How can you use demographic statistics in public health programs and studies?
1. Describe the Context- Demographic statistics can provide a general background for describing a community with whom you are working. Example
2. Assessing stage of demographic transition: Demographers have identified a long-term evolution of demographic processes, known as the Demographic Transition. The Demographic Transition is key to being able to portray the demographic “status” of a society in terms of its fertility and mortality levels. There are 4 main stages of the Demographic Transition:
- Pre-transitional: In the pre-transitional stage, both fertility and mortality are high, and the rate of population growth is very slow. Many societies were in the “pre-transitional” stage prior to the 1800’s. Current examples of pre-transitional societies are Angola, Mali, Niger, Somalia, Uganda (all are African nations)
- Early Transition: The early transition is a phase of high birth rates but falling death rates, leading to rapid population growth rates. The More Developed countries entered this phase in the early 1800’s, and most developing countries were in this phase prior to 1970. Current examples of countries in Early Transition are: Benin, Guinea, Mozambique, Rwanda, Zambia
- Transitional: The transitional phase is marked by declining fertility and fairly low mortality rates, with moderate to slow population growth rates. The Transitional/less developed countries entered this phase in the 1970’s and 1980’s, and many are still in this phase. Examples: Botswana, Bangladesh, Cambodia, Jordan
- Within countries still in the high fertility stage, there are regions or cities which have entered this later stage.
- Post-Transitional: The final stage of the demographic transition is characterized by low birth and death rates, and a return to very slow or negative growth rates. Many of the More Developed countries are now in this stage, and many of the formerly “Less Developed.” Each stage is reflected by different fertility and mortality levels, as well as different social, cultural, and economic behaviors and customs. Examples: Sri Lanka, Mauritius, Guadeloupe, Uruguay, Kazakhstan, virtually all Western European countries, Canada, US, Australia and New Zealand, Japan, Korea (both), China
Until societies reach the Post-transitional stage, demographic rates are in an evolutionary process which the transition stages represent. Therefore, knowing the demographic transitional stage of a society is very useful, as it provides guidelines for the likely demographic and social behaviors, current and future.
While the demographic transition theory can be used to describe the most probable direction in which demographic rates evolve over time, there are exceptions, and they primarily occur with regard to mortality. Reversals in the mortality transition have been documented in association with widespread famines and long lasting wars, and now in many Sub-Saharan African nations, with the AIDS epidemic. For these countries, the demographic transition is stalled, if not reversed, as mortality rates climb and life expectancy plummets, sending countries like Botswana back from transitional to early transitional status.
In this module, we use the UN groupings of More Developed, Developing, and Least Developed, because these are the groupings used to classify demographic data presented in the module.
- More Developed Countries = Post transitional
- Developing = Transitional
- Least Developed = Early transition or pre-transitional
3. Needs assessments- When justifying or proposing a project to funders, using demographic data helps to illustrate the need/importance of the program or research. Example
4. Program design and planning- To design and set realistic goals for a program, demographic data for your population of interest is essential. Example
5. Monitoring and evaluation- Once in place, it is important to monitor and evaluate the progress of a program, to see whether the desired outcomes and effects are being achieved. To do so, public health practitioners often use indicators that are set prior to program implementation. Without demographic data, most indicators would be impossible to measure. Example
Some caveats for population statistics users
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