An information system based on data from the healthcare sector and related areas. The online portal gives researchers the opportunity to research various health topics including population, socio-economic factors, health insurance, health laws.

• Laws

• Population

  • Births
  • Deaths
  • Population Numbers
  • other

• Pregnancy, Births

• Terminations of Pregnancy

• Households, Families, Living Communities

• Social Conditions

  • Education
  • Habitation
  • Employment
  • Unemployment
  • Poverty and Disparity
  • Single parent
  • Income
  • Social Welfare
  • other

• Training of the Health Personnel

• Economy

• Research

• Private Health Insurance

• Statutory Health Insurance

  • Statutory Health Insurance: Income, Expenditure
  • Statutory Health Insurance: Members, Insured Persons
  • Statutory Health Insurance: Services
  • Statutory Health Insurance: Services (Pharmaceuticals)
  • Statutory Health Insurance: other

• Nursing Care Insurance

• Pension Insurance

  • Insured Persons
  • Rehabilitation Services
  • Pensions because of old age
  • Ppensions because of diminution of ability to wrk
  • Pensions because of death (widow/widower pensions)
  • other

• Accident Insurance, employers' liability insurance association

Background The United States has experienced high surge in COVID-19 cases since the dawn of 2020. Identifying the types of diagnoses that pose a risk in leading COVID-19 death casualties will enable our community to obtain a better perspective in identifying the most vulnerable populations and enable these populations to implement better precautionary measures. Objective To identify demographic factors and health diagnosis codes that pose a high or a low risk to COVID-19 death from individual health record data sourced from the United States. Methods We used logistic regression models to analyze the top 500 health diagnosis codes and demographics that have been identified as being associated with COVID-19 death. Results Among 223,286 patients tested positive at least once, 218,831 (98%) patients were alive and 4,455 (2%) patients died during the duration of the study period. Through our logistic regression analysis, four demographic characteristics of patients; age, gender, race and region, were deemed to be associated with COVID-19 mortality. Patients from the West region of the United States: Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming had the highest odds ratio of COVID-19 mortality across the United States. In terms of diagnoses, Complications mainly related to pregnancy (Adjusted Odds Ratio, OR:2.95; 95% Confidence Interval, CI:1.4 - 6.23) hold the highest odds ratio in influencing COVID-19 death followed by Other diseases of the respiratory system (OR:2.0; CI:1.84 – 2.18), Renal failure (OR:1.76; CI:1.61 – 1.93), Influenza and pneumonia (OR:1.53; CI:1.41 – 1.67), Other bacterial diseases (OR:1.45; CI:1.31 – 1.61), Coagulation defects, purpura and other hemorrhagic conditions(OR:1.37; CI:1.22 – 1.54), Injuries to the head (OR:1.27; CI:1.1 - 1.46), Mood [affective] disorders (OR:1.24; CI:1.12 – 1.36), Aplastic and other anemias (OR:1.22; CI:1.12 – 1.34), Chronic obstructive pulmonary disease and allied conditions (OR:1.18; CI:1.06 – 1.32), Other forms of heart disease (OR:1.18; CI:1.09 – 1.28), Infections of the skin and subcutaneous tissue (OR: 1.15; CI:1.04 – 1.27), Diabetes mellitus (OR:1.14; CI:1.03 – 1.26), and Other diseases of the urinary system (OR:1.12; CI:1.03 – 1.21). Conclusion We found demographic factors and medical conditions, including some novel ones which are associated with COVID-19 death. These findings can be used for clinical and public awareness and for future research purposes.

American Community Survey 5-year Estimates 2016-2020. Includes Age, Disability, Education, Female Population, LEP, Low Income, Place of Birth (Foreign Born), Race (Minority) and Zero Vehicle Households for MCDs.

This is the replication data for manuscript titled "Unpacking drivers of online censorship endorsement: Psychological and demographic factors" submittted to review. The abstract of the manuscript is as follows. Abstract: This study explores the complex dynamics of online censorship endorsements within a national context. We examined the impact of some of the influential psychological and demographic factors contributing to online censorship endorsement of Iranian Telegram users. Through the analysis of 517 responses to an online questionnaire, we investigated the influence of variables such as age, education level, gender, the use of state-controlled media, political interests, personal trust, religiosity, perceived similarity, and motivated resistance to censorship on individuals' attitudes toward censorship. Our findings reveal that education level, state-controlled media usage, religiosity, perceived similarity, and motivated resistance to censorship significantly shape censorship endorsements in the Iranian Telegram users. In the discussion section, we highlighted the implications of these findings and offered avenues for further research.

This study examined the mediation effect of practical training on the relationship of demographic characteristics with bystander self-efficacy in cardiopulmonary resuscitation (CPR) performance. We used nationwide, cross-sectional data from the Korea Community Health Survey and analyzed 25,082 Korean adults who participated in CPR training within the last 2 years. A mediation model was applied to explore the pathway from demographic characteristics via CPR practical training to self-efficacy in CPR performance. A multiple logistic regression analysis was performed to examine each path in the mediation model. Of the 25,082 respondents recently trained, 19,168 (76.8%) practiced on a manikin. In the unadjusted CPR practical training model, the demographic characteristics associated with high self-efficacy in CPR performance were male gender (odds ratio [OR] = 2.54); 50s age group (OR = 1.30); college or more (OR = 1.39) and high school education (OR = 1.32); white collar (OR = 1.24) and soldier (OR = 2.98) occupational statuses. The characteristics associated with low self-efficacy were 30s age group (OR = 0.69) and capital (OR = 0.79) and metropolitan (OR = 0.84) areas of residence (p < 0.05). In the adjusted CPR practical training model, the significance of the relationship between demographics and self-efficacy in CPR performance decreased in male gender, 30s age group, college or more and high school education, and soldier occupational status (i.e., partial mediation), and disappeared in metropolitan residents (i.e., complete mediation). The degree of the mediating effect of CPR practical training on self-efficacy differed for each demographic characteristic. Thus, individualized educational strategies considering recipient demographics are needed for effective practice-based CPR training and improving bystander CPR performance.

GapMaps GIS data for USA and Canada sourced from Applied Geographic Solutions (AGS) includes an extensive range of the highest quality demographic and lifestyle segmentation products. All databases are derived from superior source data and the most sophisticated, refined, and proven methodologies.

GIS Data attributes include:

1. Latest Estimates and Projections The estimates and projections database includes a wide range of core demographic data variables for the current year and 5- year projections, covering five broad topic areas: population, households, income, labor force, and dwellings.

2. Crime Risk Crime Risk is the result of an extensive analysis of a rolling seven years of FBI crime statistics. Based on detailed modeling of the relationships between crime and demographics, Crime Risk provides an accurate view of the relative risk of specific crime types (personal, property and total) at the block and block group level.

3. Panorama Segmentation AGS has created a segmentation system for the United States called Panorama. Panorama has been coded with the MRI Survey data to bring you Consumer Behavior profiles associated with this segmentation system.

4. Business Counts Business Counts is a geographic summary database of business establishments, employment, occupation and retail sales.

5. Non-Resident Population The AGS non-resident population estimates utilize a wide range of data sources to model the factors which drive tourists to particular locations, and to match that demand with the supply of available accommodations.

6. Consumer Expenditures AGS provides current year and 5-year projected expenditures for over 390 individual categories that collectively cover almost 95% of household spending.

7. Retail Potential This tabulation utilizes the Census of Retail Trade tables which cross-tabulate store type by merchandise line.

8. Environmental Risk The environmental suite of data consists of several separate database components including: -Weather Risks -Seismological Risks -Wildfire Risk -Climate -Air Quality -Elevation and terrain

Primary Use Cases for GapMaps GIS Data:

1. Retail (eg. Fast Food/ QSR, Cafe, Fitness, Supermarket/Grocery)
2. Customer Profiling: get a detailed understanding of the demographic & segmentation profile of your customers, where they work and their spending potential
3. Analyse your trade areas at a granular census block level using all the key metrics
4. Site Selection: Identify optimal locations for future expansion and benchmark performance across existing locations.
5. Target Marketing: Develop effective marketing strategies to acquire more customers.

6. Integrate AGS demographic data with your existing GIS or BI platform to generate powerful visualizations.

7. Finance / Insurance (eg. Hedge Funds, Investment Advisors, Investment Research, REITs, Private Equity, VC)

8. Network Planning

9. Customer (Risk) Profiling for insurance/loan approvals

10. Target Marketing

11. Competitive Analysis

12. Market Optimization

13. Commercial Real-Estate (Brokers, Developers, Investors, Single & Multi-tenant O/O)

14. Tenant Recruitment

15. Target Marketing

16. Market Potential / Gap Analysis

17. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)

18. Customer Profiling

19. Target Marketing

20. Market Share Analysis

The data relates to the paper that analyses the determinants or factors that best explain student research skills and success in the honours research report module during the COVID-19 pandemic in 2021. The data used have been gathered through an online survey created on the Qualtrics software package. The research questions were developed from demographic factors and subject knowledge including assignments to supervisor influence and other factors in terms of experience or belonging that played a role (see anonymous link at https://unisa.qualtrics.com/jfe/form/SV_86OZZOdyA5sBurY. An SMS was sent to all students of the 2021 module group to make them aware of the survey. They were under no obligation to complete it and all information was regarded as anonymous. We received 39 responses. The raw data from the survey was processed through the SPSS statistical, software package. The data file contains the demographics, frequencies, descriptives, and open questions processed.     The study...

Abstract Purpose Knowledge, positive attitude and good preventive practices are keys to successful myopia control, but information on these is lacking in Africa. This study determined the KAP on myopia in Ghana. Methods This was a population-based cross-sectional survey conducted among adults (aged 18 years and older) living across 16 regions of Ghana between May and October 2021. Data on socio-demographic factors (sex, age, gender, level of education, working status, type of employment, monthly income, and region of residence), respondents’ awareness, and knowledge, attitude and preventive practices (KAP) about myopia were collected. Composite and mean scores were calculated from eleven knowledge (total score = 61), eight attitude (48), and nine preventive practice items (33). Differences in mean scores were assessed using one-way analysis of variance (ANOVA) and standardized coefficients (β) with 95% confidence intervals (CI), using multiple linear regression to determine the associations between the dependent (KAP) and demographic variables. Results Of the 1,919 participants, mean age was 37.4 ± 13.4 years, 42.3% were aged 18–30 years, 52.6% were men, 55.8% had completed tertiary education, and 49.2% had either heard about myopia, or accurately defined myopia as short sightedness. The mean KAP scores were 22.9 ± 23.7, 33.9 ± 5.4, and 22.3 ± 2.8, respectively and varied significantly with many of the demographic variables particularly with age group, region, marital status, and type of employment. Multiple linear regression analyses revealed significant associations between region of residence and knowledge (β =—0.54, 95%CI:-0.87, -0.23, p < 0.001), attitude (β =—0.24, 95%CI:-0.35,-0.14, p < 0.001) and preventive practices (β = 0.07, 95%CI: 0.01, 0.12, p = 0.015). Preventive practices were also associated with type of employment (self-employed vs employee: β = 0.25, 95%CI: 0.15, 4.91, p < 0.05). Knowledge scores were significantly higher in those who lived in the Greater Accra (39.5 ± 18.5) and Eastern regions (39.1 ± 17.5) and lower among those who lived in the Upper West region (6.4 ± 15.6). Government employees and those with tertiary education had significantly higher mean knowledge scores compared with non-government employees (β = 4.56, 95%CI 1.22, 7.89, p = 0.007), and those with primary/no education (β = 18.35, 95%CI: 14.42, 22.27, p < 0.001). Conclusion Ghanaian participants had adequate knowledge of myopia but showed poor attitude and low preventive practices, which varied significantly between regions and were modified by socio-demographic factors. Further research into how education can be used to stimulate Ghanaians’ engagement in preventive practices is needed.

This statistic shows the demographic and socio-economic factors most likely to shape global industries according to executive respondents from large companies worldwide, as of July 2015. 44% of executives believe that the changing nature of work or flexible work will cause major change in their industry by 2020.

Hong Kong Social Contact Dynamics

Understanding Age, Gender and Network Dynamics

By [source]

About this dataset

This dataset provides an in-depth look at the dynamics of social interaction, particularly in Hong Kong. It contains comprehensive information regarding individuals, households and interactions between individuals such as their ages, frequency and duration of contact, and genders. This data can be utilized to evaluate various social and economic trends, behaviors, as well as dynamics observed at different levels. For example, this data set is an ideal tool to recognize population-level trends such as age and gender diversification of contacts or investigate the structure of social networks in addition to the implications of contact patterns on health and economic outcomes. Additionally, it offers valuable insights into dissimilar groups of people including their permanent residence activities related to work or leisure by enabling one to understand their interactions along with contact dynamics within their respective populations. Ultimately this dataset is key for attaining a comprehensive understanding of social contact dynamics which are fundamental for grasping why these interactions are crucial in Hong Kong's society today

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How to use the dataset

This dataset provides detailed information about the social contact dynamics in Hong Kong. With this dataset, it is possible to gain a comprehensive understanding of the patterns of various forms of social contact - from permanent residence and work contacts to leisure contacts. This guide will provide an overview and guidelines on how to use this dataset for analysis.

Exploring Trends and Dynamics:

To begin exploring the trends and dynamics of social contact in Hong Kong, start by looking at demographic factors such as age, gender, ethnicity, and educational attainment associated with different types of contacts (permanent residence/work/leisure). Consider the frequency and duration of contacts within these segments to identify any potential differences between them. Additionally, look at how these factors interact with each other – observe which segments have higher levels of interaction with each other or if there are any differences between different population groups based on their demographic characteristics. This can be done through visualizations such as line graphs or bar charts which can illustrate trends across timeframes or population demographics more clearly than raw numbers would alone.

Investigating Social Networks:

The data collected through this dataset also allows for investigation into social networks – understanding who connects with who in both real-life interactions as well as through digital channels (if applicable). Focus on analyzing individual or family networks rather than larger groups in order to get a clearer picture without having too much complexity added into the analysis time. Analyze commonalities among individuals within a network even after controlling for certain factors that could affect interaction such as age or gender – utilize clustering techniques for this step if appropriate– then focus on comparing networks between individuals/families overall using graph theory methods such as length distributions (the average number of relationships one has) , degrees (the number of links connected from one individual or family unit), centrality measures(identifying individuals who serve an important role bridging two different parts fo he network) etc., These methods will help provide insights into varying structures between large groups rather than focusing only on small-scale personal connections among friends / colleagues / relatives which may not always offer accurate portrayals due to their naturally limited scope

Modeling Health Implications:

Finally, consider modeling health implications stemming from these observed patterns– particularly implications that may not be captured by simpler measures like count per contact hour (which does not differentiate based on intensity). Take into account aspects like viral transmission risk by analyzing secondary effects generated from contact events captured in the data – things like physical proximity when multiple people meet up together over multiple days

Research Ideas

• Analyzing the age, gender and contact dynamics of different areas within Hong Kong to understand the local population trends and behavior.
• Investigating the structure of social networks to study how patterns of contact vary among socio economic backgro...

Affinity DATA

This is the exclusive data set associated with the research "Demographic factors influencing the sharing of fake news in Brazil: A multivariate and qualitative study".

These datasets contains statements about demographic factors and outstanding members from Wiki-based knowledge (i.e., Wikipedia and Wikidata).

Group-centric dataset (sample of what is it about):

• Demographic factors of winners of Nobel Prize in Physics include: male, physicist, american, university teacher, and researcher. Outstanding members in this group include Maria Curie (who isn't male but female) and Wilhelm Röntgen (who isn't a citizen of the U.S. but Germany).

Subject-centric dataset (sample of what is it about):

• Fun trivia about Max Planck include: unlike 93% of winners of Liebig Medal (an award by Society of German Chemists), Planck was not a chemist, but a physicist.

This data can be also browsed at: https://wikiknowledge.onrender.com/demographics/

Urban population growth has been constant for several decades in France. Between 1960 and 2022, it rose from 61.88 percent to 81.51 percent. The phenomenon of urbanization was more significant in the 1960s. Indeed, over this period, the rate of the French population living in cities increased by ten points. The evolution was more weighted over the next 50 years, rising from 71.06 percent in 1970 to 80.98 percent in 2020.An increase in urbanization was accompanied over the same period by a sharp rise in the overall French population, from 55.57 million inhabitants in 1982 to around 68 million in 2024. Paris, an urban giant in France Like in the United Kingdom, the French-style centralized system has led to a high concentration of population around economic, financial, cultural and political centers, all located in the British and French capitals. London and Paris (and its conurbation) are among the largest urban centers on the continent, with Moscow being the most populous. This centralization of power has led to a very heterogenous distribution of population density. The Paris region has a density of more than 1000 inhabitants per km², which is ten times higher than the Haut-de-France region, the second densest region in Metropolitan France.This centralization of power attracts a strong French and foreign workforce. The French capital is by far the most populated city in France. If solely the municipality of Paris is taken into account, it had more than two million inhabitants in 2019, which is more than twice as many as in Marseille and four times as many as in Lyon, the country's second and third most populous cities. Future challenges for French cities Access to employment is no longer the only reason to settle in a town. Other factors come into play in the life choices of city dwellers. In 2019, more than 90% of the French estimated that the presence of green areas was important to settle or not in a district. The pollution level of the city was also considered in the choice of the city. In order to address these pollution problems, municipalities must resolve transportation issues on their own territory. Previously the king of the town, the car is increasingly losing ground to public transport in urban areas. Cities like Paris are relying more on public transport. Between 2011 and 2016, RATP and SNCF have built more than 60 kilometers of tramway tracks . Moreover, the construction of additional train and metro lines in the Grand Paris project aimed at better connecting the suburbs to each other without passing through intramural Paris.Making it easier to travel by bicycle is one of the options chosen by many conurbations to relieve congestion in their cities. Since the early 2000s, self-service bicycles have been a great success in France with more than 2,400 bicycles available in Toulouse or 4,000 in Lyon in 2017. A source of much tension between motorists, municipalities and cyclists, the sharing of the road between 4 and 2 wheelers has, however, been widely developed. In Strasbourg, for example, the municipality had around 1.04 metres of cycle lanes per inhabitant in 2017, the highest rate in France. However, the layout of cycle paths can be perilous and a majority of cyclists in France still feel unsafe on the road.

Replication Data for: The political eects of socio-demographic factors in China: an exposition of the generalized ordered logistic model

Sample distribution by Demographic Factors.

This map service displays demographic data used in EJSCREEN. All demographic data were derived from American Community Survey 2006-2010 estimates. EJSCREEN is an environmental justice screening tool that provides EPA with a nationally consistent approach to screening for potential areas of EJ concern that may warrant further investigation. The EJ indexes are block group level results that combine multiple demographic factors with a single environmental variable (such as proximity to traffic) that can be used to help identify communities living with the greatest potential for negative environmental and health effects. The EJSCREEN tool is currently for internal EPA use only. It is anticipated that as users become accustomed to this new tool, individual programs within the Agency will develop program use guidelines and a community of practice will develop around them within the EPA Geoplatform. Users should keep in mind that screening tools are subject to substantial uncertainty in their demographic and environmental data, particularly when looking at small geographic areas, such as Census block groups. Data on the full range of environmental impacts and demographic factors in any given location are almost certainly not available directly through this tool, and its initial results should be supplemented with additional information and local knowledge before making any judgments about potential areas of EJ concern.

Description

This comprehensive dataset provides a wealth of information about all countries worldwide, covering a wide range of indicators and attributes. It encompasses demographic statistics, economic indicators, environmental factors, healthcare metrics, education statistics, and much more. With every country represented, this dataset offers a complete global perspective on various aspects of nations, enabling in-depth analyses and cross-country comparisons.

Key Features

• Country: Name of the country.
• Density (P/Km2): Population density measured in persons per square kilometer.
• Abbreviation: Abbreviation or code representing the country.
• Agricultural Land (%): Percentage of land area used for agricultural purposes.
• Land Area (Km2): Total land area of the country in square kilometers.
• Armed Forces Size: Size of the armed forces in the country.
• Birth Rate: Number of births per 1,000 population per year.
• Calling Code: International calling code for the country.
• Capital/Major City: Name of the capital or major city.
• CO2 Emissions: Carbon dioxide emissions in tons.
• CPI: Consumer Price Index, a measure of inflation and purchasing power.
• CPI Change (%): Percentage change in the Consumer Price Index compared to the previous year.
• Currency_Code: Currency code used in the country.
• Fertility Rate: Average number of children born to a woman during her lifetime.
• Forested Area (%): Percentage of land area covered by forests.
• Gasoline_Price: Price of gasoline per liter in local currency.
• GDP: Gross Domestic Product, the total value of goods and services produced in the country.
• Gross Primary Education Enrollment (%): Gross enrollment ratio for primary education.
• Gross Tertiary Education Enrollment (%): Gross enrollment ratio for tertiary education.
• Infant Mortality: Number of deaths per 1,000 live births before reaching one year of age.
• Largest City: Name of the country's largest city.
• Life Expectancy: Average number of years a newborn is expected to live.
• Maternal Mortality Ratio: Number of maternal deaths per 100,000 live births.
• Minimum Wage: Minimum wage level in local currency.
• Official Language: Official language(s) spoken in the country.
• Out of Pocket Health Expenditure (%): Percentage of total health expenditure paid out-of-pocket by individuals.
• Physicians per Thousand: Number of physicians per thousand people.
• Population: Total population of the country.
• Population: Labor Force Participation (%): Percentage of the population that is part of the labor force.
• Tax Revenue (%): Tax revenue as a percentage of GDP.
• Total Tax Rate: Overall tax burden as a percentage of commercial profits.
• Unemployment Rate: Percentage of the labor force that is unemployed.
• Urban Population: Percentage of the population living in urban areas.
• Latitude: Latitude coordinate of the country's location.
• Longitude: Longitude coordinate of the country's location.

Potential Use Cases

• Analyze population density and land area to study spatial distribution patterns.
• Investigate the relationship between agricultural land and food security.
• Examine carbon dioxide emissions and their impact on climate change.
• Explore correlations between economic indicators such as GDP and various socio-economic factors.
• Investigate educational enrollment rates and their implications for human capital development.
• Analyze healthcare metrics such as infant mortality and life expectancy to assess overall well-being.
• Study labor market dynamics through indicators such as labor force participation and unemployment rates.
• Investigate the role of taxation and its impact on economic development.
• Explore urbanization trends and their social and environmental consequences.

The City of Rochester and its staff use data about individuals in our community to inform decisions related to policies and programs we design, fund, and carry out. City staff must understand and be accountable to best practices and standards to guide the appropriate use of this information in an ethical and accurate manner that furthers the public good. With these disaggregated data standards, the City seeks to establish useful, uniform standards that guide City staff in their collection, stewardship, analysis, and reporting of information about individuals and their demographic characteristics.This internal guide provides recommended standards and practices to City of Rochester staff for the collection, analysis, and reporting of data related to following characteristics of an individual: Race & Ethnicity; Nativity & Citizenship Status; Language Spoken at Home & English Proficiency; Age; Sex, Gender, & Sexual Orientation; Marital Status; Disability; Address / Geography; Household Income & Size; Housing Tenure; Computer & Internet Use; Employment Status; Veteran Status; and Education Level. This kind of data that describes the characteristics of individuals in our community is disaggregated data. When we summarize data about these individuals and report the data at the group level, it becomes aggregated data. These disaggregated data standards can help City staff in different roles understand how to ask individuals about various demographic traits that may describe them, the collection of which may be useful to inform the City’s programs and policies. Note that this standards document does not mandate the collection of every one of these demographic factors for all analyses or program data intake designs – instead, it prompts City staff to intentionally design surveys and other data intake tools/applications to collect the right level of data to inform the City’s decision-making while also respecting the privacy of the individuals whose information the City seeks to gather. When a City team does choose to collect any of the above-mentioned demographic information about individuals in our community, we advise that they adhere to these standards.

The world's population first reached one billion people in 1803, and reach eight billion in 2023, and will peak at almost 11 billion by the end of the century. Although it took thousands of years to reach one billion people, it did so at the beginning of a phenomenon known as the demographic transition; from this point onwards, population growth has skyrocketed, and since the 1960s the population has increased by one billion people every 12 to 15 years. The demographic transition sees a sharp drop in mortality due to factors such as vaccination, sanitation, and improved food supply; the population boom that follows is due to increased survival rates among children and higher life expectancy among the general population; and fertility then drops in response to this population growth. Regional differences The demographic transition is a global phenomenon, but it has taken place at different times across the world. The industrialized countries of Europe and North America were the first to go through this process, followed by some states in the Western Pacific. Latin America's population then began growing at the turn of the 20th century, but the most significant period of global population growth occurred as Asia progressed in the late-1900s. As of the early 21st century, almost two thirds of the world's population live in Asia, although this is set to change significantly in the coming decades. Future growth The growth of Africa's population, particularly in Sub-Saharan Africa, will have the largest impact on global demographics in this century. From 2000 to 2100, it is expected that Africa's population will have increased by a factor of almost five. It overtook Europe in size in the late 1990s, and overtook the Americas a decade later. In contrast to Africa, Europe's population is now in decline, as birth rates are consistently below death rates in many countries, especially in the south and east, resulting in natural population decline. Similarly, the population of the Americas and Asia are expected to go into decline in the second half of this century, and only Oceania's population will still be growing alongside Africa. By 2100, the world's population will have over three billion more than today, with the vast majority of this concentrated in Africa. Demographers predict that climate change is exacerbating many of the challenges that currently hinder progress in Africa, such as political and food instability; if Africa's transition is prolonged, then it may result in further population growth that would place a strain on the region's resources, however, curbing this growth earlier would alleviate some of the pressure created by climate change.