempt to account for such mortality reductions by predicting the mortality rate at each age for a birth cohort over the course of their lifetime. The purpose of this analysis was to compare life expectancy predictions based on the 2010 CDC period life table versus the 2010 SSA cohort life table and discuss the implications. Based on the 2010 CDC period and SSA cohort life tables. We simulated the remaining life expectancies at birth and at age 65, undiscounted and discounted at 3%. The absolute difference in life expectancies (CDC versus SSA) increased with age. Undiscounted life expectancies at birth and at age 65 were 78.6 and 19.1 based on the CDC life table, lower by 5.4% (vs. 83.1) and by 14.3% (vs. 22.3) compared to SSA, respectively. Discounted life expectancies at birth and at age 65 were 30.4 and 14.1 from the CDC life table, lower by 1.6% (vs. 30.9) and 10.8% (vs. 15.8) compared on SSA-based estimates. At $150,000 per quality-adjusted life-year, CDC-based life expectancy underestimated value per individual by $75,000 at birth and $255,000 at age 65. In cost-effectiveness Markov models that consider background mortality, use of period rather than cohort models will generally underestimate life years gained. These differences can substantially lower the monetary value of health gains.