Social Transition in the North (STN), was a four-year research study funded by the National Science Foundation (NSF; OPP-9213137 and OPP-9496351). STN was a longitudinal study analyzing four circumpolar regions, two in Russia (Chukotka and Kamchatka) and two in Alaska (Nana and Aleutian-Pribilof Islands), looking at demographic, epidemiologic, and domestic social transitions (Mason, 2004). Demographic transitions were the study of change in mortality and birth rate. Epidemiologic transitions were studied by watching the change of infectious disease and increase of lifestyle diseases. The third transition was domestic, and is summarized as the redefinition of family, family member roles, and the family’s role within the community. The overall goal was to predict future changes, especially of high-risk conditions, and encourage institutional change that would improve services for these conditions. During the final year of the study, while in the Russian region of Chukotka, the principal investigators, two additional research staff, and 10 villagers, died in a tragic boating accident in September of 1995. It was decided that the documents would be given to the Institute for Circumpolar Health Studies (ICHS) at the University of Alaska Anchorage where they are now housed. If researchers are interested in accessing any STN material, a data use agreement will be set in place with the following requirements: to submit an application the UAA IRB, to honor the content of the original consent forms, and in their UAA IRB application specify how they intend to be responsive to the NSF Principles for the Conduct of Research in the Arctic. Further, ICHS will require a copy of UAA IRB's approval prior to release of STN materials. Anyone interested in accessing the data can also contact: Dr. Janet Johnston (jmjohnston2@alaska.edu) or the University of Alaska at Anchorage Institute for Circumpolar Health Studies (uaa_ichs@alaska.edu)

Data set of a community based cross-sectional survey done to find the prevalence , its correlates and patterns in a population of a district in southern Kerala, IndiaBackground: Multi-morbidity is the coexistence of multiple chronic conditions in the same individual. With advancing epidemiological and demographic transitions, the burden of multi-morbidity is expected to increase India. The state of Kerala in India is also in an advanced phase of epidemiological transition. However, very limited data on prevalence of multi-morbidity are available in the Kerala population.

Methods: A cross sectional survey was conducted among 410 participants in the age group of 30-69 years. A multi-stage cluster sampling method was employed to identify the study participants. Every eligible participant in the household were interviewed to assess the household prevalence. A structured interview schedule was used to assess socio-demographic variables, behavioral risk factors and prevailing clinical conditions, PHQ-9 questionnaire for screening of depression and active measurement of blood sugar and blood pressure. Co-existence of two or more conditions out of 11 was used as multi-morbidity case definition. Bivariate analyses were done to understand the association between socio-demographic factors and multi-morbidity. Logistic regression analyses were performed to estimate the effect size of these variables on multi-morbidity.

Results: Overall, the prevalence of multi-morbidity was 45.4% (95% CI: 40.5-50.3%). Nearly a quarter of study participants (25.4%) reported only one chronic condition (21.3-29.9%). Further, 30.7% (26.3-35.5), 10.7% (7.9-14.2), 3.7% (2.1-6.0) and 0.2% reported two, three, four and five chronic conditions, respectively. Nearly seven out of ten households (72%, 95%CI: 65-78%) had at least one person in the household with multi-morbidity and one in five households (22%, 95%CI: 16.7-28.9%) had more than one person with multi-morbidity. With every year increase in age, the propensity for multi-morbidity increased by 10 percent (OR=1.1; 95% CI: 1.1-1.2). Males and participants with low levels of education were less likely to suffer from multi-morbidity while unemployed and who do recommended level of physical activity were significantly more likely to suffer from multi-morbidity. Diabetes and hypertension was the most frequent dyad.

Conclusion: One of two participants in the productive age group of 30-69 years report multi-morbidity. Further, seven of ten households have at least one person with multi-morbidity. Preventive and management guidelines for chronic non-communicable conditions should focus on multi-morbidity especially in the older age group. Health-care systems that function within the limits of vertical disease management and episodic care (e.g., maternal health, tuberculosis, malaria, cardiovascular disease, mental health etc.) require optimal re-organization and horizontal integration of care across disease domains in managing people with multiple chronic conditions.

Key words: Multi-morbidity, cross-sectional, household, active measurement, rural, India, pattern

The recent demographic transitions to lower mortality and fertility rates in most human societies have led to changes and even quick reversals in phenotypic selection pressures. This can only result in evolutionary change if the affected traits are heritable, but changes in environmental conditions may also lead to subsequent changes in the genetic variance and covariance (the G matrix) of traits. It currently remains unclear if there have been concomitant changes in the G matrix of life history traits following the demographic transition. Using 300 years of genealogical data from Finland, we found that four key life history traits were heritable both before and after the demographic transition. The estimated heritabilities allow a quantifiable genetic response to selection during both time periods, thus facilitating continued evolutionary change. Further, the G matrices remained largely stable but revealed a trend for an increased additive genetic variance and thus evolutionary potenti...

This study explores the relationship between grandparental co-residence and net fertility – measured as the number of children under five – in Mexico across three key phases of its demographic transition: 1930 (pre-transitional), 1970 (population growth), and 2015 (fertility decline). Using census microdata and Poisson and multinomial regression models, we assess how intergenerational household structures interact with family socioeconomic status and cultural context to influence fertility outcomes. A central innovation is the use of a reconstructed 10% sample of the 1930 census, complemented by an imputation strategy to infer kinship ties not recorded in the original data. This enabled one of the earliest large-scale analyses of family co-residence and reproduction in historical Mexico. Findings reveal that the effects of grandparental co-residence vary by context. In 1930, cohabitation with grandmothers – especially in rural indigenous households – was associated with lower fertility, while cohabitation with grandfathers in non-indigenous rural areas corresponded to higher fertility. In 1970, amid pronatalist policies and economic growth, these effects weakened overall but persisted modestly in rural contexts. By 2015, co-residence – particularly with both grandparents – was associated with higher fertility and lower variability in fertility (CV), suggesting a stabilizing role in reproductive behavior. In contrast, households without grandparents exhibited lower fertility and greater heterogeneity, appearing to lead the shift toward reduced fertility. These findings illustrate how extended family structures both reflect and shape reproductive adaptation across shifting demographic contexts. By integrating evolutionary concepts such as cooperative breeding and social learning biases, the study offers insight into how kin networks can either support or constrain fertility depending on historical, socioeconomic, and cultural conditions. In doing so, it also contributes methodologically by addressing the complexity of nested and interactive effects – an essential step for understanding fertility dynamics in culturally diverse populations undergoing demographic transformation.

In 2022, India overtook China as the world's most populous country and now has almost 1.46 billion people. China now has the second-largest population in the world, still with just over 1.4 billion inhabitants, however, its population went into decline in 2023. Global population As of 2025, the world's population stands at almost 8.2 billion people and is expected to reach around 10.3 billion people in the 2080s, when it will then go into decline. Due to improved healthcare, sanitation, and general living conditions, the global population continues to increase; mortality rates (particularly among infants and children) are decreasing and the median age of the world population has steadily increased for decades. As for the average life expectancy in industrial and developing countries, the gap has narrowed significantly since the mid-20th century. Asia is the most populous continent on Earth; 11 of the 20 largest countries are located there. It leads the ranking of the global population by continent by far, reporting four times as many inhabitants as Africa. The Demographic Transition The population explosion over the past two centuries is part of a phenomenon known as the demographic transition. Simply put, this transition results from a drastic reduction in mortality, which then leads to a reduction in fertility, and increase in life expectancy; this interim period where death rates are low and birth rates are high is where this population explosion occurs, and population growth can remain high as the population ages. In today's most-developed countries, the transition generally began with industrialization in the 1800s, and growth has now stabilized as birth and mortality rates have re-balanced. Across less-developed countries, the stage of this transition varies; for example, China is at a later stage than India, which accounts for the change in which country is more populous - understanding the demographic transition can help understand the reason why China's population is now going into decline. The least-developed region is Sub-Saharan Africa, where fertility rates remain close to pre-industrial levels in some countries. As these countries transition, they will undergo significant rates of population growth

Today, globally, women of childbearing age have an average of approximately 2.2 children over the course of their lifetime. In pre-industrial times, most women could expect to have somewhere between five and ten live births throughout their lifetime; however, the demographic transition then sees fertility rates fall significantly. Looking ahead, it is believed that the global fertility rate will fall below replacement level in the 2050s, which will eventually lead to population decline when life expectancy plateaus. Recent decades Between the 1950s and 1970s, the global fertility rate was roughly five children per woman - this was partly due to the post-WWII baby boom in many countries, on top of already-high rates in less-developed countries. The drop around 1960 can be attributed to China's "Great Leap Forward", where famine and disease in the world's most populous country saw the global fertility rate drop by roughly 0.5 children per woman. Between the 1970s and today, fertility rates fell consistently, although the rate of decline noticeably slowed as the baby boomer generation then began having their own children. Replacement level fertility Replacement level fertility, i.e. the number of children born per woman that a population needs for long-term stability, is approximately 2.1 children per woman. Populations may continue to grow naturally despite below-replacement level fertility, due to reduced mortality and increased life expectancy, however, these will plateau with time and then population decline will occur. It is believed that the global fertility rate will drop below replacement level in the mid-2050s, although improvements in healthcare and living standards will see population growth continue into the 2080s when the global population will then start falling.

This paper contributes evidence documenting the continued decline in all-cause mortality and changes in the cause of death distribution over time in four developing country populations in Africa and Asia. We present levels and trends in age-specific mortality (all-cause and cause-specific) from four demographic surveillance sites: Agincourt (South Africa), Navrongo (Ghana) in Africa; Filabavi (Vietnam), Matlab (Bangladesh) in Asia. We model mortality using discrete time event history analysis. This study illustrates how data from INDEPTH Network centers can provide a comparative, longitudinal examination of mortality patterns and the epidemiological transition. Health care systems need to be reconfigured to deal simultaneously with continuing challenges of communicable disease and increasing incidence of non-communicable diseases that require long-term care. In populations with endemic HIV, long-term care of HIV patients on ART will add to the chronic care needs of the community.

In 2025, there are six countries, all in Sub-Saharan Africa, where the average woman of childbearing age can expect to have between 5-6 children throughout their lifetime. In fact, of the 20 countries in the world with the highest fertility rates, Afghanistan and Yemen are the only countries not found in Sub-Saharan Africa. High fertility rates in Africa With a fertility rate of almost six children per woman, Chad is the country with the highest fertility rate in the world. Population growth in Chad is among the highest in the world. Lack of healthcare access, as well as food instability, political instability, and climate change, are all exacerbating conditions that keep Chad's infant mortality rates high, which is generally the driver behind high fertility rates. This situation is common across much of the continent, and, although there has been considerable progress in recent decades, development in Sub-Saharan Africa is not moving as quickly as it did in other regions. Demographic transition While these countries have the highest fertility rates in the world, their rates are all on a generally downward trajectory due to a phenomenon known as the demographic transition. The third stage (of five) of this transition sees birth rates drop in response to decreased infant and child mortality, as families no longer feel the need to compensate for lost children. Eventually, fertility rates fall below replacement level (approximately 2.1 children per woman), which eventually leads to natural population decline once life expectancy plateaus. In some of the most developed countries today, low fertility rates are creating severe econoic and societal challenges as workforces are shrinking while aging populations are placin a greater burden on both public and personal resources.

The aims of the project were to examine and analyse demographic processes of fertility, nuptiality, marital fertility, mortality and migration during periods encompassing the demographic transition in England and Wales. In particular, the goal was to reveal underlying relationships between demographic processes in the context of changing socio-economic conditions. With this goal in mind, population, occupational, and education data were compilated, and demographic and statistical models were employed to estimate key measures and indicators of demographic change. The large majority of the data and estimates were compiled and made at the registration district level for the period 1851-1911. In addition decennial inter-county migration flows were estimated for the period 1851-1911.

Long-Term Care Market Outlook

According to our latest research, the global Long-Term Care market size reached USD 1.25 trillion in 2024, reflecting a robust demand for comprehensive care services across the globe. The market is poised for significant expansion, projected to reach USD 2.34 trillion by 2033, growing at a steady CAGR of 7.2% during the forecast period. This sustained growth is primarily driven by the rapidly aging global population, increasing prevalence of chronic diseases, and a shifting preference towards home-based and personalized care services.

One of the principal growth factors for the Long-Term Care market is the demographic shift characterized by an unprecedented rise in the elderly population. Globally, the proportion of individuals aged 65 and above is increasing at a faster rate than any other age group. This demographic trend is particularly pronounced in developed economies such as Japan, Germany, and the United States, where advancements in healthcare have significantly extended life expectancy. As a result, there is a surging demand for a continuum of care services that cater to age-related health issues, functional disabilities, and chronic illnesses. This demographic transition is compelling both public and private sectors to invest in innovative long-term care solutions, including assisted living facilities, home healthcare, and nursing care, further fueling market expansion.

Another key driver propelling the Long-Term Care market is the increasing prevalence of chronic diseases such as diabetes, cardiovascular disorders, and neurodegenerative conditions like Alzheimer’s and Parkinson’s disease. The management of these long-term conditions often requires ongoing medical attention, rehabilitation, and support with daily activities, which are integral services provided by long-term care providers. The growing burden of non-communicable diseases, coupled with the rising cost of acute care, is prompting healthcare systems and families to seek cost-effective alternatives that ensure quality of life and reduce hospital readmissions. The integration of technology, such as remote monitoring and telehealth, is also enhancing the efficiency and accessibility of long-term care services, making them more appealing to both patients and caregivers.

Furthermore, evolving societal attitudes towards aging and care are shaping the Long-Term Care market. There is a noticeable shift towards personalized, patient-centric care models that prioritize dignity, independence, and quality of life for the elderly and individuals with disabilities. This has led to the proliferation of diverse care settings, including adult day care centers, hospice and palliative care, and community-based services, all designed to provide holistic support. Additionally, government initiatives and policy reforms aimed at improving care standards, expanding insurance coverage, and incentivizing private investment are creating a favorable environment for market growth. However, the sector continues to face challenges such as workforce shortages, regulatory complexities, and disparities in access, which necessitate ongoing innovation and policy attention.

Regionally, North America dominates the Long-Term Care market, driven by a large aging population, well-established healthcare infrastructure, and robust insurance coverage. Europe follows closely, with significant investments in elder care and supportive government policies. The Asia Pacific region is emerging as a high-growth market, propelled by rapid urbanization, rising disposable incomes, and increasing awareness of long-term care needs. Latin America and the Middle East & Africa, while currently accounting for a smaller share, are witnessing gradual growth due to improving healthcare systems and demographic changes. Each region presents unique challenges and opportunities, shaped by cultural, economic, and policy factors that influence the adoption and delivery of long-term care services.

Service Type Analysis

The Long-Term Care

ResourcesMapTeacher guide Student worksheetGet startedOpen the map.Use the teacher guide to explore the map with your class or have students work through it on their own with the worksheet.New to GeoInquiriesTM? See Getting to Know GeoInquiries.Science standardsAPES: III. B. – Population biology concepts.APES: II.B.1. – Human population dynamics - historical population sizes; distribution; fertility rates; growth rates and doubling times; demographic transition; age-structure diagrams.Learning outcomesStudents will predict total historical population trends from age-structure information.Students will relate population growth to k (carrying capacity) or r (reproductive factor) selective environmental conditions.

Anticipating critical transitions in spatially extended systems is a key topic of interest to ecologists. Gradually declining metapopulations are an important example of a spatially extended biological system that may exhibit a critical transition. Theory for spatially extended systems approaching extinction that accounts for environmental stochasticity and coupling is currently lacking. Here, we develop spatially implicit two-patch models with additive and multiplicative forms of environmental stochasticity that are slowly forced through population collapse, through changing environmental conditions. We derive patch-specific expressions for candidate indicators of extinction and test their performance via a simulation study. Coupling and spatial heterogeneities decrease the magnitude of the proposed indicators in coupled populations relative to isolated populations, and the noise regime and the degree of coupling together determine trends in summary statistics. This theory may be readily applied to other spatially extended ecological systems, such as coupled infectious disease systems on the verge of elimination.

1. Environmental variation can induce life-history changes that can last over a large part of the lifetime of an organism. If multiple demographic traits are affected, expected changes in climate may influence environmental covariances among traits in a complex manner. Thus, examining the consequences of environmental fluctuations requires that individual information at multiple life stages is available, which is particularly challenging in long-lived species. 2. Here, we analyse how variation in climatic conditions occurring in the year of hatching of female goshawks Accipiter gentilis (L.) affects age-specific variation in demographic traits and lifetime reproductive success (LRS). LRS decreased with increasing temperature in April in the year of hatching, due to lower breeding frequency and shorter reproductive life span. In contrast, the probability for a female to successfully breed was higher in years with a warm April, but lower LRS of the offspring in these years generated a negative covariance among fecundity rates among generations. 3. The mechanism by which climatic conditions generated cohort effects was likely through influencing the quality of the breeding segment of the population in a given year, as the proportion of pigeons in the diet during the breeding period was positively related to annual and LRS, and the diet of adult females that hatched in warm years contained fewer pigeons. 4. Climatic conditions experienced during different stages of individual life histories caused complex patterns of environmental covariance among demographic traits even across generations. Such environmental covariances may either buffer or amplify impacts of climate change on population growth, emphasizing the importance of considering demographic changes during the complete life history of individuals when predicting the effect of climatic change on population dynamics of long-lived species.

Abstract

The SWEDD is a regional project aiming to accelerate the demographic transition by addressing both supply- and demand-side constraints to family planning and reproductive and sexual health. To achieve its objective, the project targets adolescent girls and young women mainly between the ages of 8 and 24, who are vulnerable to early marriage, teenage pregnancy, and early school drop-out. The project targeted 9 countries of the Sahel and Western Africa (Benin, Burkina Faso, Cameroon, Chad, Côte d’Ivoire, Guinea, Mali, Mauritania, and Niger) and is expanding in other African countries. The SWEDD is structured into three main components: component 1 seeks to generate demand for reproductive, maternal, neonatal, child health and nutrition products and services; component 2 seeks to improve supply of these products and qualified personnel; and component 3 seeks to strengthen national capacity and policy dialogue.

The World Bank Africa Gender Innovation Lab and its partners conducted a baseline survey to support the interventions under component 1. These were (i) safe spaces to empower girls through the provision of life skills and SRH education; (ii) Cash transfer to cover girls’ expenses (transportation cost, food…); (iii) support to income-generating activities (IGA) with the provision of grants and entrepreneurship training and finally (iv) community sensitization by religious and village leaders. The latter two have the objective to change restrictive social norms and create an enabling environment for girls’ empowerment.

These data represent the first round of data collection (baseline) for the impact evaluation. The sample comprises 2,165 households and girls living in the regions of Hadjar Lamis, Kanem, Lac and Salamat.

The information gathered from the survey may aid decision makers in the formulation of economic and social policies to: - reduce fertility and child marriage by improving access to contraceptive methods and improving reproductive health knowledge. - foster women’s empowerment through enhancing their access to economic activities.

The survey can be an important source of information for planners to know how to improve the quality of people's living standards, in particular women’s living conditions. The Ministry of Social Affairs, Children and Families and the Ministry of National Education of Chad would benefit from the data of this survey, together with other public organizations working on girls and women empowerment and reproductive health. District Authorities, Research Institutions, Non-Governmental Organizations and the general public will also benefit from the survey data.

Geographic coverage

Four regions of Chad : Hadjar Lamis, Kanem, Lac and Salamat.

Analysis unit

Households, individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample covers 2,165 girls across 40 secondary schools and 200 rural villages in the regions of Hadjer Lamis, Kanem, Lac, and Salamat. A share of the sample targets in-school girls aged 10 to 19. For these, the Ministry of National Education provided a list of 48 eligible lower and upper secondary schools.

For the community-based safe spaces, which target out-of-school or never-schooled girls aged 12 to 24, 130 rural villages located around the 16 treatment schools were selected.

The objective of the baseline survey was to build a comprehensive dataset that captures the pre-intervention characteristics of all targeted populations, thereby serving as a reliable reference point for evaluating changes attributable to the program during subsequent follow-up rounds.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The data consists of responses from households to questions pertaining to: 1. List of household members 2. Education and employment of household members 3. Characteristics of housing and durable goods 4. Chocs and food security 5. Household head's aspirations for their children 6. Attitudes on women's empowerment and gender equality

The questionnaire administrated to girls contains the following sections: 1. Education 2. Marriage and children 3. Aspirations 4. Reproductive health and family planning 5. Psycho-social 6. Women's empowerment 7. Gender-based violence 8. Income-generating activities 9. Savings and credits 10. Personal relationships and social networks 11. Migration

The household questionnaire was administered to the head of the household or to an authorized person capable of answering questions about all individuals in the household. The adolescent questionnaire was administered to an eligible pre-selected girl within the household. Considering the modules of the adolescent questionnaire, it was only administered by female enumerators. The questionnaires were written in French and programmed on tablets in French using the CAPI program.

BackgroundFollowing the epidemiologic and demographic transition, non-communicable disease mortality is the leading cause of death in Iran. Projecting mortality trend can provide valuable tools for policy makers and planners. In this article, we have estimated the trend of non-communicable disease mortality during 2001–2015 and have projected it until 2030 at national and subnational levels in Iran.MethodsThe data employed was gathered from the Iranian death registration system and using the Spatio-temporal model, the trends of 4 major categories of non-communicable diseases (cancers, cardiovascular diseases, asthma and COPD, and diabetes) by 2030 were projected at the national and subnational levels.ResultsThe results indicated that age standardized mortality rate for cancers, CVDs, and Asthma and COPD will continue to decrease in both sexes (cancers: from 81.8 in 2015 to 45.2 in 2030, CVDs: 307.3 to 173.0, and Asthma and COPD: from 52.1 to 46.6); however, in terms of diabetes, there is a steady trend in both sexes at national level (from 16.6 to 16.5). Age standardized mortality rates for cancers and CVDs, in males and females, were high in all provinces in 2001. The variation between the provinces is clearer in 2015, and it is expected to significantly decrease in all provinces by 2030.ConclusionGenerally, the age standardized mortality rate from NCDs will decrease by 2030. Of course, given the experience of the past two decades in Iran, believing that the mortality rate will decrease may not be an easy notion to understand. However hard to believe, this decrease may be the result of better management of risk factors and early detection of patients due to more comprehensive care in all segments of society, as well as improved literacy and awareness across the country.

Environmental change influences fitness-related traits and demographic rates, which in herbivores are often linked to resource-driven variation in body condition. Coupled body condition-demographic responses may therefore be important for herbivore population dynamics in fluctuating environments, such as the Arctic. We applied a transient Life-Table Response Experiment (‘transient-LTRE’) to demographic data from Svalbard barnacle geese (Branta leucopsis), to quantify their population-dynamic responses to changes in body mass. We partitioned contributions from direct and delayed demographic and body condition-mediated processes to variation in population growth. Declines in body condition (1980-2017), which positively affected reproduction and fledgling survival, had negligible consequences for population growth. Instead, population growth rates were largely reproduction-driven, in part through positive responses to rapidly advancing spring phenology. The virtual lack of body condition-m...

The region of present-day China has historically been the most populous region in the world; however, its population development has fluctuated throughout history. In 2022, China was overtaken as the most populous country in the world, and current projections suggest its population is heading for a rapid decline in the coming decades. Transitions of power lead to mortality The source suggests that conflict, and the diseases brought with it, were the major obstacles to population growth throughout most of the Common Era, particularly during transitions of power between various dynasties and rulers. It estimates that the total population fell by approximately 30 million people during the 14th century due to the impact of Mongol invasions, which inflicted heavy losses on the northern population through conflict, enslavement, food instability, and the introduction of bubonic plague. Between 1850 and 1870, the total population fell once more, by more than 50 million people, through further conflict, famine and disease; the most notable of these was the Taiping Rebellion, although the Miao an Panthay Rebellions, and the Dungan Revolt, also had large death tolls. The third plague pandemic also originated in Yunnan in 1855, which killed approximately two million people in China. 20th and 21st centuries There were additional conflicts at the turn of the 20th century, which had significant geopolitical consequences for China, but did not result in the same high levels of mortality seen previously. It was not until the overlapping Chinese Civil War (1927-1949) and Second World War (1937-1945) where the death tolls reached approximately 10 and 20 million respectively. Additionally, as China attempted to industrialize during the Great Leap Forward (1958-1962), economic and agricultural mismanagement resulted in the deaths of tens of millions (possibly as many as 55 million) in less than four years, during the Great Chinese Famine. This mortality is not observable on the given dataset, due to the rapidity of China's demographic transition over the entire period; this saw improvements in healthcare, sanitation, and infrastructure result in sweeping changes across the population. The early 2020s marked some significant milestones in China's demographics, where it was overtaken by India as the world's most populous country, and its population also went into decline. Current projections suggest that China is heading for a "demographic disaster", as its rapidly aging population is placing significant burdens on China's economy, government, and society. In stark contrast to the restrictive "one-child policy" of the past, the government has introduced a series of pro-fertility incentives for couples to have larger families, although the impact of these policies are yet to materialize. If these current projections come true, then China's population may be around half its current size by the end of the century.

Population dynamics, its types. Population migration (external, internal), factors determining it, main trends. Impact of migration on population health.

Under the guidance of Moldoev M.I. Sir By Riya Patil and Rutuja Sonar

Abstract

Population dynamics influence development and vice versa, at various scale levels: global, continental/world-regional, national, regional, and local. Debates on how population growth affects development and how development affects population growth have already been subject of intensive debate and controversy since the late 18th century, and this debate is still ongoing. While these two debates initially focused mainly on natural population growth, the impact of migration on both population dynamics and development is also increasingly recognized. While world population will continue growing throughout the 21st century, there are substantial and growing contrasts between and within world-regions in the pace and nature of that growth, including some countries where population is stagnating or even shrinking. Because of these growing contrasts, population dynamics and their interrelationships with development have quite different governance implications in different parts of the world.

1. Population Dynamics

Population dynamics refers to the changes in population size, structure, and distribution over time. These changes are influenced by four main processes:

Birth rate (natality)

Death rate (mortality)

Immigration (inflow of people)

Emigration (outflow of people)

Types of Population Dynamics

Natural population change: Based on birth and death rates.

Migration-based change: Caused by people moving in or out of a region.

Demographic transition: A model that explains changes in population growth as societies industrialize.

Population distribution: Changes in where people live (urban vs rural).

2. Population Migration

Migration refers to the movement of people from one location to another, often across political or geographical boundaries.

Types of Migration

External migration (international):

Movement between countries.

Examples: Refugee relocation, labor migration, education.

Internal migration:

Movement within the same country or region.

Examples: Rural-to-urban migration, inter-state migration.

3. Factors Determining Migration

Migration is influenced by push and pull factors:

Push factors (reasons to leave a place):

Unemployment

Conflict or war

Natural disasters

Poverty

Lack of services or opportunities

Pull factors (reasons to move to a place):

Better job prospects

Safety and security

Higher standard of living

Education and healthcare access

Family reunification

4. Main Trends in Migration

Urbanization: Mass movement to cities for work and better services.

Global labor migration: Movement from developing to developed countries.

Refugee and asylum seeker flows: Due to conflict or persecution.

Circular migration: Repeated movement between two or more locations.

Brain drain/gain: Movement of skilled labor away from (or toward) a country.

5. Impact of Migration on Population Health

Positive Impacts:

Access to better healthcare (for migrants moving to better systems).

Skills and knowledge exchange among health professionals.

Remittances improving healthcare affordability in home countries.

Negative Impacts:

Migrants’ health risks: Increased exposure to stress, poor living conditions, and occupational hazards.

Spread of infectious diseases: Especially when health screening is lacking.

Strain on health services: In receiving areas, especially with sudden or large influxes.

Mental health challenges: Due to cultural dislocation, discrimination, or trauma.

Population dynamics is one of the fundamental areas of ecology, forming both the basis for the study of more complex communities and of many applied questions. Understanding population dynamics is the key to understanding the relative importance of competition for resources and predation in structuring ecological communities, which is a central question in ecology.

Population dynamics plays a central role in many approaches to preserving biodiversity, which until now have been primarily focused on a single species approach. The calculation of the intrinsic growth rate of a species from a life table is often the central piece of conservation plans. Similarly, management of natural resources, such as fisheries, depends on population dynamics as a way to determine appropriate management actions.

Population dynamics can be characterized by a nonlinear system of difference or differential equations between the birth sizes of consecutive periods. In such a nonlinear system, when the feedback elasticity of previous events on current birth size is larger, the more likely the dynamics will be volatile. Depending on the classification criteria of the population, the revealed cyclical behavior has various interpretations. Under different contextual scenarios, Malthusian cycles, Easterlin cycles, predator–prey cycles, dynastic cycles, and capitalist–laborer cycles have been introduced and analyzed

Generally, population dynamics is a nonlinear stochastic process. Nonlinearities tend to be complicated to deal with, both when we want to do analytic stochastic modelling and when analysing data. The way around the problem is to approximate the nonlinear model with a linear one, for which the mathematical and statistical theories are more developed and tractable. Let us assume that the population process is described as:

(1)Nt=f(Nt−1,εt)

where Nt is population density at time t and εt is a series of random variables with identical distributions (mean and variance). Function f specifies how the population density one time step back, plus the stochastic environment εt, is mapped into the current time step. Let us assume that the (deterministic) stationary (equilibrium) value of the population is N* and that ε has mean ε*. The linear approximation of Eq. (1) close to N* is then:

(2)xt=axt−1+bϕt

where xt=Nt−N*, a=f

f(N*,ε*)/f

N, b=ff(N*,ε*)/fε, and ϕt=εt−ε*

The term population refers to the members of a single species that can interact with each other. Thus, the fish in a lake, or the moose on an island, are clear examples of a population. In other cases, such as trees in a forest, it may not be nearly so clear what a population is, but the concept of population is still very useful.

Population dynamics is essentially the study of the changes in the numbers through time of a single species. This is clearly a case where a quantitative description is essential, since the numbers of individuals in the population will be counted. One could begin by looking at a series of measurements of the numbers of particular species through time. However, it would still be necessary to decide which changes in numbers through time are significant, and how to determine what causes the changes in numbers. Thus, it is more sensible to begin with models that relate changes in population numbers through time to underlying assumptions. The models will provide indications of what features of changes in numbers are important and what measurements are critical to make, and they will help determine what the cause of changes in population levels might be.

To understand the dynamics of biological populations, the study starts with the simplest possibility and determines what the dynamics of the population would be in that case. Then, deviations in observed populations from the predictions of that simplest case would provide information about the kinds of forces shaping the dynamics of populations. Therefore, in describing the dynamics in this simplest case it is essential to be explicit and clear about the assumptions made. It would not be argued that the idealized population described here would ever be found, but that focusing on the idealized population would provide insight into real populations, just as the study of Newtonian mechanics provides understanding of more realistic situations in physics.

Population migration

The vast majority of people continue to live in the countries where they were born —only one in 30 are migrants.

In most discussions on migration, the starting point is usually numbers. Understanding changes in scale, emerging trends, and shifting demographics related to global social and economic transformations, such as migration, help us make sense of the changing world we live in and plan for the future. The current global estimate is that there were around 281 million international migrants in the world in 2020, which equates to 3.6 percent of the global population.

Overall, the estimated number of international migrants has increased over the past five decades. The total estimated 281 million people living in a country other than their countries of birth in 2020 was 128 million more than in 1990 and over three times the estimated number in 1970.

There is currently a larger number of male than female international migrants worldwide and the growing gender gap has increased over the past 20 years. In 2000, the male to female split was 50.6 to 49.4 per cent (or 88 million male migrants and 86 million female migrants). In 2020 the split was 51.9 to 48.1 per cent, with 146 million male migrants and 135 million female migrants. The share of

Comparison of notable scenarios for the Population-Income-Education system according to five succession rules.

This dataset accompanies an article titled, 'Untangling the contribution of survey design and demographic change to observed differences in age-stratified contact patterns', by T. Harris et al. A preprint can be found at https://arxiv.org/abs/2406.01639.

Abstract:

Social contact patterns are key drivers of infectious disease transmission. During the COVID-19 pandemic, differences between pre-COVID and COVID-era contact rates were widely attributed to non-pharmaceutical interventions such as lockdowns. However, the factors that drive changes in the distribution of contacts between different subpopulations remain poorly understood. Here, we present a clustering analysis of 33 contact matrices generated from surveys conducted before and during the COVID-19 pandemic, and analyse key features distinguishing their structures such as the extent of age assortativity. Our analysis demonstrates that, while aspects of pandemic scenarios (such as the implementation of lockdowns) could account for some of these distinguishing features, they can also be explained by differences in study design and, to a lesser extent, long-term demographic trends. Our results caution against using survey data from different studies in counterfactual analysis of epidemic mitigation strategies. Doing so risks attributing differences stemming from survey design choices or long-term changes to the short-term effects of interventions.

See 'README.md' in main upload folder for information on repository structure.