Data for Policy Decisions
As globalization increases the number and reach of global health policies and initiatives, the data and statistics that inform policies and decision-making become increasingly important. Today, data collected in one context may affect policies that are implemented in others. The need for accurate data and informed analysis is therefore paramount.
Accurate data can be hard to find. Population health measures can suffer from limitations in collection and reporting mechanisms, or be skewed - upward or downwards - to meet political needs. For example, advocates bidding for scarce resources may exaggerate estimates of the numbers affected by a particular condition. Alternatively, disease-prevalence figures may be revised down for fear that they will discourage tourism or investment. Other difficulties include:
- Data may be partial and fragmented. In many countries even basic data on the number of deaths from particular causes each year are not available. Morbidity data, which look at the impact on population health of non-fatal disease and injury, such as dementia or blindness, are even more rare. Health-adjusted life expectancy (HALE) is a summary measure for population health that helps to mitigate this problem by adjusting life expectancy at birth for time spent in poor health.
- Policy-makers may not be able to compare the relative cost-effectiveness of different interventions. At a time when people's expectations of health services are growing and funds are tightly constrained, such information is essential to aid the rational allocation of resources. WHO's work on the cost-effectiveness of essential interventions (CHOICE) aims to fill this gap.
In the past, HIV/AIDS data have been particularly vulnerable to distortions owing to fear, denial, or lack of capacity to report. This was particularly so in Africa, where data collection systems are weak. Recognition of this problem, and increasing international interest in and commitment to HIV/AIDS, now mean that HIV/AIDS data collection and analysis can attract more resources and technical support than similar work on other diseases.
There is a need to disaggregate data for the rich and poor within countries, and for men and women, to show differences in health risk and access to health services. It is also important to disaggregate global statistics and data to highlight experiences in the developing world. For example, at the conclusion of the 1994 Uruguay Round it was forecast that the Round would lead to an extra US$200-500 billion in global income over the first seven years; however, it was predicted that sub-Saharan Africa would lose US$1.2 billion. Similarly, while the world is on target to meet some of the Millennium Development Goals, regions such as sub-Saharan Africa are not.
Gender-related distortions are of particular concern. Much of women's work is informal and is not reflected in gross national product statistics. As a result, women's contributions to development, as well as the impacts that policies have on them, are often ignored.
Most statistics relate only to the formal sector - that part of the economy that is regulated and overseen by the government. However, the formal sector frequently represents less than 50% of a country's “real” economy. The informal sector is the part of the economy that escapes regulation by government authorities and therefore cannot be monitored. In developing countries, this sector is thought to be enormous. As a result, figures for labour, production and even trade are frequently extremely inaccurate. In health, differences between government and nongovernment health provision are captured through population-based surveys, which are used to complement data reported by state services.
Microeconomics is the area of economics concerned with individual decisions and decision-making units, for example the family or a small business, and the way in which individuals' decisions interact to determine the quality and the price of, for example, goods, services and labour. Family decision-making about health expenditure is key to this area of study. The Commission on Macroeconomics and Health looked at household impoverishment through health payment and concluded that: “poor households are rarely insured against catastrophic illness, and therefore required to sell their few assets, such as farm implements and animals, or to mortgage their land in order to maintain minimal consumption in the face of lost earnings and to pay for urgent medical care. This depletion of productive assets can lead to a poverty trap at the household level even after the acute illness is overcome, since impoverished households will have a hard time re-capitalizing their productive assets.” Thus lack of health insurance at the household level can have important consequences for the economy.
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