As of the end of 2024, Syria had the highest number of refugees who had fled the country. That year, about 5.95 million refugees had left the country, mainly due to the civil war that started in 2011. The second highest number of refugees in the world came from Afghanistan after years of civil wars, instability, and the Taliban regaining power in 2021. Third on the list is Ukraine, after almost six million people left the country since Russia's invasion in February 2022. What are refugees? Refugees are people who have been forced to leave their country because of dangerous circumstances, such as war, violence, famine, or persecution. These circumstances arise from race, religious, ethnic, and/or political persecution, and refugees are unable to return to their home countries due to these conflicts. As of 2024, Turkey hosted the largest number of Syrian refugees. Refugees worldwide The number of refugees, internally displaced people, and asylum seekers worldwide has significantly increased since 2012, much due to the civil wars in Syria and Libya that started in 2011, but also due to increasing instability in the Sahel, the Sudan civil war, the Israel-Hamas war, and the Russia-Ukraine war. An internally displaced person is someone who is forced to leave their home but still stays in their country. They are not legally considered a refugee. Asylum seekers are people who leave their home country and enter another country to apply for asylum, which is internationally recognized protection.

Over 1.2 million refugees from Ukraine due to the Russian invasion fled to Germany as of April 2025. Furthermore, the second-highest number was recorded in Poland. In total, around 5.1 million Ukrainian refugees were registered across Europe and 5.6 million worldwide as of May 2025. Most of them fled the country by crossing the border with Poland. Ukrainian refugees in Germany The first increases in the number of Ukrainian refugees in Germany were registered in March and April 2022. The figure exceeded one million refugees in September of that year. Germany had the highest monthly financial allowance for Ukrainians who fled the war compared to other European countries as of June 2022. Temporary protection for Ukrainian refugees in the EU European Union (EU) members implemented the Temporary Protection Directive (TPD), which guaranteed access to accommodation, welfare, and healthcare to refugees from Ukraine. People fleeing the war had a right to a residence permit in the EU, enter the labor market, and enroll children in educational institutions. The protection is granted until March 4, 2026, but it can be extended in the future depending on the situation in the country.

As of the end of 2024, Iran was the largest refugee-hosting country in the world. According to data available by the UN Refugee Agency UNHCR, there were nearly 3.5 million refugees in Iran. Turkey was second with more than 2.9 million. The data refers to the total number of refugees in a given country, not considering the date of their application for asylum or the date of their flight.

In 2024, Turkey was the country that hosted the highest number of Syrian refugees, amounting up to 2.9 million refugees. Lebanon was second, hosting 755,426 Syrian refugees. The data refers to the total number of Syrian refugees in a given country, not considering the date of their application for asylum or the date of their flight.

The total number of refugees in France has gradually increased for more than ten years. Since the 20th century, France has been considered an important host country for immigrants. During the past century, the country welcomed political refugees, immigrants from other European nations, and immigrants from the former French territories in Africa. The distribution of refugees in France and Europe More recently, the refugee crisis has partly shaped immigration in France. In 2023, there were ******* asylum applications in the country, compared to nearly ****** in 2008. Since 2015, the European Union (EU) has implemented the Relocation Scheme, which transfers persons needing international protection from one Member State to another. With most immigrants using the Mediterranean Route to reach Europe, France relocated ***** refugees from Greece and Italy in 2018, while Germany keeps being the European country to host most refugees in Europe. Refugees worldwide Migration is a common challenge for the members of the EU. But it is also a worldwide phenomenon. In 2023, Iran was the largest refugee-hosting country with more than *** million refugees. The number of refugees worldwide, whether internally displaced persons or not, has increased since 2013. Ten years later, in 2023, the situation of countries such as South Sudan or Afghanistan has forced millions of people to flee and seek refuge elsewhere.

The rating reflects the number of migrants in the country. It allows you to assess how attractive the country is for foreigners to live and work, as well as in terms of migration legislation.

In the year to June 2025, approximately 898,000 people migrated to the United Kingdom, while 693,000 people migrated from the country, resulting in a net migration figure of 204,000. There have consistently been more people migrating to the United Kingdom than leaving it since 1993 when approximately 1,000 more people left the country than arrived. Although migration from the European Union has declined since the Brexit vote of 2016, migration from non-EU countries accelerated rapidly from 2021 onwards. In the year to June 2023, 968,000 people from non-EU countries migrated to the UK, compared with 129,000 from EU member states. Immigration and the 2024 election Since late 2022, immigration, along with the economy and healthcare, has consistently been seen by UK voters as one of the top issues facing the country. Despite a pledge to deter irregular migration via small boats, and controversial plans to send asylum applicants to Rwanda while their claims are being processed, Rishi Sunak's Conservative government lost the trust of the public on this issue. On the eve of the last election, 20 percent of Britons thought the Labour Party would be the best party to handle immigration, compared with 13 percent who thought the Conservatives would handle it better. Sunak and the Conservatives went on to lose this election, suffering their worst defeat in modern elections. Historical context of migration The first humans who arrived in the British Isles, were followed by acts of conquest and settlement from Romans, Anglo-Saxons, Danes, and Normans. In the early modern period, there were also significant waves of migration from people fleeing religious or political persecution, such as the French Huguenots. More recently, large numbers of people also left Britain. Between 1820 and 1957, for example, around 4.5 million people migrated from Britain to America. After World War Two, immigration from Britain's colonies and former colonies was encouraged to meet labour demands. A key group that migrated from the Caribbean between the late 1940s and early 1970s became known as the Windrush generation, named after one of the ships that brought the arrivals to Britain.

Abstract

The UNHCR Standardized Expanded Nutrition Surveys (SENS) provide regular nutrition data that plays a key role in delivering effective and timely interventions to ensure good nutritional outcomes among populations affected by forced displacement.

The refugee complex of Dadaab is home to an estimate of 208,000 registered refugees of which the vast majority are Somalis who fled conflict and drought in their home country several decades ago. The Dadaab refugee complex is situated in northeastern Kenya, near the border with Somalia. Dadaab was established in the year 1991 following the beginning of the civil war in Somalia. Somalis were forced to flee as the war worsened, leaving to neighbouring countries including Kenya, Ethiopia and Sudan. Today, Dadaab is home to refugees from many countries in eastern and central Africa, including South Sudan, Burundi, Congo, Ethiopia, Eritrea and Somalia. Somali refugees make up more than 90% of the population. Until early 2017, it consisted of five refugee camps. However, one of the camps, Kambioos, which was also the newest, was closed in March 2017 as refugees began returning to Somalia and the few remaining moved into the other camps. Ifo 2 camp was closed in May 2018 in line with the cam consolidation approach, with refugees either moving to the other camps or being repatriated voluntarily. Refugees live in mud-walled houses with iron sheeting roofs, while some, especially new arrivals, live in tents.

The Standardised Expanded Nutrition Survey (SENS) was conducted between 20 August and 8 September 2018 in the 3 Dadaab refugee camps (Dagahaley, Ifo and Hagadera) to assess the magnitude and severity of malnutrition, assess trends by comparison with previous years and support programmatic decisions.

The weighted prevalence of global acutemalnutrition, the most important indicator, was 8.0% overall, falling within the POOR category (5-9%). However, there was a marked improvement from 9.7% in 2017. Only Ifo camp was within the SERIOUS category (10-14%). The high prevalence of anaemia remains a major concern, as shown by the anaemia prevalence among children which remained above the 40% critical threshold, despite having decreased. Anaemia prevalence among non-pregnant women jumped to 48.9% overall, from 43.6% in 2017 and was above the 40% threshold for all camps. Some improvement was recorded in terms of infant and young chid feeding indicators, although there is still room for improvement. The access to safe drinking water also continued to be satisfactory, while gaps were still observed in terms of sanitation. The duration of the food ration and dietary diversity basically reflect what has been observed in recent surveys.

Geographic coverage

Dadaab Refugee Camps (Ifo, Dagahaley and Hagadera), in Northern Kenya

Analysis unit

Children 0-23 months Children 6-59 months Women 15-49 years Households

Universe

Children 0-59 months Women 15-49 years Refugee households

Kind of data

Sample survey data [ssd]

Sampling procedure

A two-stage cluster survey with probability proportion to size sampling was employed in this survey. Standardized Monitoring and Assessment of Relief and Transitions (SMART) methodology to collect and analyse data on child anthropometry and UNHCR's Standardised Expanded Nutrition Survey (SENS) Guidelines for Refugee Populations was used to guide data collection for other indicators.

The same households sampled by SMART were used in all indicators. Anaemia sample was drawn from the SMART sample size, as recommended by the UNHCR Standardised Expanded Nutrition Survey (SENS) Guidelines. For each of the indicators used, households and individuals were sampled as follows:

Household-level indicators: - WASH: every household - Food Security: every other household - Mosquito net: every other household

Individual-level indicators: - Children 0-59 months: all eligible children in all households were assessed (based on the above calculations) - Women 15-49: all eligible women in every other household were assessed.

The 2-stage cluster sampling method was used to select 30 clusters from each of the 3 camps. At the first stage, a list of blocks was made before the required number were selected using sampling with probability proportional to size (PPS) using ENA softwareIn nearly all cases, a cluster was the equivalent of a block. However, there were exceptions where, for some larger blocks, more than 1 cluster was selected. In this case, the blocks were split further to cater for more than one cluster. In the event that a selected block had more than 250 households, according to SMART guidance, segmentation was done, after which one of the segments was randomly selected to be the cluster.

All households in the selected clusters were labelled before data collection. At the second stage, the required number of households were selected using systematic random sampling from a list of households. A random number was selected between 1 and the sampling interval, which was calculated by dividing the total number of households in the cluster with the required number of households. The selected number became the first household to be surveyed. Subsequent households were selected by adding the sampling interval until the required number of households were completed. All eligible children below 5 years of age from all selected households were surveyed for the Child Anthropometry and Health, and Infant and Young Child Feeding (IYCF), and WASH. Half of the selected households were selected for the Food Security and Women questionnaire. The survey respondents were the primary caretakers of children below 5 years. Abandoned households were not included in the sampling frame. Absent households or households where children were absent were re-visited before the end of the day. If they were found to be empty, they were recorded as missing and were not replaced. Children who were in health centres at the time of the survey were recorded as absent.

Mode of data collection

Face-to-face [f2f]

Research instrument

1) Children 6-59 months (SENS Modules 1-2): Anthropometric status, oedema, enrolment in selective feeding programmes and blanket feeding programmes (CSB++), immunisation (measles), vitamin A supplementation in last six months, de-worming, morbidity from diarrhoea in past two weeks, haemoglobin assessment. 2) Children 0-23 months (SENS Module 3): Questions on infant and young children feeding practices. 3) Women 15-49 years (SENS Module 2): Pregnancy status, coverage of iron-folic acid pills and post-natal vitamin A supplementation, MUAC measurements for pregnant and lactating women (PLW), and haemoglobin assessment for non-pregnant women. 4) Food Security (SENS Module 4): Access and use of the general food ration (GFR), coping mechanisms when the GFR ran out ahead of time and household food dietary diversity using the food consumption score. 5) WASH (SENS Module 5): Water, sanitation and hygiene- Questions on quality and quantity of drinking water, satisfaction with the drinking water supply, and sanitation facilities

This table contains 45 series, with data for years 2014 - 2014 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada) Countries visited (15 items: United States; Mexico; United Kingdom; France; ...) Travel characteristics (3 items: Visits; Nights; Spending in country).

This table contains 25 series, with data for years 1955 - 2013 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...) Last permanent residence (25 items: Total immigrants; France; Great Britain; Total Europe ...).

Abstract

Darien is Panama’s largest and poorest province. Bordering Colombia, the province hosts a 60-mile-deep jungle, the only breaking point of the Pan-American Highway, linking the continent from south to north. Darien is a transit location for thousands of refugees and migrants, most of them coming from Haiti, Venezuela, Cuba, and African and South Asian nations. According to official statistics of the Panamanian National Migration Service (SNM), from 2010 to 2020 some 120,000 people crossed through this area. In 2021 and 2022, Panama faced an unprecedented mixed movement crisis, with nearly 400,000 people making this perilous journey through the jungle. In 2022, a total of 248,284 people entered the country irregularly through Darien. Before they arrived at the Darien jungle, many of these refugees and migrants had previously crossed multiple countries often through unofficial border crossings, resulting in increased protection risks. These vulnerabilities were further exacerbated, during their journey through the jungle, by many risks and incidents which include, but are not limited to, thefts, physical and sexual attacks, murder, and extortion. Many of these refugees and migrants were also undocumented, limiting their access to basic services. Due to protection concerns, in July 2022, UNHCR started collecting monthly information on the characteristics, vulnerabilities, and protection needs of refugees and migrants entering Panama through the Darien jungle.

Geographic coverage

Data collection happened in key transit points in Darien and Chiriqui provinces such as: reception points San Vicente, Lajas Blancas, and Los Planes de Gualaca, in addition to the city of David and the town of Paso Canoas.

Analysis unit

Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

Given the dynamic and challenging context for data collection in these locations, the surveys were collected using non-probability quota sampling. A stratified quota was drawn using proportions from the Panamanian National Migration Service’s official statistics, based on the top ten nationalities in transit. A minimum of 100 surveys were collected during each monthly data collection cycle.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire contained the following sections: introduction, personal information, reasons for leaving the country of origin, reasons for leaving the country of establishment, destination country, arrival and access to territory, protection incidents, specific condition, access to food and basic services.

This archive contains the full dataset of the project "Skills acquisition and employability through volunteering by displaced youth in Uganda", also known as Refugee Youth Volunteering Uganda (RYVU), an interdisciplinary research project funded by the UK’s Global Challenges Research Fund (GCRF) and Economic and Social Research Council (ESRC). The archive includes the project's quantitative and qualitative data collected in four locations in Uganda (Kampala city, and the refugee settlements of Bidibidi, Nakivale and Rwamwanja).

This project investigates the capacity of volunteering to reduce inequalities experienced by displaced youths in Uganda and to build their skills and employability. Forced displacement has become one of the most intractable challenges of the 21st century, with 65.6 million people displaced worldwide at the end of 2016 - a number which is predicted to rise further in the coming years.

1.4 million of these refugees are currently seeking refuge in Uganda, fleeing from conflicts in the Central African countries of South Sudan, the Democratic Republic of Congo, Burundi, and Somalia to the east. The majority of these refugees are children, and so building the skills and employability of the many young people (understood in this research as aged 15 - 25) - caught up in this crisis is critical not only to their own future prospects, but to the long-term stability of their host country and region.

Often, however, economic and other inequalities will exclude young refugees from formal schooling and wider opportunities for skills acquisition; while they will also frequently "fall through the cracks" of humanitarian programming. Many, though, are engaged in volunteering, a practice increasingly identified with building skills and enhancing employability. Thus, the aim of this research is to develop a new conceptual framework and produce a body of data and evidence for critically analysing whether volunteering by displaced youths in Uganda helps their skills acquisition and employability and reduces the inequalities they experience.

The project will take an interdisciplinary (Youth Studies, Volunteering Studies, Refugee Studies, Urban Studies and Development Studies) mixed method approach, and establish and exploit collaborative links with global South refugee NGOs, volunteers and leading global volunteering and development actors. Fieldwork will be conducted in four case study regions - Kampala city, North Western Uganda, South Uganda, where two of the populations are in the same district, and South West Uganda - and proceed through the following three phases.

In Phase 1, the research team will carry out a series of workshops, key informant interviews and field visits in order to build stakeholder engagement, refine and confirm the impact plan, and establish an initial typology of forms and understandings of volunteering to inform the large-scale quantitative survey in phase 2.

In Phase 2, the research team will design, develop, pilot and launch a large quantitative survey of young refugees involved in volunteering. Preliminary analysis of the data arising from this survey will inform the questions and focus of phase 3.

Comprising 6 main activities - participatory mapping, participatory photography, one to one semi-structured interviews, life history interviews, and stakeholder interviews - Phase 3 will deepen our understanding of where and how young refugees volunteer, address the factors shaping volunteering activity, and its impacts on skills acquisition and employability.

The main outputs from the project will include 10 international peer-reviewed journal articles; presentations at major national and international conferences; a project website, containing findings, updates and working notes targeted at different audiences; a compendium of policy briefings; a (touring) photographic exhibition (and accompanying booklet), drawing on images solicited in the context of the participatory photography exercise; and a volunteering for skills acquisition and employability toolkit.

By developing a conceptual framework and body of data and evidence on the impact of volunteering by displaced youths in Uganda on skills acquisition, employability and inequality, the research will contribute directly to knowledge which supports how creative solutions to meeting the Sustainable Development Goal challenges work with programmes to develop education and skills.

EUROPOP2019 are the latest Eurostat population projections produced at national and subnational levels for 31 countries: all 27 European Union (EU) Member States and four European Free Trade Association (EFTA) countries, covering the time horizon from 2019 to 2100.

Population projections are 'what-if scenario' that aim to show the hypothetically developments of the population size and its structure based on a sets of assumptions for fertility, mortality and net migration; they are presented for a long time period that covers more than a half-century (50 years).

The datasets at national level are composed by the baseline population projections and five sensitivity tests, namely:

• no migration – it is assumed that the net migration is set to zero in each year of the entire horizon of projections;
• lower migration – it is assumed that the net migration is 33% lower than in the baseline assumptions, in each year of the entire horizon of projections;
• higher migration – it is assumed that the net migration is 33% higher than in the baseline assumptions, in each year of the entire horizon of projections;
• lower fertility - it is assumed that the fertility rates are lower 20% than in the baseline assumptions, in each year of the entire horizon of projections;
• lower mortality - it is assumed that the mortality rates are decreased such that the life expectancy at birth will increase of about two years by 2070 when compared with the baseline assumptions.

Data are available by single year time interval, as follows:

• Projected population on 1 January by age and sex;
• Assumptions on future age-specific fertility rates, probabilities of dying and net migration levels;
• Projected life expectancy by age (in completed years) and sex.

Moreover, the demographic balances and indicators are available for the baseline projections and the five sensitive variants:

• Total numbers of the projected live births and deaths;
• Projected population structure indicators: proportions of broad age groups in total population, age dependency ratios and median ages of the population (for each sex component).

The dataset at regional level is composed by the baseline population projections and covers all 1169 regions classified as NUTS level 3 corresponding to the NUTS-2016 classification (the Nomenclature of Territorial Units for Statistics) and the 47 Statistical Regions (SR) agreed between European Commission and EFTA countries. Statistical regions are defined according to principles similar to those used in the establishment of the NUTS classification.

For all 1216 regions NUTS-3 level, data are available by single year time interval as follows:

• Projected population on 1 January by age and sex;
• Assumptions on future age-specific fertility rates, probabilities of dying and net migration levels;
• Projected deaths by age and sex;
• Projected life expectancy by age (reached during the year) and sex, which is computed according to the method described in the https://ec.europa.eu/eurostat/cache/metadata/Annexes/proj_19n_esms_an_24.pdf" target="_self">Technical note - Alternative life table (with annex)

In addition to the baseline projections, datasets on projected population at regional level are available for two sensitivity tests:

• no migration - it is assumed that migration is zero for both international and internal components in each year of the entire horizon of projections;
• no inter-regional migration - it is assumed that only internal migration is zero in each year of the entire horizon of projections.

Moreover, the demographic balances and indicators are available for the baseline projections and the two sensitive variants:

• Total numbers of the projected live births by sex and deaths;
• Projected population structure indicators: proportions of broad age groups in total population, age dependency ratios and median ages of the population (for each sex component).

The additional dataset called ‘Short-term update of the projected population (2022-2032)’ [proj_stp22] was published on 28 September 2022. While EUROPOP2019 remain the main set of reference for population projections, this new dataset includes updates of baseline projections for the total population, population in the age group 15 to 74 years (considered as the population in the working-age group), and its share in the total population. In addition, two sensitivity tests are carried out – high and very high number of refugees – by introducing in the baseline projections a shock due to the mass-influx of refugees fleeing the war in Ukraine, and who have received temporary protection in the EU countries.

The updated EUROPOP2019 projections were constructed from cumulative sums of weighted averages of annual population changes of two series: the original EUROPOP2019 projection and a new short-term population projection computed from the latest available data over the period of 10 years.

The two sensitivity tests were built on the following assumptions:

• High number of refugees sensitivity test – assumes that the influx of refugees occurs during 2022 only, and is followed by annual returns at a constant rate such that at the end of 2031 the remaining number of refugees is 10% of the total influx in 2022;
• Very high number of refugees sensitivity test – assumes that the influx of refugees occurs during 2022 and 2023, and is followed by annual returns at a constant rate such that at the end of 2031 the remaining number of refugees is 15% of the cumulated influx in 2022 and 2023.

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.

Data Source: This dataset was compiled from multiple data sources

If this was helpful, a vote is appreciated ❤️ Thank you 🙂

This data file contains details of various nations and their flags. In this file the fields are separated by spaces (not commas). With this data you can try things like predicting the religion of a country from its size and the colours in its flag.

10 attributes are numeric-valued. The remainder are either Boolean- or nominal-valued.

Attribute Information:

1. name: Name of the country concerned
2. landmass: 1=N.America, 2=S.America, 3=Europe, 4=Africa, 4=Asia, 6=Oceania
3. zone: Geographic quadrant, based on Greenwich and the Equator; 1=NE, 2=SE, 3=SW, 4=NW
4. area: in thousands of square km
5. population: in round millions
6. language: 1=English, 2=Spanish, 3=French, 4=German, 5=Slavic, 6=Other Indo-European, 7=Chinese, 8=Arabic, 9=Japanese/Turkish/Finnish/Magyar, 10=Others
7. religion: 0=Catholic, 1=Other Christian, 2=Muslim, 3=Buddhist, 4=Hindu, 5=Ethnic, 6=Marxist, 7=Others
8. bars: Number of vertical bars in the flag
9. stripes: Number of horizontal stripes in the flag
10. colours: Number of different colours in the flag
11. red: 0 if red absent, 1 if red present in the flag
12. green: same for green
13. blue: same for blue
14. gold: same for gold (also yellow)
15. white: same for white
16. black: same for black
17. orange: same for orange (also brown)
18. mainhue: predominant colour in the flag (tie-breaks decided by taking the topmost hue, if that fails then the most central hue, and if that fails the leftmost hue)
19. circles: Number of circles in the flag
20. crosses: Number of (upright) crosses
21. saltires: Number of diagonal crosses
22. quarters: Number of quartered sections
23. sunstars: Number of sun or star symbols
24. crescent: 1 if a crescent moon symbol present, else 0
25. triangle: 1 if any triangles present, 0 otherwise
26. icon: 1 if an inanimate image present (e.g., a boat), otherwise 0
27. animate: 1 if an animate image (e.g., an eagle, a tree, a human hand) present, 0 otherwise
28. text: 1 if any letters or writing on the flag (e.g., a motto or slogan), 0 otherwise
29. topleft: colour in the top-left corner (moving right to decide tie-breaks)
30. botright: Colour in the bottom-left corner (moving left to decide tie-breaks)

In 2022, a total of than 16,415 refugee claimants in Canada came from Mexico, the most out of any country. Haiti, Turkey, Colombia, and Iran rounded out the top five countries for refugee claimants in Canada in that year.

Has the curve flattened?

Countries around the world are working to “flatten the curve” of the coronavirus pandemic. Flattening the curve involves reducing the number of new COVID-19 cases from one day to the next. This helps prevent healthcare systems from becoming overwhelmed. When a country has fewer new COVID-19 cases emerging today than it did on a previous day, that’s a sign that the country is flattening the curve.

On a trend line of total cases, a flattened curve looks how it sounds: flat. On the charts on this page, which show new cases per day, a flattened curve will show a downward trend in the number of daily new cases.

This analysis uses a 5-day moving average to visualize the number of new COVID-19 cases and calculate the rate of change. This is calculated for each day by averaging the values of that day, the two days before, and the two next days. This approach helps prevent major events (such as a change in reporting methods) from skewing the data. The interactive charts below show the daily number of new cases for the 10 most affected countries, based on the reported number of deaths by COVID-19.

This datas were last updated on Saturday, April 25, 2020 at 11:51 PM EDT.

Abstract

In order to develop various methods of comparable data collection on health and health system responsiveness WHO started a scientific survey study in 2000-2001. This study has used a common survey instrument in nationally representative populations with modular structure for assessing health of indviduals in various domains, health system responsiveness, household health care expenditures, and additional modules in other areas such as adult mortality and health state valuations.

The health module of the survey instrument was based on selected domains of the International Classification of Functioning, Disability and Health (ICF) and was developed after a rigorous scientific review of various existing assessment instruments. The responsiveness module has been the result of ongoing work over the last 2 years that has involved international consultations with experts and key informants and has been informed by the scientific literature and pilot studies.

Questions on household expenditure and proportionate expenditure on health have been borrowed from existing surveys. The survey instrument has been developed in multiple languages using cognitive interviews and cultural applicability tests, stringent psychometric tests for reliability (i.e. test-retest reliability to demonstrate the stability of application) and most importantly, utilizing novel psychometric techniques for cross-population comparability.

The study was carried out in 61 countries completing 71 surveys because two different modes were intentionally used for comparison purposes in 10 countries. Surveys were conducted in different modes of in- person household 90 minute interviews in 14 countries; brief face-to-face interviews in 27 countries and computerized telephone interviews in 2 countries; and postal surveys in 28 countries. All samples were selected from nationally representative sampling frames with a known probability so as to make estimates based on general population parameters.

The survey study tested novel techniques to control the reporting bias between different groups of people in different cultures or demographic groups ( i.e. differential item functioning) so as to produce comparable estimates across cultures and groups. To achieve comparability, the selfreports of individuals of their own health were calibrated against well-known performance tests (i.e. self-report vision was measured against standard Snellen's visual acuity test) or against short descriptions in vignettes that marked known anchor points of difficulty (e.g. people with different levels of mobility such as a paraplegic person or an athlete who runs 4 km each day) so as to adjust the responses for comparability . The same method was also used for self-reports of individuals assessing responsiveness of their health systems where vignettes on different responsiveness domains describing different levels of responsiveness were used to calibrate the individual responses.

This data are useful in their own right to standardize indicators for different domains of health (such as cognition, mobility, self care, affect, usual activities, pain, social participation, etc.) but also provide a better measurement basis for assessing health of the populations in a comparable manner. The data from the surveys can be fed into composite measures such as "Healthy Life Expectancy" and improve the empirical data input for health information systems in different regions of the world. Data from the surveys were also useful to improve the measurement of the responsiveness of different health systems to the legitimate expectations of the population.

Kind of data

Sample survey data [ssd]

Sampling procedure

5,350 named individuals in the United Kingdom were systematically selected from the Electoral Register, which was stratified by local authority, and ordered by postcode.

Addresses were checked against Laing & Bussion.s Care Home and Hospital Information database and 14 addresses were removed. A further 336 named individuals were systematically selected and removed from the remaining sample, using a random start and fixed interval method on the sample sorted by local authority and postcode, leaving 5,000 addresses for the usable sample.

The 5,000 sampled individuals were sorted by local authority and postcode.

Mode of data collection

Mail Questionnaire [mail]

Cleaning operations

Data Coding At each site the data was coded by investigators to indicate the respondent status and the selection of the modules for each respondent within the survey design. After the interview was edited by the supervisor and considered adequate it was entered locally.

Data Entry Program A data entry program was developed in WHO specifically for the survey study and provided to the sites. It was developed using a database program called the I-Shell (short for Interview Shell), a tool designed for easy development of computerized questionnaires and data entry (34). This program allows for easy data cleaning and processing.

The data entry program checked for inconsistencies and validated the entries in each field by checking for valid response categories and range checks. For example, the program didn’t accept an age greater than 120. For almost all of the variables there existed a range or a list of possible values that the program checked for.

In addition, the data was entered twice to capture other data entry errors. The data entry program was able to warn the user whenever a value that did not match the first entry was entered at the second data entry. In this case the program asked the user to resolve the conflict by choosing either the 1st or the 2nd data entry value to be able to continue. After the second data entry was completed successfully, the data entry program placed a mark in the database in order to enable the checking of whether this process had been completed for each and every case.

Data Transfer The data entry program was capable of exporting the data that was entered into one compressed database file which could be easily sent to WHO using email attachments or a file transfer program onto a secure server no matter how many cases were in the file. The sites were allowed the use of as many computers and as many data entry personnel as they wanted. Each computer used for this purpose produced one file and they were merged once they were delivered to WHO with the help of other programs that were built for automating the process. The sites sent the data periodically as they collected it enabling the checking procedures and preliminary analyses in the early stages of the data collection.

Data quality checks Once the data was received it was analyzed for missing information, invalid responses and representativeness. Inconsistencies were also noted and reported back to sites.

Data Cleaning and Feedback After receipt of cleaned data from sites, another program was run to check for missing information, incorrect information (e.g. wrong use of center codes), duplicated data, etc. The output of this program was fed back to sites regularly. Mainly, this consisted of cases with duplicate IDs, duplicate cases (where the data for two respondents with different IDs were identical), wrong country codes, missing age, sex, education and some other important variables.