45.4C: Age Structure, Population Growth, and Economic Development - Biology

45.4C: Age Structure, Population Growth, and Economic Development - Biology

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A population’s growth is strongly influenced by the proportions of individuals in different age brackets, which in turn is influenced by economic development.

Learning Objectives

  • Explain how age structure in a population is associated with population growth and economic development

Key Points

  • Population dynamics are influenced by age structure, which is characteristic for populations growing at different rates.
  • Age structure varies according to the age distribution of individuals within a population.
  • Fast-growing populations with a high proportion of young people have a triangle-shaped age structure, representing younger ages at the bottom and older ages at the top.
  • Slow-growing populations with a smaller proportion of young people have a column-shaped age structure, representing a relatively even distribution of ages.
  • Improvements in health care have led to the population explosion in underdeveloped countries, causing a “youth bulge” which is associated with social unrest.

Key Terms

  • population dynamics: Variation among populations due to birth and death rates, by immigration and emigration, and concerning topics such as aging populations or population decline.
  • youth bulge: Age structure typical of fast-growing populations in which a majority of the population are relatively young.
  • age structure: The composition of a population in terms of the proportions of individuals of different ages; represented as a bar graph with younger ages at the bottom and males and females on either side.

The variation of populations over time, also known as population dynamics, depends on biological and environmental processes that determine population changes. A population’s growth rate is strongly influenced by the proportions of individuals of particular ages. With knowledge of this age structure, population growth can be more accurately predicted. Age structure data allow the rate of growth (or decline) to be associated with a population’s level of economic development.

For example, the population of a country with rapid growth has a triangle-shaped age structure with a greater proportion of younger individuals who are at or close to reproductive age. This pattern typically occurs where fewer people live to old age because of sub-optimal living standards, such as occurs in underdeveloped countries.

Changing Population Age Structure: This 3:28 minute movie discusses age structures and gives examples.

Some developed countries, including the United States, have a slowly-growing population. This results in a column-shaped age structure diagram with steeper sides. In these cases, the population has fewer young and reproductive-aged individuals, with a greater proportion of older individuals. Some developed countries, such as Italy, have zero population growth. Countries with declining populations, such as Japan, have a bulge in the middle of their age structure diagram. The bulge indicates relatively-few young individuals, and a higher proportion of middle-aged and older individuals.

Globally, less-economically developed countries in Africa and Asia have the highest growth rates, leading to populations consisting mostly of younger people. Improved health care, beginning in the 1960s, is one of the leading causes of the increased growth rates that created the population explosion. For example, in the Middle East and North Africa, around 65 percent of the population is under the age of 30. These high growth rates lead to the so-called “youth bulge,” which some experts believe is a cause of social unrest and economic problems such as high unemployment.

All of the factors above also have an impact on the average life expectancy. As economic development and quality of health care increase, the life expectancy also increases.

Zero population growth countries

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Age is the strongest demographic risk factor for most human malignancies, including breast cancer [1]. About 80% of all breast cancers occur in women older than age 50 the 10-year probability of developing invasive breast cancer increases from ρ.5% at age 40, to about 3% at age 50 and to Ϥ% by age 70, resulting in a cumulative lifetime risk of 13.2% (one in eight) and a near ninefold higher incidence rate in women older than age 50 as compared with their younger counterparts [2,3]. Despite awareness that breast cancer and other cancers are primarily age-related diseases, molecular and cellular hypotheses explaining the cancer𠄺ging relationship have only recently emerged and remain clinically unproven [4].

At the subcellular level, normal human aging has been linked to increased genomic instability [5,6], to global and promoter-specific epigenetic changes [7,8], and to altered expression of genes involved in cell division and extracellular matrix remodeling [5,6]. These associations have led to the hypothesis that the cancer-prone phenotype of an older individual results from the combined effects of cumulative mutational load, increased epigenetic gene silencing, telomere dysfunction and altered stromal milieu [9]. Given the worrisome social, economic and medical consequences of an aging worldwide population, proposed biological mechanisms linking cancer with aging must be established in order to develop effective interventions.

As with normal organs and tissues, tumor biology can also change with aging [10,11]. For sporadic breast cancer in particular, correlations between patient age at diagnosis, tumor biology and clinical prognosis have long been appreciated if not fully understood [12-16]. Younger age at diagnosis (≤ 45 years old) is associated with more aggressive breast cancer biomarkers, including overexpression of ERBB2/HER2 and ERBB1/HER1 growth factor receptors [13], abnormal p53 expression [13,15], estrogen receptor (ER) negativity [12-16], higher nuclear grade and higher Ki-67 proliferation index [12-14,16]. These breast cancer biomarkers are also interdependent, however in particular, ER expression is inversely correlated with abnormal p53 [15], overexpression of ERBB2 [15], high Ki-67 and nuclear grade, and poor patient prognosis [17]. It therefore remains unclear whether the age-specific biomarker features of breast cancer reflect the pleotropic background effects of aging on the normal mammary gland or age-specific differences in breast tumorigenesis also, since most age-specific biomarkers strongly associate with the ER status, the effects of aging must be studied in histologically similar breast cancer phenotypes controlled for ER status.

The molecular and cellular effects of aging on both normal and malignant breast tissue are superimposed on a continuum of developmental changes that normally occur between puberty and menopause, heavily influenced by menstrual history and parity. In general, the normal mammary gland ER content (fmol receptor/g tissue) as well as the proportion of ER-expressing (ER-positive) ductal epithelial cells increase with each decade of age, and reach a plateau with menopause at about age 50 [18,19]. In contrast, breast cancer ER expression continues to rise beyond menopause, reaching a near 25-fold differential between normal and malignant mammary gland ER expression in patients by age 70 [18].

Curiously, expression of some ER-inducible gene markers, such as progesterone receptor (PR), pS2, Bcl2 and cathepsin D, does not show any significant relationship with the age at diagnosis [13,18], while other markers show increased expression in breast cancers arising earlier in life [20] – suggesting that the effects of aging may in part be attributed to age-related differences in estrogen-inducible ER pathways. Important in this regard is the age-related change in PR coexpression within ER-positive breast cancers, since PR has long been used as a clinical indicator for a functioning ER pathway in tumors likely to respond to endocrine therapy [21]. Among all ethnic patient groups, ER-positive/PR-negative breast cancers show the greatest age-related increase in incidence after age 40 [22]. Potentially relevant to this ER-positive/PR-negative phenotype is the fact that growth-factor-activated pathways downregulate PR expression [22-25], and that the inverse correlation between overexpression of the ERBB2 growth factor receptor and PR positivity is only seen in breast cancers arising after age 40 [26]. Surprisingly, the natural perimenopausal decline in ovarian-produced estrogen serum levels do not fully account for age-related changes in ER-regulated mammary epithelial pathways, since the marked age-related increase in stromal and epithelial aromatase expression produces postmenopausal mammary gland estrogen levels comparable with those measured in premenopausal women [27].

To better understand the molecular and cellular influences of aging on breast cancer biology and clinical behavior, we performed a detailed study of phenotypically similar breast cancers arising in two disparate patient age groups. The DNA and the RNA were prospectively extracted from cryobanked samples of stage-matched and histology-matched ER-positive breast cancers diagnosed in either younger (age ≤ 45 years) or older (age ≥ 70 years) Caucasian women. These samples were analyzed by array comparative genomic hybridization (CGH) and by high-throughput expression microarrays to look for genetic and epigenetic differences between the age cohorts. Unsupervised hierarchical clustering of the combined data from both cohorts was used to search for age biases in clustered subsets, and this was followed by supervised comparisons between the two cohorts to delineate potential age-related genomic and transcriptome differences. Finally, a predictive analysis of microarrays (PAM) performed on the two age cohorts produced an age-specific expression signature that proved to have 㺀% predictive accuracy when validated against two other independent breast cancer datasets.

45.4C: Age Structure, Population Growth, and Economic Development - Biology

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Conservation Status of an Endemic Kinosternid, Kinosternon sonoriense longifemorale, in Arizona

J. Daren Riedle, 1 Philip C. Rosen, 2 Richard T. Kazmaier, 3 Peter Holm, 4 Cristina A. Jones 5

1 Department of Agriculture and Environmental Sciences, Lincoln University, Jefferson City, Missouri 69105 USA [ ] [email protected]
2 School of Natural Resources and the Environment, and USGS Sonoran Desert Research Station, University of Arizona, Tucson, Arizona 85721 USA [ ] [email protected]
3 Department of Life, Earth, and Environmental Sciences, West Texas A&ampM University, Canyon, Texas 79015 USA [ ] [email protected]
4 National Park Service, Organ Pipe Cactus National Monument, Ajo, Arizona 85321 USA [ ] [email protected]
5 Arizona Game and Fish Department, Nongame Branch, 5000 West Carefree Highway, Phoenix, Arizona 85086 USA [ ] [email protected]

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The Sonoyta mud turtle ( Kinosternon sonoriense longifemorale ) is a member of the unique desert riparian fauna isolated along the Rio Sonoyta watershed in northern Sonora, Mexico, and southern Arizona. This subspecies occupies six sites along the Rio Sonoyta, a pool at Quitovac in Sonora, and one pond at Quitobaquito Springs in Organ Pipe Cactus National Monument in Arizona. Since the mid-1980s, population estimates for the US population have ranged from 39–153 individuals. In 2006–2007, the human-made Quitobaquito Pond began losing water, and discussions were held concerning the fate of the turtles. During three salvage efforts all Sonoyta mud turtles encountered were captured and transported to temporary holding facilities. Because the minimum number of turtles needed for re-establishment was unknown, we conducted a Population Viability Analysis (PVA) to determine the number of Sonoyta mud turtles that should be held in an assurance colony. Results from both our PVA and previous work suggested that juvenile survivorship has the strongest effect on female transition rates from nonreproductive to reproductive age classes and in turn population growth thus, a wide range of age classes should be maintained in an assurance colony.

Chelonian Research Foundation

J. Daren Riedle , Philip C. Rosen , Richard T. Kazmaier , Peter Holm , and Cristina A. Jones "Conservation Status of an Endemic Kinosternid, Kinosternon sonoriense longifemorale, in Arizona," Chelonian Conservation and Biology 11(2), 182-189, (1 December 2012).

Received: 2 December 2011 Accepted: 1 January 2012 Published: 1 December 2012


The United Nation's Population Division publishes high & low estimates (by gender) & density.

UN World Population Projections (Average Estimates) [10]
Year Total population
2021 7,874,965,732
2022 7,953,952,577
2023 8,031,800,338
2024 8,108,605,255
2025 8,184,437,453
2026 8,259,276,651
2027 8,333,078,318
2028 8,405,863,301
2029 8,477,660,723
2030 8,548,487,371
2031 8,618,349,454
2032 8,687,227,873
2033 8,755,083,512
2034 8,821,862,705
2035 8,887,524,229
2036 8,952,048,885
2037 9,015,437,616
2038 9,077,693,645
2039 9,138,828,562
2040 9,198,847,382
2041 9,257,745,483
2042 9,315,508,153
2043 9,372,118,247
2044 9,427,555,382
2045 9,481,803,272
2046 9,534,854,673
2047 9,586,707,749
2048 9,637,357,320
2049 9,686,800,146
2050 9,735,033,900
2051 9,782,061,758
2052 9,827,885,441
2053 9,872,501,562
2054 9,915,905,251
2055 9,958,098,746
2056 9,999,085,167
2057 10,038,881,262
2058 10,077,518,080
2059 10,115,036,360
2060 10,151,469,683
2061 10,186,837,209
2062 10,221,149,040
2063 10,254,419,004
2064 10,286,658,354
2065 10,317,879,315
2066 10,348,098,079
2067 10,377,330,830
2068 10,405,590,532
2069 10,432,889,136
2070 10,459,239,501
2071 10,484,654,858
2072 10,509,150,402
2073 10,532,742,861
2074 10,555,450,003
2075 10,577,288,195
2076 10,598,274,172
2077 10,618,420,909
2078 10,637,736,819
2079 10,656,228,233
2080 10,673,904,454
2081 10,690,773,335
2082 10,706,852,426
2083 10,722,171,375
2084 10,736,765,444
2085 10,750,662,353
2086 10,763,874,023
2087 10,776,402,019
2088 10,788,248,948
2089 10,799,413,366
2090 10,809,892,303
2091 10,819,682,643
2092 10,828,780,959
2093 10,837,182,077
2094 10,844,878,798
2095 10,851,860,145
2096 10,858,111,587
2097 10,863,614,776
2098 10,868,347,636
2099 10,872,284,134
2100 10,875,393,719
2101 10,877,000,004
2102 10,877,008,846

Walter Greiling projected in the 1950s that world population would reach a peak of about nine billion, in the 21st century, and then stop growing after a readjustment of the Third World and a sanitation of the tropics. [11]

Estimates published in the 2000s tended to predict that the population of Earth would stop increasing around 2070. [12] In a 2004 long-term prospective report, the United Nations Population Division projected the world population would peak at 7.85 billion in 2075. After reaching this maximum, it would decline slightly and then resume a slow increase, reaching a level of 5.11 billion by 2300, about the same as the projected 2050 figure. [13]

This prediction was revised in the 2010s, to the effect that no maximum will likely be reached in the 21st century. [14] The main reason for the revision was that the ongoing rapid population growth in Africa had been underestimated. A 2014 paper by demographers from several universities and the United Nations Population Division forecast that the world's population would reach about 10.9 billion in 2100 and continue growing thereafter. [15] In 2017 the UN predicted a decline of global population growth rate from +1.0% in 2020 to +0.5% in 2050 and to +0.1% in 2100. [16]

Jørgen Randers, one of the authors of the seminal 1972 long-term simulations in The Limits to Growth, offered an alternative scenario in a 2012 book, arguing that traditional projections insufficiently take into account the downward impact of global urbanization on fertility. Randers' "most likely scenario" predicts a peak in the world population in the early 2040s at about 8.1 billion people, followed by decline. [17]

The population of a country or area grows or declines through the interaction of three demographic drivers: fertility, mortality, and migration. [2]

Fertility Edit

Fertility is expressed as the total fertility rate (TFR), a measure of the number of children on average that a woman will bear in her lifetime. With longevity trending towards uniform and stable values worldwide, the main driver of future population growth will be the evolution of the fertility rate. [7]

Where fertility is high, demographers generally assume that fertility will decline and eventually stabilize at about two children per woman. [2]

During the period 2015–2020, the average world fertility rate was 2.5 children per woman, [7] about half the level in 1950-1955 (5 children per woman). In the medium variant, global fertility is projected to decline further to 2.2 in 2045-2050 and to 1.9 in 2095–2100. [7]

Mortality Edit

If the mortality rate is relatively high and the resulting life expectancy is therefore relatively low, changes in mortality can have a material impact on population growth. Where the mortality rate is low and life expectancy has therefore risen, a change in mortality will have much less an effect. [2]

Because child mortality has declined substantially over the last several decades, [2] Global life expectancy at birth, which is estimated to have risen from 47 years in 1950–1955 to 67 years in 2000–2005, [18] is expected to keep rising to reach 77 years in 2045–2050. [19] In the More Developed regions, the projected increase is from 79 years today [18] to 83 years by mid-century. [19] Among the Least Developed countries, where life expectancy today is just under 65 years, [18] it is expected to be 71 years in 2045–2050. [19]

The population of 31 countries or areas, including Ukraine, Romania, Japan and most of the successor states of the Soviet Union, is expected to be lower in 2050 than in 2005.

Migration Edit

Migration can have a significant effect on population change. Global South-South migration accounts for 38% of total migration, and Global South-North for 34%. [20] For example, the United Nations reports that during the period 2010–2020, fourteen countries will have seen a net inflow of more than one million migrants, while ten countries will have seen a net outflow of similar proportions. The largest migratory outflows have been in response to demand for workers in other countries (Bangladesh, Nepal and the Philippines) or to insecurity in the home country (Myanmar, Syria and Venezuela). Belarus, Estonia, Germany, Hungary, Italy, Japan, the Russian Federation, Serbia and Ukraine have experienced a net inflow of migrants over the decade, helping to offset population losses caused by a negative natural increase (births minus deaths). [21]

2050 Edit

The median scenario of the UN 2019 World Population Prospects [22] predicts the following populations per region in 2050 (compared to population in 2000), in billions:

2000 2050 growth %/yr
Asia 3.74 5.29 +41% +0.7%
Africa 0.81 2.49 +207% +2.3%
Europe 0.73 0.71 −3% −0.1%
South/Central America
0.52 0.76 +46% +0.8%
North America 0.31 0.43 +39% +0.7%
Oceania 0.03 0.06 +100% +1.4%
World 6.14 9.74 +60% +0.9%

After 2050 Edit

Projections of population reaching more than one generation into the future are highly speculative: Thus, the United Nations Department of Economic and Social Affairs report of 2004 projected the world population to peak at 9.22 billion in 2075 and then stabilise at a value close to 9 billion [23] By contrast, a 2014 projection by the United Nations Population Division predicted a population close to 11 billion by 2100 without any declining trend in the foreseeable future. [15]

United Nations projections Edit

The UN Population Division report of 2019 projects world population to continue growing, although at a steadily decreasing rate, and to reach 10.9 billion in 2100 with a growth rate at that time of close to zero. [22]

This projected growth of population, like all others, depends on assumptions about vital rates. For example, the UN Population Division assumes that Total fertility rate (TFR) will continue to decline, at varying paces depending on circumstances in individual regions, to a below-replacement level of 1.9 by 2100. Between now (2020) and 2100, regions with TFR currently below this rate, e.g. Europe, will see TFR rise. Regions with TFR above this rate, will see TFR continue to decline. [7]

Other projections Edit

  • A 2020 study published by The Lancet from researchers funded by the Global Burden of Disease Study projects world population to peak in 2064 at 9.7 billion and then decline to 8.8 billion in 2100. In this case TFR is assumed to decline more rapidly than the UN's projection, to reach 1.7 in 2100. . [24]
  • An analysis from the Wittgenstein Center IISA predicts global population to peak in 2070 at 9.4 billion and then decline to 9.0 billion in 2100. [25]

Other assumptions can produce other results. Some of the authors of the 2004 UN report assumed that life expectancy would rise slowly and continuously. The projections in the report assume this with no upper limit, though at a slowing pace depending on circumstances in individual countries. By 2100, the report assumed life expectancy to be from 66 to 97 years, and by 2300 from 87 to 106 years, depending on the country. Based on that assumption, they expect that rising life expectancy will produce small but continuing population growth by the end of the projections, ranging from 0.03 to 0.07 percent annually. The hypothetical feasibility (and wide availability) of life extension by technological means would further contribute to long term (beyond 2100) population growth . [26] [27] [28]

Evolutionary biology also suggests the demographic transition may reverse itself and global population may continue to grow in the long term. [29] In addition, recent evidence suggests birth rates may be rising in the 21st century in the developed world. [30]

The table below shows that from 2020 to 2050, the bulk of the world's population growth is predicted to take place in Africa: of the additional 1.9 billion people projected between 2020 and 2050, 1.2 billion will be added in Africa, 0.7 billion in Asia and zero in the rest of the world. Africa's share of global population is projected to grow from 17% in 2020 to 26% in 2050 and 39% by 2100, while the share of Asia will fall from 59% in 2020 to 55% in 2050 and 43% in 2100. [22] [8] The strong growth of the African population will happen regardless of the rate of decrease of fertility, because of the exceptional proportion of young people already living today. For example, the UN projects that the population of Nigeria will surpass that of the United States by about 2050. [6]

The population of the More Developed regions is slated to remain mostly unchanged, at 1.3 billion for the remainder of the 21 st century. All population growth comes from the Less Developed regions. [22] [31]

The table below breaks out the UN's future population growth predictions by region [8]

Between 2020 and the end of this century, the UN predicts that all six regions will experience declines in population growth, that by the end of the century three of them will be experiencing population decline, and the world will have reached zero population growth.

The UN Population Division has calculated the future population of the world's countries, based on current demographic trends. Current (2020) world population is 7.8 billion. The 2019 report projects world population in 2050 to be 9.7 billion people, and possibly as high as 11 billion by the next century, with the following estimates for the top 14 countries in 2020, 2050, and 2100: [22]

Population Growth of the Top 14 Countries in 2020, 2050, and 2100
Country Pop 2020 (mil) Pop 2050 (mil) Pop 2100 (mil) 2020 Rank 2050 Rank 2100 Rank
China 1,439 1,402 1,065 1 2 2
India 1,380 1,639 1,447 2 1 1
United States 331 379 434 3 4 4
Indonesia 273 331 321 4 6 7
Pakistan 221 338 403 5 5 5
Brazil 212 229 181 6 7 12
Nigeria 206 401 733 7 3 3
Bangladesh 165 192 151 8 10 14
Russia 146 136 126 9 14 19
Mexico 129 155 141 10 12 17
Japan 126 106 75 11 17 36
Ethiopia 115 205 294 12 8 8
Philippines 110 144 146 13 13 15
Egypt 102 160 225 14 11 10
Democratic Republic of the Congo 90 194 362 16 9 6
Tanzania 60 135 286 24 15 9
Niger 24 66 165 56 30 13
Angola 33 77 188 44 24 11
World 7,795 9,735 10,875

From 2017 to 2050, the nine highlighted countries are expected to account for half of the world's projected population increase: India, Nigeria, the Democratic Republic of the Congo, Pakistan, Ethiopia, Tanzania, the United States, Uganda, and Indonesia, listed according to the expected size of their contribution to that projected population growth. [21]

Large urban areas are hubs of economic development and innovation, with larger cities underpinning regional economies and local and global sustainability initiatives. Currently, 757 million humans live in the 101 largest cities [32] these cities are home to 11% of the world's population. [32] By the end of the century, the world population is projected to grow, with estimates ranging from 6.9 billion to 13.1 billion [32] the percentage of people living in the 101 largest cities is estimated to be 15% to 23%. [32]

The following 101 metropolitan areas with the largest population projections for the years 2025, 2050, 2075, and 2100 are listed below. [32]

We found at least 10 Websites Listing below when search with population age structure on Search Engine

Population Aging: How a Population's Age Structure Changes DA: 9 PA: 50 MOZ Rank: 59

  • Based on the age structure diagrams shown for Country A and B, which of the following predictions is probably true? a
  • Country A's population will expand rapidly due to high numbers of pre

Age Structure, Population Growth, and Economic Development

  • The age structure of a population is an important factor in population dynamics. Age structure is the proportion of a population in different age classes
  • Models that incorporate age structure allow better prediction of population growth, plus the ability to associate this growth with the level of economic development in a region.

America’s Age Profile Told through Population Pyramids DA: 14 PA: 50 MOZ Rank: 66

  • Age structure is often displayed using a population pyramid
  • You can learn about the makeup of the U.S. population as a whole by looking at its population pyramid, below
  • An examination of this population pyramid reveals peaks and valleys

-3 How Does a Population's Age Structure Affect Its Growth

A Population's Age Structure Helps Us to Make Projections An important factor determining whether the popula­ tion of a country increases or decreases is its age struc­ ture: the numbers or percentages of males and females in young, middle, and older age groups in that population

What are the different types of population pyramids

A population pyramid, or age structure graph, is a simple graph that conveys the complex social narrative of a population through its shape.

Population age structure and sex composition in sub DA: 12 PA: 50 MOZ Rank: 67

  • Age structure is a direct product of past and ongoing demographic processes and, as such, reflects those development-related factors that determine mortality, fertility and migration
  • Complex interactions can make it difficult to isolate the discrete role of age structure on its own
  • Nonetheless, shifting distributions of population by age and

US Population by Age and Generation in 2020 DA: 10 PA: 50 MOZ Rank: 66

  • Gen-Z has overtaken Millennials by nearly 4 million to become the largest generation in the United States
  • Baby Boomers are the third-largest generation with the population of 69 million persons in 2020
  • With a current population of around 86 million, the Gen-Z generation is expected to grow to 88 million over the next 20 years because of migration, according to the United Nations' latest

Age-Structured Matrix Population Models

  • A typical life cycle of a population with age class structure is: The age classes themselves are represented by circles
  • In this example, we are considering a population with just four age classes
  • The horizontal arrows between the circles represent

What is the age structure of a population DA: 13 PA: 42 MOZ Rank: 63

  • (The median marks the point where half the population is older than that age and half is younger)
  • But over the next 50 years, the median age will jump to 37, according projections made by the United Nations
  • By 2050, 17 developed countries are expected to have a median age of 50 or more

Age and Sex Composition in the United States: 2019 DA: 14 PA: 50 MOZ Rank: 73

  • Marital Status of the Population 15 Years and Over by Sex and Age: 2019 [<1.0 MB] Table 3
  • Educational Attainment of the Population 15 Years and Over by Sex and Age: 2019 [<1.0 MB] Table 4
  • Nativity and Citizenship Status by Sex and Age: 2019 [<1.0 MB] Table 5
  • Year of Entry of the Foreign-Born Population by Sex and Age: 2019 [<1.0 MB]

Population: Age Structure, Sex Composition and Rural

  • Age Structure: The age structure of a population refers to the number of people in different age groups
  • A larger size of population in the age group of 15-59 years indicates the chances of having a larger working population
  • On the other hand, if the number of children in the …

45.4C: Age Structure, Population Growth, and Economic

  • A population’s growth rate is strongly influenced by the proportions of individuals of particular ages
  • With knowledge of this age structure, population growth can be more accurately predicted
  • Age structure data allow the rate of growth (or decline) to be associated with a population’s level of economic development.

Ecology – Population Structure

Age Structure in Populations • Basically, this is a function of the age or age class distribution in a population • There are several ways to evaluate this: – Follow a cohort of individuals through time and obtain direct measurement of number of individuals at each age class (this is ideal)

UNCTAD Handbook of Statistics 2020

  • Globally, for every 100 persons of working age there were 39 children and 14 older persons
  • The proportion of children in the population has steadily declined from the peak of 38 per cent in 1966, to 26 per cent in 2019, while the proportion of the older than 64 rose from 5 …

CensusScope -- Population Pyramid and Age Distribution DA: 19 PA: 18 MOZ Rank: 51

  • AGE DISTRIBUTION When drawn as a "population pyramid," age distribution can hint at patterns of growth
  • A top heavy pyramid, like the one for Grant County, North Dakota, suggests negative population growth that might be due to any number of factors, including high death rates, low birth rates, and increased emigration from the area.

Simple 8 Steps to Create a Population Pyramid Chart in DA: 15 PA: 20 MOZ Rank: 50

  • A population pyramid also called an age pyramid or age picture is a graphical illustration that shows the distribution of various age groups in a population, which forms the shape of a pyramid when the population is growing
  • In Excel, we can create population

Changing population age structures and sustainable … DA: 10 PA: 50 MOZ Rank: 76

The gradual shift from a younger to an older population age structure is encapsulated by the term “population ageing”, which is often measured by the increase in the median age or in the proportion

List of countries by age structure

The following list of countries by age structure sorts the countries of the world according to the age distribution of their population.The population is divided into three groups: Ages 0 to 14 years: children and adolescents Ages 15 to 64 years: working population or population in education Over the age of 65: retirees elderly The age structure of a country has a strong impact on society and

How to Build a Population Pyramid in Excel: Step-by-Step

  • How to Build a Population Pyramid in Excel: Step-by-Step Guide
  • By Carol | September 10, 2018 Making a population pyramid, or age-sex distribution graph, in Excel has never been easier than with this step-by-step guide that takes you all the way from gathering the needed demographic data to the finished product – a graphically-accurate (and beautiful!) population pyramid.

Hungary Demographics 2020 (Population, Age, Sex, Trends

  • A Population pyramid (also called "Age-Sex Pyramid") is a graphical representation of the age and sex of a population
  • Types: Expansive - pyramid with a wide base (larger percentage of people in younger age groups, indicating high birth rates and high fertility rates) and narrow top (high death rate and lower life expectancies).


Population Age Structure 5-9 All countries’ populations can be classified into one of four major age structure types based on their progression through the demographic transition, which is the decades-long shift that many countries have followed from high mortality and fertility rates to longer life expectancies and later, to smaller family size.


age structure In late 2011, the world’s population surpassed the 7 billion mark and is currently growing by an additional 82 million persons every year (United Nations, 2013a).

China: population distribution by age group Statista DA: 16 PA: 50 MOZ Rank: 88

  • The age cohort from birth to four years only made up 5.74 percent of the population
  • A breakdown of the population by broad age groups reveals that 64.0 percent of …

Census of India: Age Structure And Marital Status

  • Age- sex structure is one of the most important characteristics of population composition
  • Almost all population characteristics vary significantly with age
  • Age statistics form an important component of population analysis, as most of the analysis is based on age-sex structure of the population.

Population age structure and consumption expenditure

The same statistics referred to the population age structure (three-indicators structure, less than 20 years old, working age between 20 and 64, a2064, and aged 65 and over, a65), highlight that, on average, the share of persons aged less than 20 has decreased (from 26.5% to 21.4%), mostly in the favor of those aged 65 and more (from 14% to 17.8%).

Predicting population age structures of China, India, and

  • The changing population age structure has a significant influence on the economy, society, and numerous other aspects of a country
  • This paper has innovatively applied the method of compositional data forecasting for the prediction of population age changes of the young (aged 0–14), the middle-aged (aged 15–64), and the elderly (aged older than 65) in China, India, and Vietnam by …

APES Unit 8: Population Age Structure Flashcards Quizlet DA: 11 PA: 50 MOZ Rank: 87

  • Start studying APES Unit 8: Population Age Structure
  • Learn vocabulary, terms, and more with flashcards, games, and other study tools.

UNdata record view Population by age, sex and urban DA: 11 PA: 10 MOZ Rank: 48

  • 1 - Data have not been adjusted for underenumeration, estimated at 5.0 per cent for urban population and 10.0 per cent for rural population
  • 3 - Data refer to the settled population based on the 1979 Population Census and the latest household prelisting
  • The refugees of Afghanistan in Iran, Pakistan, and an

Age-Sex and Population Pyramids DA: 17 PA: 49 MOZ Rank: 94

  • This age-sex pyramid for Afghanistan shows very rapid growth
  • This age-sex pyramid of Afghanistan's population breakdown in 2015 displays a fast growth rate of 2.3 percent annually, which represents a population doubling time of about 30 years.
  • We can see the distinctive pyramid-like shape to this graph, which displays a high birth rate.

Singapore population by age Statista DA: 16 PA: 50 MOZ Rank: 95

The average age of its resident population is projected to increase to just under 53 years old by 2050.

Author information


Medical Innovation Center TMK Project, Graduate School of Medicine, Kyoto University, Kyoto, Japan

Department of Nephrology, Graduate School of Medicine, Kyoto University, Kyoto, Japan

Yuki Sato & Motoko Yanagita

Institute for the Advanced Study of Human Biology (ASHBi), Kyoto University, Kyoto, Japan

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar


Both authors contributed equally to all aspects of article preparation.

Corresponding authors

Fossil Pollen Analysis and the Reconstruction of Plant Invasions

The chapter examines the application of fossil pollen analysis to the study of plant invasions and the report progress made while reconstructing range expansion, population growth, and competitive interactions. The chapter also outlines important uncertainties that exist in the use of fossil pollen for these purposes. Fossil pollen records have provided important insight into the population biology of invading plant species that are unavailable from any other source. In respect to the strong leptokurtic nature of pollen dispersal gradients, the relationship between plant population growth and pollen deposition is likely to be dependent on the spatial characteristics of the invasion. The analysis of large sample sets from many lakes of similar size in similar settings allows the calculation of average rates of population growth from pollen records. The presence of macrofossils of invading plants in these lakes provides critical evidence regarding the presence of plant species in low densities during period when pollen abundance is very low. Such evidence would be useful in testing the hypothesis that exponential and logistic increases in pollen represent the growth of local populations from very low densities. In view of the uncertainties outlined in this chapter, particular care should be taken when using rates and areas of geographic spread, rates of population growth, and competition coefficients derived solely from fossil pollen data to parameterize or test mathematical models of plant invasion.

A palaeoecological study of an upper late glacial and holocene sequence from “de borchert”, The Netherlands

A section more than 3 m deep from “De Borchert” (near Denekamp, The Netherlands), which included part of the Younger Dryas and almost the whole of the Holocene, was studied by analysing the micro- and macrofossils per 0.8 cm in order to obtain maximum information regarding the regional and local vegetational succession and any climatic changes that might have taken place during that time interval.

Newly recognised and recorded microfossils (fungi, algae, fossils of unknown taxonomic identity) are illustrated, described and interpreted. The analysis of pollen and other palynomorphs in combination with the analysis of macrofossils, permitted the following main conclusions:

During the later part of the Younger Dryas the presence of certain herbaceous forms (e.g., Typha latifolia) indicates a minimum average July temperature of 12–13°C.

A detailed reconstruction of the vegetational and climatic changes during the Preboreal could be made the Friesland phase was a period of rising mean summer temperatures resulting in an expansion of Betula species (B. pubescens and B. nana). Drought was not yet a factor restricting the development of vegetation.

During the Rammelbeek phase the increasing summer temperature caused a further increase in the rate of evaporation. Dry conditions prevailed which rendered the climate more continental. Regionally the decline of birches and an appreciable extension of grasses is characteristic of the Rammelbeek phase. The contemporaneous occurrence of thermophilous plants (Nymphaea alba, Ceratophyllum, and representatives of the Zygnemataceae) points to relatively warm summers (with mean July temperature of 13–15°C or even higher). The deeper depressions (e.g., the sampling site of the present section) did not dry out and during the Rammelbeek phase an acceleration of filling-in with vegetation (mainly Drepanocladus) could be observed.

During the Late Preboreal the depression tracks apparently reached northwestern Europe. The summers became humid enough for the growth of Sphagnum. The amount of precipitation and the milder winters favoured the further spreading of trees.

During the Atlantic period the small Sphagnum bog developed into a Betula carr.


This prospectively designed study addresses a pressing need to evaluate molecular and cellular hypotheses proposed to explain age-related differences in breast cancer incidence and clinical behavior. It is hard to reconcile the evidence gathered in this study of ER-positive breast cancers with the more general cancer-aging postulate that the breast-cancer-prone phenotype of an older woman results from genomic instability and age-accumulated mutational loads secondary to telomeric dysfunction and/or progressive DNA damage [9]. More consistent with the present evidence is the likelihood that ER-positive breast cancers arising in older women relative to younger women do so by a fundamentally different tumorigenic process, manifested more by epigenetic transcriptome differences such as those regulated by HOX genes, and less by genomic differences that were not detected using state-of-the-art BAC-based CGH analyses. More pronounced expression of cell cycle and proliferation-associated genes emerged as a strong defining feature of ER-positive breast cancers arising in younger women, perhaps even driving their earlier clinical appearance this observation is certainly consistent with the more aggressive clinical nature of early-age-onset breast cancer.

Age cohort study designs of this type are needed to not only confirm the specific transcriptome differences noted here, but also to look for common age-associated differences in gene classes and functional pathways that may enable us to generalize about the age-related biological differences driving ER-negative breast tumorigenesis as well as the many other age-associated epithelial malignancies other than breast cancer.


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