Understanding Demographics & Rates: Population Projections, Study notes of Public Health

Download Understanding Demographics & Rates: Population Projections and more Study notes Public Health in PDF only on Docsity! Handout for Part 1 Introduction to Population Projections Slides Slide Content Closed Captions Introduction to DemProj an e-learning course This course is the first e-learning course created by the United States Agency for International Development Health Policy Initiative. My name is John Ross, and I will be your instructor for Part 1. This course was created in response to the demand for technical assistance for use of the suite of models known as Spectrum. The Spectrum software was created by predecessor projects with funding from USAID. The Spectrum models are used as planning tools around the world by policymakers, program managers, students, and people working in the field of public health. This course focuses on the first module of Spectrum—DemProj—which, as its name implies, is used to make population projections based on demographics. Overview of Course Part 1: Introduction to population projections Part 2: Introduction to the DemProj model Part 3: Programmatic applications of DemProj This course is divided into 3 parts. Part 1 is an introduction to population projections. Part 2 is an introduction to the DemProj Model. Part 3 presents some programmatic applications of DemProj. To begin this course, users should have a basic understanding of Windows software. Spectrum is a Windows-based program that will run in either Windows 95 or higher versions. The Spectrum program requires approximately 16MB of hard disk space. The tutorials that we are about to cover can be installed from a CD or over the web. 1 Part 1: Introduction to DemProj: An E-learning Course Introduction to Population Projections The first part of the course is an introduction to population projections. Objectives The objectives of part 1 of the course are as follows: Introduce basic concepts related to population projections including fertility, mortality, and migration Explain how population projections can be useful decision-making tools for policy makers Describe principal determinants of population growth Provide an overview of the impact of AIDS on population growth The objectives of Part 1 are as follows: Introduce basic concepts related to population projections, including fertility, mortality, and migration. Explain how population projections can be useful decisionmaking tools for policymakers. Describe principal determinants of population growth. Provide an overview of the impact of AIDS on population growth. 1. Population Projections The next few slides give an overview of population projections. 2 Part 1: Introduction to DemProj: An E-learning Course Why make population projections? Planning – Assessing the need for new jobs, teachers, schools, doctors, nurses, urban housing, food, etc. Policy dialogue – Helping policymakers understand that problems exist – Developing solutions Why do we make population projections? Population projections are useful for a number of reasons and help stakeholders plan for the near and distant future. If we know how many people are in a country or region, this puts us in a better position to assess the need for new jobs, teachers, schools, doctors, nurses, urban housing, food, and requirements for resources. For example, in order to plan an immunization program at some time in the future, governments, donors, and healthcare staff need to know how many children will be alive in the future. Population projections can help us estimate future population size. Population projections are also important for raising awareness of issues among policymakers. For example, a population projection can help illustrate the impact of an increased population on the use of fuel wood and the potential threat to the forests or the need for affordable housing projects to accommodate the large and growing population. Summary of Key Population Concepts A population is the total number of men, women, boys, and girls, of different ages, living in a particular place at a particular point in time. A population pyramid is a graphic representation of the age and sex distribution. A population projection is an estimate of the number of people expected to be alive at a future date, based on assumptions of population size, births, deaths, and migration. Population projections are useful tools for program planning and policy dialogue. In summary, a population is the total number of men, women, boys, and girls of different ages, living in a particular place at a particular point in time. A population pyramid is a graphic representation of the age and sex distribution. A population projection is an estimate of the number of people expected to be alive at a future date, based on assumptions of population size, births, deaths, and migration. Population projections are useful tools for program planning and policy dialogue. 5 Part 1: Introduction to DemProj: An E-learning Course 2. Fertility Now that we know the basics of population, I’m going to walk you through concepts related to fertility. I’m sure many of you are familiar with measures of fertility, and for some of you, this may be a review. However, it is important to understand these concepts prior to doing an application in DemProj. Fertility Fertility refers to the number of children born to women. Fertility is determined by biological and social factors. What is fertility? Fertility refers to the number of children born to women. Why are we concerned with women and not men? Fertility also refers to men, but demographers have found that it is much harder to measure the fertility of men, and therefore, in most cases, fertility is measured in relation to women. Fertility is determined by biological and social factors. Measures of Fertility There are several different measures of fertility. We measure fertility to estimate the number of children that women are having in a given population during a specific time period. Data for measuring fertility can come from different sources. There are several different measures of fertility. You may come across many of these measures in your work, and some find their way into popular newspapers. Familiarizing yourself with them will make you a better consumer of the information, and it will help you better understand fertility trends and levels. Data used to measure fertility and other population processes come from various sources. Some of these sources may produce more complete information than others.The more information you have, the more accurate your estimates will be. Some common sources for finding fertility rates include: national fertility surveys, Demographic and Health Surveys, Centers for Disease Control and Prevention (CDC) Reproductive Health Surveys, the Population 6 Part 1: Introduction to DemProj: An E-learning Course Reference Bureau’s World Population Data Sheet, and the World Bank’s World Development Indicators. Fertility The biological component of fertility is the physical ability of a woman to reproduce (fecundity). Over the course of her life, a woman could bear between 13 and 17 children, in the absence of any other factors. The number of children born to a woman varies by social factors that affect when she starts childbearing, the spacing between children, and when she stops childbearing. The biological component of fertility is the physical ability of a woman to become pregnant and carry the pregnancy to a live birth; this is also called fecundity. Over the course of her life, a woman could bear between 13 and 17 children, in the absence of any other factors. The biological ability to bear children is similar across different societies; although, it is also affected by health factors such as nutrition and disease. The number of children a woman will have depends not only on her fecundity but also on social factors that affect when she starts childbearing, the spacing between children, and when she stops childbearing. For example, families in agricultural societies often have more children than families in industrialized areas. Measures of Fertility Crude birth rate (CBR): The number of live births per 1,000 persons in a given year CBR = ( # births in a year ) X 1000 Mid-year population The crude birth rate is the number of live births per 1,000 population in a given year. This measure tells us how many children will be born in a given population in a given year. It does not tell us who is having children, or how many children the typical woman might have. 7 Part 1: Introduction to DemProj: An E-learning Course Measures of Fertility Total fertility rate (TFR): The approximate number of births that a woman will have if she moves through her reproductive years having births at the current age-specific birth rates. TFR is a synthesis measure of the number of births women of different ages are having now. Total fertility rate (TFR): The approximate number of births that a woman will have if she goes through her reproductive years having births at the current age- specific birth rates. The TFR is a synthesis of the number of births women are having today. Measures of Fertility TFR is the sum of the age-specific rates (ASFRs) multiplied by 5 and divided by 1000. TFR is expressed as a rate per woman. TFR can be compared across populations because it is not influenced by differences in age structure. TFR is calculated as the sum of the ASFRs through all ages, multiplied by 5, and then divided by 1,000. The multiplication by 5 is because a woman will spend 5 years of her life in each 5-year age group. We divide by 1,000 because the original rates were per 1,000, and we want to express the rate per individual woman. The TFR is interpreted as the number of births per individual woman. It can be compared across populations because it is not influenced by differences in age structure. Calculating TFR from ASFR Calculation of TFR from ASFR: (135+192+135+83+41+16+3)*5 = 3.0 1,000 *Note that we multiply total ASFRs by 5 because each age group covers five years. Let’s use the same example from Bangladesh in 2004 to calculate the TFR. We calculate the TFR by summing all the age-specific fertility rates and multiplying by 5, because each age group covers 5 years, and then divide by 1,000. The TFR in Bangladesh in 2004 was 3.0. 10 Part 1: Introduction to DemProj: An E-learning Course Comparison of TFR among Countries Country TFR Nigeria (2003) 5.7 Ghana (2003) 4.4 Bangladesh (2004) 3.0 Colombia (2005) 2.4 USA (2006) 2.1 United Kingdom (2006) 1.7 You will rarely have to calculate the TFR from the ASFRs by hand. The TFR values above show that societal factors influence the rate. The TFRs across these 6 countries are markedly different. Notice that the first three countries, which are largely agrarian, have higher fertility rates. Measures of Fertility Replacement level fertility: The TFR at which women have exactly the number of births required to replace themselves and their partners. Another common concept is replacement-level fertility. Replacement-level fertility is the TFR at which women have the number of births required to replace themselves and their partners but no more. If no children died before reaching adulthood, replacement- level fertility would be 2 births per woman (1 to replace the mother and 1 to replace her spouse). If death rates are low, replacement-level fertility averages about 2.12, since not all children will survive to reach reproductive age. If mortality rates are high, replacement-level fertility will be higher. Sex ratio at Birth Sex ratio at birth: The number of male births per 100 female births. In most countries, this value is about 105 for first births. Another measure associated with births is the sex ratio at birth. It is measured as the number of male births per every 100 female births. In most countries, this value is 103–105, which means that for every 100 girls born, there are between 103 and 105 boys born. This is also an input in the DemProj Model. 11 Part 1: Introduction to DemProj: An E-learning Course Summary of Fertility Fertility has both biological and social components. Several fertility measures are used in making projections with DemProj: TFR, ASFR, and the sex ratio at birth. TFR is calculated from ASFR. In summary, fertility has both a biological and social component. There are several fertility measures used in DemProj: TFR, ASFR, and the sex ratio at birth. However, you will see in the next section that ASFR is not an input of the DemProj Model but is instead an output. Age distribution of fertility is actually the input used in the model. 3. Mortality Now that we have a good understanding of fertility measures, we can move to mortality rates. There are several ways to talk about the deaths, or mortality, that occur in the population. Mortality and Population Growth Declining mortality (and not rising fertility) has been the cause of the accelerating pace of world population growth. It is important to note that declining mortality (and not rising fertility) has been the cause of the accelerating pace of world population growth. 12 Part 1: Introduction to DemProj: An E-learning Course Life Tables Life tables tell what would happen to a new birth cohort if the current age-specific death rates were to remain constant over its entire lifetime experience. Life tables give the distribution of deaths that would occur within each age group. Life tables also produce values for life expectancy. A life table shows us what would happen to a new birth cohort if the age-specific death rates for a given period were to remain constant and apply throughout the full life-time experience. Life tables give the distribution of deaths by age group and can also be used to calculate values of life expectancy. In DemProj, life expectancy is an input that links to a life table and a set of mortality ratios (DemProj uses this information in the projection). You will learn more about life tables when we get to the DemProj Model. Life Tables Because many countries do not have accurate data on mortality by age groups, DemProj uses model life tables based on expected mortality rates at different levels of life expectancy and patterns of mortality in various regions of the world. The life tables shown are for males and females in Nigeria in 2000. Summary of Mortality Declining mortality has been the cause of the accelerating pace of world population growth. IMR is an important indicator of a country’s development; increased development is associated with a lower IMR. DemProj uses life expectancy (and its associated age-specific survival ratios) and life tables as inputs for population projections. Life tables show the distribution of deaths by age group and are the source for survival ratios and values of life expectancy. In summary, declining mortality has been the cause of the accelerating pace of world population growth. The infant mortality rate is an important indicator of a country’s development: increased development is associated with a lower IMR. DemProj uses Life Expectancy and model life tables (which are selected based on CDR and IMR) as inputs for population projections. Life tables illustrate the distribution of mortality by age group and are the source for survival ratios and values of life expectancy. 15 Part 1: Introduction to DemProj: An E-learning Course 4. Migration There are other measures of a population that help us understand population projections. The size of a population is not only affected by births and deaths but is also affected by the number of people coming into and out of a place. Measuring Migration People move different distances. Some migrants are ‘return migrants.’ Some migrants are not official/legal and may view their ‘residence’ differently. Generally, migration is more difficult to measure than fertility and mortality because of some complexities noted here. First, we need to define whether we are trying to measure domestic or international migration. People move within the country all the time. Some migrants are “return migrants.” For example, many migrant workers in southern Africa migrate to South Africa to work in the diamond mines on a seasonal basis. Some migrants are not official/legal and may view their “residence” differently. For example, there are many people moving across the border into refugee camps. It’s not only difficult to find data on this, but it is also difficult to define permanent residency. Nevertheless, when data are available, migration is measured with rates that are similar to fertility and mortality measures. 16 Part 1: Introduction to DemProj: An E-learning Course Measures of Migration Net migration: The difference between those who move in and those who move out of an area Net migration is the measure used as an input to a population projection in DemProj. It is the difference between those who move in and those who move out of the area for which the population projection is being prepared. If the projection is for a country, then it is international migration. If the projection area is a region or city, then migration refers to people moving into or out of the region or city. Measures of Migration Net migration: Negative net migration implies that there are more people moving out than in. Positive net migration implies that there are more people moving in than out. Zero net migration does not necessarily mean that nobody is moving in or out of an area. Negative net migration implies that there are more people moving out. Positive net migration implies that there are more people moving in. Note that zero net migration does not necessarily mean that we do not have people moving in and out of an area in a given year. It might mean that equal numbers of people are moving in and out. Measures of Migration Net international migration is not a major component of population change in most countries. Age and sex patterns of migration vary considerably. Net migration can be temporary or can vary throughout the year. Net international migration is not a major component of population change in most countries. Often, migration can be ignored without a significant effect on the population projection. However, for some countries, and also for cities, migration can be very important. Age and sex patterns of migration vary considerably. In Nairobi, for example, migrants to the city consist largely of young males seeking work. In other cities, migrants to the city are composed primarily of entire families. As noted above, net migration can also be temporary. For example, in Jordan, there was a significant outflow of migrants during the oil boom in the Persian Gulf states in the 1970s and 1980s. However, during the 1990s, there was a net inflow of migrants as families returned to Jordan due to reduced 17 Part 1: Introduction to DemProj: An E-learning Course Population Growth Rate The population growth rate is difficult to calculate using the previous formula. More conventionally it is calculated using the formula below: Population=Population(time2)-Population(time 1) x 100 growth rate starting population (+1) The population growth rate is difficult to calculate using the previous formula. More conventionally, it is calculated using this formula. Population Momentum Momentum is the tendency for a population to continue to grow even after replacement-level fertility has been achieved. Population momentum occurs because the age structure has large numbers of women in the childbearing years, so there are many more births than deaths for a long time to come. Therefore, even when replacement-level fertility is achieved, it can take the population growth rate a long time to reach zero! Population momentum is the tendency for a population to continue to grow even after replacement-level fertility has been achieved. This occurs because the age structure has large numbers of women in the childbearing years, so there are many more births than deaths for a long time to come. Even though each woman has only two births, there are so many women that there are many births, and the population keeps growing. So it is important to remember that even when replacement-level fertility is achieved, it can still take the population growth rate a long time to reach zero! Population Momentum Population momentum is mainly a function of a population’s age structure. In other words, the larger the number of children entering their reproductive years, the faster the population will grow. Population momentum is mainly a function of a population’s age structure. In other words, the larger the number of children entering their reproductive years, the faster the population will grow. For example, in this pyramid of Country A, the momentum is maintained by the children at the base of the pyramid who will be entering their childbearing years. 20 Part 1: Introduction to DemProj: An E-learning Course Population Momentum A larger vehicle (train) takes a longer time to stop than a smaller vehicle (bus or car). Similarly, a population heavily concentrated in younger ages will take a longer time to reach zero population growth. We can think of a population with a large population momentum like a train. A large train takes longer to stop than a car or even a bus. Similarly, a population heavily concentrated in younger ages will take longer to reach zero population growth. Even if each couple has only two surviving children (replacement level fertility rates), there will be many births, as we saw in the previous slide. Country A’s Population Growth, 1950–2000 Between 1950 and 1980, country A’s population doubled. Country A’s past population growth provides an illustration of population momentum. Over a 50-year period, the population doubled twice. The first time it doubled it took 30 years, from 1950–1980. Country A’s Population Growth, 1950–2000 Between 1980 and 2000, Country A’s population almost doubled again! The second time it doubled it took only 20 years, from 1980–2000. Thus, over 50 years, the population increased, from 32 million to 125 million! 21 Part 1: Introduction to DemProj: An E-learning Course Country A’s Population Growth, 1950–2000 Even if Country A’s fertility rate were reduced to replacement level today (i.e., 2.1 births), the population would continue to increase for the next 25 years due to the large number of girls who will enter the reproductive ages over the next several years. Even if Country A’s fertility rate were reduced to replacement level today (i.e., 2.1 births), the population would continue to rise for the next 25 years due to the large number of girls who will enter the reproductive ages over the next several years. This information is important for those policymakers who expect to see an immediate impact from family planning programs. Let’s look at the impact of two fertility scenarios on population growth. Country A’s Population Growth Under Two Fertility Scenarios, 2007–2050 This graph shows population growth between 2007 and 2050 for two different fertility scenarios. Under the first scenario, the total fertility rate decreases only gradually from 5.4 to 3.1, between 2007 and 2030 and then between 3.1 to 2.1 between 2030 and 2050. In the second scenario, the total fertility rate decreases instantly to replacement level in 2007. (We know this would never happen because behavior change takes a long time, but we assume this for illustrative purposes.) Even if we achieved replacement-level fertility immediately, the population of Country A would keep growing; it would not begin to stabilize until 2050. This graph shows how population continues to grow even when the fertility rate has leveled off. Country A’s Population in 2050 Under Two Fertility Scenarios This slide shows how the population structure would change by 2050 if replacement-level fertility were achieved today. The age structure of the population would change dramatically. You see on the left the population structure that would occur with just a gradual reduction in the total fertility rate. The pyramid on the right shows how the population structure of Country A would look in 2050 if replacement-level fertility had been achieved in 2007. 22 Part 1: Introduction to DemProj: An E-learning Course Impact of AIDS on Mortality Impact on infant mortality Impact on life expectancy Impact on population structure In many countries, HIV/AIDS has had a big impact on all measures of mortality, including infant mortality, life expectancy, and mortality in the economically productive ages of 15–49. The greater the HIV prevalence, the larger the impact on mortality. As mentioned in the previous slide, the epidemic also affects fertility, although to a lesser extent. HIV/AIDS also affects the population structure, as people dying from AIDS-related illnesses are mainly in their most productive years. Also, fewer births change the age structure at the bottom of the pyramid. Impact on Infant Mortality The high prevalence of HIV has increased the IMR significantly in several African countries. Infant mortality rates in some African countries are higher than they were in 1990. This is because without prevention of mother-to-child transmission, an HIV+ pregnant woman has a 30% probability of transmitting the HIV virus to her newborn. In Zimbabwe, AIDS causes 70% of the deaths among children less than 5 years of age. When thinking about these rates, it’s important to think about the IMR prior to the epidemic. For example, prior to the epidemic, Swaziland made great strides in decreasing its IMR, but the IMR is now high due to AIDS. However, Mozambique has always had a high IMR, so we see a smaller relative impact from the epidemic. As with all the data you see, it’s important to think about why numbers may be what they are over time. As public health professionals, we need to think about what is behind the data. 25 Part 1: Introduction to DemProj: An E-learning Course Impact on Life Expectancy AIDS has set back or erased improvements in life expectancy achieved prior to the epidemic. AIDS also kills young adults, who would otherwise have low mortality. The combination of increased infant mortality and increased mortality among young adults has resulted in severe set-backs and in some countries has erased, many improvements in life expectancy that have been achieved in the last 20 years. The most dramatic effect has been in sub- Saharan Africa. Impact of AIDS on Life Expectancy Life Expectancy Country 1990 1995 2010 Zimbabwe 56 48 40 Zambia 50 42 33 South Africa 63 57 48 Life expectancy at birth in Zimbabwe, Zambia, and South Africa declined between 1990 and 1995 and is expected to decrease further by 2010. AIDS Deaths vs. All Deaths among Adults (15-49), Nigeria With treatments still out of reach for many people, and no cure in sight, HIV infection ultimately results in death. Let’s look at a historical representation of Nigeria to illustrate the impact of AIDS on mortality. In the early 1990s, deaths from AIDS began to appear. By 2000, annual AIDS deaths were nearly half of all deaths among the 15–49 population. Clearly, AIDS was killing large numbers of people. 26 Part 1: Introduction to DemProj: An E-learning Course Impact of AIDS on Population Structure In the absence of treatment, AIDS will affect the population distribution by reducing the number of adults in the reproductive years. AIDS also affects the population structure. The dark orange and dark blue represent the age cohorts that are most affected by HIV. The sexual mode of transmission of AIDS implies that it is the adult population that experiences most deaths. That distorts the population pyramid, removing greater numbers of adults in the sexually active ages than from other age groups. AIDS also results in a higher percentage of infant deaths. Also, the surviving infants have fewer adults to take care of them due to adult deaths. Estimated Effect of AIDS on Population Growth Nigeria WITH AIDS EPIDEMIC* WITHOUT AIDS EPIDEMIC Total Population (millions) Growth Rate Total Population (millions) Growth Rate 1990 89 89 2000 118 2.7 119 2.9 2005 132 2.1 135 2.4 2010 145 1.6 151 2.1 2015 155 1.3 167 1.9 Estimated Effect of AIDS on Population Growth. Many Nigerians have AIDS already, and many others with HIV will soon have AIDS, and deaths will occur for many years to come. The red projection reflects the expected deaths, and it shows smaller populations in the future than in the green projection. The growth rates are also less with the red projection. Summary of the Impact of HIV/AIDS The HIV/AIDS epidemic has increased the IMR significantly in several Sub-Saharan African countries. Life expectancy in many Sub-Saharan African countries has decreased as a result of AIDS. AIDS affects the population distribution by reducing the number of adults of reproductive age and the number of infants. Summary of the Impact of HIV/AIDS. Because of HIV and AIDS, mortality has risen in several sub-Saharan African countries. The IMR has risen, as have death rates in the reproductive years, among both men and women. Consequently, overall life-expectancy has fallen. In addition, the age structure has changed due to the depletion of adults of reproductive age and of infants. 27