NEW PROJECTIONS OF MORTALITY AND CAUSES OF DEATH TO YEAR 2060

Back in 2006, I published a paper in PLoS Medicine with detailed projections of deaths by age, sex and cause for all regions of the world, from year 2002 to 2030 (https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.0030442).  That paper has proved very popular, with over 9000 citations to date.  I’ve updated these projections to most recent WHO baseline estimates several times, and following the release of the latest update of causes of death for year 2016 by WHO earlier this year (http://www.who.int/healthinfo/global_burden_disease/estimates/en/), I have done another update, extending the projections for the first time beyond 2030 to 2060.

This has now been released by WHO on its website at http://www.who.int/healthinfo/global_burden_disease/projections/en/ where regional and global projections can be downloaded in spreadsheet form, along with a methods note. Apart from synchronising the new projections with the 2016 cause of death estimates, the cause-specific trends in the near term are synchronized with estimated recent trends over the last 10 to 15 years. In the longer term, broad trends are largely driven by projection equations which model the epidemiological transition from infectious to non-communicable diseases in terms of projections of average income per capita, average years of education, time, and for some causes also projections of smoking impact.

In the original projections, separate projection models were developed for HIV/AIDS, tuberculosis, lung cancer, diabetes mellitus and chronic respiratory diseases.  These models were revised and updated for this latest update. Additional special projection models were also been developed for malaria, maternal deaths, road injury, homicide, natural disasters and war and conflict.

At the global level, age-standardized death rates for most important causes are falling with time, faster in most cases for infectious, maternal and perinatal causes than for non-communicable diseases (see figures below). The main exceptions are for diabetes, breast cancer and road injuries. The specific projection model for diabetes is based on projections of the prevalence of overweight and obesity and that for road injury is based on projections of vehicles per capita with continued economic development.

IHD = Ischaemic heart disease, COPD = Chronic Obstructive Pulmonary Disease,
ARI = Acute respiratory infection (mainly pneumonia), TB = tuberculosis

However, for many of these causes, the total projected deaths are rising with time because of population growth and ageing. Only the relatively fast declining infectious, maternal and perinatal causes are likely to also have declining total numbers of deaths (see the following two figures).

The projections of deaths by cause are not intended as forecasts of what will happen in the future but as projections of current and past trends, based on certain explicit assumptions. The methods used base the disease burden projections largely on broad mortality projections driven by projections of future growth in income and increases in human capital in different regions of the world, together with a model relating these to cause-specific mortality trends based on the historical observations in countries with death registration data over the last 60 years. The results depend strongly on the assumption that future mortality trends in poor countries will have a similar relationship to economic and social development as has occurred in the higher income countries. If governments give increased priority and resources to achievement of the Sustainable Development Goals by the year 2030 and progress towards Universal Health Coverage with the most relevant cost-effective interventions, it is entirely possible and certainly to be hoped that future declines in mortality for many causes will be faster than these business-as-usual projections.  On the other hand, if economic growth in low income countries is lower than the forecasts used here, and global warming results in additional adverse impacts on economic and social development, then the world may achieve slower progress and widening of health inequalities.

Despite these uncertainties, projections provide a useful perspective on sustainable health trends and health policies, provided that they are interpreted with a degree of caution. Projections enable us to appreciate better the implications for health and health policy of currently observed trends, and the likely impact of fairly certain future trends, such as the ageing of the population, and the continuation of the epidemiological transition in developing countries.

Projected global deaths in 2030 and 2060 under the business-as-usual scenario  are 68.2 million and 101.8 million respectively. Projected global deaths in 2030 under the UN medium variant projections of the World Population Prospects 2017 (WPP2017) are 2% higher in 2030 at 69.5 million and 0.7% lower at 101.1 million in 2060. These global projections for all-cause mortality are remarkably close to the UN projections given that these are the sum of independent projections for 20 separate cause groups, whereas the UN projections are based on estimated trends in all-cause mortality and fertility. The results are also very similar for all regions except the South East Asian Region and the African Region.  For the South East Asian Region, the UN projections are higher, with 3.5% more deaths in 2030 rising to 12% more deaths in 2060. For the African Region, the UN projections are slightly higher in 2030 but by 2060 are 12% lower.

The following figure compares projected life expectancies at birth for the world and for countries grouped by income. The projections are very similar for the world and for middle- and high-income countries. The main difference is for low income countries (predominantly African) where the projected life expectancy is 1 year lower than the UN projected life expectancy in 2030 rising to 3.9 years difference in 2060.

These updated projections have drawn heavily on WHO official statistics, and data sets and analyses carried out by my former colleagues in WHO, as well as collaborators in other UN agencies and universities. The development of some of the modelling approaches has benefited from discussions with Majid Ezzati, Vasilis Kontis, Margie Peden, Gretchen Stevens and Dan Hogan.

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Are countries on track to meet the global targets for noncommunicable diseases?

More than half of all countries are predicted to fail to reach the UN target to reduce premature deaths from cancers, cardiovascular disease, chronic respiratory disease, and diabetes by 2030, according to a new analysis published by the NCD Countdown 2030 collaborators in The Lancet ahead of the third UN High-Level Meeting on NCDs commencing on 27 September 2018. WHO is a member of the Countdown 2030 and the paper makes use of the most recent WHO estimates for causes of death released by my Unit earlier this year.

Cancers, cardiovascular diseases, chronic respiratory diseases, and diabetes were responsible for 12.5 million deaths among people aged 30-70 years worldwide in 2016. The following figure from the paper shows the of dying between ages 30 and 70 from these four non-communicable disease groups (referred to below as NCD4) for men and women.

The paper warns that more than half of all countries are not on track to achieve a UN target for year 2030 to reduce by a third the probability of dying between ages 30 and 70 from NCD4. The maps below show the years at which countries will achieve the UN Target if current trends continue.

One in 10 countries have seen death rates stagnate or worsen – for instance in the USA, NCD rates for women have stagnated, with almost one in eight 30 year old women dying from one of the four NCDS before their 70th birthday, compared with 1 in 20 women in the best performing country (South Korea).

Back around 2013-2014, when indicators and targets for 2030 were under discussion within UN agencies, a WHO expert group proposed that the NCD mortality indicator be chosen as the probability of dying between exact ages 30 and 70 if the cohort was subject only to the current age-sex-specific mortality rates for the NCD4 causes observed in each population.  This is a life table concept and is calculated using a life table where the mortality rates are those for NCD4 only.  Demographers refer to this indicator as an “unconditional” probability of dying from the specified causes.  If other causes, “competing” causes are also included in the mortality risk, then the probabilities become dependent on other causes as well as NCD4 and hence are “conditional”.

To see this, consider a population in which 50% of the men die between ages 30 and 40 from HIV. If competing causes are not excluded from the calculation, the risk of dying between 30 and 70 from NCD4 will be approximately half that for an identical population where there is no HIV mortality.

This indicator was chosen because it provides numbers that are immediately understandable by policy makers and the general public. It is quite easy to understand what it means to say that 10% or 30% of 30 year olds can expect to die from NCD4 by age 70, if nothing else kills them. In contrast, the numbers associated with the more tradional age-specific or age-standardized death rate indicators are not immediately meaningful to policy makers or the general public.  Is a death rate of 250 per 100,000 population aged 30-69 a desirable or undesirable level?

However, in WHO consultations with Member States and more generally, it turned out that this indicator has been an endless source of confusion for many.  It seems that life table concepts are not familiar to many epidemiologists and public health professionals, and the issues around “competing causes” has also been a source of much confusion. We discovered that the term “unconditional” is used in several different senses, and is generally not interpreted the way demographers use it.  Also, a number of epidemiologists have argued that exclusion of other causes is resulting in bias, rather than being necessary to produce an indicator which is influenced only by NCD mortality level s. So WHO has ceased to use the term “unconditional” and learned to live with the NCD target being described in terms of “death rates”, “premature death rates”, or even numbers of deaths. The indicator being used however is clearly defined as a life table probability in technical descriptions of the indicator.

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Noncommunicable disease mortality in tropical countries

The recent WHO update of causes of death for 186 countries in 2016 (www.who.int/evidence/bod) has been used as the basis for an assessment of the importance of non-communicable diseases (NCDs) in tropical countries. The paper “Acting on non-communicable diseases in low- and middle-income tropical countries” was published last week in Nature journal (Ezzati, Pearson-Stuttard, Bennett & Mathers, Nature https://doi.org/10.1038/s41586-018-0306-9). The paper showed that most NCDs cause more deaths at every age in low- and middle-income tropical countries than in high-income Western countries.

The following graph from the paper compares NCD mortality in low- and middle-income tropical countries with that in high-income Western countries. The map (a) shows the share of deaths from NCDs, and map (b) shows the age-standardized death rates from NCDs.  The latter provides a standard measure of the risk of death from NCDs, which removes the effect of different population age structures.  It is clear that NCD mortality risks are higher in most tropical low- and middle-income countries than high income countries.  In contrast, the NCD share of deaths is higher in high income countries, because infectious disease death rates are much lower.

The paper goes on to examine the causes of NCDs in low- and middle-income countries, which  include poor nutrition and living environment, infections, insufficient taxation and regulation of tobacco and alcohol, and under-resourced and inaccessible healthcare. The paper also identifies a comprehensive set of actions across health, social, economic and environmental sectors that could confront NCDs in low- and middle-income tropical countries and reduce global health inequalities.

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Latest information on the state of the world’s health released by WHO

In mid-May, WHO released World Health Statistics 2018: Monitoring health for the SDGs  (WHS2018). This WHO flagship publication compiles data from the organization’s 194 Member States on 36 health-related Sustainable Development Goals (SDG) indicators, providing a snapshot of both gains and threats to the health of the world’s people. While the quality of health data has improved significantly in recent years, many countries still do not routinely collect high-quality data to monitor more than 50 health-related SDG indicators. Nine of the SDG health indicators reported in the WHS2018 are mortality indicators drawn from the latest update of the WHO Global Health Estimates released in April this year.

This update of estimates of death by cause, age and sex for years 2000 to 2016 for 187 countries and for 236 causes and cause groups is available at www.who.int/evidence/bod.  Finalizing this update was a major focus of my work during my last months at WHO before retirement, and indeed, I continued to do some work after retirement to finalize numbers and documentation. So the 2018 World Health Statistics will be the last to which I have made substantial contributions. I have to recognize the huge efforts by my former team to produce and publish this report, in particular Annet Mahanani who was the overall project manager and editor, and Gretchen Stevens who played an important role both in the update of the Global Health Estimates and in the preparation of material and text for the WHS2018.

Since 2016 the World Health Statistics series has served as WHO’s annual report on the health-related Sustainable Development Goals (SDGs) and the 2018 report includes a section summarizing the current status of the health SDGs.

Making sense of the often complex available data on health indicators can be highly challenging. Health data derived from health information systems, including health-facility records, surveys or vital statistics, may not be representative of the entire population of a country and in some cases may not even be accurate. Comparisons between populations or over time can also be complicated by differences in data definitions and/or measurement methods. Although some countries may have multiple sources of data for the same year, it is more usual for data not to be available for every population or year. For example, measurement frequency for data collected through household surveys is typically every 3–5 years. This means that the years for which data are available differ by country. To overcome these and other issues and allow for comparisons to be made across countries and over time, WHO and collaborators have developed mathematical and statistical models with the aim of producing unbiased estimates that are representative and comparable.

As well as reporting on SDG health indicators, the WHS2018 includes several stories focusing on particular topics. One of these is the coverage of essential health services (which was discussed in an earlier blog entry on Universal Health Coverage) and a story on the rising tide of obesity in the young.

The world has seen a more than ten-fold increase in the number of obese children and adolescents aged 5¬19 years in the past four decades ¬ from just 11 million in 1975 to 124 million in 2016. An additional 213 million were overweight in 2016 but fell below the threshold for obesity. Taken together this means that in 2016 almost 340 million children and adolescents aged 5-19 years – or almost one in every five (18.4%) – were overweight or obese globally.

Analysis of these trends has shown that although population growth has played a role in the increase in numbers of obese children and adolescents, the primary driver has been an increase in the prevalence of obesity. Globally, the prevalence of obesity among children and adolescents aged 5-19 years increased from 0.8% in 1975 to 6.8% in 2016. Although high-income countries continue to have the highest prevalence, the rate at which obesity among children and adolescents aged 5-19 years is increasing is much faster in LMIC (see the following figure).

One of the six core functions of WHO is monitoring of the health situation, trends and determinants in the world. Over the years that I have worked for WHO (2000-2018) it has cooperated closely with other UN partner agencies like UNICEF, UNAIDS, UNFPA and the UN Population Division to collect and compile global health statistics. There are a number of established UN multi-agency expert group mechanisms for  cross cutting topics such as child mortality (the UN-IGME including UNICEF/WHO/UN Population Division/World Bank), and specific diseases such as HIV/AIDS (UNAIDS Reference Group), maternal mortality (MMEIG including WHO/UNICEF/UNFPA/World Bank), tuberculosis (WHO STAG), malaria (Malaria Reference Group and Roll Back Malaria- Malaria Monitoring and Evaluation Reference Group). Additionally, WHO collaborates with a network of academics (MCEE) to estimate child causes of death. This collaboration succeeds the former Child Health Epidemiology Reference Group (CHERG) of WHO and UNICEF.

Something that has been immensely satisfying to me over the last two decades is the efforts from all the UN agencies involved in health statistics, as well as the World Bank, and some academic collaborators, to work towards consistent and coherent health statistics used by all the UN agencies and based in a common demographic statistical framework of births, population numbers and deaths prepared by the UN Population Division in their biennial World Population Prospects datasets. When I started working for WHO in 2000, the UN Population Division, the World Bank, UNICEF and WHO all independently produced statistics for child mortality which were not entirely consistent. This was just one example of the lack of coherence across agencies, which has been drastically reduced during the time I have been involved with UN statistics.

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Disease Control Priorities, Edition 3

The third edition of Disease Control Priorities was launched by the WHO Director General, Dr Tedros Adhanom, in London earlier this month. These nine volumes provide up-to-date evidence on priorities that countries should consider in order to reach Universal Health Coverage. The 9th and last volume includes a chapter from my team that summarizes global and regional patterns of causes of death for 2015 and trends for 2000–15 (chapter-deaths-cause-2000-and-2015). Further information available on the DCP3 website at http://dcp-3.org/

Launch of the DCP3 nine volumes in London on 6th December. At this end of the front row is Dr Ala Alwan, former WHO Regional Director for the Eastern Mediterranean Region and next to him is the new Director General of WHO, Dr Tedros Adhanom.

The chapter colleagues and I contributed to Volume 9 summarizes global and regional patterns of causes of death for 2015 and trends for 2000–15 using the 2015 Global Health Estimates (GHE 2015) released by WHO in early 2017. This period marks the end point for the Millennium Development Goals (MDG) and the starting point for the Sustainable Development Goals (SDGs) for the year 2030. This chapter documents major changes during the MDG era. Progress toward the MDGs, on the whole, has been remarkable, including, for instance, poverty reduction, improved education, and increased access to safe drinking water. Progress on the three health goals and targets has also been considerable. Globally, the HIV/AIDS, tuberculosis, and malaria epidemics have been “turned around,” and child mortality and maternal mortality have decreased greatly (53 percent and 44 percent, respectively, since 1990), despite falling short of the MDG targets. Large reductions in mortality have occurred in Sub-Saharan Africa since the early 2000s, coinciding with increased coverage of HIV/AIDS treatment, methods of malaria control, and scale-up of vaccination coverage. Despite this progress, major challenges remain in achieving further progress on child and maternal mortality and on infectious diseases such as HIV/AIDS, tuberculosis, malaria, neglected tropical diseases, and hepatitis.

The rate of increase in life expectancy in LICs over the past 15 years has exceeded the rate of growth observed for life expectancy in the countries with the highest life expectancies. Longer life expectancies and population aging have resulted in an increased focus on NCDs and their risk factors in LMICs and in HICs. Three-quarters of NCD-related deaths occurred in LMICs in 2015.

The SDGs expand the focus of health targets from the unfinished Millennium Development Goals (MDG) agenda for child and maternal mortality and priority infectious diseases to a broader agenda including noncommunicable diseases (NCDs), injuries, health emergencies, and health risk factors as well as a strong focus on universal health care. The GHE 2015 estimates of trends and levels of mortality by cause will contribute to WHO and UN monitoring and reporting of progress toward the SDG health goals and targets.

Overall, the nine volumes identify 21 essential packages of health interventions across five delivery platforms (population based, community level, health centre, first-level hospital, and referral hospital). DCP3 defines essential universal health coverage in terms of 218 cost-effective interventions that provides a starting point for country-specific analysis of priorities. In the Foreword to Volume 9, Bill and Melinda Gates describe DCP3 as innovately addressing the different needs of countries at different stages in the development of their health systems. DCP3 maps out pathways—essential packages of related, cost-effective interventions—that countries can follow to accelerate progress toward universal health coverage.

Dean Jamison

The Disease Control Priorities projects provide fitting book-ends to my career in international health statistics.  The first Disease Control Priorities project stimulated the original Global Burden of Disease Study for the year 1990 at a time when I was just getting involved in the international work on healthy life expectancy and summary measures of population health. I was experimenting with a form of disability-adjusted life years when the World Development Report 1993, edited by Dean Jamison, published the first global and regional DALY results. I applied the DALY and burden of disease methodology to Australian data to produce the first Australian Burden of Disease study in 1996 and a few years later moved to the World Health Organization to work on updating the Global Burden of Disease Study  with Chris Murray and Alan Lopez.  I joined Dean Jamison, Alan Lopez, Chris Murray and Majid Ezzati in producing the second volume of the Disease Control Priorities (Edition 2): Global Burden of Disease and Risk Factors in 2006.  And now with the third edition, I have contributed another chapter on the latest WHO assessment of global and regional causes of death in 2015, the end of the MDG period and the start of the SDG period.

The nine volumes of DCP3

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Universal health coverage

A WHO report monitoring progress towards Universal Health Coverage (UHC) was released yesterday. It reports the proportion of the population that can access essential health services, and the proportion that are pushed into poverty by healthcare expenses, for 183 countries. Staff of my unit were responsible for the service coverage measurement, and estimated that at least half the world’s population do not have full coverage of essential services. The report is available at http://www.who.int/healthinfo/universal_health_coverage/en/

The report uses 16 essential health services as indicators of the level and equity of coverage in countries. More details on the service coverage index have been published simultaneously in the Lancet Global Health:

http://www.thelancet.com/journals/langlo/article/PIIS2214-109X(17)30472-2/fulltext

Currently, 800 million people spend at least 10 percent of their household budgets on health expenses for themselves, a sick child or other family member. For almost 100 million people these expenses are high enough to push them into extreme poverty, forcing them to survive on just $1.90 or less a day.

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Child mortality continues to decline, but large disparities remain

The United Nations Inter-agency Group for Child Mortality Estimation (UN-IGME) released new data last week showing that the world has made substantial progress in reducing child mortality in the last several decades. The total number of child deaths has dropped to 5.6 million in 2016 from 12.6 million in 1990. Under-five mortality rates per 1,000 live births have dropped by 56% between 1990 and 2016. If all countries achieved the average mortality of high-income countries, 87 per cent of under-five deaths could have been averted and almost 5 million lives could have been saved in 2016. The report and country-level data are available at www.childmortality.org

At current trends, 60 million children will die before their fifth birthday between 2017 and 2030, half of them newborns, according to the report released by UNICEF, the World Health Organization, the World Bank and the Population Division of UNDESA which make up the Inter-agency Group for Child Mortality Estimation (IGME).

Most newborn deaths occurred in two regions: Southern Asia (39 per cent) and sub-Saharan Africa (38 per cent). Five countries accounted for half of all new-born deaths: India (24 per cent), Pakistan (10 per cent), Nigeria (9 per cent), the Democratic Republic of the Congo (4 per cent) and Ethiopia (3 per cent).

                                                                                                                 Source: UN-IGME 2017 report

The report notes that many lives can be saved if global inequalities are  reduced. If all countries achieved the average mortality of high-income countries, 87 per cent of under-five deaths could have been averted and almost 5 million lives could have been saved in 2016.

                                                                                          Source: UN-IGME 2017 report

The United Nations Inter-agency Group for Child Mortality Estimation or UN IGME was formed in 2004 to share data on child mortality, harmonize estimates within the UN system, improve methods for child mortality estimation report on progress towards child survival goals and enhance country capacity to produce timely and properly assessed estimates of child mortality. At that time, the UN Population Division, WHO and UNICEF were all producing their own estimates of child mortality, and I was instrumental in bringing UNICEF and WHO together to share data, information and to harmonize statistics.

Some years later, the Bill and Melinda Gates Foundation funded the Institute for Health Metrics and Evaluation (IHME) at the University of Washington in Seattle, and they started to release independent estimates of child mortality. This stimulated methods improvements in both UN IGME and IHME, as well as closer examination of available data and assumptions.  While initial IHME estimates of child mortality published in 2007 concluded that child mortality was declining faster than assessed by UN-IGME (Lancet 2007, estimates from both groups have tended to converge in subsequent revisions, until this year.

According to the Global Burden of Disease (GBD) 2016 update, published in September this year (Lancet 2017 ), IHME’s most recently published estimate of child mortality for the year 2016 is just under 5 million, 642,000 deaths lower than the UN-IGME estimate.  This is a dramatic change in their assessment of global child mortality.  What is the reason for the change?

HME GBD 2016 and UN IGME estimates of the global under-five mortality rate in 2016 are actually quite similar at 38.4 deaths per 1000 live births and 40.8 respectively, as are most country estimates. The main reason in the discrepancies in the number of deaths is that different sets of estimates of live births are used by the UN IGME and IHME. UN IGME used the live birth estimates from the UN Population Division (UNPD), while IHME used its own live birth estimates. In the IHME estimates there were 128.8 million live births in 2016, 12.2 million lower than the 141 million estimated by UNPD. Previously they had also used the UNPD birth estimates, but starting from this round they have developed their own estimates.  The consequence is that the IHME number of deaths in this round (5 million) is much lower than its last round estimate. Last year IHME estimated 5.8 million under-five deaths in 2015, which is very close to the UN IGME estimate of 5.9 million.

UN IGME estimates of under-five and neonatal deaths are based on UN IGME mortality rates and UNPD estimates of the annual number of live births in each country from the World Population Prospects: the 2017 revision.  UNPD estimates of live births (like UN IGME estimates of under-five mortality) are based on a comprehensive analysis of the population dynamics and fertility levels and trends in each country taking into account all sources of data available (https://esa.un.org/unpd/wpp/DataSources/).

IHME relies on UNPD estimates of female population and age pattern of fertility for each country, but IHME estimates lower fertility levels and trends for many countries. While the information published by IHME is insufficient to fully understand all the differences, a cursory comparison suggests that about 85% of the differences in the number of live births estimates by IHME is concentrated in 25 countries (mostly in Asia and Africa) with nearly half in China and India. IHME estimates there were 11.2 million births in China in 2016, 44% lower than the 16.9 million estimated by the UN Population Division, which is quite similar to the number reported by the national Burea of Statistics of China.

Despite a very large reduction in estimated numbers of child deaths, IHME has not increased its uncertainty ranges to take into account the additional uncertainty in estimates of births.  The uncertainty range for the most recent estimate (4.8-5.2 million) is similar width to the previous IHME estimate (5.7 -6.0 million) despite the fact that there is a very substantial non-overlap between these successive revisions.

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