Mortality Sector

Purpose and Perspective

In order to capture the demographic impact of changes in the socio-economic context, iSDG provides an endogenous representation of mortality. Since the various factors that influence mortality have age-specific impacts, the model includes age-specific death rates. To accomplish this we specify initial values of life expectancy at birth for females and males and then use life expectancy to look up age specific death rates, based on tabulated numeric relationships empirically estimated between life expectancy- at-birth and the age- and gender-specific death rates in various regions [1]. These rates are modified over time by a range of effects caused by changes in per capita GDP, access to basic health care, average years of schooling, sufficiency of nourishment, access to electricity, exposure to air pollution, access to clean water and sanitation, and political stability and absence of violence. Mortality rates by cause are also tracked. These causes include the thirteen major categories of causes from WHO’s burden of disease data [2]. An initial distribution of mortality causes is specified, over time the distribution is altered by changes in the factors mentioned above.

Outputs of the sector are deaths (person/year) by age and sex. From deaths, life expectancy, infant mortality, under five mortality, and maternal mortality are derived. Life expectancy, a key indicator for the Human Development Index, influences important variables in other model sectors, for example productivity in agriculture, industry, and services sectors.

Sector Structure and Major Assumptions

Exogenous Input Variables


Initialization Variables

  • Initial perceived per capita real GDP - Units: RLCU/person/year

  • Initial life expectancy by sex - Units: Years

  • Initial proportion of mortality due to PM 2.5 - Units: Dmnl

  • Initial proportion of mortality due to lack of access to water and sanitation - Units: Dmnl

Modeling Details

The use of gender differentiation and one year cohorts means that death rates and their causes are separately calculated for each age group. This allows introducing the impact of specific interventions exclusively on the target age groups.

Footnotes and References

[1] Demographers at the Population Council found that the pattern of age specific death rates vary slightly in different regions of the world. Specifically, they found that there are four patterns: (a) West (base); (b) East (higher infant mortality rates than in west, increasingly high rates over age 50, and lower for other ages, relative to West); (c) North (lower infant mortality rates, low rates beyond 45 or 50, and higher for other ages, relative to West); and (d) South (higher mortality rates for ages under 5, lower mortality between 40 -65, and higher over 65, relative to West). We have tested these four life tables in ten countries, and they have proven to be extremely accurate.

[2] The causes of mortality used in the model are: aids, diarrhoeal, parasite and vector, respiratory, maternal, neonatal, nutritional, neoplasms, diabetes, cardiovascular, road, violence, other.

[3] Baker, D., Leon, J., Smith Greenaway, E. G.; Collins, J., & Marcela, M. (2011, June). The Education Effect on Population Health: A Reassessment. Population and Development Revue, 37(2): 307–332.

Carrin, G., Mathauer, I., Xu, K., Evans, D. B. (2008). Bulletin of the World Health Organization, 86, 11: 817-908.

Preston, S.A. (1975). The Changing Relation between Mortality and Level of Economic Development. Population Studies, 29(2): 2231-2248.

[4] Kunitz, S. J., (2007). The health of populations: General theories and particular realities. Oxford University Press.

[5] Kunitz, S. J., (2007). The health of populations: General theories and particular realities. Oxford University Press.

[6] Fogel, R.W. (1984). Nutrition and the Decline in Mortality Since 1700: Some Preliminary Findings. NBER Working Paper No. 1402.

[7] Ezzati, M., & Kammen, D.M. (2002). Evaluating the health benefits of transitions in household energy technologies in Kenya. Energy Policy, 30, 815–826

World Bank, (2008). The Welfare Impact of Rural Electrification: A Reassessment of the Costs and Benefits, An IEG Impact Evaluation. Washington, DC: World Bank.

[8] World Health Organization (2012). Global costs and benefits of drinking-water supply and sanitation interventions to reach the MDG target and universal coverage. Geneva: World Health Organization.

[9] World Health Organization (2013). Health effects of particulate matter. Copenhagen: World Health Organization Regional Office for Europe.

[10] Lin, R.-T., Chien, L.-C., Chen, Y.-M., & Chan, C.-C. (2014). Governance matters: An ecological association between governance and child mortality. International Health Advance Access. April 7, 2014

[11] Kopits, E., Cropper, M. (2008). Why Have Traffic Fatalities Declined in Industrialised Countries? Journal of Transport Economics and Policy, 42, 1: 129–154.

[12] World Bank (2010). The Costs to Developing Countries of Adapting to Climate Change, New Methods and Estimates from the Global Report of the Economics of Adaptation to Climate Change Study. Washington, DC: World Bank.

[13] Collste, D., Pedercini, M. & Cornell, S.E. (2017). Policy coherence to achieve the SDGs: Using integrated simulation models to assess effective policies. Sustainability Science, 12: 921-931.