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Projections of Global Health Outcomes from 2005 to 2060 Using the International

Futures Integrated Forecasting Model

October 18, 2010

Barry B. Hughes, Professor and Director, Frederick S. Pardee Center for International Futures,Josef Korbel School of International Studies, University of Denver

Randall Kuhn, Assistant Professor and Director, Global Health Affairs Program, Josef KorbelSchool of International Studies, University of Denver

Cecilia Mosca Peterson, Frederick S. Pardee Center for International Futures, Josef KorbelSchool of International Studies, University of Denver

Dale S. Rothman, Associate Professor, Frederick S. Pardee Center for International Futures,Josef Korbel School of International Studies, University of Denver

José Roberto Solórzano, Frederick S. Pardee Center for International Futures, Josef KorbelSchool of International Studies, University of Denver

Colin D. Mathers, Senior Scientist, Evidence and Information for Policy Cluster, World Health

Organization

Janet R. Dickson, Frederick S. Pardee Center for International Futures, Josef Korbel School of International Studies, University of Denver

Corresponding Author:

Randall KuhnJosef Korbel School of International Studies

2201 S. Gaylord St.Denver, CO 80208(303) [email protected]

Running Head: Projections of Global Health Outcomes



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Abstract

Background: Long-term forecasts of mortality and disease burdens are essential for settingcurrent and future health system priorities, yet few forecasts cover a wide range of nations over along time-span or situate changes in mortality into an integrated framework. Building on the

work of the Global Burden of Disease project (GBD), we developed an integrated healthforecasting model as part of the larger International Futures (IFs) modeling system.

Methods: IFs health begins with historical relationships between economic/social developmentand cause-specific mortality used by GBD, but we build forecasts from endogenous projectionsof these drivers, incorporating forward linkages from health outcomes themselves back to inputslike population and economic growth. The hybrid IFs system adds alternative structuralformulations for causes not well served by regression models (e.g., HIV/AIDS) and accounts forchanges in proximate health risk factors. We forecast to 2100 but report findings to 2060.

Findings: The base model projects that deaths related to communicable disease (CD) will

decline by 50 percent while those related to both non-communicable diseases (NCD) and injuriesmore than double. Considerable cross-national convergence in life expectancy is expected.Climate-induced variations in agricultural yield cause surprisingly little excess childhood CDmortality, though we do not explore other climate-health pathways. Optimistic and pessimisticscenarios deviate considerably, with the former producing, in 2060, 39 million fewer deaths anda 20 percent relative increase in GDP per capita, in spite of a billion additional people. SouthAsia would experience the greatest mortality and economic benefit.

Interpretation: While reduction of CD risk factors remains critical over the short-term, long-term success depends on targeting NCD and injury risk factors and broader social determinantsof health. Economic growth effects are always positive, but are modest and should not constitutea singular rationale for health investment. Economic impacts could be enhanced by sequencedinvestments in family planning, nutrition, and education.

Conclusions: Long-term, integrated forecasting advances our understanding of the connectionsbetween health and other markers of human progress, offering powerful insight into our currentpath and key points of leverage for future improvements.



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Introduction

Long-term forecasts of mortality and disease burdens are essential for setting current and futurehealth system priorities, yet few forecasts cover a wide range of nations over a long time-span.Even fewer situate changes in age-specific mortality rates into an integrated framework to

account for the effects of mortality variation on population size, age-structure, and drivers of mortality such as income. This paper describes an approach to addressing this gap by building anintegrated, long-range health forecasting model into the existing International Futures (IFs)global modeling system (1). IFs is a software tool whose central purpose is to facilitateexploration of possible global futures through the creation and analysis of alternative scenarios.Alongside health, IFs incorporates models of population, economics, education, energy, food andagriculture, aspects of the environment, and socio-political change and represents dynamicconnections among them. We build on the groundbreaking work of WHO’s Global Burden of Disease (GBD) project, which has produced the only published global forecasts of regional andcause-specific health outcomes to date (2,3). GBD was not, however, designed to produce long-term, integrated forecasts; available analyses extend to 2030, now only 20 years distant.

The need for long-term, integrated forecasting is evident in emerging health risks and populationtrends. The epidemiologic transition in leading causes of death from communicable disease (CD)to non-communicable disease (NCD) and injuries demands that models capture complex, long-term relationships like the effects of global agriculture production on obesity-related mortalitydecades later; the effects of infrastructural investment on road traffic accidents; and the potentialeffects of anthropogenic climate change on a constellation of CD and NCD risks (3-5). The shiftfrom CDs to NCDs also relates to ongoing processes of population growth and aging, whichdetermine both the number and distribution of deaths in a society. Finally, the burden of diseaseand population can have profound forward impacts on subsequent economic growth, therebyfurther altering health trajectories in a synergistic fashion (6) .

Many wealthy nations produce long-term forecasts of age-sex-cause-specific death rates and theeconomic consequences of aging, yet few are integrated to explore scenarios in which, forexample, a major shift in tobacco consumption could simultaneously affect mortality, morbidity,population size/structure, and productivity. Few poor countries produce any long-range forecasts,yet many are in the midst or on the threshold of the above transitions. Finally, no forecastexplores these issues in a global system in which rich and poor countries interact.

In spite of this dearth of forecasting tools, the global community has begun to set global healthtargets that are both long-term and based on an integrated understanding of health risk. In 2009,the WHO Commission on Social Determinants of Health (CSDH) set ambitious targets for thereduction of levels and gaps in life expectancy, under-5 mortality (U5MR), and adult mortality(AMR, the probability of dying between age 15 and 49) (7). CSDH stressed the role of the socialenvironment in driving mortality, yet no model exists to evaluate these pathways.

To address this gap, we extend the work of GBD in a number of ways. IFs forecasts to 2100,allows end-users to explore country-level outcomes, embeds mortality and morbidity patternswithin larger global systems, forecasts proximate drivers (e.g., obesity, child underweight), andreplaces some regression-based formulations with richer structural formulations. The flexible



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structure of IFs allows users to vary model assumptions and undertake alternative scenarioanalysis. This paper introduces the IFs health model and presents results from our base case,optimistic, and pessimistic scenarios out to 2060. The model is freely available online atwww.ifs.du.edu, along with complete technical documentation and results (8,9).

Methods

GBD broke new ground with its forecasting approach, measures, and methodology. It was thefirst multi-country forecast to disaggregate mortality into multiple causes of death, importantbecause the driver-outcome relationships vary with cause of death as well as with age and sex.(10). Rather than relying heavily on extrapolative techniques, GBD employed a structural,regression-based projection model using exogenous projections of independent distal-drivervariables to forecast health outcomes. This approach led to the development of three alternativescenarios of the future mortality and morbidity for over 100 diseases based on differing incomeand education assumptions for the eight global regions of their analysis. Mathers and Loncar(2006) incorporated more extensive data, updated driver-variable forecasts, created regression

models specific to low- and lower-middle-income countries, and developed separate projectionmodels for diseases such as HIV/AIDS that are not easily forecast using distal drivers (3).

We take another step forward by integrating GDB formulations into the existing IFs forecastingframework and explicitly capturing many of the linkages between health and other dimensions of human progress. IFs draws upon standard modeling approaches from a wide range of disciplinesincluding economics, politics, population, agriculture, energy, technology, education, and theenvironment. For example , the demographic model incorporates a true “cohort-component”representation, tracking country-specific populations and events (including birth, death, andmigration) over time by age and sex (11). IFs Global Health begins with the distal driver-outcome formulations developed for the GBD project. These can be represented as:

)ln(**))(ln(*)ln(*)ln(*)ln( ,,542

321,,,,, Rk a R R Rik a Rik a SI T Y HC Y C M β β β β β +++++= (1)

where M is Mortality Level in deaths per 100,000 for a given age group a, sex k, cause i andcountry or Region R; Y is GDP per capita; HC is Total Years of Adult Education (for adults 25and older) ; T is time (Year-1900); and SI is Smoking Impact. Deaths are distributed from GBD'slarge age categories into five-year age categories (up to age 100, infants separated from 1-4) sothat decedents can be removed from population age-sex structures. While linkage of healthoutcomes into our existing cohort-component system is itself a substantial advancement, wefurther incorporate improved forecasts of distal and proximate health drivers, structuralrepresentations of key health issues, and forward linkages from health to population and

development, as shown in Figure 1.

In contrast to GBD, IFs includes endogenous forecasts of distal drivers of health. The existingIFs framework already included the basic linkages required to integrate changes in health tochanges in the drivers of health, for instance the effect of an additional person on the labor-capital ratio or an additional elder or child on the dependency ratio. We update that system toaddress a broader range of pathways through which mortality and disease are known to impacteconomic growth (6,12,13), including linkages from reduced mortality/morbidity to reduced



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improvements in life expectancy in high-income countries, particularly for males. Comparingour results for 2050 to those from UN Population Division, we anticipate comparable but slightlybetter life expectancy outcomes, with high-income countries performing about one year betterand poor countries performing up to two years better.

CSDH set the goal of reducing the gap in life expectancy at birth between the 60 longest- andshortest-lived countries by 10 years, or from 18.8 years in 2000 to 8.8 years in 2040. Figure 3depicts this gap historically and in the IFs base case forecast. This goal appears unlikely to bemet until after 2060 (in fact, not until near the end of the century). This is due to the aggressivegoals set by CSDH and continued advancement in the life expectancies of high income countries.

Prospects for achieving such ambitious goals will depend on continued reductions in CDmortality and action against the rising burden of NCD and injuries mortality. Globally, IFsforecasts a shift away from CD deaths to NCD (already the major cause category in 2005),shown in Figure 4. We forecast a CD reduction of just over 40 percent by 2030 and almost 70percent by 2060, in spite of substantial population increase. This is consistent with historical

patterns of progress against most CDs, though considerable uncertainty attends to the pace of reduction in HIV/AIDS and malaria. Even in SSA, we forecast that the balance of deaths willshift to NCDs by around 2030; by 2060 NCD deaths would outnumber CD by more than 5 to 1.These shifts reflect changing age-specific death rates and an older population structure.

Scenario Analysis

To leverage the integrative qualities of the IFs system and address the well-known "optimismbias" existing in many forecasts (24,25), our scenario analysis moves beyond the symmetrical,upside-downside variations incorporated in other studies to consider realistic scenarios. Weincorporate two symmetric elements aimed at capturing variations in technology (via a 50%increase or decrease in the pace of mortality reduction due to technology over time) and in theproximate drivers of health (via a one standard error increase or decrease in each proximate riskfactor, phased in over time). To better capture potential positive human action above and beyondour eight proximate drivers, we allow countries currently underperforming their projections, suchas the Russian Federation and South Africa, to gradually converge to expectation (26-28). Twofurther adjustments capture a realistic pessimistic scenario, particularly for low income countries.First, to account for lingering effects of the Great Recession (2008-2011 in the IFs base case), wemodel reductions in GDP growth in all countries and greater reductions in low income countries.Finally, although we introduce no direct change in biological assumptions, our pessimisticscenario incorporates slowed reductions in CD mortality, particularly for HIV/AIDS (29).

The optimistic and pessimistic scenarios carry significant global implications. Annual globaldeaths in the pessimistic scenario grow to be 34 million more by 2060 than in the optimisticscenario. In terms of death rates, the gap is still larger, because population diverges markedlybetween the two scenarios, from just over 9 billion (pessimistic) to just over 10 billion(optimistic), compared to a base case value of 9.4 billion. Of 1 billion additional people in theoptimistic scenario, the great majority—about 800 million—are 65 and older, with an additional236 million working-age adults and 39 million fewer people under 15. Due to population agingand the high probability of some reduction in CD risks, both scenarios suggest an ongoing global



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shift from CD to NCD burdens. In neither scenario do CDs in 2060 account for more than 12percent of deaths, though CD burdens remain much higher in the pessimistic scenario.

Figure 5 depicts variation in critical life-course mortality probabilities based on forecast life tableestimates. While U5MR is a well-known target of the Millennium Development Goals,

continued aging and CD mortality reduction push our attention to trends in AMR. CSDH setexplicit targets for reducing U5MR by 95% and AMR by 50% in all country, gender, and socialgroups between 2004 and 2040. To address a broader spectrum of life-course mortality trends,we also track the life table probability of dying between age 60 and 79 as an indicator of olderadult mortality. Relative differences in mortality probabilities are very large across all regions,with mortality probabilities in the two scenarios varying quite often by a factor of 2 or evenmore. Differences are especially great for U5MR, particularly for SSA and South Asia. For low-income countries, the pessimistic scenario is extremely pessimistic, with an actual increase inchild deaths in coming years and a large gap, exceeding three million child deaths annually formost of the forecast horizon, between the patterns of that scenario and the optimistic one.

Scenarios variations in AMR are equally striking, with SSA and especially South Asia showingthe greatest deviation. For SSA, the scope of the HIV/AIDS epidemic is so sweeping and thelikelihood of some progress so great that adult mortality falls from 388 per 1000 in 2005 to 219per 1000 in 2060 even in the pessimistic scenario; in the optimistic scenario it falls much further,to 105 per 1000. In South Asia and in Europe and Central Asia, where NCDs largely drive adultmortality, the two scenarios differ dramatically in the rate of progress over the next half-century.In South Asia for instance, AMR in 2005 was 217 per 1000. In the optimistic scenario, SouthAsia’s AMR plunges to 64 per 1000 in 2060, comparable to today’s high-income societies,whereas in the pessimistic scenario the rate only falls to 165 per 1000.

Regional life expectancy also differs dramatically by scenario, as shown in Figure 6. In 2005,aggregate life expectancy for SSA was 28 years behind that of high-income countries (52 versus80) while South Asia experienced a 15-year deficit (65 versus 80). IFs forecasts considerableconvergence in the optimistic scenario, with SSA's disadvantage narrowing to 12 years by 2060(80 versus 92) and South Asia narrowing to only 5 years (87 versus 92). In the pessimisticscenario, however, SSA's life expectancy remains 23 years behind today's high-income countries(63 versus 86). Variation is even more striking for South Asia, which in a pessimistic scenariowould remain 15 years behind high-income countries (71 versus 86), and would lose ground withrespect to SSA. This reflects both the high likelihood of some improvement in HIV/AIDSmortality in SSA and the dependence of future gains in South Asia on NCD mortality reductions.

Proximate Risk Factor Variation

While variations in proximate risk factor prevalence included in the optimistic and pessimisticscenarios weigh heavily on the pace of short-term mortality reductions, their effects tend to erodeover time. This is especially true for CD mortality, which is projected to decrease eventuallybased on distal drivers alone but would decline much faster under favorable proximate driverscenarios. We estimate, for instance, that between 2005 and 2060, 131.6 million cumulative CDdeaths, or 23.4% of total CD deaths, could be eliminated by gradually reducing four proximatedrivers (childhood underweight; unsafe water, sanitation, hygiene; indoor air pollution; global



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climate change). This relatively high population attributable fraction masks substantial variationover time, from 35.6% of CD deaths averted in 2010 to only 7.9% CD deaths in 2060.

Our forecast of substantial declines in baseline CD mortality and childhood underweight sets thecontext for our climate change impact forecast. As noted above, we only estimate the effects of

climate change on childhood underweight through the pathway of diminished food production.We analyze impacts in the context of a fully integrated social, economic, and environmentalstructure. In our base run, the atmospheric concentration of carbon dioxide rises to 550 parts permillion in 2060, translating to a further increase in global temperature of 1.6 degrees Celsiusfrom 2005, with specific temperature and precipitation changes at country level. Figure 7presents the projected effect of this additional climate change on mortality from communicablediseases other than HIV/AIDS among children under 5. This particular result ignores anypotential positive carbon fertilization effects on yield. We employ a 10-year moving average tosmooth the effects of periodic mortality spikes in climate-affected countries with low foodreserves. Such spikes should be anticipated, but not necessarily in the exact years we forecast.

The annual number of additional child CD deaths rises to above 50,000 by 2030, peaking in 2050at over 70,000, and declining thereafter. The vast majority of additional deaths occurs in SouthAsia and SSA, both characterized by greater food insecurity and higher baseline levels of CDmortality. The overall pattern reflects the increasing relative risks of CD mortality due tochildhood underweight as a result of falling yields due to climate change, but also decliningbaseline levels of CD mortality and of childhood underweight due to increased food production.This should not be taken as conclusive evidence of limited climate change impacts on mortality,but it does point to the importance of accounting for baseline improvements in childhoodunderweight, which is not done in a number of other climate impact forecasts (30).

Economic Impacts

One final contribution of an integrated forecast is the opportunity to evaluate the potential socialand economic impacts of health scenarios. Our analysis suggests that an optimistic healthscenario results in positive economic returns relative to the base case, in spite of the requirementsassociated with extra population. Figure 8 presents the potential magnitude of the positive impactby showing the ratios of GDP per capita of the optimistic scenario to the adjusted base case. Inall regions except East Asia and Pacific, the optimistic scenario increases per capita GDP relativeto the adjusted base case. The different result in East Asia and Pacific flows from the largenumber of older adults (and of the elderly who are beyond the 60-79 age range) that China willexperience relative to its working-age population in coming years—most of the reducedmortality for the region occurs in those age categories and intensifies the fiscal pressures theelderly will likely place on the society. The same phenomenon appears to a lesser degree in high-income countries, leading to an absence of economic difference between the two scenarios.

South Asia would benefit most in the optimistic scenario, followed by sub-Saharan Africa andthe Middle East and North Africa. The swing in GDP per capita for South Asia between the twoscenarios reaches 37 percent in 2060 in spite of increased population. South Asia’s relative gainsstem from the imminent arrival of demographic dividend cohorts into prime working ages; their



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health will matter considerably. Sub-Saharan Africa would experience about a 22 percent swingin GDP by 2060, while Middle East and North Africa would gain about 15 percent.

Simultaneous attention to other aspects of development, including fertility reduction andincreased food production (and therefore nutrition of larger populations), could further enhance

the gains shown for the optimistic scenario by itself. The incremental gain for sub-SaharanAfrica would be especially great, moving the 22 percent improvement in GDP per capita fromhealth alone to 36 percent.

The economic boost of the optimistic scenario accumulates over more than a generation, witheffects peaking for most regions and the world as a whole by 2060. Given the more rapid agingof populations in the optimistic scenario, dependency ratios rise and economic gains diminishafter about 2040. Sub-Saharan Africa experiences peak relative economic gains in 2065.

Discussion and Conclusion

Although forecasting of human population size and characteristics routinely extends to mid-century, and often to the end of the century or beyond, forecasting of health has for the most partnot looked beyond 2030, and has been relatively rare in general. Yet an increasing number of global actors and governments take a long-term approach to setting goals for health, as with therecent WHO Commission on Social Determinants of Health (CSDH), which set goals to 2040.Societies and global actors not only want to understand the possible future health of citizens,they want to know how to improve it.

Our analysis reinforces and extends our understanding of changing global and regional mortalitypatterns, both the trends toward improvement almost everywhere and toward reduction in theburden from communicable diseases and (relative) increase in those from non-communicable

diseases and injuries. As dramatic as improvements are in our base case, however, they will notlead to achievement of the goals of the Commission on Social Determinants of Health except inthe optimistic scenario. We have found that convergence, the closing of the gaps in mortalitypatterns between less- and more-developed countries is likely to occur very slowly in our basecase, quite substantially (but by no means fully) across the next half century in our optimisticscenario, and not at all in our pessimistic scenario.

Because human action is such a large part of the difference in the assumptions that underlie theoptimistic and pessimistic scenarios, action on multiple fronts is critical to convergence,particularly for regions such as South Asia that sit at the threshold of epidemiologic transition.Our analysis shows how effective proximate drivers interventions can be over the short-term, but

these impacts diminish in the long-term. While proximate drivers should be added to our modeland new proximate targets will emerge, long-term success nonetheless requires higher-levelaction, as spelled out by CSDH. Remaining excess risks are no doubt related to intersectoralfactors such as governance, health systems, and social environment (often called "super distaldrivers") that are currently captured in the convergence term of our optimistic scenario.

The benefits of an intersectoral, interdisciplinary understanding of health futures is illustratedboth in our exploration of climate change impacts on mortality and of economic impacts of



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health. In the case of climate change, our relatively low estimates of additional child CD deathsresulting from food insecurity illustrate the need for climate impact studies to account forbroader processes of change like the ongoing trend of improvements in childhood underweight.While such improvements are no more inevitable than any of our other findings, they do reflect aplausible trajectory that is not fully addressed by most existing climate impact studies. As

evidence improves, the IFs health forecast will incorporate further connections climate-relatedoutcomes and conditions such as vector-borne disease, heat-related illness, and natural disaster.

In the case of economic impacts, our model suggests a modest but positive contribution of healthto economic growth, particularly in South Asia and Sub-Saharan Africa. Although improvementsin health, by themselves, should be adequate to justify personal and social investments in them,debate remains as to whether such investments also generate societal economic returns. Ourextensive analysis of possible pathways for such return, in combination with maintaining fullaccounting of the diversion of such investments from other possible uses of funds, suggests thatthey should in fact generate positive returns. These impacts could be enhanced if investments infamily planning, nutrition, and education were to dampen the magnitude and burden of short-

term population growth, particularly in areas yet to experience any fertility decline. The modestlevel of economic return, however, suggests the need for care in using economic returns as asimple justification for health investments; the best justification remains better and longer lives.

There is, of course, more that we wish to do or see done. That includes the extension of the set of proximate drivers to include such drivers as alcohol use and physical activity level. We couldalso better capture the social and political context of health, including representation of sub-national variation (beyond sex differences), accounting for national inequality as a distal driver,and modeling spatial and social transmission of health risks from nation to nation. Our scenarioanalysis would benefit from deeper exploration of a variety of extreme events, including bothmajor plagues and dramatic breakthroughs in life extension. More immediately, IFs will soon beable to evaluate the potential health and economic impacts of specific policy and governancescenarios beginning with the Global Framework Convention on Tobacco Control. We also hopeextend and refine of our forward linkage analysis to provide a more systematic consideration of the synergistic relationships between health, economics, and institutional change that helpexplain recent economic breakthroughs in East Asia and Europe (6,12).

Finally, let us reiterate that the system used for this analysis is available for others to use inreplication or alternative analysis, and also to develop further.

Authors' contributions: ICMJE criteria for authorship read and met: RK BBH CMP DSR JRSJRD CDM. Agree with the manuscript’s results and conclusions: RK BBH CMP DSR JRS JRDCDM. Designed the experiments/the study: RK BBH CMP DSR JRS. Analyzed the data: BBHCMP DSR JRS. Wrote the first draft of the paper: RK CMP. Contributed to the writing of thepaper: RK BBH CMP DSR JRD CDM.

Conflict of interest: none.



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Role of funding source: This work was supported in part by the Frederick S. Pardee Center forInternational Futures at the University of Denver. No funding bodies had any role in studydesign, data collection and analysis, decision to publish, or preparation of the manuscript.

Ethics committee approval: not applicable.



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