The price of curing cancer

Background Health care systems around the world struggle with high prices for new cancer drugs. The purpose of this study was to conduct a gedankenexperiment and calculate how much health expenditures would change if a cure for cancer through pharmaceutical treatment were made available. The cancer cure was conceived to eliminate both cancer deaths and the underlying morbidity burden of cancer. Furthermore, the cure was hypothesized to arrive in incremental steps but at infinitesimally small time intervals (resulting, effectively, in an immediate cure). Methods The analysis used secondary data and was conducted from the viewpoint of the German social health insurance. As its underlying method, it used a cause-elimination life-table approach. To account for the age distribution of the population, the study weighted age-specific increases in remaining life expectancy by age-specific population sizes. It considered drug acquisition costs as well as savings and life extension costs from eliminating cancer. All cancer drugs that underwent a mandatory early benefit assessment in Germany between 2011 and 2015/16 and were granted an added benefit were included. Data on age- and gender-specific probabilities of survival, population sizes, causes of death, and health expenditures, as well as data on cancer costs were taken from the German Federal Office of Statistics and the German Federal Social Insurance Office. Results Based on the cause-elimination life-table approach and accounting for the age structure of the German population, curing cancer in Germany yields an increase in average remaining life expectancy by 2.66 life years. Based on the current incremental cost-effectiveness ratio of new cancer drugs, which is on average €101,493 per life year gained (€39,751/0.39 life years), the German social health insurance would need to pay €280,497 per insuree to eliminate cancer. Dividing this figure by current average remaining lifetime health expenditures yields a ratio of 2.07, which represents a multiplier of current health expenditures. Conclusions Eliminating cancer at current price levels would more than triple total health expenditures in Germany. As the current price of a cure requires a drastic reduction of non-health consumption, it appears that current prices for cancer drugs already on the market (i.e., small steps towards a cure) need careful reconsideration.

Geographic variation in gender gap among older adults in India and China: Application of a new ageing measure

A continuous rise in the life expectancy of females above that of the males among older adults in India and China may give an impression that the gender gap in health is decreasing. However, given the systemic bias against females in these countries across multiple facets, and the diversity across provinces, a fuller understanding of gender gap calls for (a) understanding the gender gap in multiple dimensions of health, and (b) understanding the variations across provinces. We estimate a multi-dimensional old-age threshold (MOAT) across provinces in India and China, that specifies different old-age thresholds for female and male populations after simultaneously accommodating for multiple dimensions related to their health. These aspects of health include remaining life expectancy, intellectual and functional health. We estimate the gender gap across provinces in these countries by differencing the MOAT of males against that of females. In addition, we also illustrate the gender gap across individual dimensions of health. Our results show that females in almost all the provinces of India and China have a lower MOAT than their male counterparts, showing an earlier advent of ‘old-age’ among females compared to males. The estimates based on remaining life expectancy shows gender gap in favor of females, but the estimates of multi-dimensional gender gap are higher and biased against females. A huge variation is seen across provinces, with Karnataka and Hubei showing lower levels of gender gap and Rajasthan and Yunnan showing higher gender gaps in India in China respectively.

Grandparents in Italy: trends and changes in the demography of grandparenthood from 1998 to 2016

In this article we explore the last two decades of changes in the demography of grandparenthood in Italy, by means of a set of measures: the proportion of men and women becoming grandparents by age and time, the age at transition to grandparenthood and its crossing with a set of life events and the length of grandparenthood. We used data from the four waves of the Survey on Family and Social Subjects carried out by the Italian National Institute of Statistics in 1998, 2003, 2009 and 2016. Overall, the median age at which half of the population over 35 is made up of grandparents moved forward by at least 5 years during the two observed decades. The postponement of grandparenthood is evident in middle age: between 55 and 64 the ratio of grandparents to non-grandparents decreased significantly by about 10 per cent. Overall, among people who had ever had children, the median age at the transition to grandparenthood advanced by three years from 1998 to 2016, both for men (59 to 62) and women (54 to 57). This difference is greater than that observed for age at parenthood and equal to the advantage gained in terms of life expectancy at age 60. Thus, although grandparenthood has been postponed over the last two decades in Italy, the great gains in remaining life expectancy result in grandparent-grandchildren lifetime not being reduced.

A model of lifetime health outcomes in cardiovascular disease based on clinical trials and large cohorts

Background and purpose Cardiovascular disease (CVD) risk of individuals depends on their socio-demographic characteristics, clinical risk factors, and treatments, and strongly influences their quality of life and survival. Individual-based long-term disease models, which aim to more accurately calculate the lifetime consequences, can help to target treatments, develop disease management programmes, and assess the value of new therapies. We present a new micro-simulation CVD model. Methods This micro-simulation model was developed using individual participant data from the Cholesterol Treatment Trialists' collaboration (CTT: 118,000 participants; 15 trials) and calibrated (with added socioeconomic deprivation, ethnicity, physical activity, mental illness, cancer and incident diabetes) in the UK Biobank cohort (UKB: 502,000 participants). Parametric survival models estimated risks of key endpoints (myocardial infarction (MI), stroke, coronary revascularisation (CRV), diabetes, cancer and vascular (VD) and nonvascular death (NVD) using participants' age, sex, ethnicity, physical activity, socioeconomic deprivation, smoking history, lipids, blood pressure, creatinine, previous cardiovascular diseases, diabetes, mental illness and cancer at entry and non-fatal incidents of the key endpoints during follow-up. The model integrates the risk equations and enables annual projection of endpoints and survival over individuals' lifetimes. The model was used to project remaining life expectancy across UK Biobank participants. Results Nonfatal cardiovascular events and age were the major determinants of CVD risk and, together with incident diabetes and cancer, of individuals' survival. The cumulative incidence of the key endpoints predicted by the CTT-UKB model corresponded well to their observed incidence in the UK Biobank cohort, overall (Figure 1) and in categories of participants by age, sex, prior CVD and CVD risk. Predicted remaining life expectancy across UK Biobank participants without history of CVD ranged between 22 and 43 years in men and between 24 and 46 years in women, depending on their age and CVD risk (Figure 2). Among UK Biobank participants with history of CVD, depending on their age, predicted remaining life expectancy ranged from 20 to 32 years in men and from 26 to 38 years in women. Conclusion This new lifetime CVD model accurately predicts morbidity and mortality in a large UK population cohort. It will be made available to provide individualised projections of expected lifetime health outcomes and benefits of treatments. FUNDunding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): UK National Institute for Health Research (NIHR) Health Technology Assessment (HTA) Programme, UK Medical Research Council (MRC), British Heart Foundation Figure 1. Predicted (in black) versus observed (95% CI; in red) incidence of major clinical outcomes in the UK Biobank. Figure 2. Predicted remaining life expectancy of participants in UK Biobank cohort, by age and CVD risk or previous CVD at entry. QRISK, a 10-year CVD risk scoring algorithm for people without previous CVD, recommended for use in the UK National Health Service.

Growing Income-Based Inequalities in Old-Age Life Expectancy in Sweden, 2006–2015

Sweden is known for high life expectancy and economic egalitarianism, yet in recent decades it has lost ground in both respects. This study tracked income inequality in old-age life expectancy and life span variation in Sweden between 2006 and 2015, and examined whether patterns varied across levels of neighborhood deprivation. Income inequality in remaining life expectancy at ages 65, 75, and 85 increased. The gap in life expectancy at age 65 grew by more than a year between the lowest and the highest income quartiles, for both men (from 3.4 years in 2006 to 4.5 years in 2015) and women (from 2.3 to 3.4 years). This widening income gap in old-age life expectancy was driven by different rates of mortality improvement: individuals with higher incomes increased their life expectancy at a faster rate than did those with lower incomes. Women with the lowest incomes experienced no improvement in old-age life expectancy. Furthermore, life span variation increased in the lowest income quartile, while it decreased slightly among those in the highest quartile. Income was found to be a stronger determinant of old-age life expectancy than neighborhood deprivation.

Vaccinating the oldest against COVID-19 saves both the most lives and most years of life

Many competing criteria are under consideration for prioritizing COVID-19 vaccination. Two criteria based on age are demographic: lives saved and years of future life saved. Vaccinating the very old against COVID-19 saves the most lives, but, since older age is accompanied by falling life expectancy, it is widely supposed that these two goals are in conflict. We show this to be mistaken. The age patterns of COVID-19 mortality are such that vaccinating the oldest first saves the most lives and, surprisingly, also maximizes years of remaining life expectancy. We demonstrate this relationship empirically in the United States, Germany, and South Korea and with mathematical analysis of life tables. Our age-risk results, under usual conditions, also apply to health risks.

Healthy life expectancy by frailty state in Europe from 2004 to 2015: findings from SHARE

Background As populations age, the possible consequences of increased frailty are a major concern for the health sector. Here, we investigate how life expectancy with and without frailty has changed during a 10–11-year-period across Europe. Methods The Sullivan method was used to investigate changes in life expectancy with and without frailty in 10 European countries. Frailty status (non-frail, pre-frail and frail) was determined by use of the Survey of Health, Ageing and Retirement in Europe Frailty Instrument (SHARE-FI). Data on frailty prevalence was obtained from 21 698 individuals in wave 1 (2004–05) and 38 859 individuals in wave 6 (2015) of the SHARE. Information on mortality was obtained from the Eurostat Database. Results In 2015, women aged 70 spent 25.0% (95% CI: 24.0–26.1) of their remaining life expectancy in a frail state, and the number for men was 11.5% (95% CI: 10.7–12.3). Southern Europeans spent 24.2% (95% CI: 22.9–25.4) of their remaining life expectancy in a frail state and the numbers for Central Europeans and Northern Europeans were 17.0% (95% CI: 16.0–17.9) and 12.2% (95% CI: 10.9–13.5), respectively. From 2004–05 to 2015, life expectancy increased by 1.1 years (from 15.3 to 16.4 years) for 70-year-old Europeans. Similarly, non-frail life expectancy increased by 1.1 years (95% CI: 0.8–1.4), whereas no significant changes in life expectancy in frail states were observed. Conclusions This study suggests that Europeans today spend more years in a non-frail state than Europeans did 10–11 years ago. Our findings reflect a considerable inequality by gender and region.