8.2 Multiple geographical scales

Inequality is a pervasive issue in health care management. An implicit goal for health care planners is generally to achieve a degree of equity in the provision of health services, such that all members of a given population have equal access to the health care they need. Health care inequality is a multi-faceted concept that encompasses differences in levels of access to health services for different groups within a population. These groups may be defined by factors such as gender, socio-economic status, religion, ethnicity, education and so on. Of particular interest from a GIS perspective, however, are those differences in access to health services that are manifest spatially. As with most geographically-varying phenomena, these differences can be observed at a wide range of geographical scales from the international to the very local. The geographical scale of health care inequalities is important because of the implications for health care planners who seek to address these inequalities. These scales also have important implications for the way inequalities can be measured and subsequently investigated using GIS.

“Countries with the lowest relative need have the highest numbers of health workers, while those with the greatest burden of disease must make do with a much smaller health workforce”.

(World Health Organisation, 2006)

 

The existence of health inequalities at the international scale (i.e. existing between countries and continents) is widely documented. The World Health Report, produced annually by the World Health Organisation (WHO), contains startling figures on the disparity between health care provision and health care need in different regions of the world (World Health Organisation, 2006). The region of the Americas contains around 10% of the world’s burden of disease, but spends over 50% of the worlds financial resources for health care. This is in marked contrast to Africa which contains 24% of the world’s disease burden but spends less than 1% of the world’s health resources. Other metrics of health service provision reveal similar global inequalities. Table 1 summarises the disparity in the provision of health workers for different regions of the world (Data from World Health Organisation (2006)). Inequalities occurring at the international scale are often the hardest to measure and quantify reliably, as well as being the most daunting to address. International organisations such as the WHO seek to quantify inequalities between different regions in order to set priorities for global health initiatives. Global inequalities are also of interest to large charitable health organisations such as the Gates Foundation, and to governmental bodies such as the UK’s Department for International Development and the United States Agency for International Development (see References for web links to these organisations).

 
Inequalities in health care provision can often be striking at the scale of individual countries, and these can often be obscured by national-level statistics. The density of health workers and health facilities often varies dramatically, and this variation is often most pronounced in those countries which are generally under-resourced. A common pattern is for rural areas within a country to be less well served than urban areas (Noor et al., 2003). Internationally it is estimated that whilst less than 55% of the world’s population live in urban areas, more than 75% of doctors, over 60% of nurses and 58% of other health workers also live in urban areas (World Health Organisation, 2006). Within-country inequalities are of interest to national governments and other health service providers when assessing the distribution of national health resources.

Inequalities are also present at much smaller geographical scales, such as within a state, neighbourhood, or hospital catchment area. Such inequalities are often studied in considerable detail using GIS approaches in order to help local health system planners. Analysing local differences in service provision or utilisation can identify possible soicio-economic or demographic explanations (Odoi et al., 2005) or assist in the planning of new service provision (Messina et al., 2006).

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