Human development/Description

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GISMO model to assess human development in IMAGE 3.0
Flowchart Human development. See also the Input/Output Table on the introduction page.

Model description of Human development

GISMO assesses the impacts of global environmental change on human development through the impacts on human health either directly, for example the impact of climate change on malaria, or indirectly through, for instance, the impact of climate change on food availability. In addition to environmental factors, human health is also driven by socioeconomic factors, including income and education levels.

To take account of the interrelationships between the various factors, GISMO consists of three modules that address human health, poverty and education (Figure Flowchart). The modules are linked through a cohort component population model that includes endogenous fertility and mortality (for details see Hilderink, 2000). Fertility levels are modelled using a convergence level that is determined by female educational levels, and speed of convergence determined by the human development index, and mortality rates by the health module. Future trends in migration, including urbanisation, are exogenous inputs to the model (for details see Hilderink, 2000).

The Human development index (HDI), which was introduced in the UNDP Human Development Report 1990 to rank development achievements, is a composite index of life expectancy, education, and income indices (UNDP, 1990; UNDP, 2010). While the underlying indicators have been refined several times, the three elements have remained the same. The index links to the three GISMO model components.

GISMO health module

This module describes the causal chains between health-risk factors and health outcomes (morbidity and mortality) and takes into account the effect of health services. The mortality rate is modelled by a risk-factor-attributable component and a non-attributable component. Historically, the non-attributable component represents mortality not covered by the risk factors included. For future projections, this component is assumed to reduce by the average regional historical rates of reduction.

The risk-factor-attributable component is based on a multi-state approach that distinguishes exposure, disease and death (WHO, 2002; Cairncross and Valdmanis, 2006). This implies that incidence and case fatality rates (ratio of the number of deaths from a specific disease to the number of diagnosed cases) are taken into account for various health-risk factors. Case fatality rates are modified by the level of health services. This method is used for malaria, diarrhoea and pneumonia. The method for projecting mortality due to other causes (non-communicable chronic diseases, other communicable diseases and injuries) follows the global burden of disease (GBD) approach. This method uses a parsimonious regression technique to relate mortality rates with GDP, smoking behaviour and human capital, in ten major disease clusters (Mathers and Loncar, 2006). This method is also used in determining death related to urban air pollution.

Table: Cause of death and environmental risk factors
Cause of death Risk factors
Malaria Climate suitable for malaria vectors
Protein deficiency Prevalence of underweight
Diarrhoea Lack of safe drinking water and basic sanitation
Pneumonia, Chronic obstructive pulmonary disease (COPD), Lung cancer Use of solid fuels (traditional biomass or coal) for cooking and heating
Lung cancer, Cardiopulmonary diseases, Acute respiratory infections (ARI) Exposure to PM10 and PM2.5, related to NOx, SO2 and black carbon emissions

The GISMO health module takes into account mortality due to a range of diseases and conditions. These include:

  • malaria;
  • communicable and infectious diseases associated with undernourishment, limited access to safe drinking water and basic sanitation and poor indoor air quality;
  • diseases caused by poor outdoor air quality;
  • chronic diseases including high blood pressure and obesity.

Only the first three causes of mortality are considered because these are linked to environmental factors. The mortality rate due to a specific disease is a multiplication of the incidence rate (fraction of the population with the specific disease) and the case fatality rate (the fraction of people who die from a specific disease), distinguishing for the two sexes and five-year age cohorts. These mortality rates can then be used to calculated age-specific life expectancy (for details see Hilderink, 2000).

Malaria risk

Incidence rates of malaria are determined by the areas suitable for the malaria mosquito, based on monthly temperature and precipitation, see Component Water (Craig et al., 1999). Incidence rates are decreased by the level of insecticide treated bed nets and indoor residual spraying, modelled separately as potential policy options. The case fatality rate of malaria is increased by level of underweight people and decreased by case management (treatment).

Access to food, water and energy

GISMO relates incidence and case fatality rates for specific diseases to access to food, water and energy (the table above). Access is defined by per capita food availability, access to safe drinking water and improved sanitation, and access to modern energy sources for cooking and heating. The future per capita food availability (Kcal/cap/day) is obtained from IMAGE (Component Agriculture and land use). The levels of access to safe drinking water and improved sanitation are modelled separately by applying linear regression. The explanatory variables include GDP per capita, urbanisation rate and population density. Improvements in water supply are assumed to be implemented ahead of sanitation. Access to water supply and sanitation follows a pathway from no sustainable access to safe drinking water and basic sanitation, to improved water supply only, improved water supply and sanitation, household connection for water supply, to household connection to water supply and sanitation. Three levels of access to modern energy sources for cooking and heating are distinguished: traditional biomass and coal on traditional stoves; traditional biomass and coal on improved stoves; and use of modern energy carriers (electricity, natural gas, LPG, kerosene, modern biofuels and decentralised renewable sources). Trends in access to modern energy sources are taken from the TIMER energy demand module.

Underweight children and prevalence of undernourishment

For children under the age of five, undernourishment is expressed as underweight (measured as weight-for-age), and prevalence of undernourishment is used for the rest of the population. The direct effect of undernourishment is protein deficiency, which for mortality rates of the under fives is scaled to their underweight status and for other age groups to the level of undernourishment. Undernourishment indirectly increases the incidence of diarrhoea and pneumonia, and the case fatality of malaria, diarrhoea and pneumonia. These indirect effects are only modelled for children under the age of five. Underweight children as the result of chronic undernourishment is modelled as a function of improvements in average food intake, ratio of female to male life expectancy at birth, female enrolment in secondary education and access to clean drinking water (Smith and Haddad, 2000). Based on a normal distribution, the total number of underweight children is divided into three groups of mildly, moderately and severely underweight (De Onis and Blossner, 2003).

The prevalence of undernourishment is calculated from per-capita food availability and minimum energy requirements (FAO, 2003). The calculations use a lognormal distribution function determined by mean food consumption and a coefficient of variation, which decreases over time as a function of per capita GDP. The minimum requirement of dietary energy is derived by aggregating region-specific, sex-age energy requirements weighted by the proportion of each sex and age group in the total population, including a pregnancy allowance. Incidence rates of pneumonia, chronic obstructive pulmonary disease (COPD) and lung cancer are increased by indoor air pollution caused by cooking and heating with traditional biomass and coal. Simultaneously, incidence rates and case fatality rates are increased by child underweight levels. Incidence rates of diarrhoea depend on levels of access to drinking water and sanitation, levels of underweight children, and also on climate change. Case fatality rates are increased by underweight levels and decreased by the level of oral rehydration therapy.

Mortality associated with urban air pollution

Mortality rates of lung cancer, cardiopulmonary diseases and acute respiratory infections due to urban air pollution (PM10 and PM2.5 concentration levels) are derived using the GBD method (Mathers and Loncar, 2006). Based on emissions of NOx, SO2 and black carbon (Component Emissions), PM10 concentration levels are determined using the Global Urban Air quality Model (GUAM model). This model originates from the GMAPS model (Pandey et al., 2006), which determines PM10 concentration levels by economic activity, population, urbanisation and meteorological factors. PM2.5 concentrations are obtained using a region-specific PM10–PM2.5 ratio. Based on these levels and the exposed population, mortality attributable to causes of death is derived using relative risks obtained from epidemiology studies (Dockery et al., 1993; Pope et al., 1995).

GISMO poverty module

The poverty line is commonly defined as the level at which consumption or income levels fall below that required to meet basic needs. In the model, the poverty head count (people living below the poverty line) is conducted by applying a log-normal distribution using per-capita income and a GINI coefficient to describe poverty distribution over a population. The poverty module can assess the number of people living below a poverty line, including the international poverty line defined as USD 1.25 per day, at 2005 PPP, by the World Bank (Ravallion et al., 2008).

GISMO education module

The education module assesses future developments in school enrolment and educational attainment, including literacy rates at three levels of education: primary, secondary and tertiary. The model tracks the proportion of the highest level of education completed and the average number of years of schooling per cohort. The enrolment ratios per educational level are determined using cross-sectional relationships with per-capita GDP (PPP). The age at which a certain educational level is attained is assumed to be identical in all regions. Literacy rates are determined by the proportion of the population over the age of 15 who have completed at least primary education. Furthermore, to take account of autonomous increases in literacy levels, literacy levels of the population between the age of 15 and 65 is increased by 0.3%, annually.