Showing papers by "Christopher J L Murray published in 2002"

Global and regional estimates of cancer mortality and incidence by site: II. results for the global burden of disease 2000

Abstract: Mortality estimates alone are not sufficient to understand the true magnitude of cancer burden. We present the detailed estimates of mortality and incidence by site as the basis for the future estimation of cancer burden for the Global Burden of Disease 2000 study. Age- and sex- specific mortality envelope for all malignancies by region was derived from the analysis of country life-tables and cause of death. We estimated the site-specific cancer mortality distributions from vital records and cancer survival model. The regional cancer mortality by site is estimated by disaggregating the regional cancer mortality envelope based on the mortality distribution. Estimated incidence-to-mortality rate ratios were used to back calculate the final cancer incidence estimates by site. In 2000, cancer accounted for over 7 million deaths (13% of total mortality) and there were more than 10 million new cancer cases world wide in 2000. More than 60% of cancer deaths and approximately half of new cases occurred in developing regions. Lung cancer was the most common cancers in the world, followed by cancers of stomach, liver, colon and rectum, and breast. There was a significant variations in the distribution of site-specific cancer mortality and incidence by region. Despite a regional variation, the most common cancers are potentially preventable. Cancer burden estimation by taking into account both mortality and morbidity is an essential step to set research priorities and policy formulation. Also it can used for setting priorities when combined with data on costs of interventions against cancers.

Armed conflict as a public health problem

Abstract: Armed conflict is a major cause of injury and death worldwide, but we need much better methods of quantification before we can accurately assess its effect Armed conflict between warring states and groups within states have been major causes of ill health and mortality for most of human history. Conflict obviously causes deaths and injuries on the battlefield, but also health consequences from the displacement of populations, the breakdown of health and social services, and the heightened risk of disease transmission. Despite the size of the health consequences, military conflict has not received the same attention from public health research and policy as many other causes of illness and death. In contrast, political scientists have long studied the causes of war but have primarily been interested in the decision of elite groups to go to war, not in human death and misery. We review the limited knowledge on the health consequences of conflict, suggest ways to improve measurement, and discuss the potential for risk assessment and for preventing and ameliorating the consequences of conflict. #### Summary points Conflict related death and injury are major contributors to the global burden of disease Information systems break down during conflict, leading to great uncertainty in the magnitude of mortality and disability The World Health Survey may provide a reliable and valid basis for assessing conflict related mortality and disability Forecasting models may provide a plausible basis for assessing risk of conflict and thus prevention Improved collaboration between political scientists and experts in public health would benefit measurement, prediction, and prevention of conflict related death The impact of war on populations arises both from the direct effects of combat—namely, battle deaths—and from the indirect consequences of war, which may occur for several years after a conflict ends.1 Indirect effects of conflict on mortality can be formally …

Global and regional estimates of cancer mortality and incidence by site: I. Application of regional cancer survival model to estimate cancer mortality distribution by site

Abstract: The Global Burden of Disease 2000 (GBD 2000) study starts from an analysis of the overall mortality envelope in order to ensure that the cause-specific estimates add to the total all cause mortality by age and sex. For regions where information on the distribution of cancer deaths is not available, a site-specific survival model was developed to estimate the distribution of cancer deaths by site. An age-period-cohort model of cancer survival was developed based on data from the Surveillance, Epidemiology, and End Results (SEER). The model was further adjusted for the level of economic development in each region. Combined with the available incidence data, cancer death distributions were estimated and the model estimates were validated against vital registration data from regions other than the United States. Comparison with cancer mortality distribution from vital registration confirmed the validity of this approach. The model also yielded the cancer mortality distribution which is consistent with the estimates based on regional cancer registries. There was a significant variation in relative interval survival across regions, in particular for cancers of bladder, breast, melanoma of the skin, prostate and haematological malignancies. Moderate variations were observed among cancers of colon, rectum, and uterus. Cancers with very poor prognosis such as liver, lung, and pancreas cancers showed very small variations across the regions. The survival model presented here offers a new approach to the calculation of the distribution of deaths for areas where mortality data are either scarce or unavailable.

The Epidemiologic Transition Revisited: Compositional Models for Causes of Death by Age and Sex

Abstract: For decades, researchers have noted systematic shifts in cause-of-death patterns as mortality levels change. The notion of the “epidemiologic transition” has influenced thinking about the evolution of health in different societies and the response of the health system to these changes. This article re-examines the epidemiologic transition in terms of empirical regularities in the cause composition of mortality by age and sex since 1950, and considers whether the theory of epidemiologic transition presents a durable framework for understanding more recent patterns. Age-sex-specific mortality rates from three broad cause groups are analyzed: Group 1 (communicable diseases, maternal and perinatal causes, and nutritional deficiencies); Group 2 (noncommunicable diseases); and Group 3 (injuries), using the most extensive international database on mortality by cause, including 1,576 country-years of observation, and new statistical models for compositional data. The analyses relate changes in cause-of-death patterns to changing levels of all-cause mortality and income per capita. The results confirm that declines in overall mortality are accompanied by systematic changes in the composition of causes in many age groups. These changes are most pronounced among children, for whom Group 1 causes decline as overall mortality falls, and in younger adults, where strikingly different patterns are found for men (shift from Group 3 to Group 2) compared to women (shift toward Group 2 then Group 3). The underlying patterns that emerge from this analysis offer insights into the epidemiologic transition from high-mortality to low-mortality settings.

Comparative analyses of more than 50 household surveys on health status

Abstract: The objective of this study is to determine whether existing data sources from household interview based surveys conducted in a large number of countries may be used towards estimating the distribution and levels of severity of non-fatal health at the population level. Operationally, this objective addresses two main questions: 1. Is there information content on the severity and distribution of non-fatal health status in data collected through household interview surveys? 2. May this information be compared in a valid and reliable manner across countries? The paper first reviews current approaches to measure health status within interview based surveys and the limitations of these approaches concerning the cross-population comparability of data collected, not only the comparability of questions. Issues demanding further attention concerning the cross-population comparability include inconsistent reporting and differences in end-points and cut-points on reference scales. These limitations prevent the meaningful comparison of survey data within and across populations. The paper then describes and tests a methodology to extract information on non-fatal health status. This approach is specific to self-reported data from different surveys conducted in different populations, as a first attempt to improve comparability of data given that no external means to calibrate responses are available. Sixty four data sets from an array of surveys in 46 countries are analyzed using factor analysis, based on the hypothesis that one underlying latent construct, non-fatal health, is similar across surveys and populations. A factor score of the level of non-fatal health is estimated for each individual and all scores are re-scaled within each population to improve comparability of end-points. Results are provided by age groups and sex, for data from each of the 46 countries included in this study. Based on an examination of the results within countries, the evidence concerning the information content of surveys is mixed. Despite efforts to improve the comparability of existing household interview data on non-fatal health, the paper concludes that the valid comparison of existing data from household interview surveys across countries is limited. Even where the survey methodologies and data collection approaches are standardized, biases in the self-report of health status prevent a meaningful comparison of non-fatal health status across populations. These results are disappointing given that a growing number of countries carry out household interview surveys addressing health topics, coupled with the WHO mandate to improve the use and comparability of existing data on non-fatal health. Yet WHO realizes that household interview …

Statistical Models for Enhancing Cross-Population Comparability

Abstract: Measuring the health state of individuals is important for the evaluation of health interventions, monitoring individual health progress, and as a critical step in measuring the health of populations. Self-report responses in household survey data are widely used for assessing the non-fatal health status of populations. These data typically take the form of ordered categorical (ordinal) responses. Over the past three decades, there has been great progress in developing instruments to measure the multiple domains of health that are reliable and demonstrate within population validity [31],[22].

Describing population health in six domains: comparable results from 66 household surveys

Abstract: One of the World Health Organization's longest standing mandates is the collection and routine reporting of information on population health. In addition to estimates of mortality and disease, assessment of health status from population based surveys contribute to estimates of population health. The first section of the paper briefly introduces the conceptual and operational basis to measure health, where health is measured through six domains (affect, cognition, pain, mobility, self-care and usual activities). The second section briefly notes that the main objective of this paper is to report on the average levels of health by age and sex groups for each domain of health across 66 population based surveys. The third section of this paper describes how we have applied the hierarchical ordered probit (HOPIT) model using vignettes to calibrate responses across survey populations, to self-reported levels of health on six domains. The data comes from the WHO Health Survey Study 2000-2001, from 66 population based surveys in 57 countries, representative of individuals 18 years and older. The fourth section provides results on comparable levels of health for each domain across populations, by age groups and sex. In order to further facilitate comparisons across countries, age-standardized aggregated results across all age groups, by sex, are also presented and compared to external data, such as GDP per capita (PPP) and life expectancy. The fifth section discusses the information content of the surveys, the added-value of the multi-dimensional approach and the comparability of responses across countries. The final section recommends additional analyses to be conducted. Comments on this discussion paper are most welcome and should be forwarded to: Dr. Ritu Sadana Evidence and Information for Policy World Health Organization Avenue Appia 20 CH-1211 Geneva 27 Switzerland Email: sadanar@who.int

Causal decomposition of summary measures of population health.

Abstract: The part 6 deals with the causal decomposition of summary measures of population health. It provides the description of the construction of the summary measures for different health problems so that the planners could prioritize the resources. The causal attribution of health expectancies due to cause-specific mortality and related morbidity conditions has been described clearly. The authors have stressed that there is a need to identify principal cause of morbidity to incorporate in health information system. The second chapter has covered the epidemiological measures of causality theory which is the basic objective of the subject epidemiology. Different models given by the statisticians and epidemiologists have been well presented. The theory of cause effect has been related with actions and interventions. This is one of the most interesting sections of the book.

Global Patterns of Healthy Life Expectancy for Older Women

Abstract: This paper focuses on patterns of healthy life expectancy for older women around the globe in the year 2000, and on the determinants of differences in disease and injury for older ages. Our study uses data from the World Health Organization for women and men in 191 countries. These data include a summary measure of population health, healthy life expectancy (HALE), which measures the number of years of life expected to be lived in good health, and a complementary measure of the loss of health (disability-adjusted life years or DALYs) due to a comprehensive set of disease and injury causes. We examine two topics in detail: (1) cross-national patterns of female-male differences in healthy life expectancy at age 60; and (2) identification of the major injury and disability causes of disability in women at older ages. Globally, the male-female gap is lower for HALE than for total life expectancy. The sex gap is highest for Russia (10.0 years) and lowest in North Africa and the Middle East, where males and females have similar levels of healthy life expectancy, and in some cases, females have lower levels of healthy life expectancy. We discuss the implications of the findings for international health policy.

Summary measures of population health: conclusions and recommendations.

Abstract: We hope that this book will provide a major contribution to that debate by assembling the views and arguments of health policy-makers and experts from a wide range of disciplines including epidemiology demography health statistics health economics philosophy and ethics. In this concluding chapter we summarize the important conceptual empirical and ethical issues identified and debated by contributors and draw some conclusions and recommendations for the future evolution of summary measures of population health. (excerpt)

Health gaps: an overview and critical appraisal.

Abstract: Chapter 1.2 defined two major families of summary measures of population health (SMPH): health expectancies and health gaps. In this chapter, we review the concept of health gaps, present an overview of the key conceptual and methodological issues around the construction of health gaps, and compare different types of health gaps against the desirable criteria for summary measures of population health proposed by Murray et al. in chapter 1.2.

The individual basis for summary measures of population health.

Abstract: In chapter 1.2 we reviewed some of the uses and conceptual debates relating to summary measures of population health (SMPH) and presented minimal criteria for evaluating SMPH. Much of the literature on SMPH has grown out of the demographic and epidemiological traditions which take a population perspective as their starting point. For some uses such as measuring inequalities in health across individuals or measuring the health of individuals in clinical settings or intervention trials it is important to formulate SMPH in terms of the health of some set of individuals. Many of the challenges identified in chapter 1.2 are intimately related to the linkage between population and individual health measures. Distinctions between incidence and prevalence perspectives or period and cohort perspectives for example can be recast in terms of different choices as to the set of individuals (real or hypothetical) whose health is aggregated into a population measure. Recent efforts have been made to develop formal expressions of population health as aggregations of individual health measures (Cutler and Richardson 1997; 1998; Fleurbaey forthcoming). In this paper we attempt to set out a systematic framework for characterizing the individual basis for summary measures of population health. To facilitate later debates in this volume this paper addresses the question “When is one person healthier than another?” Five different answers to this question are formalized in terms of individual-level analogues to population-level health expectancies and health gaps. Precise formalization of these concepts often reveals important issues that will need to be addressed and reflected upon in future work. We end the chapter with some thoughts on the implications of this work for the development of alternative SMPH. (excerpt)

Epidemiological evidence: improving validity through consistency analysis

Abstract: Information about the incidence and preva-lenceofdiseasesandinjuriesatthepopulationlevelisfrequentlyrequiredforbenchmarking,for advocacy of particular policies, to assistin setting funding priorities, for monitoringachievements towards internationallyaccepted goals and targets, and to guidetechnical strategies and responses. In thisissue of the Bulletin (pp. 622–628), Kruijshaaret al. examine the use of incidence–preva-lence–mortality (IPM) models to improveestimates of disease epidemiology.The Global Burden of Disease (GBD)study (1, 2) developed explicit methods,including the DisMod software, to ensureinternal consistency of epidemiologicaland mortality estimates for specific causes.WHO is now undertaking a new assessmentof the GBD for the year 2000 (GBD 2000)and subsequent years (3). Explicit aims aretoprovidevalid,internallyconsistentestimatesof the incidence, prevalence, duration, andmortality for 135 disease and injury causesfor major geographical regions, and toanalyse the attributable burden of majorphysiological, behavioural, and social riskfactors. The use of IPM models is crucialfor achieving these objectives, and Murray& Barendregt have developed an improvedsoftware tool, DisMod II, for use in GBD2000. This is available at no cost from WHOfor use in other analyses (4).A disease process can be described bya number of variables, such as incidence,prevalence, remission, case fatality, duration,and mortality. In principle, these can all bemeasured in populations, but with differentdegrees of difficulty. Mortality, for example,can be relatively easily measured usingnational vital registration systems, but theunderlying cause of death can bemisreportedormisclassified.Nevertheless,manycountrieshave cause-of-death statistics that usuallyconstitute the most reliable and comparablesource of disease data at population level.Measuring incidence and prevalence ofdiseases, injuries, or impairments is usuallymuchmoredifficultthanmeasuringmortality.Data collection, when done, is often limitedin time and geographical area; and problemsof case definition abound. Not surprisingly,data are frequently incomplete, and theirvalidity may be in doubt. In particular, giventhe different nature of the disease variablesand the differences in the way the data arecollected, it is inevitable that the observationsare internally inconsistent. For example,when more incident cases than mortality aremissed, the observed incidence will be toosmall for the observed mortality.Both the GBD and national burdenof disease studies have identified inconsis-tencies between incidence, prevalence, andmortality data for specific diseases of publichealthimportance.Kruijshaaretal.givesomeexamples in their paper. Using IPM analysis,the Australian Burden of Disease and InjuryStudy found that incidence estimates froma recent meta-analysis of incidence ofdementia (5) were inconsistent with previousprevalence meta-analyses (6, 7) unlessimplausibly high case-fatality rates wereassumed (8). Such inconsistencies inepidemiological evidence are common, andto maximize the validity and usefulness ofsuchevidenceforhealthpolicy,itisimportantto assess internal consistency.Kruijshaar et al. conclude that use ofIPM models does not permit determinationof whether inconsistencies in empirical dataare due to data inaccuracies or to past trendsin incidence or mortality. To enable users toaddress this issue, both DisMod and DisModII include an option to specify past trendsin input parameters (which may be assessedfrom empirical evidence or expert opinion)in order to allow analysis of their effect oninternal consistency for the time periodof interest.When IPM models are used to analyseempirical evidence, it is crucial to includeexcess mortality from other causes as well asdirect mortality due to the cause of interest.Formanydiseases,andforinjuries,theremaybe excess mortality due to a wide range ofcausesassociatedwithcommonriskfactorsorwith disease or injury sequelae or treatment.Thus, for example, for many cancers thereis likely to be excess mortality from othercauses such as cardiovascular disease,diabetes, and chronic respiratory disease,associated with common dietary risk factorsand other behavioural, environmental, andgenetic risk factors. Failure to include allexcessmortalityriskintheIPMmodel,aswasthecasewiththeanalysesforthefourcancersreported by Kruijshaar et al., will result inincorrect assessment of consistency betweenincidence and prevalence observations.Health planning often proceeds on thebasis of incomplete or biased epidemiologicalevidence that is not comparable acrosspopulation groups. We argue that in all cases,health policy should be informed by validand internally consistent epidemiologicalestimates.Theremaywellbewideuncertaintyaround some estimates due to the lack ofreliable information, but the uncertaintyshould be quantified and relayed to decision-makers to aid their planning. In this respect,IPM models provide an important toolto assist in the development of evidence forhealth policy.

of Healthy Life Expectancy for Older Women

Abstract: SUMMARY. This paper focuses on patterns of healthy life expectancy for older women around the globe in the year 2000, and on the determinants of differences in disease and injury for older ages. Our study uses data from the World Health Organization for women and men in 191 countries. These data include a summary measure of population health, healthy life expectancy (HALE), which measures the number of years of life expected to be lived in good health, and a complementary measure of the loss of health (disability-adjusted life years or DALYs) due to a comprehensive set of