Summary Measures Of Population Health Research Articles

Healthy Lifespan Statistics Derived From Cross-Sectional Prevalence Data Using the Sullivan Method are Informative Summary Measures of Population Health

Health expectancy (HE), commonly derived from cross-sectional prevalence data using the Sullivan method, serves as the most frequently used summary measure of population health. Like lifespan distribution statistics, which are often discussed alongside life expectancy (LE) in demographic studies, analogous statistics on healthy lifespans can provide valuable information on population health. We examine whether healthy lifespan distribution statistics beyond HE can be estimated based on cross-sectional prevalence data and the life table, the data inputs of the Sullivan method. To do so, we treat the Sullivan method as an extension of the stationary population model to health and distinguish between health conditions with and without recovery from the state of decreased health. Our empirical demonstration is based on the prevalence of chronic diseases in selected European countries in 2017 from the Survey of Health, Ageing and Retirement in Europe (SHARE), as well as on life tables from EUROSTAT. We find that the Sullivan method, when considered as an extension of the stationary population model to health, allows for the estimation of a healthy survival distribution and its statistics, beyond HE, for health characteristics with no recovery from the state of decreased health. We show that for such health conditions, the method requires that the number of persons in full health in a stationary population does not increase with age. We argue that for such health dimensions, HE conditional on being in good health at the life table radix age is of relevance for health policy interventions. In our empirical application, we show that the conditional and unconditional measures of HE can give substantially different pictures of population health. Furthermore, we show that across European countries, in contrast to the negative relationship between LE and lifespan inequality, higher HE is associated with greater inequality in healthy years lived when conditional on being healthy at age 50. Overall, the Sullivan method, when considered as an extension of the stationary population model, proves to be a valuable tool for deriving summary statistics of population health beyond HE, which are highly relevant to public policy. * This article belongs to a special issue on “Levels and Trends of Health Expectancy: Understanding its Measurement and Estimation Sensitivity”.

Trends and Patterns of Cancer Burdens by Region and Income Level in Korea: A National Representative Big Data Analysis.

This study aimed to elucidate the trends and characteristics of the cancer burden in Korea by cancer site, region, and income level. Korean National Burden of Disease research methodology was applied to measure the cancer burden in Korea from 2008 to 2018. The cause of death and national health insurance claims data were obtained from Statistics Korea and the National Health Insurance Service, respectively. An incidence-based approach was applied to calculate the disability-adjusted life-years, which is a summary measure of population health. In the past decade, the cancer burden in Korea increased from 2,088 to 2,457 person-years per 100,000 population. Among the cancer burden, the years of life lost decreased, and the years lived with disabilities increased. Cancers of the trachea, bronchus, and lung had the highest disease burden, followed by those of the stomach, colon and rectum, liver, and breast. The findings of this study can provide valuable quantitative data for prioritizing and evaluating cancer prevention strategies and implementing cancer policies. Estimating the difference in cancer burden according to region and income level within a country can yield useful data to understand the nature of the cancer burden and scale of the problem. In addition, the results of this study provide a better understanding of the causes of cancer patterns, thereby generating new hypotheses regarding its pathogenesis.

Slowing down or returning to normal? Life expectancy improvements in Britain compared to five large European countries before the COVID-19 pandemic.

Life expectancy is an important summary measure of population health. In the absence of a significant event like war or disease outbreak, trends should, and historically have, increase over time, albeit with some fluctuations. Data were extracted from the human mortality database for life expectancy at birth and age 65 years from 1980 to the latest available year for England & Wales, Scotland (Great Britain), France, Spain, Italy, the Netherlands and Germany. It is well established that life expectancy improvements in Great Britain have stalled in recent years, and that a similar stalling was seen in other high-income countries during the mid-2010s. The significance and causes of the slowdown in improvement in life expectancy in Britain are disputed. First, was Britain's slowdown in progress in life expectancy in the 2010s a deviation 'from' earlier sustained improvements or simply returning to normal (slower) improvement rates following fastergains in the 2000s? Second, did other European countries have slowdowns comparable to that in Britain? Life expectancy, as a summary measure, conceals inequalities. Other measures, such as lifespan disparity, complement it in understanding changing trends. While annual fluctuations in life expectancy are expected, continued stalls should raise concern. The three British nations examined were the only ones among these European countries to experience stalling of life expectancy gains in both sexes. While it is clear that Britain is making less progress in health than similar countries, more research is needed to explain why.

Burden of disease assessment for Germany and its regions – results from the BURDEN 2020 study

Abstract Background Epidemiological measures such as incidence, prevalence, or deaths are essential for monitoring population health. However, evaluating them in isolation cannot adequately compare and assess the relative importance of different diseases. Assessments of the burden of disease (BoD) are therefore of growing importance in supporting health policy decisions. Using disability-adjusted life years (DALY) as a summary measure of population health, BoD integrates morbidity and mortality in a transparent approach. Methods Within BoD methodology, deviations in the health of the population from an ‘ideal’ health status is quantified in the unit of life years. DALY are the sum of years of life lost due to death (YLL) and years lived with disability (YLD). While YLL describe the gap between age at death and statistical life expectancy, the indicator YLD quantifies years lived with a disability or disease. Calculations were based on different primary and secondary data sources for Germany, especially cause-of-death statistics, epidemiological survey data, and statutory health insurance data. Results In Germany, there were about 12 million DALY in 2017, the equivalent of 14,584 DALY per 100,000 population. Coronary heart disease contributes the most to the overall burden of disease, followed by lower back pain and lung cancer. In women, headache disorders and dementias account for more DALY as compared to men. Men have a higher burden of disease from lung cancer or alcohol use disorders. Pain disorders and alcohol use disorders lead the DALY rankings for both sexes in younger adulthood. The burden due to cardiovascular disease, COPD, and diabetes mellitus increases with age and also varies by region. Conclusions The results suggest age- and gender-specific prevention as well as regional health care needs. BoD studies therefore provide comprehensive data for population health surveillance and can support health policy decisions. Key messages • The importance of specific diseases as measured by DALY differs greatly by age and gender, highlighting the need for targeted prevention measures. • Regional patterns emerge for cardiovascular disease, COPD, and depressive disorders, among others, which may indicate health care needs.

Well-Being Adjusted Health Expectancy: A New Summary Measure of Population Health

We propose a new summary measure of population health (SMPH), the well-being-adjusted health expectancy (WAHE). WAHE belongs to a subgroup of health-adjusted life expectancy indicators and gives the number of life years equivalent to full health. WAHE combines health and mortality information into a single indicator with weights that quantify the reduction in well-being associated with decreased health. WAHE's advantage over other SMPHs lies in its ability to differentiate between the consequences of health limitations at various levels of severity and its transparent, simple valuation function. Following the guidelines of a Committee on Summary Measures of Population Health, we discuss WAHE's validity, universality, feasibility sensitivity and ensure its reproducibility. We evaluate WAHE's performance compared to life expectancy, the most commonly used indicators of health expectancy (HE) and disability-adjusted life expectancy (DALE) in an empirical application for 29 European countries. Data on health and well-being are taken from the 2018 EU-SILC, and the life tables are from Eurostat. DALE is taken from the database of the Global Burden of Disease Programme. WAHE's sensitivity to univariate and multivariate state specifications is studied using the three Minimum European Health Module health dimensions: chronic morbidity, limitations in activities of daily living, and self-rated health. The empirical tests of the indicators’ correspondence reveal that WAHE has the strongest correlation with the other SMPHs. Moreover, WAHE estimates are in agreement with all other SMPHs. Additionally, WAHE and all other SMPHs form a group of reliable indicators for studying population health in European countries. Finally, WAHE estimates are robust, regardless of whether health is defined across one or multiple simultaneous dimensions of health. We conclude that WAHE is a useful and reliable indicator of population health and performs at least as well as other commonly used SMPHs.

Comparative analysis of health expectancy indicators in older adult men and women in selected European countries

The study concerns the assessment of the health of older adults in selected European countries using summary measures of population health. These measures show to what extent increasing life span goes hand in hand with a healthier life, and to what extent it is burdened by health limitations and disability. This paper covers such health domains as self-perceived health, functional limitations, chronic morbidity and disability. Data on the health of people aged 60 and over came from Wave 7 of SHARE, and Sullivan’s method was used to calculate health expectancy measures. The multivariate nature of the health status measurement prompted us to also use a multivariate comparative analysis and to determine a synthetic measure of health status. The results showed large disproportions in health expectancy indicators in the later years of life of men and women in the European countries analysed, and these differences are greater than those relating to life expectancy.

A Review of the Types and Characteristics of Healthy Life Expectancy and Methodological Issues.

An index that evaluates the health level of a population group considering both death and loss of function due to disease is called a summary measure of population health (SMPH). SMPHs are broadly divided into life year indices and life expectancy indices, the latter of which comprise healthy life expectancy (HLE). HLE is included as a policy target in various national and regional level healthcare plans, and the term “HLE” is commonly used in academia and by the public. However, the overall level of understanding of HLE—such as the precise definition of HLE and methods of calculating HLE—still seems to be low. As discussed in this study, the types of HLE are classified into disability-free life expectancy, disease-free life expectancy, quality-adjusted life expectancy, self-rated HLE, and disability-adjusted life expectancy. Their characteristics are examined to facilitate a correct understanding and appropriate utilization of HLE. In addition, the Sullivan method, as a representative method for calculating HLE, is presented in detail, and major issues in the process of calculating HLE, such as selection of the population group and age group, estimation of death probability, calculation of life years, and incorporation of health weights, are reviewed. This study will help researchers to select an appropriate HLE type and evaluate the validity of HLE research results, and it is expected to contribute to the vitalization of HLE research.

9.B. Workshop: Burden of disease of COVID-19: strengthening the collaboration for national studies

 The spread of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was categorized as a pandemic by the World Health Organization in March 2020. Since the outbreak was first identified in December 2019 in Wuhan, China, the public health impact of the disease globally has been enormous. Estimating the burden of the disease at national level is useful for comparing its impact with other diseases in the population and across populations. The disability-adjusted life year (DALY) is one of the most used summary measure of population health, and the key metric in the Global Burden of Disease study. The European Burden of Disease Network (burden-eu) was established in 2019 to act as a technical platform for integrating and strengthening capacity in burden of disease assessment across Europe and beyond. It is structured in technical and disease-focused working groups focus. Since the start of the pandemic, burden-eu has developed tools and started initiatives to support countries in estimating the national burden of COVID-19. These include developing and harmonizing methodologies, disseminating technical materials, launching a working group and online forum, exchanging experiences and supporting capacity building, and planning future studies. This workshop consists of four presentations. In the first, we present a consensus methodology, data requirements and solutions to data gaps to estimate national-level burden of COVID-1. Next, we present as examples results of studies conducted in Germany and Scotland, and the status of an international effort to harvest real-world data derived from the health systems' response across Europe and internationally during the pandemic. The fourth presentation describes burden-eu's activities to promote capacity building to estimate the burden of COVID-19, and discusses the importance, needs and lessons learned of sharing experience and of communicating COVID-19 data. The workshop will end with ample time for discussions on current approaches and opportunities for methodological developments, challenges, possible solutions and potential for collaborations to increase implementation and utility of studies of COVID-19 burden globally.Key messages National burden of disease studies offer a means of monitoring the direct population health impact of COVID-19 infection in countries.International initiatives have emerged to promote harmonization of methods, share knowledge and support international collaboration across countries and public health authorities worldwide.

Well-being adjusted health expectancy - a new summary measure of population health

Well-being adjusted health expectancy - a new summary measure of population health

Burden of Disease of Guillain-Barré Syndrome in Brazil before and during the Zika virus epidemic 2014-2016.

To estimate the burden of disease of Guillain-Barré syndrome (GBS) in Brazil in 2014, 1year before the Zika virus epidemic, and in 2015 and 2016 during the epidemic. The burden of disease of GBS was estimated using the summary measure of population health: Disability Adjusted Life Years (DALY), that combines both mortality (Years of Life Lost YLLs) and morbidity (Years Lived with Disability) components. The study population was composed of GBS hospitalised cases and deaths from the information systems of the Brazilian Unified Health System. The GBS incidence rate in 2014, 2015 and 2016 was 0.74, 0.96, 1.02/100000 respectively, and the mortality rate in the same period was 0.08, 0.009 and 0.11/100000 habitants. The DALYs calculated using the point estimate of GBS disability weight and its values of the confidence interval (0.198 and 0.414) were 5725.90 (5711.79-5742.89) in 2014, 6054.61 (6035.57-6077.54) in 2015 and 7588.49 (7570.20-7610.51) in 2016. The DALYs were high among the male population and in age groups between 20 and 50years. The increase in DALYs in the years 2015 and 2016 compared to 2014 probably resulted from the introduction of ZIKV in Brazil, reinforcing the importance of investments in the prevention of ZIKV infection and in the care of GBS patients.

30.L. Workshop: European burden of disease network: strengthening the collaboration

Abstract The burden of disease (BoD) approach has become one of the foundations of descriptive epidemiology. Central to this approach is the use of the Disability-Adjusted Life Year as a comprehensive and comparable summary measure of population health. Driven by the impact of the Global Burden of Disease (BoD) study, several researchers and health institutes across the world have adopted the BoD approach to assess the health impact of diseases and risk factors, supporting a more rational allocation of available resources. Despite the increasing prominence of the BoD approach, several challenges remain. The BoD methodology is complex and highly data intensive, which has led to major disparities across researchers and nations in their capacity to perform BoD studies, to interpret the soundness of available BoD estimates, or to advocate for the use of BoD methods. Often, these disparities follow geographical boundaries–for instance, over half of all published BoD studies in Europe were set in the Netherlands, Spain and UK, while only 15% were set in eastern European countries. BoD as a generally standardized approach nonetheless requires different methodological choices, and lack of harmonization in these may hamper comparisons across studies. This is further aggravated by the fact that different BoD initiatives have remained scattered–there is for instance little interaction between infectious disease, nutritional and environmental epidemiologists, even though several methodological issues transcend the boundaries of diseases and risk factors. Finally, many BoD researchers are struggling to find optimal ways to translate their findings and communicate them adequately and comprehensively to decision makers and other stakeholders. In response to these needs, several countries and BoD researchers have set up ad hoc partnerships. In 2016, the WHO Regional Office for Europe (WHO-EURO) launched a European BoD network, aiming to intensify links between WHO, IHME and the WHO-EURO member states. In 2019, our group has launched a COST Action that aims to serve as a technical platform to integrate and strengthen capacity in BoD assessment across Europe and beyond. At the moment of writing, the “burden-eu” COST Action joins over 200 participants from 38 European countries, as well as several observers from non-European countries and international organizations. In this workshop, we give an overview of the current status and initial accomplishments of the COST Action, with a focus on the key challenges that the Action aims to address - i.e., increased interaction between existing efforts, methodological advances and technical capacity building at country level, and an actionable understanding of the process underlying knowledge translation. The different sessions will include interactions with the audience to learn about the needs and expectations of the attendees, and how these can be addressed by the COST Action. Key messages The burden of disease approach is increasingly used to generate comparable and comprehensive estimates of the health impact of diseases and risk factors. The 'burden-eu' COST Action offers a technical platform for integrating and strengthening capacity in burden of disease assessment across Europe and beyond.

Health-related quality of life after traumatic brain injury: deriving value sets for the QOLIBRI-OS for Italy, The Netherlands and The United Kingdom

PurposeThe Quality of Life after Brain Injury overall scale (QOLIBRI-OS) measures health-related quality of life (HRQoL) after traumatic brain injury (TBI). The aim of this study was to derive value sets for the QOLIBRI-OS in three European countries, which will allow calculation of utility scores for TBI health states.MethodsA QOLIBRI-OS value set was derived by using discrete choice experiments (DCEs) and visual analogue scales (VAS) in general population samples from the Netherlands, United Kingdom and Italy. A three-stage procedure was used: (1) A selection of health states, covering the entire spectrum of severity, was defined; (2) General population samples performed the health state valuation task using a web-based survey with three VAS questions and an at random selection of sixteen DCEs; (3) DCEs were analysed using a conditional logistic regression and were then anchored on the VAS data. Utility scores for QOLIBRI-OS health states were generated resulting in estimates for all potential health states.ResultsThe questionnaire was completed by 13,623 respondents. The biggest weight increase for all attributes is seen from “slightly” to “not at all satisfied”, resulting in the largest impact on HRQoL. “Not at all satisfied with how brain is working” should receive the greatest weight in utility calculations in all three countries.ConclusionBy transforming the QOLIBRI-OS into utility scores, we enabled the application in economic evaluations and in summary measures of population health, which may be used to inform decision-makers on the best interventions and strategies for TBI patients.

The Korean National Burden of Disease Study: from Evidence to Policy.

Following the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), disability-adjusted life years (DALYs) have been widely used as a summary measure of population health. The DALY metric is a comprehensive measurement of population health, enabling comparison among groups. However, the available data and reliance on estimates to supplement the data gap require contextualization at the national level, and the outcomes of which are more appropriate for national policymakers. The Korean National Burden of Disease (KNBD) study was initiated by the Research and Development Project of the Ministry of Health and Welfare, funded by a 5-year grant from 2013, to contextualize the GBD method to the Korean population. Here, we provide a comprehensive overview of the KNBD study with its achievements and challenges and discuss the implications for public health policies.

Burden of disease, disability-adjusted life years and frailty prevalence.

Burden of disease (BoD) using disability-adjusted life years (DALY) is a useful summary measure of population health and estimates are provided for Ireland annually. We hypothesized that BoD may be used as a predictor of frailty prevalence. To examine the correlation between frailty measured by the accumulation of deficits (frailty index, FI) and Fried frailty phenotype (FFP) classifications and BoD, in an Irish context. Cross-sectional secondary analysis. Data were obtained from waves two and three of The Survey of Health, Ageing and Retirement in Europe for Irish adults aged ≥65 in 2007. Frailty was defined by a 70-item FI and the FFP. Years lived with disability (YLD), years of life lost (YLL) and DALY were calculated using adapted equations from the World Health Organization and, where possible, disability weights, sequelae and durations as in the Global BoD (GBD) project (2016). Of 1035 participants, 442 were ≥65 years. Mean DALY were significantly higher in those identified as frail (FI: 3.31, P < 0.0001, n = 406; FFP: 2.46, P = 0.005, n = 319). For the FI, stronger correlation was found for DALY (r = 0.5431, P < 0.0001) than for age (r = 0.275, P < 0.0001). Controlling for confounders, DALY were an independent predictor of frailty when measured with the FI (OR 1.17, 95% CI: 1.10-1.24) but not with the FFP (OR 1.079, 95%% CI 1.00-1.17). Frailty correlates significantly with DALY, and more so with the FI than the FFP, reaffirming that these measures are different constructs. GBD data could represent a predictor of population-level frailty estimates, facilitating improved comparisons.

Trend analysis of major cancer statistics according to sex and severity levels in Korea.

Existing epidemiologic reports or studies of cancer statistics in Korea lack sufficient data on cancer severity distributions and observed survival rates. This study analyzed trends in major cancer statistics according to sex and severity levels in Korea from 2006 to 2013. We included eight cancers (hepatocellular carcinoma, and thyroid, colorectal, gastric, lung, prostate, breast, and cervical cancer), using Korea Central Cancer Registry data. Severity level was classified by Surveillance, Epidemiology, and End Results (SEER) stage as follows: localized, regional, distant, or unknown. Numbers of incident cancer cases from 2006 to 2013 were described by sex and SEER stage. We estimated up to 8-year observed survival rates of major cancers by sex and SEER stage, and provided prevalence rates by sex and SEER stage in 2011, 2012, and 2013. Although increases in new cancer cases are slowing and the total number of incident cancer cases in 2013 decreased for the first time since 2006, the number of prevalent cancer cases was 663,530 in 2013, an increase of 13.3% compared to 2011. Among the five cancers affecting both sexes, sex-related differences in 5-year observed survival rates for lung cancer were greatest in the localized stage (men, 31.9%; women, 48.1%), regional stage (men, 20.0%; women, 31.3%), and unknown stage (men, 24.3%; women, 37.5%). The sum of the proportions of localized and regional stages for thyroid and breast cancer was over 90% in 2013, while the sum of the proportions of localized and regional stages for lung cancer was only 56.7% in 2013. Differences in observed survival rates between men and women were prominent in lung cancer for all SEER stages. The reported epidemiologic data from this study can be used to obtain a more valid measure of cancer burden using a summary measure of population health.

Life expectancy and poverty

Measuring the number of years that an individual is expected to live with a specific economic status is important for the following reasons: (1) to monitor the achievement of national and international poverty and mortality reduction targets (eg, the Sustainable Development Goals); and (2) to draw attention to the proportion of a population spending considerable periods of life under a defined poverty line. Riumallo-Herl and colleagues1Riumallo-Herl C Canning D Salomon JA Measuring health and economic wellbeing in the Sustainable Development Goals era: development of a poverty-free life expectancy metric and estimates for 90 countries.Lancet Glob Health. 2018; 6: e843-e858Summary Full Text Full Text PDF PubMed Scopus (6) Google Scholar contribute to this important debate by proposing a measure of poverty-free life expectancy (PFLE) that combines information on health and economic status of a population. The proposed PFLE measure is based on Sullivan's method, which assigns the same mortality to those who live in poverty and those who do not. Studies on subpopulations show the existent mortality heterogeneity in populations. For example, a gap greater than 12 years between life expectancy of Inuit indigenous peoples versus non-indigenous peoples has been noted in Canada.2Anderson I Robson B Connolly M et al.Indigenous and tribal peoples' health (The Lancet–Lowitja Institute Global Collaboration): a population study.Lancet. 2016; 388: 131-157Summary Full Text Full Text PDF PubMed Scopus (387) Google Scholar In Denmark, the average life expectancy for men with mental disorders lags behind the rest of the population by 10 years.3Erlangsen A Andersen P K Toender A Laursen TM Nordentoft M Canudas-Romo V Cause-specific life-years lost in people with mental disorders: a nationwide, register-based cohort study.Lancet Psychiatry. 2017; 4: 937-945Summary Full Text Full Text PDF PubMed Scopus (57) Google Scholar In the USA, a 14 year gap in life expectancy has been reported between the richest 1% of the population and poorest 1%.4Chetty R Stepner M Abraham S et al.The association between income and life expectancy in the United States, 2001–2014.JAMA. 2016; 315: 1750-1766Crossref PubMed Scopus (842) Google Scholar Taking into account the strong association between a person's relative position in the income hierarchy (rather than absolute income) and life expectancy,5Pickett KE Wilkinson RG Income inequality and health: a causal review.Soc Sci Med. 2015; 128: 316-326Crossref PubMed Scopus (646) Google Scholar the use of the Sullivan method is a drawback of the proposed PFLE. Any study aiming for a global effect needs a strong statement for a call to improve vital statistics and the quality of data, particularly for countries where information is still deficient.6Lo S Horton R Everyone counts—so count everyone.Lancet. 2015; 386: 1313-1314Summary Full Text Full Text PDF PubMed Scopus (18) Google Scholar This diversity in the quality of data complicates efforts to provide PFLE results for most of the world. The heterogeneity in information, combined with the problems of the method used by PFLE, further complicate the use of the results presented by Riumallo-Herl and colleagues.1Riumallo-Herl C Canning D Salomon JA Measuring health and economic wellbeing in the Sustainable Development Goals era: development of a poverty-free life expectancy metric and estimates for 90 countries.Lancet Glob Health. 2018; 6: e843-e858Summary Full Text Full Text PDF PubMed Scopus (6) Google Scholar Several countries have the data to quantify status transitions between living in poverty and out of poverty, and from each of those to death, which are needed to calculate the years lived in and out of poverty using multistate models.7Lubitz J Cai L Kramarow E Lentzner H Health, life expectancy, and health care spending among the elderly.N Engl J Med. 2003; 349: 1048-1055Crossref PubMed Scopus (326) Google Scholar An appropriate balance of quantity of the PFLE global estimates with the quality of these calculations using proper methods and data is needed to evaluate the validity of the estimates of PFLE. The assessment of the PFLE results is complex and must be used cautiously so it does not mislead policy makers. There are reasons to be sceptical about the authors' policy suggestions from PFLE estimates. The authors mention that “policies that reduce mortality in populations living below the poverty line will not add to overall PFLE in the way that reducing mortality in populations living above the poverty line will”.1Riumallo-Herl C Canning D Salomon JA Measuring health and economic wellbeing in the Sustainable Development Goals era: development of a poverty-free life expectancy metric and estimates for 90 countries.Lancet Glob Health. 2018; 6: e843-e858Summary Full Text Full Text PDF PubMed Scopus (6) Google Scholar It is careless to suggest that alleviating the burden of premature death in the poor will not improve PFLE. Changes in PFLE depend on changes in age-patterns of poverty prevalence and mortality. In the past, increases in life expectancy were driven by decreases in mortality at young age, but today, it is decreases in mortality at old age that increases life expectancy.8Oeppen J Vaupel JW Broken limits to life expectancy.Science. 2002; 296: 1029-1031Crossref PubMed Scopus (1413) Google Scholar Yet the highest levels of poverty are also found at young and old ages, as shown in figure 1 of the Article.1Riumallo-Herl C Canning D Salomon JA Measuring health and economic wellbeing in the Sustainable Development Goals era: development of a poverty-free life expectancy metric and estimates for 90 countries.Lancet Glob Health. 2018; 6: e843-e858Summary Full Text Full Text PDF PubMed Scopus (6) Google Scholar Thus, saving lives of individuals below the poverty line will yield increases in PFLE and benefits for the entire society. The authors further compare the use of healthy life expectancy by policy makers to identify health gaps with the potential use of the proposed PFLE. Both metrics are based on the Sullivan method, and healthy life expectancy corresponds to an overall population measure because the assumption that members of the population transition between healthy and unhealthy states is not unrealistic.9Jagger C Gillies C Moscone F et al.Inequalities in healthy life years in the 25 countries of the European Union in 2005: a cross-national meta-regression analysis.Lancet. 2008; 372: 2124-2131Summary Full Text Full Text PDF PubMed Scopus (211) Google Scholar However, it is difficult to take PFLE as a population measure since transitions in and out of poverty might occur for a subset of the population only and differ greatly between countries. Further discussion is needed about which measures help us move forward and which should be discarded. Riumallo-Herl and colleagues' call for an integrated approach to measure poverty and mortality should be praised. However, as well expressed in earlier research on income distribution and life expectancy: “a paradox inherent in the scientific method is that, attached though we are to the hypotheses we formulate, we must really subject them to assault and search for circumstances that really test their resilience”.10Judge K Income distribution and life expectancy: a critical appraisal.BMJ. 1995; 311: 1282Crossref PubMed Scopus (138) Google Scholar I declare no competing interests. Measuring health and economic wellbeing in the Sustainable Development Goals era: development of a poverty-free life expectancy metric and estimates for 90 countriesDifferences in PFLE between countries are substantially greater than differences in life expectancy. Despite general improvements in survival in most regions of the world in the past decades, the focus in the SDG era on ending poverty brings into sharp relief the importance of ensuring that years of added life are lived with at least a minimum standard of economic wellbeing. Although summary measures of population health provide overall measures of survivorship and functional health, our new measure of PFLE provides complementary information that can inform and benchmark policies seeking to improve both health and economic wellbeing. Full-Text PDF Open Access

Measuring health and economic wellbeing in the Sustainable Development Goals era: development of a poverty-free life expectancy metric and estimates for 90 countries.

The Sustainable Development Goals (SDGs), adopted in September, 2015, emphasise the link between health and economic development policies. Despite this link, and the multitude of targets and indicators in the SDGs and other initiatives, few monitoring tools explicitly incorporate measures of both health and economic status. Here we propose poverty-free life expectancy (PFLE) as a new metric that uses widely available data to provide a composite measure of population health and economic wellbeing. We developed a population-level measure of PFLE and computed this summary measure for 90 countries with available data. Specifically, we used Sullivan's method, as in many health expectancy measures, to incorporate the prevalence of poverty by age and sex from household economic surveys into demographic life tables based on mortality rates from the 2015 Global Burden of Disease Study (GBD). For comparison, we also recalculated all PFLE measures using life tables from WHO and the UN. PFLE estimates for each country, stratified by sex, are the average number of poverty-free years a person could expect to live if exposed to current mortality rates and poverty prevalence in that country. The average PFLE in the 90 countries included in this study was 66·0 years (95% uncertainty interval [UI] 64·5-67·3) for females and 61·6 years (60·1-62·9) for males, whereas life expectancy estimates were 76·3 years (95% UI 74·0-78·2) for females and 71·0 years (68·7-73·0) for males. PFLE varied widely between countries, ranging from 9·9 years (95% UI 9·1-10·5) for both sexes combined in Malawi, to 83·2 years (83·0-83·5) in Iceland, the latter differing only marginally from life expectancy in that country. In 67 of 90 countries, the difference between life expectancy and PFLE was greater for females than for males, indicating that women generally live more years of life in poverty than men do. Results were consistent when using GBD, WHO, or UN life tables. Differences in PFLE between countries are substantially greater than differences in life expectancy. Despite general improvements in survival in most regions of the world in the past decades, the focus in the SDG era on ending poverty brings into sharp relief the importance of ensuring that years of added life are lived with at least a minimum standard of economic wellbeing. Although summary measures of population health provide overall measures of survivorship and functional health, our new measure of PFLE provides complementary information that can inform and benchmark policies seeking to improve both health and economic wellbeing. None.

Dairy cow disability weights

Over the past 175 years, data related to human disease and death have progressed to a summary measure of population health, the Disability-Adjusted Life Year (DALY). As dairies have intensified there has been no equivalent measure of the impact of disease on the productive life and well-being of animals. The development of a disease-adjusted metric requires a consistent set of disability weights that reflect the relative severity of important diseases. The objective of this study was to use an international survey of dairy authorities to derive disability weights for primary disease categories recorded on dairies. National and international dairy health and management authorities were contacted through professional organizations, dairy industry publications and conferences, and industry contacts. Estimates of minimum, most likely, and maximum disability weights were derived for 12 common dairy cow diseases. Survey participants were asked to estimate the impact of each disease on overall health and milk production. Diseases were classified from 1 (minimal adverse effects) to 10 (death). The data was modelled using BetaPERT distributions to demonstrate the variation in these dynamic disease processes, and to identify the most likely aggregated disability weights for each disease classification. A single disability weight was assigned to each disease using the average of the combined medians for the minimum, most likely, and maximum severity scores. A total of 96 respondents provided estimates of disability weights. The final disability weight values resulted in the following order from least to most severe: retained placenta, diarrhea, ketosis, metritis, mastitis, milk fever, lame (hoof only), calving trauma, left displaced abomasum, pneumonia, musculoskeletal injury (leg, hip, back), and right displaced abomasum. The peaks of the probability density functions indicated that for certain disease states such as retained placenta there was a relatively narrow range of expected impact whereas other diseases elicited a wider breadth of impact. This was particularly apparent with respect to calving trauma, lameness and musculoskeletal injury, all of which could be redefined using gradients of severity or accounting for sequelae. These disability weight distributions serve as an initial step in the development of the disease-adjusted lactation (DALact) metric. They will be used to assess the time lost due to dynamic phases of dairy cow diseases and injuries. Prioritizing health interventions based on time expands the discussion of animal health to view profits and losses in light of the quality and length of life.

장애가중치 산출 연구에서의 쟁점 검토

Summary measures of population health (SMPHs) have been used to estimate the burden of diseases. Among various type of SMPHs, disability adjusted life year (DALY) and healthy life expectancy (HALE) have been calculated in the global and national burden of disease studies. In order to calculate DALY and HALE, disability weight is an essential element. Disability weights quantify the level of disability for health states or diseases and have values between 0 (full health) to 1 (being dead). In this study, we reviewed the main disability weights studies and determined their meaning and limitations. Furthermore, we provided the whole process of typical disability weight study and reviewed key issues as follows: health state or disease description development, panel composition, valuation method, validation of disability weight, cross-cultural variability in health state or disease, and so on. The results from this study will be helpful to conduct future disability weight studies for adapting disability weights and developing new methodologies.

Healthy life expectancy of oral squamous cell carcinoma patients aged 75 years and older

ObjectivesHealthy life expectancy, an extension of the concept of life expectancy, is a summary measure of population health that takes into account the mortality and morbidity of a population. The aim of the present study was to retrospectively analyze the self-reliance survival times of oral squamous cell carcinoma (OSCC) patients. Materials and methodsOne hundred and twelve patients aged 75years or older with primary OSCC were included and examined at Shinshu University Hospital. To investigate healthy life expectancy, OSCC patients older than 75years were divided into 3 groups: 75–79, 80–84, and older than 85years. The Kaplan-Meier method was used to estimate the median times of healthy life expectancy. The Log-rank test was used to test significant differences between actual curves. ResultsThe median self-reliance survival times of patients aged 75–79, 80–84, and older than 85years were 5.7, 1.6, and 1.4years, respectively. Most patients with early stage cancers underwent curative treatments and showed a health expectancy of more than 5years. In patients with advanced cancers, health expectancy was poor (less than one year), except among patients aged 75–79years who underwent standard treatments. ConclusionsIt seems that in patients with advanced cancers, health expectancy was poor (less than 1year), except among patients aged 75–79years who underwent standard treatments. In elderly patients, healthy life expectancy (self-reliance survival time) may be one of the measures of patient prognosis as well as overall survival times.