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14.1 INTRODUCTION

Projections or forecasts can be a useful input into the health policy debate on infrastructure, human capital and research investments. There has been an extensive literature on health projections, with a range of different types of approaches. A set of alternative projection scenarios were developed as part of the GBD 1990 study, and efforts are currently underway to build on these previous efforts with an expanded data set and improved statistical methods.

In this chapter we present a brief overview of the 1990 projection effort and a preview of some of the ongoing work on extending the exercise for the GBD 2000.

For researchers undertaking a national burden of disease study, we do not recommend independent efforts to include projections in these projects, as the analyses must draw on extensive databases from all countries for which data is available in order to fit models that may be used for developing forecasts.

14.2 GBD1990PROJECTION APPROACH

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For the GBD 1990 projections, a simple regression model was developed relating age- and cause-specific mortality rates to a short-list of distal socio-economic determinants and one major risk factor (smoking). The models in the GBD 1990 exercise were fit as follows:

Causes of death were divided into nine clusters of causes: Group I (communicable, maternal, perinatal and malnutrition), malignant neoplasms, cardiovascular diseases, digestive diseases, chronic respiratory

diseases, other Group II diseases (noncommunicable), road traffic accidents, other unintentional injuries, and intentional injuries. These clusters were selected because mortality trends over the last 40 years in

countries with good vital registration data suggest that the more specific causes within each cluster have followed a similar time trend.

Equations were developed relating age, sex and cause-specific death rates to the following explanatory variables:

GDP per capita

Human capital – average number of years of schooling Smoking intensity – based on lung cancer death rates Time – used as a proxy for technological progress. A log-log functional form has been used for most causes:

LnM

=C

+

β

LnY+

β

LnHC+

β

T

where Ca,k,i is a constant term; Ma,k,i is the mortality level for age group a, sex k , and cause i; and Y, HC and

T denote GDP per capita, human capital and time, respectively.

We omitted some of the variables where we thought epidemiological relation would not be sensible; for example mortality rate from infectious diseases for children would likely to be not affected by smoking intensity. A further selection of variables proceeded after F-tests were used to weed out insignificant variables. For some causes we posited quadratic relation with income and for nearly all we had a constant term.

A panel data set of age, sex and cause-specific mortality rates for 47 countries from 1950-1990 was used to estimate these regression equations.

P

I

V

Alternative projection scenarios were constructed by projecting the independent variables over the projection period. For the 1990 GBD, we constructed baseline, pessimistic and optimistic scenarios. Many more scenarios including some lower probability scenarios could be constructed.

Regional income per capita projections, were based on the World Bank Global Economic Prospects estimates of expected growth over the period 1995-2004.

Forecasts for the period 2005 to 2020 were constructed assuming that the growth rate of income per capita would tend toward the historical average of EME over the last 40 years (around 3% per year). Optimistic and pessimistic scenarios were constructed by examining the highest and lowest regional growth rates over the last 40 years and selecting a plausible but arbitrary rate.

For projections of human capital, we first estimated the growth rate in human capital as a function of level of human capital that has been achieved.

r=0 043 0 004.

−

.

HC

Projections assumed that this relationship would hold over the projection period. Optimistic and pessimistic variants were developed by arbitrarily modifying the coefficient on HC.

1. The GBD 1990 projections of smoking intensity were patterned after the evolution of the smoking epidemic in the UK from 1900 to 1990. We assume that the lagged relationship between cigarettes consumed per capita and the lung cancer rate minus the non-smoker lung cancer rate would be maintained in each region as in the UK. The 1990 consumption of cigarettes is thus used to estimate the smoking intensity in 2020. This presumes that the relationship between 1990 consumption and 2020 lung cancer incidence is fully determined.

2. The residual shift in the relationship between income, education and smoking with mortality rates was modeled by including time in the regressions, which captures the effects of technological

improvements.

P

M

D

C

Once mortality projections for the nine cause clusters were developed, projections for the detailed causes within each cluster were developed in one of two ways:

1. Regression models were estimated relating rates for detailed causes to the rates for the cause cluster. Where these models were good predictors, the regression results were used.

2. For other causes, we assumed that the relative distribution of mortality within each cause cluster would be maintained over the projection period.

3. HIV was treated as a special case because the regression model was fit to data that dated, for the most part, from the pre-HIV era. Separate models of HIV trends were adapted from those used by the former Global Programme on AIDS. In India and sub-Saharan Africa, tuberculosis projections were also modified based on projections of large HIV epidemics in these regions and the important interactions between HIV and TB.

YLL

YLDP

YLL projections were calculated based on the mortality projections. YLDs were then added assuming, depending on the cause, either that the ratio of YLD to YLL would remain constant, that age specific YLD rates would decline faster than the mortality trend, or that these rates would remain constant.

14.3 NEW PROJECTION MODELS FOR GBD2000

Ongoing work on extending previous projection models is being undertaken as part of the GBD 2000 project, using a substantially expanded data set on mortality by country, age, sex and cause that has been compiled at WHO. In addition, new statistical methods have been developed to address some of the major

In terms of statistical methods, the regression models used in the GBD 1990 left a number of opportunities for improvements:

• Listwise deletion was applied for missing observations.

• The regression models did not include time series processes, i.e., death rates for one year were not used in predicting death rates for the following year.

• It was assumed that the same relationships between mortality rates and income, education, smoking and time would apply to all countries.

• Separate models were estimated for each age, sex and cause cluster, which does not take advantage of different degrees of relatedness along each of these dimensions.

A number of improvements have been realized since the GBD 1990 project was completed, including the development of algorithms for imputing missing data and more sophisticated statistical techniques to reduce the bias created by pooling observations from different countries, and to take advantage of the hierarchical data structure. Another important area of work is in providing meaningful measures of the uncertainty around projections. This work is currently ongoing for the GBD 2000 study.

The weakest aspect of the projection method developed for the GBD 1990 is the simple approach to

projecting YLDs, which assumes in many cases that the relationship between mortality and non-fatal health outcomes will remain constant. There is some cross-sectional evidence that the ratio of YLDs to YLLs may decline as mortality declines. In addition, for some causes of disability there may be predictable trends independent of mortality trends. For example, the decline in heart disease mortality during the 80s and 90s in many western countries is mostly due to improved survival with heart disease, thus inflating the numbers of heart disease patients.On the other hand, due to better treatment possibilities, e.g. for heart failure, the YLD may actually be dropping. In current work, additional disease specific projection sub-models are being considered.

The final output of the ongoing projections work for the GBD 2000 will be the first set of country-specific projections of mortality and burden of disease. For research teams undertaking a national burden of disease exercise, revised projections may be developed in collaboration with WHO by including the results from the national burden study as the baseline from which forecasts extend.

REFERENCES

Bulutao R. Mortality by cause, 1970-2015. in Gribble JN, Preston SH, eds. The epidemiology transition. Policy and Planning Implications for developing countries. Work shop Proceedings

Lopez, AD and Hakama M. Approaches to the Projection of Health Status. in World Health Organization,

Health Projections in Europe: Methods and Applications

Murray CJL and Lopez AD. The global burden of disease Summers R and Heston A. The Penn World Table (version 5.5). World Bank. The Adult Health Policy Challenge