Globally, about 25 percent of the population is under 15 years of age and 10 percent is over 65 years of age. Africa has the youngest population worldwide. In Sub-Saharan Africa, more than 40 percent of the population is below 15 years, and only three percent are above 65, indicating the low life expectancy in several of the countries. In Europe, on the other hand, a higher share of the population is above 65 years than the population under 15 years. Fertility rates The high share of children and youth in Africa is connected to the high fertility rates on the continent. For instance, South Sudan and Niger have the highest population growth rates globally. However, about 50 percent of the world’s population live in countries with low fertility, where women have less than 2.1 children. Some countries in Europe, like Latvia and Lithuania, have experienced a population decline of one percent, and in the Cook Islands, it is even above two percent. In Europe, the majority of the population was previously working-aged adults with few dependents, but this trend is expected to reverse soon, and it is predicted that by 2050, the older population will outnumber the young in many developed countries. Growing global population As of 2025, there are 8.1 billion people living on the planet, and this is expected to reach more than nine billion before 2040. Moreover, the global population is expected to reach 10 billions around 2060, before slowing and then even falling slightly by 2100. As the population growth rates indicate, a significant share of the population increase will happen in Africa.

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.

Analysis of ‘COVID-19 State Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/nightranger77/covid19-state-data on 30 September 2021.

--- Dataset description provided by original source is as follows ---

This dataset is a per-state amalgamation of demographic, public health and other relevant predictors for COVID-19.

Deaths, Infections and Tests by State

The COVID Tracking Project: https://covidtracking.com/data/api

Used positive, death and totalTestResults from the API for, respectively, Infected, Deaths and Tested in this dataset. Please read the documentation of the API for more context on those columns

Predictor Data and Sources

Population (2020)

Density is people per meter squared https://worldpopulationreview.com/states/

ICU Beds and Age 60+

https://khn.org/news/as-coronavirus-spreads-widely-millions-of-older-americans-live-in-counties-with-no-icu-beds/

GDP

https://worldpopulationreview.com/states/gdp-by-state/

Income per capita (2018)

https://worldpopulationreview.com/states/per-capita-income-by-state/

Gini

https://en.wikipedia.org/wiki/List_of_U.S._states_by_Gini_coefficient

Unemployment (2020)

Rates from Feb 2020 and are percentage of labor force
https://www.bls.gov/web/laus/laumstrk.htm

Sex (2017)

Ratio is Male / Female
https://www.kff.org/other/state-indicator/distribution-by-gender/

Smoking Percentage (2020)

https://worldpopulationreview.com/states/smoking-rates-by-state/

Influenza and Pneumonia Death Rate (2018)

Death rate per 100,000 people
https://www.cdc.gov/nchs/pressroom/sosmap/flu_pneumonia_mortality/flu_pneumonia.htm

Chronic Lower Respiratory Disease Death Rate (2018)

Death rate per 100,000 people
https://www.cdc.gov/nchs/pressroom/sosmap/lung_disease_mortality/lung_disease.htm

Active Physicians (2019)

https://www.kff.org/other/state-indicator/total-active-physicians/

Hospitals (2018)

https://www.kff.org/other/state-indicator/total-hospitals

Health spending per capita

Includes spending for all health care services and products by state of residence. Hospital spending is included and reflects the total net revenue. Costs such as insurance, administration, research, and construction expenses are not included.
https://www.kff.org/other/state-indicator/avg-annual-growth-per-capita/

Pollution (2019)

Pollution: Average exposure of the general public to particulate matter of 2.5 microns or less (PM2.5) measured in micrograms per cubic meter (3-year estimate)
https://www.americashealthrankings.org/explore/annual/measure/air/state/ALL

Medium and Large Airports

For each state, number of medium and large airports https://en.wikipedia.org/wiki/List_of_the_busiest_airports_in_the_United_States

Temperature (2019)

Note that FL was incorrect in the table, but is corrected in the Hottest States paragraph
https://worldpopulationreview.com/states/average-temperatures-by-state/
District of Columbia temperature computed as the average of Maryland and Virginia

Urbanization (2010)

Urbanization as a percentage of the population https://www.icip.iastate.edu/tables/population/urban-pct-states

Age Groups (2018)

https://www.kff.org/other/state-indicator/distribution-by-age/

School Closure Dates

Schools that haven't closed are marked NaN https://www.edweek.org/ew/section/multimedia/map-coronavirus-and-school-closures.html

Note that some datasets above did not contain data for District of Columbia, this missing data was found via Google searches manually entered.

--- Original source retains full ownership of the source dataset ---

This dataset is a per-state amalgamation of demographic, public health and other relevant predictors for COVID-19.

Deaths, Infections and Tests by State

The COVID Tracking Project: https://covidtracking.com/data/api

Used positive, death and totalTestResults from the API for, respectively, Infected, Deaths and Tested in this dataset. Please read the documentation of the API for more context on those columns

Predictor Data and Sources

Population (2020)

Density is people per meter squared https://worldpopulationreview.com/states/

ICU Beds and Age 60+

https://khn.org/news/as-coronavirus-spreads-widely-millions-of-older-americans-live-in-counties-with-no-icu-beds/

GDP

https://worldpopulationreview.com/states/gdp-by-state/

Income per capita (2018)

https://worldpopulationreview.com/states/per-capita-income-by-state/

Gini

https://en.wikipedia.org/wiki/List_of_U.S._states_by_Gini_coefficient

Unemployment (2020)

Rates from Feb 2020 and are percentage of labor force
https://www.bls.gov/web/laus/laumstrk.htm

Sex (2017)

Ratio is Male / Female
https://www.kff.org/other/state-indicator/distribution-by-gender/

Smoking Percentage (2020)

https://worldpopulationreview.com/states/smoking-rates-by-state/

Influenza and Pneumonia Death Rate (2018)

Death rate per 100,000 people
https://www.cdc.gov/nchs/pressroom/sosmap/flu_pneumonia_mortality/flu_pneumonia.htm

Chronic Lower Respiratory Disease Death Rate (2018)

Death rate per 100,000 people
https://www.cdc.gov/nchs/pressroom/sosmap/lung_disease_mortality/lung_disease.htm

Active Physicians (2019)

https://www.kff.org/other/state-indicator/total-active-physicians/

Hospitals (2018)

https://www.kff.org/other/state-indicator/total-hospitals

Health spending per capita

Includes spending for all health care services and products by state of residence. Hospital spending is included and reflects the total net revenue. Costs such as insurance, administration, research, and construction expenses are not included.
https://www.kff.org/other/state-indicator/avg-annual-growth-per-capita/

Pollution (2019)

Pollution: Average exposure of the general public to particulate matter of 2.5 microns or less (PM2.5) measured in micrograms per cubic meter (3-year estimate)
https://www.americashealthrankings.org/explore/annual/measure/air/state/ALL

Medium and Large Airports

For each state, number of medium and large airports https://en.wikipedia.org/wiki/List_of_the_busiest_airports_in_the_United_States

Temperature (2019)

Note that FL was incorrect in the table, but is corrected in the Hottest States paragraph
https://worldpopulationreview.com/states/average-temperatures-by-state/
District of Columbia temperature computed as the average of Maryland and Virginia

Urbanization (2010)

Urbanization as a percentage of the population https://www.icip.iastate.edu/tables/population/urban-pct-states

Age Groups (2018)

https://www.kff.org/other/state-indicator/distribution-by-age/

School Closure Dates

Schools that haven't closed are marked NaN https://www.edweek.org/ew/section/multimedia/map-coronavirus-and-school-closures.html

Note that some datasets above did not contain data for District of Columbia, this missing data was found via Google searches manually entered.

Population, total in World was reported at 8142056446 in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. World - Population, total - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

Associated with manuscript titled: Fifty Muslim-majority countries have fewer COVID-19 cases and deaths than the 50 richest non-Muslim countriesThe objective of this research was to determine the difference in the total number of COVID-19 cases and deaths between Muslim-majority and non-Muslim countries, and investigate reasons for the disparities. Methods: The 50 Muslim-majority countries had more than 50.0% Muslims with an average of 87.5%. The non-Muslim country sample consisted of 50 countries with the highest GDP while omitting any Muslim-majority countries listed. The non-Muslim countries’ average percentage of Muslims was 4.7%. Data pulled on September 18, 2020 included the percentage of Muslim population per country by World Population Review15 and GDP per country, population count, and total number of COVID-19 cases and deaths by Worldometers.16 The data set was transferred via an Excel spreadsheet on September 23, 2020 and analyzed. To measure COVID-19’s incidence in the countries, three different Average Treatment Methods (ATE) were used to validate the results. Results published as a preprint at https://doi.org/10.31235/osf.io/84zq5(15) Muslim Majority Countries 2020 [Internet]. Walnut (CA): World Population Review. 2020- [Cited 2020 Sept 28]. Available from: http://worldpopulationreview.com/country-rankings/muslim-majority-countries (16) Worldometers.info. Worldometer. Dover (DE): Worldometer; 2020 [cited 2020 Sept 28]. Available from: http://worldometers.info

List of Army personnel in the world, and the population of the respective country. The data is extracted and scrapped from 1. https://worldpopulationreview.com/country-rankings/military-size-by-country 2. https://en.wikipedia.org/wiki/List_of_countries_by_number_of_military_and_paramilitary_personnel

Mbouda Population 2023
This dataset falls under the category Traffic Generating Parameters Population.
It contains the following data:
This dataset was scouted on 2022-02-14 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: https://worldpopulationreview.com/world-cities/mbouda-population

Mombasa Population 2022
This dataset falls under the category Traffic Generating Parameters Population.
It contains the following data: Mombasa Population 2022
This dataset was scouted on 2022-02-13 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: https://worldpopulationreview.com/world-cities/mombasa-population

Clinical studies, especially randomized controlled trials, are essential for generating evidence for clinical practice. However, generalizability is a long-standing concern when applying trial results to real-world patients. Generalizability assessment is thus important, nevertheless, not consistently practiced. We performed a systematic scoping review to understand the practice of generalizability assessment. We identified 187 relevant papers and systematically organized these studies in a taxonomy with three dimensions: (1) data availability (i.e., before or after trial [a priori vs a posteriori generalizability]), (2) result outputs (i.e., score vs non-score), and (3) populations of interest. We further reported disease areas, underrepresented subgroups, and types of data used to profile target populations. We observed an increasing trend of generalizability assessments, but less than 30% of studies reported positive generalizability results. As a priori generalizability can be assessed using only study design information (primarily eligibility criteria), it gives investigators a golden opportunity to adjust the study design before the trial starts. Nevertheless, less than 40% of the studies in our review assessed a priori generalizability. With the wide adoption of electronic health records systems, rich real-world patient databases are increasingly available for generalizability assessment; however, informatics tools are lacking to support the adoption of generalizability assessment practice.

Methods We performed the literature search over the following 4 databases: MEDLINE, Cochrane, PychINFO, and CINAHL. Following the Institute of Medicine’s standards for systematic review and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), we conducted the scoping review in the following six steps: 1) gaining an initial understanding about clinical trial generalizability assessment, population representativeness, internal validity, and external validity, 2) identifying relevant keywords, 3) formulating four search queries to identify relevant articles in the 4 databases, 4) screening the articles by reviewing titles and abstracts, 5) reviewing articles’ full-text to further filter out irrelevant ones based on inclusion and exclusion criteria, and 6) coding the articles for data extraction.

Study selection and screening process

We used an iterative process to identify and refine the search keywords and search strategies. We identified 5,352 articles as of February 2019 from MEDLINE, CINAHL, PychINFO, and Cochrane. After removing duplicates, 3,569 records were assessed for relevancy by two researchers (ZH and XT) through reviewing the titles and abstracts against the inclusion and exclusion criteria. Conflicts were resolved with a third reviewer (JB). During the screening process, we also iteratively refined the inclusion and exclusion criteria. Out of the 3,569 articles, 3,275 were excluded through the title and abstract screening process. Subsequently, we reviewed the full texts of 294 articles, among which 106 articles were further excluded based on the exclusion criteria. The inter-rater reliability of the full-text review between the two annotators is 0.901 (i.e., Cohen’s kappa, p < .001). 187 articles were included in the final scoping review.

Data extraction and reporting

We coded and extracted data from the 187 eligible articles according to the following aspects: (1) whether the study performed an a priori generalizability assessment or a posteriori generalizability assessment or both; (2) the compared populations and the conclusions of the assessment; (3) the outputs of the results (e.g., generalizability scores, descriptive comparison); (4) whether the study focused on a specific disease. If so, we extracted the disease and disease category; (5) whether the study focused on a particular population subgroup (e.g., elderly). If so, we extracted the specific population subgroup; (6) the type(s) of the real-world patient data used to profile the target population (i.e., trial data, hospital data, regional data, national data, and international data). Note that trial data can also be regional, national, or even international, depending on the scale of the trial. Regardless, we considered them in the category of “trial data” as the study population of a trial is typically small compared to observational cohorts or real-world data. For observational cohorts or real-world data (e.g., EHRs), we extracted the specific scale of the database (i.e., regional, national, and international). For the studies that compared the characteristics of different populations to indicate generalizability issues, we further coded the populations that were compared (e.g., enrolled patients, eligible patients, general population, ineligible patients), and the types of characteristics that were compared (i.e., demographic information, clinical attributes and comorbidities, treatment outcomes, and adverse events). We then used Fisher’s exact test to assess whether there is a difference in the types of characteristics compared between a priori and a posteriori generalizability assessment studies.

This dataset is the Supplementary Material for a review of uncertainty in petrel population estimates. It contains raw data from the literature review, source code for the full analysis, and additional text accompanying the manuscript.

Raw data were extracted from a literature review of petrel population estimates on islands. References were sourced from the Web of Science bibliographic index searched on 20 January 2020 using the search terms "burrowing seabird" OR "burrow-nesting seabird" OR "burrow-nesting petrel" OR "burrowing petrel" OR “scientific name” OR “common name” (taxonomy followed HBW and BirdLife International, 2018) for all species in the families Procellariidae, Hydrobatidae and Oceanitidae, AND “abundance” OR “population” in the title, abstract or keywords.

The data contain the original reference with metadata on year, journal, species studied, island studied, motivations for the study. We extracted published population estimates reported in each paper. Most represented a mean, but where only minima or maxima were reported we used this as the estimate, and where only minima and maxima were reported we used their average as the estimate. To allow comparison between studies we extracted basic dispersion statistics and manipulated them to approximate confidence intervals (see paper for methods).

The full dataset includes: 1. data.csv - the raw data from the literature review including information for 60 variables.

1. supplementary_code.rmd - full code for the analysis.

2. Supplementary material.docx - supporting text including methods, results and references.

This data reports hydroelectricity consumption (in million tonnes oil equivalent) by country for the years 1965 to 2005. The data comes from the British Petroleum Statistical Review of World Energy. www.bp.com

This data reports natural gas consumption (in billion cubic meters) by country for the years 1965 to 2005. The data comes from the British Petroleum Statistical Review of World Energy. www.bp.com

BackgroundThe prevalence of diabetes in West Africa is increasing, posing a major public health threat. An estimated 24 million Africans have diabetes, with rates in West Africa around 2–6% and projected to rise 129% by 2045 according to the WHO. Over 90% of cases are Type 2 diabetes (IDF, World Bank). As diabetes is ambulatory care sensitive, good primary care is crucial to reduce complications and mortality. However, research on factors influencing diabetes primary care access, utilisation and quality in West Africa remains limited despite growing disease burden. While research has emphasised diabetes prevalence and risk factors in West Africa, there remains limited evidence on contextual influences on primary care. This scoping review aims to address these evidence gaps.Methods and analysisUsing the established methodology by Arksey and O’Malley, this scoping review will undergo six stages. The review will adopt the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Review (PRISMA-ScR) guidelines to ensure methodological rigour. We will search four electronic databases and search through grey literature sources to thoroughly explore the topic. The identified articles will undergo thorough screening. We will collect data using a standardised data extraction form that covers study characteristics, population demographics, and study methods. The study will identify key themes and sub-themes related to primary healthcare access, utilisation, and quality. We will then analyse and summarise the data using a narrative synthesis approach.ResultsThe findings and conclusive report will be finished and sent to a peer-reviewed publication within six months.ConclusionThis review protocol aims to systematically examine and assess the factors that impact the access, utilisation, and standard of primary healthcare services for diabetes in West Africa.

IntroductionStroke incidence data with methodologically acceptable design in Southeast Asia countries is limited. This study aimed to determine incidence of age-, sex- and subtype-specific first-ever stroke (FES) in Vietnam.MethodsWe conducted a hospital-based retrospective study, targeting all stroke cases hospitalized at a solo-provider hospital in our study site of Nha Trang from January 2009 to December 2011 with International Classification of Diseases, 10th revision (ICD-10) codes I60-69. We calculated positive predictive values (PPVs) of each ICD-10-coded stroke by conducting a detailed case review of 190 randomly selected admissions with ICD-10 codes of I60-I69. These PPVs were then used to estimate annual incident stroke cases from the computerized database. National census data in 2009 was used as a denominator.Results2,693 eligible admissions were recorded during the study period. The crude annual incidence rate of total FES was 90.2 per 100,000 population (95% CI 81.1–100.2). The age-adjusted incidence of FES was 115.7 (95% CI 95.9–139.1) when adjusted to the WHO world populations. Importantly, age-adjusted intracerebral hemorrhage was as much as one third of total FES: 36.9 (95% CI 26.1–51.0).ConclusionsWe found a considerable proportion of FES in Vietnam to be attributable to intracerebral hemorrhage, which is as high or exceeding levels seen in high-income countries. A high prevalence of improperly treated hypertension in Vietnam may underlie the high prevalence of intracerebral hemorrhagic stroke in this population.

This data reports natural gas proved reserves (in trillion cubic meters) by country for the years 1980 to 2005. The data comes from the British Petroleum Statistical Review of World Energy. www.bp.com

BackgroundAlthough previous studies demonstrated no association between depression and tamoxifen in patients with breast cancer, there is still a limited amount of long-term follow-up data. This study aimed to evaluate the relationship between endocrine treatment and the risk of depression.MethodsThis nationwide population-based cohort study used data obtained over a 14-year period (January 2007 to December 2021) from the Korean National Health Insurance claims database. All female patients with breast cancer were included. We examined the incidence of depression in patients who underwent endocrine treatment, and those who did not undergo endocrine treatment constituted the control group.ResultsThe data from 11,109 patients who underwent endocrine treatment and 6,615 control patients between 2009 and 2010 were analyzed. After performing matching for comorbidities and age, both groups comprised 6,532 patients. The median follow-up were 119.71 months. Before and after matching was performed, the endocrine treatment was not a significant risk factor for developing depression (p=0.7295 and p=0.2668, respectively), nor was it a significant factor for an increased risk for suicide attempt (p=0.6381 and p=0.8366, respectively).ConclusionsUsing a real-world population-based cohort, this study demonstrated that there is no evidence that the endocrine treatment increases the risk of depression.

BackgroundClinical and sociodemographic characteristics of differentiated thyroid cancer (DTC) patients with synchronous bone metastasis (SBM) remain unclear. This real-world study aimed to elucidate the incidence and prognosis of DTC patients with SBM using population-based data.MethodsData of patients with newly diagnosed DTC from 2010 to 2016 were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Multivariable logistic regression analysis was utilized to identify predictors of developing SBM in patients with DTC and was further evaluated by receiver operator characteristics (ROC) analysis. Multivariable Cox regression was applied to identify prognostic factors associated with overall survival (OS) and cancer-specific survival (CSS).ResultsA total of 67,176 patients with DTC were screened from the database, with 0.36% (244/67,176) developed SBM. The age-adjusted incidence of SBM in patients with DTC was relatively stable during the study period with an average annual percentage change (AAPC) of 2.52. Multivariable logistic regression analysis recognized seven factors (older age, male gender, black race, other races, follicular histology, the American Joint Committee on Cancer (AJCC) T2, T3, T4 staging, and N1 staging) as predictors of developing SBM among the entire cohort, with the value of area under the curve (AUC) of 0.931 (95% CI: 0.915–0.947). The median survival time of DTC patients with SBM was 22 months (interquartile range, 7–47 months). The multivariable Cox regression analysis indicated multiple metastatic sites, surgical procedures, and chemotherapy as predictors for the survival of patients.ConclusionsPredictors and prognostic factors of SBM in patients with DTC were identified in this study. Patients with risk factors should be given more attention in clinical practice.

This dataset provides information about 2007 Endowment figures across Colleges and Universities in the World (mainly in the United States). The Study was conducted by NACUBO. Results are also listed for 2006 and percentage change has also been listed between the two years. Locations are mapped by the lat/lon coordinates of the institution. More information on the study can be found at http://www.nacubo.org/ The National Endowment Study is the largest and longest running annual survey studying the endowment holdings of higher education institutions and their foundations. Information is collected and calculated on behalf of NACUBO by TIAA-CREF. Seven hundred and eighty-five (785) institutions in the United States and Canada participated in the 2007 NES, which is the largest number in the 35-year history of the study and the seventh consecutive year of record-breaking participation since NACUBO began its partnership with TIAA-CREF in 2000. NACUBO, (National Association of College and University Business Officers) founded in 1962, is a nonprofit professional organization representing chief administrative and financial officers at more than 2,100 colleges and universities across the country. NACUBOs mission is to promote sound management and financial practices at colleges and universities. Data was accessed on 1/23/2008 http://www.nacubo.org/Images/All%20Institutions%20Listed%20by%20FY%202007%20Market%20Value%20of%20Endowment%20Assets_2007%20NES.pdf

This dataset tracks the average applied tariff rates in both industrial and developing countries. Data is averaged for the years 1981-2005. Figures for 2005 have been estimated. Notes: All tariff rates are based on unweighted averages for all goods in ad valorem rates, or applied rates, or MFN rates whichever data is available in a longer period. Tariff data is primarily based on UNCTAD TRAINS database and then used WTO IDB data for gap filling if possible. Data in 1980s is taken from other source.** Tariff data in these countries came from IMF Global Monitoring Tariff file in 2004 which might include other duties or charges. Country codes are based on the classifications by income in WDI 2006, where 1 = low income, 2 = middle income, 3 = high incone non-OECDs, and 4 = high income OECD countries. Sources: UNCTAD TRAINS database (through WITS); WTO IDB database (through WITS); WTO IDB CD ROMs, various years and Trade Policy Review -- Country Reports in various issues, 1990-2005; UNCTAD Handbook of Trade Control Measures of Developing Countries -- Supplement 1987 and Directory of Import Regimes 1994; World Bank Trade Policy Reform in Developing Countries since 1985, WB Discussion Paper #267, 1994 and World Development Indicators, 1998-2006; The Uruguay Round: Statistics on Tariffs Concessions Given and Received, 1996; OECD Indicators of Tariff and Non-Tariff Trade Barriers, 1996 and 2000; and IMF Global Monitoring Tariff data file 2004. Data source: http://go.worldbank.org/LGOXFTV550 Access Date: October 17, 2007