Calculate Life Expectancy Research Articles

Trend in age-specific cancer mortality and cancer-free life expectancy in China, 2008–17: a cross-sectional survey of nationwide data

BackgroundCancer is the main chronic disease that affects the health of Chinese residents; however, there have been few systematic studies on cancer-free life expectancy in China. We aimed to explore age-specific cancer mortality and changes in population lifespan after accounting for the removal of the burden of cancer to provide important insights and a basic reference on the development of relevant health policies, such as cancer control or elderly care. MethodsWe used population data from the National Bureau of Statistics and data on cancer mortality from the China Health Statistics Yearbook to produce estimates of the number of deaths from 2008 to 2017 for 18 different age groups, female and male individuals, urban areas and rural areas, and different cancer types. We analysed the change in mortality for the 18 different age groups from 2008 to 2017 and inputted the population mortality and deaths caused by cancer into a life table system to calculate life expectancy for all age groups and cancer-free life expectancy for all age groups. FindingsFrom 2008 to 2017, total mortality associated with cancer in China decreased at a steady trend per year in urban areas and increased in rural areas. Mortality of the various age groups decreased during the 10-year period, whereas overall mortality rate increased due to population ageing. Lung cancer in rural areas increased by 2·8% (SD 1·2–4·4) per year, and liver cancer and stomach cancer significantly decreased by 2·1–2·2% per year in rural areas (p<0·0001). Life expectancy of the entire population increased from 74·6 years in 2008 to 75·6 years in 2017. Life expectancy in women (77·5–78·4 years) was higher than in men (72·0–73·1 years) from 2008 to 2017, and life expectancy in urban areas (76·0–76·2 years) was higher than rural areas (73·1–75·0 years). After removing the effects of cancer, life expectancy of the total population increased by about 3 years. Lung cancer had a greater effect on life expectancy than other types of cancer, and predominantly affects the male population younger than 54 years. If cancer were to be effectively controlled, the life expectancy of the population aged 50–84 years would be significantly improved. InterpretationCancer is a serious disease that harms the health of the population in China. The development of medicine is the main reason for the rapid increase in life expectancy in rural areas. Increased smoking and obesity are the main factors affecting the life expectancy of the Chinese population, although the development of new drugs and some new vaccines can effectively control cancer deaths. The study of life expectancy and healthy life expectancy will be important topics of research in the future. FundingNational High-tech R&D Program (863 Program) for High-end Electronic Medical Record Technology Development and System Application Demonstration (2012AA02A607).

John Graunt at 400: Fighting Disease with Numbers

Abstract Demographers, epidemiologists and statisticians working today are building on foundations laid by John Graunt, a London haberdasher born in 1620, who sought to analyse the many causes of death and calculate life expectancy. By Timandra Harkness

Lifetime Burden of Adult Congenital Heart Disease in the USA Using a Microsimulation Model.

Congenital heart defects (CHD) represent a growing burden of illness among adults. We estimated the lifetime health, education, labor, and social outcomes of adults with CHD in the USA using the Future Adult Model, a dynamic microsimulation model that has been used to study the lifetime impacts of a variety of chronic diseases. We simulated a cohort of adult heads of households > 25years old derived from the Panel Survey of Income Dynamics who reported a childhood heart problem as a proxy for CHD and calculated life expectancy, disability-free and quality-adjusted life years, lifetime earnings, education attainment, employment, development of chronic disease, medical spending, and disability insurance claiming status. Total burden of disease was estimated by comparing to a healthy cohort with no childhood heart problem. Eighty-seven individuals reporting a childhood heart problem were identified from the PSID and were used to generate the synthetic cohort simulated in the model. Life expectancy, disability-free, quality-adjusted, and discounted quality-adjusted life years were an average 4.6, 6.7, 5.3, and 1.4years lower than in healthy adults. Lung disease, cancer, and severe mental distress were more common compared to healthy individuals. The CHD cohort earned $237,800 less in lifetime earnings and incurred higher average total medical spend by $66,600 compared to healthy individuals. Compared to healthy adults, the total burden of CHD is over $500K per adult. Despite being among the healthiest adults with CHD, there are significant decrements in life expectancy, employment, and lifetime earnings, with concomitant increases in medical spend.

Evidencing the gap between health expectancy and life expectancy for ethnic groups in Scotland.

Background&#x0D; Recent evidence has shown that ethnic minorities live longer than the majority population in Scotland. This mortality advantage in ethnic minorities is not unique to Scotland. However, whether morbidity patterns by ethnicity align with mortality patterns by ethnicity is unknown. Thus, this study explores ethnic differences in health expectancies (HE) in Scotland and contrasts HE with life expectancy (LE) findings.&#x0D; Methods&#x0D; The Scottish Health and Ethnicity Linkage study anonymously links the Scottish Census 2001 for 4.6 million people to mortality records. The Scottish Census 2001 collected two measures of self-assessed health, self-declared ethnicity, age, and sex. Utilising the life tables used to calculate life expectancy by ethnicity and sex in Scotland, the Sullivan method was employed to calculate two measures of health expectancy (healthy life expectancy and disability-free life expectancy) by ethnicity and sex. 95% confidence intervals were calculated to detect significant differences compared to the majority White Scottish population, taken as reference.&#x0D; Results&#x0D; Longer health expectancies were found in males and females of Other White British, Other White, and Chinese origins as well as in Indian males compared to White Scottish populations. Any Mixed Background and Pakistani populations had the shortest healthy life expectancies. Patterns of health expectancy by ethnicity mostly aligned with patterns of life expectancy by ethnicity with the clear exception of the Pakistani population who showed among the longest life expectancies with the shortest health expectancies. Contrasting HE with LE findings, the number of years in an unhealthy state was greater in females than in males for each ethnic group. In relation to ethnicity, Pakistani and Indian populations had the highest number of years in an unhealthy state in Scotland. Pakistani females showed the strong-est disadvantage in this respect.&#x0D; Conclusion&#x0D; Pakistani populations had the shortest health expectancies contrasting with the longest life expectancies in Scotland. Future research should aim to understand why such a discrepancy occurs while policy makers ensure that fair and adapt-ed care is provided to offer better quality of life for the most vulnerable.

Overall survival (OS) and healthcare utilization results of a randomized controlled trial (RCT) assessing a patient navigation (PN) intervention to increase early access to supportive care (SC) for patients with metastatic cancer in a resource-limited setting.

12112 Background: We previously reported improvements in access to SC, advance directive completion, and pain control in a RCT comparing a patient navigator-led early SC intervention vs. usual care among patients with newly-diagnosed metastatic cancer in Mexico (NCT03293849). We now present results on healthcare utilization and OS. Methods: Patients were randomized to PN or usual oncology care. Patients in the PN arm received SC interventions by a navigator-led multidisciplinary team (palliative care, physical therapy, geriatrics, psychology) in the first 12 weeks after diagnosis. At 12-weeks, patients allocated to usual care were able to cross-over to PN and receive multidisciplinary SC. We analyzed the number (no.) of emergency room (ER) visits, their cause, and whether they were potentially avoidable (as determined by expert consensus), using descriptive statistics and X2 tests. OS was estimated using the Kaplan-Meier method and the log-rank test. Results: 133 patients (median age 60, range 23-93; 52% male) were randomized (66 PN, 67 control) from 08/17 to 04/18. Median follow-up was 22.8 months. 61% had gastrointestinal tumors, and 45% had a calculated life expectancy ≤6 months. 69% of patients randomized to usual care crossed-over to PN and received SC interventions. 80% of patients attended the ER ≥once (median no. of visits = 2). No difference was found between patients randomized to early SC or usual care in ER visits (2.4 vs. 2.3, p = 0.58). Out of a total 316 ER visits, the most common reason was infections (n = 69, 22%), followed by pain (n = 40, 13%), and indwelling catheter-related complications (n = 23, 7%). 41% of ER visits were considered as potentially avoidable, with no difference in avoidable visits found between arms (1.7 vs. 1.7, p = 0.49). No differences between arms were found in no. of hospitalizations (0.8 vs. 0.6 p = 0.82). Survival results were assessed after 64% of patients had died (n = 85), finding no statistically significant OS difference between the early SC intervention and the usual care arms (11.0 vs 13.0 months, p = 0.77) Conclusions: In the context of a limited-resource healthcare system, the early delivery of SC did not improve healthcare utilization, reduce avoidable ER visits, or prolong OS compared to the implementation of SC at a later time, which might be partially explained by the unavailability of hospice or home care, and by high rates of cross-over between arms. Clinical trial information: NCT03293849 .

How do ethnicity and deprivation impact on life expectancy at birth in people with serious mental illness? Observational study in the UK.

Across international contexts, people with serious mental illnesses (SMI) experience marked reductions in life expectancy at birth. The intersection of ethnicity and social deprivation on life expectancy in SMI is unclear. The aim of this study was to assess the impact of ethnicity and area-level deprivation on life expectancy at birth in SMI, defined as schizophrenia-spectrum disorders, bipolar disorders and depression, using data from London, UK. Abridged life tables to calculate life expectancy at birth, in a cohort with clinician-ascribed ICD-10 schizophrenia-spectrum disorders, bipolar disorders or depression, managed in secondary mental healthcare. Life expectancy in the study population with SMI was compared with life expectancy in the general population and with those residing in the most deprived areas in England. Irrespective of ethnicity, people with SMI experienced marked reductions in life expectancy at birth compared with the general population; from 14.5 years loss in men with schizophrenia-spectrum and bipolar disorders, to 13.2 years in women. Similar reductions were noted for people with depression. Across all diagnoses, life expectancy at birth in people with SMI was lower than the general population residing in the most deprived areas in England. Irrespective of ethnicity, reductions in life expectancy at birth among people with SMI are worse than the general population residing in the most deprived areas in England. This trend in people with SMI is similar to groups who experience extreme social exclusion and marginalisation. Evidence-based interventions to tackle this mortality gap need to take this into account.

Racial disparities in mortality in the adult hispanic population.

ObjectiveWe addressed three research questions: (1) Are there racial mortality disparities in the adult Hispanic population that resemble those observed in the non-Hispanic population in the US? (2) Does nativity mediate the race-mortality relationship in the Hispanic population? and (3) What does the Hispanic mortality advantage relative to the non-Hispanic white population look like when Hispanic race is considered?MethodsWe estimated a series of parametric hazard models on eight years of mortality follow-up data and calculated life expectancy estimates using the Mortality Disparities in American Communities database.ResultsHispanic white adults experience lower mortality than their Hispanic black, American Indian and Alaska Native, Some Other Race, and multiple race counterparts. This Hispanic white advantage is found mostly among the US born. The Hispanic advantage relative to the non-Hispanic white population operates for most Hispanic race groups among the foreign born but either disappears or converts to a disadvantage for most of the non-white Hispanic groups among the US born.ContributionOur study extends the literature on the Hispanic Mortality Paradox by revealing that the adult Hispanic population experiences racial mortality disparities that closely resemble those observed in the non-Hispanic population. The Hispanic mortality advantage is mediated not only by nativity but by race. These results indicate that race is a critical factor that should be considered in any study with the goal of understanding the health and mortality profiles of the Hispanic population in the US.

Lillies: An R package for the estimation of excess Life Years Lost among patients with a given disease or condition

Life expectancy at a given age is a summary measure of mortality rates present in a population (estimated as the area under the survival curve), and represents the average number of years an individual at that age is expected to live if current age-specific mortality rates apply now and in the future. A complementary metric is the number of Life Years Lost, which is used to measure the reduction in life expectancy for a specific group of persons, for example those diagnosed with a specific disease or condition (e.g. smoking). However, calculation of life expectancy among those with a specific disease is not straightforward for diseases that are not present at birth, and previous studies have considered a fixed age at onset of the disease, e.g. at age 15 or 20 years. In this paper, we present the R package lillies (freely available through the Comprehensive R Archive Network; CRAN) to guide the reader on how to implement a recently-introduced method to estimate excess Life Years Lost associated with a disease or condition that overcomes these limitations. In addition, we show how to decompose the total number of Life Years Lost into specific causes of death through a competing risks model, and how to calculate confidence intervals for the estimates using non-parametric bootstrap. We provide a description on how to use the method when the researcher has access to individual-level data (e.g. electronic healthcare and mortality records) and when only aggregated-level data are available.

Life expectancy and inequalities therein by income from 2016 to 2018 across the 253 electoral constituencies of the National Assembly of the Republic of Korea.

ObjectivesWe calculated life expectancy and inequalities therein by income for the period of 2016-2018 across the 253 electoral constituencies of the 20th National Assembly election in Korea.MethodsWe obtained population and death data between 2016 and 2018 from the National Health Information Database and constructed abridged life tables using standard life table procedures according to gender and income quintiles for the electoral constituencies of the 20th National Assembly election held in 2016.ResultsLife expectancy across the 253 constituencies ranged from 80.51 years to 87.05 years, corresponding to a gap of 6.54 years. The life expectancy difference by income across the 253 constituencies ranged from 2.94 years to 10.67 years. In each province, the difference in life expectancy by income across electoral constituencies was generally greater than the inter-constituency differences. Constituencies in capital and metropolitan areas showed a higher life expectancy and a lower life expectancy difference by income than constituencies in rural areas.ConclusionsPro-rich inequalities in life expectancy by income existed in every electoral constituency in Korea. Both intra-constituency and inter-constituency socioeconomic inequalities in health should be highlighted in future policy-making in the National Assembly.

Trend in Age-Specific Cancer Mortality and Cancer-Free Life Expectancy in China, 2008-2017: An Analysis of Nationwide Data

Background: Cancer is the main chronic disease that affects the health of Chinese residents, however, less systematic study on cancer-free life expectancy in China. We aimed to explore age-specific cancer mortality and changes in population lifespan after removal of cancer to provide important insights and a basic reference on the development of relevant health policies such as cancer control or elderly care. Methods: We use the available data to produce estimates of the number of deaths from 2008 to 2017 for 18 age groups, both sexes, urban areas and rural areas, cancer and the three high-incidence cancers. Data used include the population data from the National Bureau of Statistics and cancer mortality from China Health Statistics Yearbook. We analyzed the change in mortality over the years, and at the same time input the population and deaths into the life table system to calculate life expectancy for all ages and cancer-free. Findings: From 2008 to 2017, the total mortality rate of cancers in China decreased at a steady trend per year in urban areas and increased in rural areas. The mortality rate of the different age groups decreased during the 10-year period while the overall mortality rate increased. The lung cancer in rural areas increased by 2.8% per year, and liver cancer and stomach cancer significantly decrease by 2.1-2.2% per year (P<0.05). Life expectancy of the entire population from 74.6 years in 2008 to 75.6 years in 2017, nearly increases about 1 year. Life expectancy in female is higher than male, urban areas are higher than rural areas. After removing the effects of cancer, life expectancy of the population increases by about 3 years. Lung cancer has a greater impact on life expectancy of the population, mainly concentrated in the male population under 54 years old. If the cancer is effectively controlled, life expectancy of population in 50-84 years will be significantly improved. Interpretation: Cancer is a seriously disease harms the health of the population in China. The development of medical is the main reason for the rapid increase in life expectancy in rural areas. Increasing smoking and obesity are the main factors affecting life expectancy of people, meanwhile, the development of new drugs and some new vaccines can effectively control cancer deaths. The study on life expectancy and healthy life expectancy are important trends in the future. Funding Statement: National High-tech R&D Program (863 Program) for High-end Electronic Medical Record Technology Development and System Application Demonstration (2012AA02A607) Declaration of Interests: The authors declare no competing interests. Ethics Approval Statement: Missing.

LIFE EXPECTANCY: A Brief International Comparison

Life-expectancy data are essential to many fields, notably demography, health care, insurance, military recruiting, and forensic economics. Professionals using global rankings find life expectancy data misleading. Policymakers need a standard protocol to measure and track life expectancies. A case study comparing four countries’ life-expectancy data against the U.S. found skewed population data caused by different definitions and different formulas across different countries. Only the U.S. Life-Expectancy Tables made adjustments for both infant mortality and fertility rates. Absent a standard protocol to measure and track life expectancies, policymakers will continue to reach wrong conclusions. A worldwide standard protocol would require all counties to define life-expectancy data in the same way, calculate life expectancy values in the same way, and uniformly adjust for rates of both infant mortality and fertility. The significant changes called for will not come quickly, but we surely can suggest a starting basis.

Ayurlakshaneeyam of Bhela Samhita- Indriya Sthana: An explorative study

Bhela Samhita is one of the prominent treatises of Ayurveda. Indriya Sthana is one among the eight sections of Bhela Samhita and it comprises 12 chapters. Ayurlakshanam Indriyam (ALI) is the first chapter of B Indriya Sthana. Though it consists of various unique concepts, it is modestly explored or unexplored till date. Previous works have explored various clinical conditions present in “Charaka Indriya Sthana”; these have prognostic significance and clinical applicability in the present era also but such types of studies are lacking in “Bhela Samhita-Indriya Sthana.” The current work is aimed at exploring the contents of ALI of “Indriya Sthana.” ALI deals with the estimation of life expectancy and age-specific mortality based on some anthropometric or phenotypic characteristics. Most of the content is unique and not explained in any other Ayurveda classical texts. Various conditions such as craniofacial anomalies/ disorders, multi-malformed infants, infant/ neonatal mortality, synophrys, mental retardation (MR), congenital, chromosomal, genetic syndromes/ conditions, neonatal intensive care unit (NICU) conditions, newborn cry-based diagnostic systems (NCDs), spina bifida (SB), hydrocephalus, congenital talipes equinovarus (CTEV), hypertrichosis, malnutrition, calculation of life expectancy based on various anthropometric indices (e.g., length and breadth of fingers, palms, ears, nose, forehead, and thighs), and phenotypic biomarkers and personality traits that are positively associated with centenarians have been documented in this chapter. Maharshi Bhela has provided techniques that are inexpensive, simple, noninvasive, highly accurate, and suitable for low- or middle-income countries for estimating life expectancy as well as for diagnosing hidden diseases. Though further research is still required to substantiate the claims made in this chapter, the current study paves the path for future research directions.

Life Expectancy in Areas around Subway Stations in the Seoul Metropolitan Area in Korea, 2008-2017.

BackgroundThis study aimed to calculate life expectancy in the areas around 614 subway stations on 23 subway lines in the Seoul metropolitan area of Korea from 2008 to 2017.MethodsWe used the National Health Information Database provided by the National Health Insurance Service, which covers the whole population of Korea. The analysis was conducted on the level of the smallest administrative units within a 200-m radius of each subway station. Life expectancy was calculated by constructing an abridged life table using the number of population and deaths in each area and 5-year age groups (0, 1–4, …, 85+) during the whole study period.ResultsThe median life expectancy in the areas around 614 subway stations was 82.9 years (interquartile range, 2.2 years; minimum, 77.6 years; maximum, 87.4 years). The life expectancy of areas around subway stations located in Seoul was higher than those in Incheon and Gyeonggi-do, but variation within the region was observed. Significant differences were observed between some adjacent subway stations. In Incheon and Gyeonggi-do, substantially higher life expectancy was found around subway stations in newly developed urban areas, and lower life expectancy was found in central Incheon and suburbs in Gyeonggi-do.ConclusionWhen using areas around subway stations as the unit of analysis, variation in life expectancy in the Seoul metropolitan area was observed. This approach may reduce the stigma associated with presenting health inequalities at the level of the smallest administrative units and foster public awareness of health inequalities.

Impact of injury-related deaths on life expectancy in China, 2016.

This study aimed to illustrate the impact of injury-related deaths on life expectancy in China in 2016 and to identify the high-risk population. Standard life tables were used to calculate life expectancy and cause-eliminated life expectancy by utilizing mortality data from the national mortality surveillance system. In 2016, the life expectancy of Chinese residents was 78.91 years. After eliminating injury-related mortality, the life expectancy of the residents increased by 1.13 years, which was higher in male and rural residents compared with female and urban residents. The life expectancy on the Eastern and Central regions increased slowly compared with the Western region. The greatest influencing factor on life expectancy was road traffic injuries, followed by falls. Falls had a greater impact on life expectancy for children under 5 years old and those aged over 65 years. This study indicates that deaths caused by injury had a noteworthy impact on life expectancy in China. More attention should be paid to road traffic injuries, and effective preventive measures should be taken to reduce deaths related to injury to increase the life expectancy of residents, especially in children under 5 years and adults over 65 years. By reducing the deaths caused by injury, the life expectancy level is likely to further improve.

Older Adult Perspectives Toward Surveillance Colonoscopy: A Qualitative Study.

Surveillance colonoscopy is the most common indication for colonoscopy in older adults, yet factors involved in patient decision-making are poorly defined. We sought to understand general perspectives of older adults toward surveillance colonoscopy. We conducted 2 in-person, 90-minute semistructured focus groups at a rural, tertiary care, academic facility with a total of 20 English-speaking participants with a history of colon polyps. We also obtained baseline characteristics including information to calculate life expectancy using the Schonberg Index, a validated measure of 5-year and 9-year mortality. Participant ages ranged from 75 to 89, 67% were female, and 61% had a life expectancy of ≤9 years. Major common themes included reasons for and against getting a surveillance colonoscopy, and preferences and opinions surrounding discontinuing surveillance. Fear of cancer, trust in the colonoscopy procedure, and provider advice played prominent roles in patient decisions to return for surveillance. Most felt they should make screening decisions with input from providers, and that providers should engage them in these decisions and base recommendations on their patients' personal history and health, not on how old they are or on actuarial data. This small, qualitative study suggests that older adults familiar with surveillance colonoscopy prefer patient-centered decision-making on when to discontinue testing and want support and information from providers when making choices. The evidence also suggests that participants value provider communication and trust their advice. Future work will use this information to create a larger patient survey on attitudes and beliefs toward surveillance colonoscopy.

Inequality in Life Expectancy in Korea according to Various Categorizations of the National Health Insurance Premiums as a Marker of Income.

The purpose of this study was to examine the degree to which the magnitude of income inequality in life expectancy according to different categorization across beneficiary types under the National Health Insurance Service (NHIS) in Korea. We used population and death data in 2017 from the National Health Information Database of the NHIS. Income quintile groups were classified in four ways according to beneficiary type (employee insured, self-employed insured, and Medical Aid beneficiaries). Standard life table procedures were used to calculate life expectancy. The life expectancy gap between the lowest and highest income quintiles was the largest when the entire population was divided into quintiles without distinguishing among types of beneficiaries. In conclusion, we suggest that income quintile indicators in NHIS data, measured without distinguishing among types of beneficiaries, may best represent the magnitude of health inequalities in Korean society. This indicator could be used for future research on health inequalities, as well as for monitoring health inequalities in Korea.

Cities of over a million people on the mortality map of Russia

The problem of excess mortality in Russia has not lost its relevance. The situation is complicated by the high level of spatial inequality in health, which is usually measured at the regional level in our country. This work is one of the first attempts to look at the dynamics and extent of spatial inequality in health in Russia at the sub-regional level, by contrasting the "center/core" (in our case, represented by the largest Russian cities) with the "periphery" (the rest of the country). Cities with a population of over a million people were chosen based on the spatial hierarchy that exists in Russia, according to which the highest level of social and economic development is concentrated in the largest cities. As a rule, a higher level of development of human capital corresponds to lower mortality. Using data provided by Rosstat, we calculated life expectancy at birth for Russian cities with a population of over a million people in 1989-2016. The results fully coincided with our expectations: the polarization in the health levels between the largest Russian cities and the rest of the country has significantly increased in the last twenty-five years, which is a reflection of those centripetal processes that have been taking place in our country during this period. Russian cities with a population of over a million people are attractive destinations for both internal and external migrants, and thus acquire, among other things, a much more educated population. Since people with higher education take better care of their health, having a more educated population is undoubtedly an essential advantage of bigger cities over the periphery when it comes to the overall health level. Without solving the structural problems that restrain social and economic development outside the largest agglomerations, convergence in mortality rates between cities with a population of over a million people and the surrounding territories is hardly possible.

The Effect on Annuities Preference of Prompts to Consider Life Expectancy: Evidence from a UK Quota Sample

State‐run pensions advice websites in the UK and the USA recommend considering how long the pension needs to last and offer a life expectancy calculator for this purpose. Theories make conflicting predictions regarding the effect on annuities preference of following this recommendation. We asked a quota sample of over 2000 UK residents to choose between an annuity or cashing out their pension. Whereas a control group made this choice without any prompt, three treatment groups were asked prior to choice how long their retirement would last, or what age they would live to, or what age they would die by. Asking about life expectancy reduced preference for annuities relative to the control condition. This research suggests that the reference to life expectancy on the UK's pension advice website exacerbates underannuitization.

LIFE EXPECTANCY WITH CHRONIC DISEASE AMONG OLDER MEXICAN AMERICANS

The Hispanic Paradox research consistently shows a survival advantage among Hispanics, where they have significantly longer life expectancy compared to non-Hispanic Whites. However, less is known about the quality of these additional years of life. Our goal was to calculate life expectancy for older Hispanics in the US, and to determine what proportion of their lives they could expect to live with chronic conditions. We used data from 2004-5 of the Hispanic Established Population for the Epidemiological Study of the Elderly (Hispanic-EPESE), linked to vital status data through 2016. To determine life expectancy with/without chronic conditions, we used Sullivan’s method. Chronic conditions included: diabetes, stroke, heart attack, arthritis, hypertension, and cancer. Finally, we mapped life expectancy without chronic conditions across neighborhood characteristics. The sample consisted of 2,069 Mexican Americans aged 75 and older. Results showed that at age 75, Mexican Americans could expect to live a large portion of their lives with at least one chronic condition (88%). The largest proportion of life lived with disease was for hypertension (61%), arthritis (59%), and diabetes (33%). There was no pattern by neighborhood characteristics for disease-free life expectancy. Gender differences could not be examined because of small sample sizes. Our findings show that regardless of neighborhood characteristics, Mexican Americans can expect to live a high proportion of their life with at least one chronic condition. This is particularly high for hypertension, diabetes, and arthritis. It is important to consider quality as well as quantity of life when exploring the Hispanic Paradox.

Predicting Life Expectancy for Older Adults with Cancer in Clinical Practice: Implications for Shared Decision-making.

The calculation of noncancer-specific life expectancy can guide shared decision-making and avoid over- and undertreatment in older adults with cancer. Several factors determine life expectancy, including socio-demographic background, comorbidities, physical performance, and geriatric assessment variables. We present an overview of existing tools to estimate life expectancy, as well as practical examples of how to take into account the patient's noncancer-specific life expectancy when discussing screening decisions, initiation of treatment, and end-of-life care. Life expectancy prognostication has been recently recommended by international societies as part of the initial assessment of all older adults with cancer. Additionally, online resources have been created in order to make life expectancy calculation tools accessible for clinicians. Understanding available methods to estimate life expectancy, as well as how to utilize them, is a fundamental part of geriatric oncology that should be integrated into everyday clinical practice.