The reintroduction of rare species in natural preserves is a commonly used restoration strategy to prevent species extinction. An essential first step in planning successful reintroductions is identifying which life stages (e.g., seeds or large adults) should be used to establish these new populations. Following this initial establishment phase, it is necessary to determine the level of survival, growth, and recruitment needed to maintain population persistence over time and identify management actions that will achieve these goals. In this 5-year study, we projected the short- and long-term population growth rates of a critically endangered long-lived shrub, Delissea waianaeensis. Using this model system, we show that reintroductions established with mature individuals have the lowest probability of quasi-population extinction (10 individuals) and the highest increase in population abundance. However, our results also demonstrate that short-term increases in population abundances are overly optimistic of long-term outcomes. Using long-term stochastic model simulations, we identified the level of natural seedling regeneration needed to maintain a positive population growth rate over time. These findings are relevant for planning future reintroduction efforts for long-lived species and illustrate the need to forecast short- and long-term population responses when evaluating restoration success.

In the middle of 2023, about 60 percent of the global population was living in Asia.The total world population amounted to 8.1 billion people on the planet. In other words 4.7 billion people were living in Asia as of 2023. Global populationDue to medical advances, better living conditions and the increase of agricultural productivity, the world population increased rapidly over the past century, and is expected to continue to grow. After reaching eight billion in 2023, the global population is estimated to pass 10 billion by 2060. Africa expected to drive population increase Most of the future population increase is expected to happen in Africa. The countries with the highest population growth rate in 2024 were mostly African countries. While around 1.47 billion people live on the continent as of 2024, this is forecast to grow to 3.9 billion by 2100. This is underlined by the fact that most of the countries wit the highest population growth rate are found in Africa. The growing population, in combination with climate change, puts increasing pressure on the world's resources.

The University of Trinidad and Tobago (UTT) has published its applicant demographic overview by gender, campus, programme, programme level, age group, and academic area using tableau software. The publication is part of UTT's Institutional Data Profile (IDP) and allows the data visualisation to be downloaded in an image, PDF and PowerPoint format. You can also filter the dashboard as well as share the link. UTT Applicant Demographic Overview: https://utt.edu.tt/?wk=62&page_key=1124

Computed prospective ages for 1950-2100 for all countries and regions based on 2017 Revision of the UN World Population Prospects.Content:1. codebook.pdf contains a brief overview of the dataset, its background and a description of the cases and variables.2. methods.pdf is a (draft but complete) write up of the calculations used to create the dataset.3. 2017_prospective-ages.csv is the human readable form of the prospective age dataset containing the calculated prospective old-age thresholds for 241 countries and regions, for the period 1950-2100, for men, women and both together, as well as the proportions of the population (male, female and total) over these thresholds.This figshare fileset is published directly from the github repository ProspectiveAgeData. For an application of this data see the factsheet on ageing in the Middle East and Northern Africa which will be published in Population Horizons journal.

The introduction of plant pathogens can quickly reshape disease dynamics in island agro-ecologies, representing a continuous challenge for local crop management strategies. Xanthomonas pathogens causing tomato bacterial spot were probably introduced in Taiwan several decades ago, creating a unique opportunity to study the genetic makeup and adaptive response of this alien population. We examined the phenotypic and genotypic identity of 669 pathogen entries collected across different regions of Taiwan in the last three decades. The analysis detected a major population shift, where X. euvesicatoria and X. vesicatoria races T1 and T2 were replaced by new races of X. perforans. After its introduction, race T4 quickly became dominant in all tomato-growing areas of the island. The genomic analysis of 317 global genomes indicates that the Xanthomonas population in Taiwan has a narrow genetic background, most likely resulting from a small number of colonization events. However, despite the apparent genetic uniformity, X. perforans race T4 shows multiple phenotypic responses in tomato lines. Additionally, an in-depth analysis of effector composition suggests diversification in response to local adaptation. These include unique mutations on avrXv3 which might allow the pathogen to overcome Xv3/Rx4 resistance gene. The findings underscore the dynamic evolution of a pathogen when introduced in a semi-isolated environment and provide insights into the potential management strategies for this important disease of tomato.

This dataset, released February 2020, contains the male Population projections for years 2020, 2025 and 2030, by 5-year age groups: 0-14, 15-24, 25-44, 45-64, 65+, 70+, 75+, 85+ years.

The data is by Population Health Area (PHA) 2016 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS).

Population Health Areas, developed by PHIDU, are comprised of a combination of whole SA2s and multiple (aggregates of) SA2s, where the SA2 is an area in the ABS structure.

For more information please see the data source notes on the data.

Source: These data are based on customised projections prepared for the Australian Government Department of Health by the Australian Bureau of Statistics and originally published by the Australian Institute of Health and Welfare. PHA data were compiled by PHIDU based on these customised projections for 2020, 2025, and 2030..

AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.

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.

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.

This data package contains model data that were used to support conclusions drawn in “Declines in low-elevation subalpine populations outpace growth in high-elevation populations with warming”, by Conlisk et al. 2017. Experimental data collected at field sites within the Alpine Treeline Warming Experiment (ATWE), and data from long-term observational plots were collected on Niwot Ridge, Colorado, USA and used to formulate models contained within the folder “Model_archive” in the zipped folder “Conlisk_etal_JEcol2017_model_archive12022020.zip”. The contents of this compressed folder are described in the data user's guide attached to this archive. There are two folders within the zipped folder - “EngelmannSpruce” and “LimberPine” - for each of the two species in the paper. Models are stored as text files and .sch files can also be opened as text files. However, please note that all these files are specific to the RAMAS Metapop population modeling software, and you will need the program in order to be able to run these models. There are two separate documents, both named “Conlisk_JofEcology_SI_01262017” within “Model_archive”. One is a Microsoft Word file, and the other is a PDF. The former can be opened with Microsoft Word, and the latter can be opened by Adobe Acrobat Reader, or any software compatible with a PDF. ------------------ 1. Species distribution shifts in response to climate change require that recruitment increase beyond current range boundaries. For trees with long lifespans, the importance of climate-sensitive seedling establishment to the pace of range shifts has not been demonstrated quantitatively. 2. Using spatially explicit, stochastic population models combined with data from long-term forest surveys, we explored whether the climate-sensitivity of recruitment observed in climate manipulation experiments was sufficient to alter populations and elevation ranges of two widely distributed, high-elevation North American conifers. 3. Empirically observed, warming-driven declines in recruitment led to rapid modeled population declines at the low-elevation, “warm edge” of subalpine forest and slow emergence of populations beyond the high-elevation, “cool edge”. Because population declines in the forest occurred much faster than population emergence in the alpine, we observed range contraction for both species. For Engelmann spruce, this contraction was permanent over the modeled time horizon, even in the presence of increased moisture. For limber pine, lower sensitivity to warming may facilitate persistence at low elevations – especially in the presence of increased moisture – and rapid establishment above treeline, and, ultimately, expansion into the alpine. 4. Synthesis. Assuming 21st century warming and no additional moisture, population dynamics in high-elevation forests led to transient range contractions for limber pine and potentially permanent range contractions for Engelmann spruce. Thus, limitations to seedling recruitment with warming can constrain the pace of subalpine tree range shifts.

The contents of the dataset relate to the population living in the province of Trento. The dataset, including resources in PDF format, is also available on the Employment Agency’s Open Data Portal at the URL: https://www.agenzialavoro.tn.it/Open-Data/I-dataset-available/Historical-Series/Demography Data are grouped by year and gender. Data are expressed in absolute values. The metadata ‘time coverage’ refers to the time interval taken into account by the Historical Series which is identified in the file name with the suffix _ST. Time coverage refers to 31 December of each year. The dataset is updated to 31 December each year with the addition of a new time series. The data released in CSV format are: Machine Readable, identified in the file name with the suffix _MR and validated with the Good Tables library. https://okfnlabs.org/blog/2015/02/20/introducing-goodtables.html ATTRIBUTION: data processed by the Office for the Study of Policies and the Labour Market on ISTAT data.

The U.S. Small-area Life Expectancy Estimates Project (USALEEP) is a partnership of NCHS, the Robert Wood Johnson Foundation (RWJF)External, and the National Association for Public Health Statistics and Information Systems (NAPHSIS)External to produce a new measure of health for where you live. The USALEEP project produced estimates of life expectancy at birth—the average number of years a person can expect to live—for most of the census tracts in the United States for the period 2010-2015. These estimates were published in September, 2018."A growing body of research is recognizing the importance of measuring mortality outcomes in small geographic areas, such as U.S. census tracts, to identify health disparities within a population. The indicator most widely identified as the ideal measure of a population’s mortality experience is life expectancy at birth. The concept of life expectancy is intuitive and easily understood by both policymakers and the lay public. Life expectancy is estimated for national populations by most developed countries, including the United States, which has produced the estimate annually since 1945 and decennially since 1900. However, its calculation is relatively complex compared with that of other summary mortality measures, because it entails the calculation of six distinct functions and requires a minimum number of age groups and total population size, below\ which the estimates become unstable and unreliable." - USALEEP Methodology Summary The methodology used to calculate the U.S. censustract abridged life tables consisted of several stages. First, through a collaboration between the National Vital Statistics System registration areas and the National Center for Health Statistics, death records of U.S. residents (excluding residents of Maine and Wisconsin) for deaths occurring in 2010 through 2015 were geocoded using decedents’ residential addresses to identify and code census tracts. Second, population estimates were produced based on the 2010 decennial census and the 2011–2015 American Community Survey 5-year survey. Third, a methodology that combined standard demographic techniques and statistical modeling was developed to address challenges posed by small population sizes and small and missing age-specific death counts. Last, standard, abridged life table methods were adjusted to account for error introduced by population estimates based on sample data. To review the full methodology, please use the following link: https://www.cdc.gov/nchs/data/series/sr_02/sr02_181.pdf

These modelled annual population estimates were created for use the GLA's population projections.

They are intended to provide a consistent series of annual population and components of change between census years with all change accounted for by the standard components of change (births, deaths, and migration).

The official mid-year population estimates published by ONS are available here.

The original detailed internal migration data published by ONS is available here.

An overview of the general approach used to create these estimates is described in this presentation delivered at the 2022 BSPS conference.

* 17 April 2023 code for producing the modelled 2021 detailed internal migration flows is now available on Github

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, for 2020, the 2020 Census provides the official counts of the population and housing units for the nation, states, counties, cities, and towns. For 2016 to 2019, the Population Estimates Program provides estimates of the population for the nation, states, counties, cities, and towns and intercensal housing unit estimates for the nation, states, and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..For more information on understanding race and Hispanic origin data, please see the Census 2010 Brief entitled, Overview of Race and Hispanic Origin: 2010, issued March 2011. (pdf format).The Hispanic origin and race codes were updated in 2020. For more information on the Hispanic origin and race code changes, please visit the American Community Survey Technical Documentation website..The 2016-2020 American Community Survey (ACS) data generally reflect the September 2018 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Abstract

The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.

Geographic coverage

National coverage

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The universe of inference includes all formal (i.e., registered) private sector businesses (with at least 1% private ownership) and with at least five employees. In terms of sectoral criteria, all manufacturing businesses (ISIC Rev 4. codes 10-33) are eligible; for services businesses, those corresponding to the ISIC Rev 4 codes 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95 are included in the Enterprise Surveys. Cooperatives and collectives are excluded from the Enterprise Surveys. All eligible establishments must be registered with the registration agency. In the case of India, the definition of registration of the 6th Economic Census (EC) was used, where registration can be from any of the following: Shops and Commercial Establishments Act; Companies Act, 1956; Factories Act, 1948; Central Excise/Sales Tax Act; Societies Registration Act; Co-operative Societies Act; Directorate of Industries; KVIC/KVIB/DC: Handloom/Handicrafts; Registered with other relevant agencies.

Kind of data

Sample survey data [ssd]

Sampling procedure

The WBES use stratified random sampling, where the population of establishments is first separated into non-overlapping groups, called strata, and then respondents are selected through simple random sampling from each stratum. The detailed methodology is provided in the Sampling Note (https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Sampling_Note-Consolidated-2-16-22.pdf). Stratified random sampling has several advantages over simple random sampling. In particular, it:

• produces unbiased estimates of the whole population or universe of inference, as well as at the levels of stratification
• ensures representativeness by including observations in all of those categories
• produces more precise estimates for a given sample size or budget allocation, and
• may reduce implementation costs by splitting the population into convenient subdivisions.

The WBES typically use three levels of stratification: industry classification, establishment size, and subnational region (used in combination). Starting in 2022, the WBES bases the industry classification on ISIC Rev. 4 (with earlier surveys using ISIC Rev. 3.1). For regional coverage within a country, the WBES has national coverage.

Note: Refer to Sampling Structure section in "The India 2022 World Bank Enterprise Survey Implementation Report" for detailed methodology on sampling.

Mode of data collection

Face-to-face [f2f]

Research instrument

The standard WBES questionnaire covers several topics regarding the business environment and business performance. These topics include general firm characteristics, infrastructure, sales and supplies, management practices, competition, innovation, capacity, land and permits, finance, business-government relations, exposure to bribery, labor, and performance. Information about the general structure of the questionnaire is available in the Enterprise Surveys Manual and Guide (https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Enterprise-Surveys-Manual-and-Guide.pdf).

The questionnaire implemented in the India 2022 WBES included additional questions covering contractual disputes, COVID-19, green economy, delayed payments, invoice discounting (TReDS or similar services), government support, attitudes towards taxes, training costs, and childcare support. These questions were selected in collaboration with the members of the WB local country team.

Response rate

Overall survey response rate was 61.8%.

Introduction: Inhibitors of programmed cell death 1 (PD1) and its ligand (PDL1) have exhibited favorable long-term survival in many types of advanced-stage cancer and current approvals have to date been granted in certain tumour types irrespective of PD-L1 status.Methods: We extracted the following information: study sample size, trial period, cancer types, intervention of treatment, type of PD-L1 antibody, immunohistochemistry (IHC) scoring method, number and percentage of PD-L1 < 1% population, and median follow- up time. PD-L1 expression was defined as percentage of number of PD-L1-stained tumor cells (TPS), area of tumor infiltrated by PD-L1-stained immune cells (IPS), number of PD-L1-stained cells (tumor cells, lymphocytes and macrophages; CPS). Different trials used distinct method to define low PD-L1 expression. The risk of bias of the included trials was assessed by using the Cochrane risk of bias tool for RCTs.Results: Here, a total of 34 trials were included to extract individual patient data (IPD) to evaluate the survival benefit of first line PD1/PDL1 inhibitors vs. standard-of-care (SOC) in patients with PDL1 < 1%. In term of anti-PD-1/PD-L1 monotherapy, OS (HR = 0.90, 0.81−1.01) and PFS (HR = 1.11, 0.97−1.27) between PD-1/PD-L1 inhibitor group and SOC group were comparable. In term of anti-PD-1/PD-L1 combination therapy, PD-1/PD-L1 inhibitor group exhibited longer OS (median 19.5 months vs. 16.3 months; HR = 0.83, 0.79−0.88, p < 0.001) and PFS than those of SOC group (median 8.11 months vs. 6.96 months; HR = 0.82, 0.77−0.87, p < 0.001).Subgroup analysis showed that survival benefit was mainly observed in non-small cell lung cancer (NSCLC) (HROS = 0.74; HRPFS = 0.69; p < 0.001), small-cell lung cancer (SCLC) (HROS = 0.58, p < 0.001; HRPFS = 0.55, p = 0.030), esophageal squamous cell carcinoma (ESCC) (HROS = 0.62, p = 0.005; HRPFS = 0.79, p < 0.001), melanoma (HROS = 0.53, p < 0.001) and nasopharyngeal carcinoma (NPC) (HRPFS = 0.35, p = 0.013).Conclusion: Anti-PD-1/PD-L1 combinational therapy rather than monotherapy exhibit survival benefit in the low PD-L1 population in the first-line setting, and the survival benefit was mainly observed in specific tumor types.

As of October 2025, 6.04 billion individuals worldwide were internet users, which amounted to 73.2 percent of the global population. Of this total, 5.66 billion, or 68.7 percent of the world's population, were social media users. Global internet usage Connecting billions of people worldwide, the internet is a core pillar of the modern information society. Northern Europe ranked first among worldwide regions by the share of the population using the internet in 2025. In the Netherlands, Norway, and Saudi Arabia, 99 percent of the population used the internet as of February 2025. North Korea was at the opposite end of the spectrum, with virtually no internet usage penetration among the general population, ranking last worldwide. Eastern Asia was home to the largest number of online users worldwide—over 1.34 billion at the latest count. Southern Asia ranked second, with around 1.2 billion internet users. China, India, and the United States rank ahead of other countries worldwide by the number of internet users. Worldwide internet user demographics As of 2024, the share of female internet users worldwide was 65 percent, five percent less than that of men. Gender disparity in internet usage was bigger in African countries, with around a 10-percent difference. Worldwide regions, like the Commonwealth of Independent States and Europe, showed a smaller usage gap between these two genders. As of 2024, global internet usage was higher among individuals between 15 and 24 years old across all regions, with young people in Europe representing the most considerable usage penetration, 98 percent. In comparison, the worldwide average for the age group of 15 to 24 years was 79 percent. The income level of the countries was also an essential factor for internet access, as 93 percent of the population of the countries with high income reportedly used the internet, as opposed to only 27 percent of the low-income markets.

Introduction: Preterm babies are a vulnerable population that experience significant short and long-term morbidity. Rehospitalisations constitute an important, potentially modifiable adverse event in this population. Improving the ability of clinicians to identify those patients at the greatest risk of rehospitalisation has the potential to improve outcomes and reduce costs. Machine-learning algorithms can provide potentially advantageous methods of prediction compared to conventional approaches like logistic regression.Objective: To compare two machine-learning methods (least absolute shrinkage and selection operator (LASSO) and random forest) to expert-opinion driven logistic regression modelling for predicting unplanned rehospitalisation within 30 days in a large French cohort of preterm babies.Design, Setting and Participants: This study used data derived exclusively from the population-based prospective cohort study of French preterm babies, EPIPAGE 2. Only those babies discharged home alive and whose parents completed the 1-year survey were eligible for inclusion in our study. All predictive models used a binary outcome, denoting a baby's status for an unplanned rehospitalisation within 30 days of discharge. Predictors included those quantifying clinical, treatment, maternal and socio-demographic factors. The predictive abilities of models constructed using LASSO and random forest algorithms were compared with a traditional logistic regression model. The logistic regression model comprised 10 predictors, selected by expert clinicians, while the LASSO and random forest included 75 predictors. Performance measures were derived using 10-fold cross-validation. Performance was quantified using area under the receiver operator characteristic curve, sensitivity, specificity, Tjur's coefficient of determination and calibration measures.Results: The rate of 30-day unplanned rehospitalisation in the eligible population used to construct the models was 9.1% (95% CI 8.2–10.1) (350/3,841). The random forest model demonstrated both an improved AUROC (0.65; 95% CI 0.59–0.7; p = 0.03) and specificity vs. logistic regression (AUROC 0.57; 95% CI 0.51–0.62, p = 0.04). The LASSO performed similarly (AUROC 0.59; 95% CI 0.53–0.65; p = 0.68) to logistic regression.Conclusions: Compared to an expert-specified logistic regression model, random forest offered improved prediction of 30-day unplanned rehospitalisation in preterm babies. However, all models offered relatively low levels of predictive ability, regardless of modelling method.

Introduction: Fluoroquinolone exposure is reportedly associated with a higher risk of tendon disorders, tendonitis, or tendon rupture. However, studies in East Asian populations have not confirmed these risks in patients with comorbidities or concomitant medication use. This cohort study was designed to investigate the associations among fluoroquinolone exposure, comorbidities, medication use, and tendon disorders in Taiwan.Materials and Methods: This population-based, nationwide, observational, cohort study used data from the National Health Insurance Research database in Taiwan, a nationwide claims database that covers more than 99% of the Taiwanese population. The study period was from January 2000 to December 2015, and the median follow-up time was 11.05 ± 10.91 years. Patients who were exposed to fluoroquinolones for more than three consecutive days were enrolled, and patients without fluoroquinolone exposure who were matched by age, sex, and index year were enrolled as controls. The associations of comorbidities and concomitant medication use with tendon disorder occurrence were analyzed using Cox regression models.Results: The incidence of tendon disorders were 6.61 and 3.34 per 105 person-years in patients with and without fluoroquinolone exposure, respectively (adjusted hazard ratio, 1.423; 95% confidence interval [1.02,1.87]; p = 0.021). Sensitivity analyses yielded similar results. Patients under 18 and over 60 years with fluoroquinolone exposure; those with chronic kidney disease, diabetes, rheumatologic disease, cardiac disease, lipid disorder, or obesity; and those who concomitantly used statins, aromatase inhibitors, or glucocorticoids, had a significantly higher risk of tendon disorders.Conclusion: The long-term risk of tendon disorders was higher in patients with fluoroquinolone exposure than in those without fluoroquinolone exposure. Clinicians should assess the benefits and risks of fluoroquinolone use in patients at high risk of tendon disorders who require fluoroquinolone administration.

Nucleus-based breeding programs are characterized by intense selection that results in high genetic gain, which inevitably means reduction of genetic variation in the breeding population. Therefore, genetic variation in such breeding systems is typically managed systematically, for example, by avoiding mating the closest relatives to limit progeny inbreeding. However, intense selection requires maximum effort to make such breeding programs sustainable in the long-term. The objective of this study was to use simulation to evaluate the long-term impact of genomic selection on genetic mean and variance in an intense layer chicken breeding program. We developed a large-scale stochastic simulation of an intense layer chicken breeding program to compare conventional truncation selection to genomic truncation selection optimized with either minimization of progeny inbreeding or full-scale optimal contribution selection. We compared the programs in terms of genetic mean, genic variance, conversion efficiency, rate of inbreeding, effective population size, and accuracy of selection. Our results confirmed that genomic truncation selection has immediate benefits compared to conventional truncation selection in all specified metrics. A simple minimization of progeny inbreeding after genomic truncation selection did not provide any significant improvements. Optimal contribution selection was successful in having better conversion efficiency and effective population size compared to genomic truncation selection, but it must be fine-tuned for balance between loss of genetic variance and genetic gain. In our simulation, we measured this balance using trigonometric penalty degrees between truncation selection and a balanced solution and concluded that the best results were between 45° and 65°. This balance is specific to the breeding program and depends on how much immediate genetic gain a breeding program may risk vs. save for the future. Furthermore, our results show that the persistence of accuracy is better with optimal contribution selection compared to truncation selection. In general, our results show that optimal contribution selection can ensure long-term success in intensive breeding programs using genomic selection.

As of July 2024, Nigeria's population was estimated at around 229.5 million. Between 1965 and 2024, the number of people living in Nigeria increased at an average rate of over two percent. In 2024, the population grew by 2.42 percent compared to the previous year. Nigeria is the most populous country in Africa. By extension, the African continent records the highest growth rate in the world. Africa's most populous country Nigeria was the most populous country in Africa as of 2023. As of 2022, Lagos held the distinction of being Nigeria's biggest urban center, a status it also retained as the largest city across all of sub-Saharan Africa. The city boasted an excess of 17.5 million residents. Notably, Lagos assumed the pivotal roles of the nation's primary financial hub, cultural epicenter, and educational nucleus. Furthermore, Lagos was one of the largest urban agglomerations in the world. Nigeria's youthful population In Nigeria, a significant 50 percent of the populace is under the age of 19. The most prominent age bracket is constituted by those up to four years old: comprising 8.3 percent of men and eight percent of women as of 2021. Nigeria boasts one of the world's most youthful populations. On a broader scale, both within Africa and internationally, Niger maintains the lowest median age record. Nigeria secures the 20th position in global rankings. Furthermore, the life expectancy in Nigeria is an average of 62 years old. However, this is different between men and women. The main causes of death have been neonatal disorders, malaria, and diarrheal diseases.