Historical chart and dataset showing Switzerland poverty rate by year from 1982 to 2021.

The number of people at risk of poverty or social exclusion in Switzerland was approximately ************ people in 2020. Between 2007 and 2020, the number of people at risk of poverty rose by around ************ people, though the increase followed an uneven trajectory rather than a consistent upward trend.

Switzerland Poverty Gap at $3.20 a Day: 2011 PPP: % data was reported at 0.000 % in 2015. This stayed constant from the previous number of 0.000 % for 2014. Switzerland Poverty Gap at $3.20 a Day: 2011 PPP: % data is updated yearly, averaging 0.000 % from Dec 2006 (Median) to 2015, with 10 observations. Switzerland Poverty Gap at $3.20 a Day: 2011 PPP: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Switzerland – Table CH.World Bank.WDI: Poverty. Poverty gap at $3.20 a day (2011 PPP) is the mean shortfall in income or consumption from the poverty line $3.20 a day (counting the nonpoor as having zero shortfall), expressed as a percentage of the poverty line. This measure reflects the depth of poverty as well as its incidence.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. The aggregated numbers for low- and middle-income countries correspond to the totals of 6 regions in PovcalNet, which include low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia). See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Switzerland Poverty Headcount Ratio at National Poverty Lines: % of Population data was reported at 15.800 % in 2021. This records an increase from the previous number of 14.700 % for 2020. Switzerland Poverty Headcount Ratio at National Poverty Lines: % of Population data is updated yearly, averaging 15.250 % from Dec 2006 (Median) to 2021, with 16 observations. The data reached an all-time high of 16.000 % in 2018 and a record low of 13.800 % in 2013. Switzerland Poverty Headcount Ratio at National Poverty Lines: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Switzerland – Table CH.World Bank.WDI: Social: Poverty and Inequality. National poverty headcount ratio is the percentage of the population living below the national poverty line(s). National estimates are based on population-weighted subgroup estimates from household surveys. For economies for which the data are from EU-SILC, the reported year is the income reference year, which is the year before the survey year.;World Bank, Poverty and Inequality Platform. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines.;;This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries.

Poverty rate at $3.2 a day of Switzerland remained constant at 0.10 % over the last 1 years. Population below $3.1 a day is the percentage of the population living on less than $3.1 a day at 2005 international prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.

Switzerland Poverty Headcount Ratio at Societal Poverty Lines: % of Population data was reported at 11.700 % in 2020. This records a decrease from the previous number of 12.100 % for 2019. Switzerland Poverty Headcount Ratio at Societal Poverty Lines: % of Population data is updated yearly, averaging 11.650 % from Dec 1982 (Median) to 2020, with 20 observations. The data reached an all-time high of 12.900 % in 2007 and a record low of 10.000 % in 2004. Switzerland Poverty Headcount Ratio at Societal Poverty Lines: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Switzerland – Table CH.World Bank.WDI: Social: Poverty and Inequality. The poverty headcount ratio at societal poverty line is the percentage of a population living in poverty according to the World Bank's Societal Poverty Line. The Societal Poverty Line is expressed in purchasing power adjusted 2017 U.S. dollars and defined as max($2.15, $1.15 + 0.5*Median). This means that when the national median is sufficiently low, the Societal Poverty line is equivalent to the extreme poverty line, $2.15. For countries with a sufficiently high national median, the Societal Poverty Line grows as countries’ median income grows.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

Percent of Population Below the Poverty Level (5-year estimate) in Switzerland County, IN was 11.10% in January of 2023, according to the United States Federal Reserve. Historically, Percent of Population Below the Poverty Level (5-year estimate) in Switzerland County, IN reached a record high of 22.80 in January of 2020 and a record low of 11.10 in January of 2023. Trading Economics provides the current actual value, an historical data chart and related indicators for Percent of Population Below the Poverty Level (5-year estimate) in Switzerland County, IN - last updated from the United States Federal Reserve on July of 2025.

Switzerland Income Share Held by Lowest 10% data was reported at 3.200 % in 2015. This stayed constant from the previous number of 3.200 % for 2014. Switzerland Income Share Held by Lowest 10% data is updated yearly, averaging 3.150 % from Dec 2006 (Median) to 2015, with 10 observations. The data reached an all-time high of 3.300 % in 2013 and a record low of 2.900 % in 2007. Switzerland Income Share Held by Lowest 10% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Switzerland – Table CH.World Bank.WDI: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Switzerland Poverty Headcount Ratio at $5.50 a Day: 2011 PPP: % of Population data was reported at 0.000 % in 2014. This stayed constant from the previous number of 0.000 % for 2013. Switzerland Poverty Headcount Ratio at $5.50 a Day: 2011 PPP: % of Population data is updated yearly, averaging 0.200 % from Dec 2006 (Median) to 2014, with 9 observations. The data reached an all-time high of 0.500 % in 2006 and a record low of 0.000 % in 2014. Switzerland Poverty Headcount Ratio at $5.50 a Day: 2011 PPP: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Switzerland – Table CH.World Bank: Poverty. Poverty headcount ratio at $5.50 a day is the percentage of the population living on less than $5.50 a day at 2011 international prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. The aggregated numbers for low- and middle-income countries correspond to the totals of 6 regions in PovcalNet, which include low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia). See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Switzerland Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data was reported at 0.980 % in 2015. Switzerland Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data is updated yearly, averaging 0.980 % from Dec 2015 (Median) to 2015, with 1 observations. Switzerland Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Switzerland – Table CH.World Bank.WDI: Poverty. The growth rate in the welfare aggregate of the bottom 40% is computed as the annualized average growth rate in per capita real consumption or income of the bottom 40% of the population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2011 Purchasing Power Parity (PPP) using the PovcalNet (http://iresearch.worldbank.org/PovcalNet). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered. The final year refers to the most recent survey available between 2011 and 2015. Growth rates for Iraq are based on survey means of 2005 PPP$. The coverage and quality of the 2011 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2011 exercise of the International Comparison Program. See PovcalNet for detailed explanations.; ; World Bank, Global Database of Shared Prosperity (GDSP) circa 2010-2015 (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).; ; The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes.

Switzerland Survey Mean Consumption or Income per Capita: Total Population: Annualized Average Growth Rate data was reported at 0.990 % in 2014. Switzerland Survey Mean Consumption or Income per Capita: Total Population: Annualized Average Growth Rate data is updated yearly, averaging 0.990 % from Dec 2014 (Median) to 2014, with 1 observations. Switzerland Survey Mean Consumption or Income per Capita: Total Population: Annualized Average Growth Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Switzerland – Table CH.World Bank: Poverty. The growth rate in the welfare aggregate of the total population is computed as the annualized average growth rate in per capita real consumption or income of the total population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2011 Purchasing Power Parity (PPP) using the PovcalNet (http://iresearch.worldbank.org/PovcalNet). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered. The final year refers to the most recent survey available between 2011 and 2015. Growth rates for Iraq are based on survey means of 2005 PPP$. The coverage and quality of the 2011 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2011 exercise of the International Comparison Program. See PovcalNet for detailed explanations.; ; World Bank, Global Database of Shared Prosperity (GDSP) circa 2010-2015 (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).; ; The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes.

Switzerland Poverty Headcount Ratio at $3.65 a Day: 2017 PPP: % of Population data was reported at 0.100 % in 2020. This records an increase from the previous number of 0.000 % for 2019. Switzerland Poverty Headcount Ratio at $3.65 a Day: 2017 PPP: % of Population data is updated yearly, averaging 0.100 % from Dec 1982 (Median) to 2020, with 20 observations. The data reached an all-time high of 0.700 % in 1992 and a record low of 0.000 % in 2019. Switzerland Poverty Headcount Ratio at $3.65 a Day: 2017 PPP: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Switzerland – Table CH.World Bank.WDI: Social: Poverty and Inequality. Poverty headcount ratio at $3.65 a day is the percentage of the population living on less than $3.65 a day at 2017 international prices.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

As a reaction to widespread poverty, a system of coercive welfare developed in Switzerland during the 19th century. Poverty was often thought to result from an individual’s misconduct rather than from structural, economic or political circumstances. People whose lifestyle deviated from the desired norm or who were unable to make a living for themselves were subjected to so-called administrative detention at institutions such as workhouses and poorhouses. The excavation of the cemetery of the correctional facility/workhouse and asylum «Realta» in Cazis offered the opportunity to gain insight into the living conditions of a marginalized group of people and to shed light on aspects of coercive welfare that have hardly been addressed in historical studies. A comprehensive study of pathological alterations was used to assess possible physical causes and effects of administrative detention. Skeletal samples from regular contemporaneous cemeteries provided data for the general population and thus allowed us to detect peculiarities in the «Realta» assemblage. Possible cases of Stickler Syndrome, microcephaly, congenital syphilis, endemic hypothyroidism and disabilities secondary to trauma may have been the reason for the affected individuals’ institutionalisation. The high prevalence of tuberculosis was linked to the socioeconomic status and the living conditions at the facility. Several cases of scurvy and osteomalacia may have resulted from various risk factors such as poverty, alcoholism, mental illness or institutionalisation. The fracture rates, especially of ribs, were extremely high. A large proportion of the fractures were incompletely healed and most likely occurred during detention due to interpersonal violence. Underlying diseases further contributed to the high fracture rates. This first study on skeletons from an institution of administrative detention in Switzerland demonstrated how pre-existing health conditions and the socioeconomic background contributed to the chance of being detained, and how detention led to further deterioration of health.

Abstract

In 2013, the EU-SILC instrument covered all EU Member States plus Iceland, Turkey, Norway, Switzerland and Croatia. EU-SILC has become the EU reference source for comparative statistics on income distribution and social exclusion at European level, particularly in the context of the "Program of Community action to encourage cooperation between Member States to combat social exclusion" and for producing structural indicators on social cohesion for the annual spring report to the European Council. The first priority is to be given to the delivery of comparable, timely and high quality cross-sectional data.

There are two types of datasets: 1) Cross-sectional data pertaining to fixed time periods, with variables on income, poverty, social exclusion and living conditions. 2) Longitudinal data pertaining to individual-level changes over time, observed periodically - usually over four years.

Social exclusion and housing-condition information is collected at household level. Income at a detailed component level is collected at personal level, with some components included in the "Household" section. Labor, education and health observations only apply to persons aged 16 and over. EU-SILC was established to provide data on structural indicators of social cohesion (at-risk-of-poverty rate, S80/S20 and gender pay gap) and to provide relevant data for the two 'open methods of coordination' in the field of social inclusion and pensions in Europe.

This is the 1st version of the 2013 Cross-Sectional User Database as released in July 2015.

Geographic coverage

The survey covers following countries: Austria; Belgium; Bulgaria; Croatia; Cyprus; Czech Republic; Denmark; Estonia; Finland; France; Germany; Greece; Spain; Ireland; Italy; Latvia; Lithuania; Luxembourg; Hungary; Malta; Netherlands; Poland; Portugal; Romania; Slovenia; Slovakia; Serbia; Sweden; United Kingdom; Iceland; Norway; Turkey; Switzerland

Small parts of the national territory amounting to no more than 2% of the national population and the national territories listed below may be excluded from EU-SILC: France - French Overseas Departments and territories; Netherlands - The West Frisian Islands with the exception of Texel; Ireland - All offshore islands with the exception of Achill, Bull, Cruit, Gorumna, Inishnee, Lettermore, Lettermullan and Valentia; United Kingdom - Scotland north of the Caledonian Canal, the Scilly Islands.

Analysis unit

• Households;
• Individuals 16 years and older.

Universe

The survey covered all household members over 16 years old. Persons living in collective households and in institutions are generally excluded from the target population.

Kind of data

Sample survey data [ssd]

Sampling procedure

On the basis of various statistical and practical considerations and the precision requirements for the most critical variables, the minimum effective sample sizes to be achieved were defined. Sample size for the longitudinal component refers, for any pair of consecutive years, to the number of households successfully interviewed in the first year in which all or at least a majority of the household members aged 16 or over are successfully interviewed in both the years.

For the cross-sectional component, the plans are to achieve the minimum effective sample size of around 131.000 households in the EU as a whole (137.000 including Iceland and Norway). The allocation of the EU sample among countries represents a compromise between two objectives: the production of results at the level of individual countries, and production for the EU as a whole. Requirements for the longitudinal data will be less important. For this component, an effective sample size of around 98.000 households (103.000 including Iceland and Norway) is planned.

Member States using registers for income and other data may use a sample of persons (selected respondents) rather than a sample of complete households in the interview survey. The minimum effective sample size in terms of the number of persons aged 16 or over to be interviewed in detail is in this case taken as 75 % of the figures shown in columns 3 and 4 of the table I, for the cross-sectional and longitudinal components respectively.

The reference is to the effective sample size, which is the size required if the survey were based on simple random sampling (design effect in relation to the 'risk of poverty rate' variable = 1.0). The actual sample sizes will have to be larger to the extent that the design effects exceed 1.0 and to compensate for all kinds of non-response. Furthermore, the sample size refers to the number of valid households which are households for which, and for all members of which, all or nearly all the required information has been obtained. For countries with a sample of persons design, information on income and other data shall be collected for the household of each selected respondent and for all its members.

At the beginning, a cross-sectional representative sample of households is selected. It is divided into say 4 sub-samples, each by itself representative of the whole population and similar in structure to the whole sample. One sub-sample is purely cross-sectional and is not followed up after the first round. Respondents in the second sub-sample are requested to participate in the panel for 2 years, in the third sub-sample for 3 years, and in the fourth for 4 years. From year 2 onwards, one new panel is introduced each year, with request for participation for 4 years. In any one year, the sample consists of 4 sub-samples, which together constitute the cross-sectional sample. In year 1 they are all new samples; in all subsequent years, only one is new sample. In year 2, three are panels in the second year; in year 3, one is a panel in the second year and two in the third year; in subsequent years, one is a panel for the second year, one for the third year, and one for the fourth (final) year.

According to the Commission Regulation on sampling and tracing rules, the selection of the sample will be drawn according to the following requirements:

1. For all components of EU-SILC (whether survey or register based), the crosssectional and longitudinal (initial sample) data shall be based on a nationally representative probability sample of the population residing in private households within the country, irrespective of language, nationality or legal residence status. All private households and all persons aged 16 and over within the household are eligible for the operation.
2. Representative probability samples shall be achieved both for households, which form the basic units of sampling, data collection and data analysis, and for individual persons in the target population.
3. The sampling frame and methods of sample selection shall ensure that every individual and household in the target population is assigned a known and non-zero probability of selection.
4. By way of exception, paragraphs 1 to 3 shall apply in Germany exclusively to the part of the sample based on probability sampling according to Article 8 of the Regulation of the European Parliament and of the Council (EC) No 1177/2003 concerning

Community Statistics on Income and Living Conditions. Article 8 of the EU-SILC Regulation of the European Parliament and of the Council mentions: 1. The cross-sectional and longitudinal data shall be based on nationally representative probability samples. 2. By way of exception to paragraph 1, Germany shall supply cross-sectional data based on a nationally representative probability sample for the first time for the year 2008. For the year 2005, Germany shall supply data for one fourth based on probability sampling and for three fourths based on quota samples, the latter to be progressively replaced by random selection so as to achieve fully representative probability sampling by 2008. For the longitudinal component, Germany shall supply for the year 2006 one third of longitudinal data (data for year 2005 and 2006) based on probability sampling and two thirds based on quota samples. For the year 2007, half of the longitudinal data relating to years 2005, 2006 and 2007 shall be based on probability sampling and half on quota sample. After 2007 all of the longitudinal data shall be based on probability sampling.

Detailed information about sampling is available in Quality Reports in Related Materials.

Mode of data collection

Mixed

ORIGINAL DATA SET FOR STUDY OF PUBLICATION AND CITATION TRENDS, TROPICAL MEDICINE, EARLY COVID 19 PANDEMIC. Background: An adequate response to health needs includes the identification of research patterns about the large number of people living in the tropics and subjected to tropical diseases. Studies have shown that research does not always match the real needs of those populations, and that citation reflects mostly the amount of money behind particular publications. Here we test the hypothesis that research from richer institutions is published in better-indexed journals, and thus has greater citation rates. Methods: The data in this study was extracted from the Science Citation Index Expanded database; the 2020 journal Impact Factor (IF2020) was updated to 30 June 2021. We considered places, subjects, institutions and journals. Results: We identified 1 041 highly cited articles with 100 citations or more in the category of tropical medicine. About a decade is needed for an article to reach peak citation. Only two Covid-19 related were highly cited in the last three years. Most cited articles were published by the journals Memorias Do Instituto Oswaldo Cruz (Brazil), Acta Tropica (Switzerland), and PLoS Neglected Tropical Diseases (USA). The USA dominated five of the six publication indicators. International collaboration articles had more citations than single-country articles. The UK, South Africa, and Switzerland had high citation rates, as did the London School of Hygiene and Tropical Medicine in the UK, the Centers for Disease Control and Prevention in the USA, and the WHO in Switzerland. Conclusions: About ten years of accumulated citations are needed to get 100 citations or more as highly cited articles in the Web of Science category of tropical medicine. Six publication and citation indicators, including authors’ publication potential and characteristics evaluated by Y-index, indicate that the currently available indexing system places tropical researchers at a disadvantage against their colleagues in temperate countries, and suggest that, to progress towards better control of tropical diseases, international collaboration should increase, and other tropical countries should follow the example of Brazil, which provides significant financing to its scientific community.Julián Monge-Nájera1, and Yuh-Shan Ho2* 1Laboratorio de Ecología Urbana, Vicerrectoría de Investigación, Universidad Estatal a Distancia, 2050 San José, Costa Rica; julianmonge@gmail.com (https://orcid.org/0000-0001-7764-2966) *Corresponding author: Trend Research Centre, Asia University, No. 500 Lioufeng Road, Wufeng, Taichung 41354, Taiwan; ysho@asia.edu.tw (https://orcid.org/0000-0002-2557-8736) {"references": ["Monge-Najera and Ho. (2023). Highly cited tropical medicine articles in the Web of Science from 1991 to 2020: A bibliometric analysis"]} Full data set in Excel, most cited tropical medicine articles at the beginning of the COVID 19 pandemic

As of 2024, there were 975 food banks in Germany. This was an increase compared to the previous year at 964. It was also the highest number of food banks since 1993, when the German Tafel scheme was set up. Food bank usage ‘Tafel’ in Germany is an organization that it similar to the concept of food banks in the United States. These food banks operate at a regional level and provide food that would otherwise be destroyed to those in need either for free or at a heavily discounted price. In 2022, around two million people were using food banks in Germany, this was the highest figure since 2014. This new peak is likely due to the large increase in food prices over the past two years. Both 2022 and 2023 saw a year-on-year increase of over 12 percent. It is not just Germany that is facing higher food prices. Countries across the world have been experiencing a rise in the price of groceries. Over 10 percent of people living in Spain, Great Britain, Germany, France, and Italy said that it was usually difficult for them to afford food items at the end of 2022. In France and Italy there were noticeably higher rates. Poverty When it came to the average financial wealth of adults in Europe, Switzerland, Iceland, and Denmark topped the list. Germany ranked 13th on the list, with average wealth of adults at 113,00 U.S. dollars. This average, however, does not represent the entire population, and there are people in Germany, as in every country, who struggle to finance day-to-day life. In 2022, there were around 16.7 percent of people at risk of living in poverty. This was a slight decrease compared to the previous year, but still significantly higher than in previous years. In certain cities the risk of living in poverty was even higher than the national average. The city of Duisburg, which is located in western Germany, had an at risk of living in poverty rate of over 30 percent. In Bremen, a city close to Hamburg, the share of those facing financial difficulties was almost 30 percent.

Suisse: Poverty, percent of population: Pour cet indicateur, La Banque mondiale fournit des données pour la Suisse de 2006 à 2021. La valeur moyenne pour Suisse pendant cette période était de 15.18 pour cent avec un minimum de 13.8 pour cent en 2013 et un maximum de 16 pour cent en 2018.

Switzerland Income Share Held by Lowest 20% data was reported at 7.800 % in 2015. This stayed constant from the previous number of 7.800 % for 2014. Switzerland Income Share Held by Lowest 20% data is updated yearly, averaging 7.800 % from Dec 2006 (Median) to 2015, with 10 observations. The data reached an all-time high of 8.100 % in 2012 and a record low of 7.400 % in 2007. Switzerland Income Share Held by Lowest 20% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Switzerland – Table CH.World Bank.WDI: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Postcranial fracture rates in individuals with and without obvious bone loss.

Prevalence of lesions on the visceral surface of ribs in Cazis/Realta and reference groups.