Latest data show accelerating rise in atmospheric carbon dioxide

Back in January, I posted on climate change (are-humans-heating-up-the-world) and included a graph of the Keeling curve showing the increase in atmospheric carbon dioxide concentration. The Mauna Loa Observatory in Hawaii has just released its latest results showing that atmospheric carbon dioxide concentration hit 415 ppm on June 3rd (two days ago). This is the second highest annual rise in the past six decades.

May is the most significant month for global carbon dioxide concentrations because it is the peak value for the year, before the growth of vegetation in the northern hemisphere starts to absorb the gas from the air. The seasonal peak and fall can be seen in the Keeling curve, named after Charles Keeling, who started the observations on Mauna Loa in 1958 because of its isolation in the Pacific Ocean.

As recently as the 1990s, the average annual growth rate was about 1.5ppm, but in the past decade that has accelerated to 2.2ppm, and is now even higher. This brings the threshold of 450ppm closer sooner than had been anticipated. Concentrations of the gas have increased every year, reflecting our burning of fossil fuels.

Scientists have warned for more than a decade that concentrations of more than 450ppm risk triggering extreme weather events and temperature rises as high as 2C, beyond which the effects of global heating are likely to become catastrophic and essentially irreversible on a human time scale.

The Scripps Institute of Oceanography has a video animation showing the dramatic rise in carbon dioxide concentration from the mid-20th century. The screenshot below from the video shows that the carbon dioxide concentration was below 300 ppm for most of the last three centuries but started to rise dramatically after World War II.

The video then zooms out to show the Keeling curve for the last 800,000 years putting the accelerating rise in atmospheric carbon dioxide this century in stark perspective. For the evidence linking carbon dioxide rise to global warming refer to my previous post.


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State-level trends and levels of child mortality in India 2000-2015

I’ve been involved for nearly 20 years in the estimation of global, regional and national causes of child death under the age of 5, in collaboration with academic researchers and a number of WHO technical departments (new-estimates-of-the-causes-of-child-death-under-age-5).  This collaboration has just published a paper in the Lancet extending this work to state level for India:

Li Liu et al. National, regional, and state-level all-cause and cause-specific under-5
mortality in India in 2000–15: a systematic analysis with implications for the
Sustainable Development Goals.

India had a population 1.35 billion in 2018 and the larger states are bigger than most of the world’s countries. If Indian states were countries, five of them would be among the 20 largest countries in the world. Uttar Pradesh is similar in size to Brazil or Indonesia, and Maharashra and Bihar are similar in size to Japan or Mexico. The map below graphically illustrates the size of Indian states by labelling them with the names of countries with similar population sizes.

The paper presents estimates and trends for causes of death in children under 5 in India  for the 23 of the 29 states, together with smaller states grouped into two categories. In 2015, there were 25 million livebirths in India and 1·2 million under-5 deaths (under-5 mortality rate 48 per 1000 livebirths). 0·7 million (58%) of these deaths occurred in the first month of life (neonatal period). This quite a contrast to the rest of the world, for which 45% of under 5 deaths occurred in the neonatal period, and India is characterized by a substantially higher proportion of deaths associated with preterm birth (25% of all under 5 deaths, see figure below).  Low birthweight deaths, as reported by the Million Deaths Study, were even higher, due to high numbers of deaths in full-term low birthweight babies.

The paper is the result of a collaboration between the Maternal and Child Epidemiology Estimation (MCEE) group, WHO staff, and the Million Death Study (MDS). The MCEE group, lead by Professor Bob Black and researchers at Johns Hopkins University, is funded by the Bill and Melinda Gates Foundation (MCEE).

India had the largest number of under-5 deaths of all countries in 2015, with substantial subnational disparities. The child mortality rates in 2015 ranged from 9.7 deaths under age 5 per 1,000 livebirths in Goa to 73.1 deaths per 1,000 livebirths in Assam. Overall, child mortality rates were lowest in the south and highest in the northeast of India. In 2015, India was ranked 158th out of 194 countries for child mortality. The graph below shows the rankings against countries of the Indian States, if they were treated as countries. Goa would have ranked 66th out of 218 “countries” and Assam 194th (or 25th worst).

Overall, the leading causes of under-5 deaths were preterm birth complications (0·330 million [95% uncertainty range 0·279–0·367]; 27·5% of under-5 deaths), pneumonia (0·191 million [0·168–0·219]; 15·9%), and intrapartum-related events (0·139 million [0·116–0·165]; 11·6%), with cause-of-death distributions varying across states and regions. In states with very high under-5 mortality, infectious-disease-related causes (pneumonia and diarrhoea) were among the three leading causes, whereas the three leading causes were all non-communicable in states with very low mortality.

India has made a sustained effort in recent years to increase vaccination coverage for children.  The national coverage for the first dose of vaccine against measles has risen from around 56% in 2000 to 88 % and that for the second dose of vaccine against measles from under 25% t0 77%.  Measles deaths in India have declined from around 90,000 in 2000 to 20,000 in 2017.

The number of child deaths under 5 reduced from 2·5 million in 2000 to 1.2 million in 2015,  yet despite this progress, India did not meet target 4 of the Millennium Development Goals (MDG 4) of a two-thirds reduction in under-5 mortality rate between 1990 and 2015.  In the post-MDG period the UN Sustainable Development Goals (SDG) target for child mortality is to achieve ≤25 under-5 deaths per 1000 livebirths and ≤12 neonatal deaths per 1000 livebirths by 2030. The paper concluded that ten major states must accelerate progress to achieve the SDG under-5 mortality target, while 17 are not on track to meet the neonatal mortality target. Efforts to reduce vaccine-preventable deaths and to reduce geographical disparities should continue to maintain progress achieved in 2000–15. Enhanced policies and programmes are needed to accelerate mortality reduction in high-burden states and among neonates to achieve the SDG child survival targets in India by 2030.

Annual rate of reduction (ARR) of U5MR, NMR, mortality rate among 1-59 month olds (PNMR) by state in India in 2000-2015 (Webappendix 11 of Liu et al, Lancet 2019).

If the average rate of decline in child mortality from 2000-2015 is sustained unchanged until 2030, how would the Indian States compare with the countries of the world in 2030?  The tables below show the change in global ranking (Indian States and other countries) for the five best and worst Indian States in 2015 and 2030.  The paper provides more detailed analysis of State-level trends in specific causes of child death to help identify areas where more efforts are needed to achieve the SDG target for child mortality for India.

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WHO and the Global Burden of Disease

As a former WHO staff member, who played a key role in the production and clearance of WHO health statistics over the last 15 years, and a long-time collaborator with the Global Burden of Disease (GBD) enterprise and with the Institute of Health Metrics and Evaluation (IHME), I was interested to see two papers that were recently published discuss the roles of IHME and WHO in production of global health statistics. The IHME is located within the University of Washington in Seattle, USA and is primarily funded by the Bill and Melinda Gates Foundation.

  1. Manjari Mahajan. The IHME in the Shifting Landscape of Global Health Metrics. Global Policy 28 January 2019. Available in the Wiley Online Library at
  2. Marless Tichenor and Devi Sridhar. Metric partnerships: global burden of disease estimates within the World Bank, the World Health Organisation and the Institute for Health Metrics and Evaluation. Wellcome Open Research, 18 February 2019. Available at

The first of these articles, by Mahajan, takes a reasonably sceptical view of the claims of the power of “big data”  and modelling, but mistakenly characterises WHO global statistics as “traditionally relying on national statistics”. In fact, WHO global health statistics also makes use of modelling and multiple sources of data, including national statistics as well as surveys and epidemiological studies to address issues of biased and missing data. However, the paper does give a reasonable overview of some of the issues arising from private sector production of global statistics  outside the UN system, and with limited ability of outside experts or national-level users to assess and understand the derivation of the statistics.

The second paper is more problematic as the authors state that they primarily used notes taken at three IHME events. There were apparently no inputs from WHO staff involved in the interaction with IHME and GBD.  This has resulted in some inaccuracies in the paper, some of which I address in the comments below. It is disappointing that an article examining the interaction between IHME and WHO/UN did not make the effort to include inputs from WHO and UN people who are closely involved in global estimates as well.

Despite what the editor of the Lancet, Richard Horton, is quoted as saying in the paper, there was no so-called “cold war” between WHO and IHME before 2012. Ties Boerma and I were members of the core scientific group for the first GBD2010. This was the central scientific decision-making group set up in 2007, with 15 members of whom 9 were from outside Chris Murray’s research group. I and many other WHO staff contributed to the work of the GBD over the next five years, though Ties and I became increasingly concerned that the external core group were being excluded from access to the data and analyses. Around the period 2011 to 2012, six of the external core group members withdrew from the core group due to this and related issues. Apart from myself and Ties Boerma from WHO, this included Bob Black and Neff Walker from Johns Hopkins University, and Ken Hill and Dean Jamison from Harvard University. From WHO’s point of view, there was no cold war (1), and various WHO staff continued to provide data and contribute to GBD analyses, and WHO continued to make use of analyses derived from the IHME GBD results. However, because we could not gain access to data and analyses, WHO staff were unable to agree to be authors on GBD papers and WHO as an institution was unable to endorse the results. Perhaps more importantly, WHO was also unable to examine areas where GBD results differed from WHO and other UN statistics in order to reconcile differences and potentially improve global health statistics.

On page 4, the paper claims that the GBD 1990 data were reworked in various ways and used for the next 25 years, until IHME undertook the GBD2010.  This is quite incorrect. During the period from 1999 through to 2008, the majority of mortality and morbidity estimates (for almost all diseases of public health importance) were revised with new inputs. This included development of new model life tables at WHO, a big growth in disease-specific modelling both at WHO and by academic collaborators, and the establishment of various UN interagency groups, particularly for MDG targeted diseases. I have reviewed WHO work on GBD during the period 1999-2008 and estimate that morbidity and disability estimates were revised using new data for around 90% of the disease and injury causes (including all those of public health importance) and mortality estimates were revised for 100% of causes. Disability weights were the main area where a comprehensive update was not carried out, though quite a few were revised using a European study (2), the World Health Surveys (3) and other sources of population information on health states.

The paper is incorrect in saying that the difference in malaria mortality estimates is because the IHME uses MAP parasite prevalence. WHO also uses the same parasite prevalence data as a major input to its estimates of malaria mortality (4).  The big difference arises from IHME interpretation of verbal autopsy data in a way which maps much more “fever of unknown cause” to malaria for adults than WHO does.

The paper notes the difference in the IHME estimated trend for maternal mortality compared to that estimated by WHO, although there is little difference in the latest year estimates. Both IHME and WHO methods estimate the proportion of all female deaths in the reproductive period that are maternal deaths, and these estimates are reasonably similar. The trend difference in numbers of deaths arises because the IHME life tables have flatter adult female mortality trends than the UN life tables (5).  The IHME life tables place greater credence on sibling history data for periods long before surveys and have flatter adult mortality trends in parts of Africa. This results in flatter maternal mortality trends.

In the discussion, the authors question the value of competition in achieving global health goals and link this to the emphasis in the GBD and indeed in all the UN global health statistics on the comparability of statistics across locations and times. While it is arguable whether the whole global targets setting process spurs healthy competition between countries, the concern about comparability in statistics is essentially a concern to have meaningful statistics. And any statistic is only meaningful and interpretable through comparison. For example, an average death rate of 8,945 per 100,000 population is uninterpretable to almost everyone, unless put in a comparative context.

Measurement only has meaning if a standard scale is used (or at least fixed scales that can be translated to each other). Since bias varies over time as well as over space, you could argue that lack of concern for comparability would be like tracking your weight with a scale whose zero is varying in an unknown way over time.

The authors do raise relevant and important issues around the potential creation of a global health data monopoly, the concentration of analytic skills in a first-world institution, and the broader governance structures and accountability for statistics. Many developing countries have little interest in the outputs of a US academic group, but are very concerned about WHO and UN statistics. UN agencies have a mandate to produce statistics and some responsibility to consult with countries. IHME has tried to spin this as “political interference” which has largely not been the case, at least in my experience carrying out a central statistical clearance role in WHO and in working with the various UN interagency groups. The downside of IHME “independence” is that there have been quite drastic changes in methods and estimates from revision to revision for some causes and topics with little responsiveness in some cases to those who pointed out problems before publication.  A recent example includes drug overdose deaths for USA, where GBD2016 excluded prescription opioid deaths (without documenting this) for unknown reasons, and GBD2017 included them, resulting in a more than doubling of drug overdose deaths. The sudden introduction of very different birth denominators in GBD2016 similarly knocked around half a million child deaths off the global total compared to UN (which previously was almost identical).

IHME is now estimating its own population and birth numbers. So the mortality and other outputs are inhabiting a parallel demographic universe to those of the UN agencies.  This makes the issues of understanding difference even more complex and opaque. And I suspect will unfortunately limit the ability of UN agencies to make direct use of IHME results.

This is a great pity, as there is a lot to be gained by collaborating more closely and working together to improve both the primary data, the analyses and the statistical assessments that are increasingly important for guiding and tracking global health progress.


  1. Boerma T, Mathers C. The World Health Organization and global health estimates: improving collaboration and capacity. BMC Medicine.2015, 13:50. doi: 10.1186/s12916-015-0286-7. Available online at
  2. Stouthard M , Essink-Bot M, Bonsel G, Barendregt J, Kramers P. Disability weights for diseases in the Netherlands. Rotterdam, Department of Public Health, Erasmus University, 1997.
  3. Ustun TB, Chatterji S, Mechbal A, Murray C.J.L, WHS Collaborating Groups. The world health surveys. In: Murray CJL, Evans D, eds. Health systems performance assessment: debates, methods and empiricism. Geneva, World Health Organization, 2003.
  4. World Health Organization (2018). World Malaria Report 2018.
  5. Gerland P, Masquelier B, Helleringer S, Hogan D, Mathers CD. Maternal mortality estimates.
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Are humans heating up the world?

I was in Australia over Christmas-New Year period and the southern parts of the country were sweltering in a heat wave. Yesterday, the Australian Bureau of Meteorology issued its 2018 climate statement, revealing a record breaking run of rising temperatures. The graph below shows average Australian temperature by year relative to the 1961-1990 average temperature.

The average maximum temperature for the country as a whole was particularly warm, 1.55 degrees Celsius above the 1961–1990 average, making 2018 Australia’s second warmest year on record for daily high temperatures. Nine of the ten warmest years on record have occurred since 2005. These rising temperatures have been accompanied by drought, bushfires, and the death of half of the Great Barrier Reef.

I was astounded to read the comments to an article in the Sydney Morning Herald on this, where many commentators expressed complete disbelief that the climate was changing, or that humans were responsible, or indeed that the science was settled and not in dispute. A recent major report by the Intergovernmental Panel on Climate Change  stated that human activities are estimated to have caused approximately 1.0°C of global warming above pre-industrial levels, with a likely range of 0.8°C to 1.2°C. Global warming is likely to reach 1.5°C between 2030 and 2052 if it continues to increase at the current rate. Achieving the global greenhouse gas emission targets set in the Paris Agreement will limit global warming to below 2°C this century.

The report also makes clear that limiting warming to 1.5°C will have huge benefits compared with allowing temperatures to surge to the 2°C level. But keeping to 1.5°C would require aggressive action to curb greenhouse gas emission, going further than the targets set in the Paris Agreement. Even if nations could achieve that, the world would look very different: entire ecosystems could be destroyed across more than 6% of the earth’s land surface, sea levels would rise between half and 1 metre, and 70–90% of coral reefs would disappear. Moreover, sea levels will continue to rise for centuries, with projected long term increases in the range 3-13 metres.

The  most important greenhouse gas is carbon dioxide, and the Keeling Curve summarizes the global accumulation of carbon dioxide in the earth’s atmosphere. It is based on continuous measurements taken at the Mauna Loa Observatory on the island of Hawaii from 1958 to the present day. The curve is named for the scientist Charles David Keeling, who started the monitoring program and supervised it until his death in 2005.

Source: Delorme [CC BY-SA 4.0 (, from Wikimedia Commons

This is one of the most important scientific results of the 20th century. It was the first significant evidence that carbon dioxide levels in the atmosphere were rapidly increasing, and in a very real sense it continues to track our performance as a species. The curve continues to rise steadily at an undiminished pace, and is a stark indictment of a species that is ready to stand by as islands submerge, coastal lands flood, human habitats burn in wildfires, entire ecosystems disappear, species extinction accelerates, and coral reefs disappear.

Data from Antarctic ice cores has enabled levels of carbon dioxide to be measured back to 800,000 years ago. The graph below shows 800,000 years of CO2 data, based air bubbles trapped in ice cores. It shows that CO2 hasn’t ever been greater than 300 parts per million, with very slow and cyclical increases and decreases about every 100,000 years. Today it’s over 400 ppm, 33% higher than it’s been in 800,000 years, and on a very sharp upwards trajectory.

                                                                                                                                                                Credit: Scripps Institution of Oceanography.

A 2009 study, published in the journal Science, analyzed shells in deep sea sediments to estimate past CO2 levels, and found that CO2 levels have not been as high as they are now for at least the past 10 to 15 million years, during the Miocene epoch.This was a time when global temperatures were substantially warmer than today, and there was very little ice around anywhere on the planet. And so the sea level was considerably higher — around 100 feet higher — than it is today.

With global CO2 emissions continuing on an upward trajectory that is likely to put CO2 concentrations above 450 ppm or higher, it is extremely unlikely that the steadily rising shape of the Keeling Curve is going to change anytime soon. Particularly, as the United States has repudiated the Paris Agreement and started dismantling climate change regulations. Australia also recently abandoned emissions targets or any policy to address climate change, effectively also abandoning the Paris Agreement

National emissions have risen each year since 2014, when the Australian government repealed laws requiring big industrial emitters to pay for their emissions. There are also no significant policies to reduce the other major sources of pollution, such as transport, agriculture, heavy industry and mining, which together generate nearly two-thirds of Australia’s carbon emissions. Australia’s conservative government has rejected four national climate policies since it was elected in 2013, and rejection of climate change policy arguably played a role in the abrupt replacement of then Prime Minister Malcolm Turnbull by a climate sceptic Scott Morrison.

The Australian government refusal to act on emissions is completely out of line with public opinion, with a recent poll finding that 68% of respondents wanted domestic climate targets in line with the Paris Agreement. Australia is among several industrialized nations that are not on track to reduce greenhouse gas emissions to keep global warming below two degrees Celsius as the Paris accord promises, according to independent analyses.

Australian climate change denialists, including the Murdoch-media, continue to claim that the science is in dispute. An oft-quoted statistic is that 3% if climate scientists don’t think that human-caused global warming is real, often followed by a claim that the 97% who do think so are involved in some vast conspiracy or have vested interests (shares in solar panel companies perhaps?).  If 3% of climate scientists really think that anthropogenic global warming is not real, then perhaps there possibly are real uncertainties about the science.

So is the 97% figure an urban myth or real?  The figure actually comes from a 2013 article in Environmental Research Letters by Cook et al. titled “Quantifying the Consensus on Anthropogenic Global Warming in the Scientific Literature.” They reported that “Among abstracts expressing a position on AGW, 97.1% endorsed the consensus position that humans are causing global warming”. The 97 percent figure went viral and, not surprisingly, the qualifying phrase “expressing a position”—the fine print, —got dropped. Most of the remaining 3% did not express a position, very different from a climate-sceptical position. A more recent review of abstracts from 2013 and 2014 (the_consensus_on_anthropogenic_global_warming) found that of 24,210 abstracts of papers on climate change, only five explicitly rejected human role in global warming. As two of these papers were by the same author, the final figure for scientists who publish on global warming and reject a human causative role is 1 in 17,352 or 0.006%. This is probably as close to unanimity as humans are capable of.

Does this mean the projections quoted above are exact? Of course not. I know perhaps more than most that projections are uncertain, sometimes highly uncertain, and there are plenty of arguments within the literature about the details and impacts of global warming.  Whether scenario X or Y is likely optimistic or pessimistic, whether the sea level will rise more than half a metre by 2100 etc etc.  But none of this means there is not a very strong consensus on the big picture, the need to act now, and the likely implications of not acting strongly enough.

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New estimates of the causes of child death under age 5

WHO has just released the latest update on global causes of death for children under age 5 for years 2000-2017. These are available for download on the WHO website at and also in the Global Health Observatory at  A collaborating group of academic researchers led by Professor Bob Black from Johns Hopkins University carried out data analysis and modelling in collaboration with a number of WHO technical departments and myself.

Despite remarkable progress in the improvement of child survival globally, with a reduction in the annual number of child deaths from 10 million in 2000 to 5.4 million in 2017, this level of progress will need to accelerate to achieve the Sustainable Development Goal for child mortality in 2030. There remain many preventable child deaths in developing countries. The causes of the 5.4 million deaths of children under 5 are summarized in the following pie chart.

Global distribution of causes of child death under age 5, 2017


Almost half of deaths under 5 are now in the neonatal period (the first four weeks of life) where the causes of death are shaded yellow above and led by prematurity and birth asphyxia or trauma.  In the period 1-11 months the leading cause of death is acute respiratory infection (ARI) which is mostly pneumonia, followed by diarrheal diseases and injuries.

Reductions in mortality rates for pneumonia, diarrhoea, neonatal intrapartum-related events, malaria, and measles were responsible for 65% of the total reduction of under 5 deaths, pr just under 3 million of the 4.6 million deaths per year averted between 2000 and 2017 (see Figure below). Most of these causes relate mainly to the period 1-59 months after the neonatal period. The faster decline in these “post-neonatal” causes over the last 15 years has resulted in preterm birth complications now being the leading cause of under 5 deaths in 2017.


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Seven WHO staff named in world’s most highly cited list

Seven staff from the World Health Organization have been included in the 2018 Clarivate Analytics 2018 Highly Cited Researchers List. This list includes more than 4,000 leading researchers in 21 fields of the sciences and social sciences from around the world, including 17 Nobel laureates.

Now in its fifth edition, the list identifies influential researchers who have demonstrated significant influence on the research community through publication of multiple highly cited papers. The Web of Science is used as the basis for identifying researchers whose citation records position them in the top 1 percent by citations for their field over the last ten years.

Five of the WHO staff included in the list this year worked at the WHO Headquarters in Geneva:  Mercedes de Onis, Chris Dye, Colin Mathers, Susan Norris, and Gretchen Stevens. The other two, Freddie Bray and Jacques Ferlay work at the International Agency for Research on Cancer (IARC), WHO’s specialized cancer agency based in Lyon, France. Chris Dye, Gretchen Stevens and myself all left WHO earlier this year.

A new cross-field category was added this year to recognize researchers with substantial influence in several fields but who do not have enough highly cited papers in any one field to be chosen. Two of the WHO staff included this year were named in the cross-field category: Gretchen Stevens and Chris Dye.

In my work on global health statistics and burden of disease, I have collaborated widely with academics across the world, and also worked with many of them on WHO expert committees. Twenty-five of these academic collaborators are also included in the 2018 list of the world’s highly cited researchers, including leading researchers from Harvard University, University of Edinburgh, Imperial College London, the London School of Hygiene and Tropical Medicine, the Universities of NSW and Melbourne, the University of Toronto, and the University of Washington.

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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 (  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 (, 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 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).

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