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Unveiling the Health and Demographics Dataset: Illuminating Life Expectancy

Description: Step into the world of global health and demographics with our rich and comprehensive dataset. It's your passport to unraveling the secrets of life expectancy and understanding the pulse of population health. Dive into a treasure trove of valuable information for public health research and epidemiology, where each column tells a unique story about a nation's health journey.

Discover the Gems in Our Dataset:

• Country: Explore the global tapestry with data from diverse nations.
• Year: Unlock the passage of time and its impact on health trends.
• Status: Understand the development status, whether "Developed" or "Developing," that shapes the course of health.
• Life Expectancy: Peer into the crystal ball of population health, revealing how long people can expect to live.
• Adult Mortality: Gauge the probabilities of survival between ages 15 and 60 per 1,000 population.
• Infant Deaths: Delve into infant health with the number of infant deaths per 1,000 live births.
• Alcohol: Raise a glass to insights on average alcohol consumption in liters per capita.
• Percentage Expenditure: Unearth health expenditure as a percentage of a country's GDP.
• Hepatitis B: Measure immunization coverage for Hepatitis B.
• Measles: Examine the impact of this preventable disease with the number of reported cases per 1,000 population.
• BMI: Step onto the scales of national health with the average Body Mass Index.
• Under-Five Deaths: Shine a spotlight on child mortality with the number of deaths under age five per 1,000 live births.
• Polio: Inspect immunization coverage for Polio.
• Total Expenditure: Track the total health expenditure as a percentage of GDP.
• Diphtheria: Assess immunization coverage for Diphtheria.
• HIV/AIDS: Witness the prevalence of HIV/AIDS as a percentage of the population.
• GDP: Follow the financial pulse of a nation with Gross Domestic Product data.
• Population: Witness the ebb and flow of a nation's populace.
• Thinness 1-19 Years: Explore the prevalence of thinness among children and adolescents aged 1-19.
• Thinness 5-9 Years: Zoom in on thinness among children aged 5-9.
• Income Composition of Resources: Decode the composite index reflecting income distribution and resource access.
• Schooling: Measure the gift of knowledge with data on average years of schooling.

Predictive Targets: - The "Life Expectancy" column is your North Star, guiding the way to predictive insights. Harness the power of data to predict life expectancy using the mosaic of health and demographic indicators at your disposal.

Journey with the Data: 1. Predicting Life Expectancy: Embark on the quest to build regression models that forecast life expectancy for diverse countries and years based on this wealth of features. 2. Identifying Influential Factors: Uncover the gems within the dataset that influence life expectancy the most, providing valuable insights for public health interventions. 3. Health Policy Analysis: Assess the impact of health expenditure, immunization coverage, and disease prevalence on life expectancy and shape policies that safeguard population health.

This dataset is your window into the intricate world of global health. Join us on a journey of discovery as we explore the factors shaping life expectancy and navigate the waters of public health, epidemiology, and population health.

Professional organizations in STEM (science, technology, engineering, and mathematics) can use demographic data to quantify recruitment and retention (R&R) of underrepresented groups within their memberships. However, variation in the types of demographic data collected can influence the targeting and perceived impacts of R&R efforts - e.g., giving false signals of R&R for some groups. We obtained demographic surveys from 73 U.S.-affiliated STEM organizations, collectively representing 712,000 members and conference-attendees. We found large differences in the demographic categories surveyed (e.g., disability status, sexual orientation) and the available response options. These discrepancies indicate a lack of consensus regarding the demographic groups that should be recognized and, for groups that are omitted from surveys, an inability of organizations to prioritize and evaluate R&R initiatives. Aligning inclusive demographic surveys across organizations will provide baseline data that can be used to target and evaluate R&R initiatives to better serve underrepresented groups throughout STEM. Methods We surveyed 164 STEM organizations (73 responses, rate = 44.5%) between December 2020 and July 2021 with the goal of understanding what demographic data each organization collects from its constituents (i.e., members and conference-attendees) and how the data are used. Organizations were sourced from a list of professional societies affiliated with the American Association for the Advancement of Science, AAAS, (n = 156) or from social media (n = 8). The survey was sent to the elected leadership and management firms for each organization, and follow-up reminders were sent after one month. The responding organizations represented a wide range of fields: 31 life science organizations (157,000 constituents), 5 mathematics organizations (93,000 constituents), 16 physical science organizations (207,000 constituents), 7 technology organizations (124,000 constituents), and 14 multi-disciplinary organizations spanning multiple branches of STEM (131,000 constituents). A list of the responding organizations is available in the Supplementary Materials. Based on the AAAS-affiliated recruitment of the organizations and the similar distribution of constituencies across STEM fields, we conclude that the responding organizations are a representative cross-section of the most prominent STEM organizations in the U.S. Each organization was asked about the demographic information they collect from their constituents, the response rates to their surveys, and how the data were used. Survey description The following questions are written as presented to the participating organizations. Question 1: What is the name of your STEM organization? Question 2: Does your organization collect demographic data from your membership and/or meeting attendees? Question 3: When was your organization’s most recent demographic survey (approximate year)? Question 4: We would like to know the categories of demographic information collected by your organization. You may answer this question by either uploading a blank copy of your organization’s survey (linked provided in online version of this survey) OR by completing a short series of questions. Question 5: On the most recent demographic survey or questionnaire, what categories of information were collected? (Please select all that apply)

Disability status Gender identity (e.g., male, female, non-binary) Marital/Family status Racial and ethnic group Religion Sex Sexual orientation Veteran status Other (please provide)

Question 6: For each of the categories selected in Question 5, what options were provided for survey participants to select? Question 7: Did the most recent demographic survey provide a statement about data privacy and confidentiality? If yes, please provide the statement. Question 8: Did the most recent demographic survey provide a statement about intended data use? If yes, please provide the statement. Question 9: Who maintains the demographic data collected by your organization? (e.g., contracted third party, organization executives) Question 10: How has your organization used members’ demographic data in the last five years? Examples: monitoring temporal changes in demographic diversity, publishing diversity data products, planning conferences, contributing to third-party researchers. Question 11: What is the size of your organization (number of members or number of attendees at recent meetings)? Question 12: What was the response rate (%) for your organization’s most recent demographic survey? *Organizations were also able to upload a copy of their demographics survey instead of responding to Questions 5-8. If so, the uploaded survey was used (by the study authors) to evaluate Questions 5-8.

Geolocet offers a rich repository of European demographic data, providing you with a robust foundation for data-driven decisions. Our datasets encompass a diverse range of attributes, but it's important to note that the attributes available may vary significantly from country to country. This variation reflects the unique demographic reporting standards and data availability in each region.

Attributes include essential demographic factors such as Age Bands, Gender, and Marital Status, as a minimum. In some countries, we provide cross-referenced attributes, such as Marital Status per Age Band, Marital Status per Gender, or even intricate combinations like Marital Status per Gender and Age. Additionally, for select countries, we offer insights into income, employment status, household composition, housing status, and many more.

🌐 Trusted Source Data

Our demographic data is derived exclusively from official census sources, ensuring the highest level of accuracy and reliability. We take pride in using data that is available under open licenses for commercial use. However, it's important to note that our data is not a direct representation of the original census data. Instead, we use this source data to create comprehensive demographic models that are tailored to your needs.

🔄 Annual Data Updates

To keep your insights fresh and accurate, our data is updated once per year. We offer annual subscriptions, allowing you to access the latest demographic information and maintain the relevance of your analyses.

🌍 Geographic Coverage

While our demographic data spans across the majority of European countries and their administrative divisions' boundaries, it's important to inquire about specific attributes and coverage for each region of interest. We understand that your data needs may vary depending on your target regions, and our team is here to assist you in selecting the most relevant datasets for your objectives.

Contact us to explore our offerings and learn how our data can elevate your decision-making processes.

🌐 Enhanced with Spatial Insights: Administrative Boundaries Spatial Data

Geolocet's demographic data isn't limited to numbers; it's brought to life through seamless integration with our Administrative Boundaries Spatial Data. This integration offers precise boundary mapping, allowing you to visualize demographic distributions, patterns, and densities on a map. This spatial perspective unlocks geo patterns and insights, aiding in strategic decision-making. Whether you're planning localized marketing strategies, optimizing resource allocation, or selecting ideal expansion sites, the geographic context adds depth to your data-driven strategies. Contact us today to explore how this spatial synergy can enhance your decision-making.

🌍 Enhanced with Robust Aggregated POI Data

Geolocet doesn't stop at demographics; we enhance your analysis by offering Geolocet's POI Aggregated Data. This data source provides a comprehensive understanding of local areas, enabling you to craft detailed local area profiles. It's not just about numbers; it's about uncovering the essence of each locality.

🔍 Crafting Local Area Profiles

When you combine our POI Aggregated Data with our Demographics Data, you have the tools to craft insightful local area profiles. Dive into the specific data points for various sectors, such as the number of hospitals, schools, hotels, restaurants, pubs, casinos, groceries, clothing stores, gas stations, and more within designated areas. This level of granularity allows you to paint a vivid picture of each locality, understanding its unique characteristics and offerings.

Contact us today to explore how this synergy can elevate your strategic decision-making and enrich your insights into local communities.

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Geolocet's Data as a Service (DaaS) offers flexibility tailored to your needs. Our transparent pricing model ensures cost-efficiency, allowing you to pay only for the data you require.

Abstract

The 1993 Turkish Demographic and Health Survey (TDHS) is a nationally representative survey of ever-married women less than 50 years old. The survey was designed to provide information on fertility levels and trends, infant and child mortality, family planning, and maternal and child health. The TDHS was conducted by the Hacettepe University Institute of Population Studies under a subcontract through an agreement between the General Directorate of Mother and Child Health and Family Planning, Ministry of Health and Macro International Inc. of Calverton, Maryland. Fieldwork was conducted from August to October 1993. Interviews were carried out in 8,619 households and with 6,519 women.

The Turkish Demographic and Health Survey (TDHS) is a national sample survey of ever-married women of reproductive ages, designed to collect data on fertility, marriage patterns, family planning, early age mortality, socioeconomic characteristics, breastfeeding, immunisation of children, treatment of children during episodes of illness, and nutritional status of women and children. The TDHS, as part of the international DHS project, is also the latest survey in a series of national-level population and health surveys in Turkey, which have been conducted by the Institute of Population Studies, Haeettepe University (HIPS).

More specifically, the objectives of the TDHS are to:

Collect data at the national level that will allow the calculation of demographic rates, particularly fertility and childhood mortality rates; Analyse the direct and indirect factors that determine levels and trends in fertility and childhood mortality; Measure the level of contraceptive knowledge and practice by method, region, and urban- rural residence; Collect data on mother and child health, including immunisations, prevalence and treatment of diarrhoea, acute respiratory infections among children under five, antenatal care, assistance at delivery, and breastfeeding; Measure the nutritional status of children under five and of their mothers using anthropometric measurements.

The TDHS information is intended to assist policy makers and administrators in evaluating existing programs and in designing new strategies for improving family planning and health services in Turkey.

MAIN RESULTS

Fertility in Turkey is continuing to decline. If Turkish women maintain current fertility rates during their reproductive years, they can expect to have all average of 2.7 children by the end of their reproductive years. The highest fertility rate is observed for the age group 20-24. There are marked regional differences in fertility rates, ranging from 4.4 children per woman in the East to 2.0 children per woman in the West. Fertility also varies widely by urban-rural residence and by education level. A woman living in rural areas will have almost one child more than a woman living in an urban area. Women who have no education have almost one child more than women who have a primary-level education and 2.5 children more than women with secondary-level education.

The first requirement of success ill family planning is the knowledge of family planning methods. Knowledge of any method is almost universal among Turkish women and almost all those who know a method also know the source of the method. Eighty percent of currently married women have used a method sometime in their life. One third of currently married women report ever using the IUD. Overall, 63 percent of currently married women are currently using a method. The majority of these women are modern method users (35 percent), but a very substantial proportion use traditional methods (28 percent). the IUD is the most commonly used modern method (I 9 percent), allowed by the condom (7 percent) and the pill (5 percent). Regional differences are substantial. The level of current use is 42 percent in tile East, 72 percent in tile West and more than 60 percent in tile other three regions. "File common complaints about tile methods are side effects and health concerns; these are especially prevalent for the pill and IUD.

One of the major child health indicators is immunisation coverage. Among children age 12-23 months, the coverage rates for BCG and the first two doses of DPT and polio were about 90 percent, with most of the children receiving those vaccines before age one. The results indicate that 65 percent of the children had received all vaccinations at some time before the survey. On a regional basis, coverage is significantly lower in the Eastern region (41 percent), followed by the Northern and Central regions (61 percent and 65 percent, respectively). Acute respiratory infections (ARI) and diarrhea are the two most prevalent diseases of children under age five in Turkey. In the two weeks preceding the survey, the prevalence of ARI was 12 percent and the prevalence of diarrhea was 25 percent for children under age five. Among children with diarrhea 56 percent were given more fluids than usual.

Breastfeeding in Turkey is widespread. Almost all Turkish children (95 percent) are breastfed for some period of time. The median duration of breastfeeding is 12 months, but supplementary foods and liquids are introduced at an early age. One-third of children are being given supplementary food as early as one month of age and by the age of 2-3 months, half of the children are already being given supplementary foods or liquids.

By age five, almost one-filth of children arc stunted (short for their age), compared to an international reference population. Stunting is more prevalent in rural areas, in the East, among children of mothers with little or no education, among children who are of higher birth order, and among those born less than 24 months after a prior birth. Overall, wasting is not a problem. Two percent of children are wasted (thin for their height), and I I percent of children under five are underweight for their age. The survey results show that obesity is d problem among mothers. According to Body Mass Index (BMI) calculations, 51 percent of mothers are overweight, of which 19 percent are obese.

Geographic coverage

The Turkish Demographic and Health Survey (TDHS) is a national sample survey.

Analysis unit

• Household
• Women age 12-49
• Children under five

Universe

The population covered by the 1993 DHS is defined as the universe of all ever-married women age 12-49 who were present in the household on the night before the interview were eligible for the survey.

Kind of data

Sample survey data

Sampling procedure

The sample for the TDHS was designed to provide estimates of population and health indicators, including fertility and mortality rates for the nation as a whole, fOr urban and rural areas, and for the five major regions of the country. A weighted, multistage, stratified cluster sampling approach was used in the selection of the TDHS sample.

Sample selection was undertaken in three stages. The sampling units at the first stage were settlements that differed in population size. The frame for the selection of the primary sampling units (PSUs) was prepared using the results of the 1990 Population Census. The urban frame included provinces and district centres and settlements with populations of more than 10,000; the rural frame included subdistricts and villages with populations of less than 10,000. Adjustments were made to consider the growth in some areas right up to survey time. In addition to the rural-urban and regional stratifications, settlements were classified in seven groups according to population size.

The second stage of selection involved the list of quarters (administrative divisions of varying size) for each urban settlement, provided by the State Institute of Statistics (SIS). Every selected quarter was subdivided according tothe number of divisions(approximately 100 households)assigned to it. In rural areas, a selected village was taken as a single quarter, and wherever necessary, it was divided into subdivisions of approximately 100 households. In cases where the number of households in a selected village was less than 100 households, the nearest village was selected to complete the 100 households during the listing activity, which is described below.

After the selection of the secondary sampling units (SSUs), a household listing was obtained for each by the TDHS listing teams. The listing activity was carried out in May and June. From the household lists, a systematic random sample of households was chosen for the TDHS. All ever-married women age 12-49 who were present in the household on the night before the interview were eligible for the survey.

Mode of data collection

Face-to-face

Research instrument

Two questionnaires were used in the main fieldwork for the TDHS: the Household Questionnaire and the Individual Questionnaire for ever-married women of reproductive age. The questionnaires were based on the model survey instruments developed in the DHS program and on the questionnaires that had been employed in previous Turkish population and health surveys. The questionnaires were adapted to obtain data needed for program planning in Turkey during consultations with population and health agencies. Both questionnaires were developed in English and translated into Turkish.

a) The Household Questionnaire was used to enumerate all usual members of and visitors to the selected households and to collect information relating to the socioeconomic position of the households. In the first part of the Household Questionnaire, basic information was collected on the age, sex, educational attainment, marital status and relationship to the head of household for each person listed as a household member

Context

This dataset contains information about rural urban demographics.

Content

This dataset contain columns: - Area ,households ,male female, transgender ,all genders ,population 1998 ,sex ratio ,average annual growth rate ,area ,population 2017 ,literacy rate 2010 ,literacy rate 2016 ,literacy rate 2019

Acknowledgements

You can download, copy and share this dataset for analysis and can easily differentiate urban and rural life and their demographics by data we can predict better and can analyze our community problems and solve them

Inspiration

1. Identifying households in different regions and their impacts on life
2. What is the population and sex ratio
3. what is the average annual growth rate in urban and rural areas

Malaysia Life Expectancy at Birth data was reported at 80.700 Year in 2050. This records an increase from the previous number of 80.500 Year for 2049. Malaysia Life Expectancy at Birth data is updated yearly, averaging 74.800 Year from Jun 1980 (Median) to 2050, with 71 observations. The data reached an all-time high of 80.700 Year in 2050 and a record low of 64.100 Year in 1980. Malaysia Life Expectancy at Birth data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s Malaysia – Table MY.US Census Bureau: Demographic Projection.

As of April 2025, almost ** percent of Instagram users in Singapore fell within the age group of 25 to 34 years. This segment comprised almost ** percent of females and ** percent of males. Notably, approximately ** percent of Instagram users within the age group of 18 to 24 years were females. Instagram in general The Facebook-owned social network counts about **** billion active users worldwide. In Singapore, Instagram ranks third among the leading social media platforms . Compared to Facebook, Instagram is a more visual-based platform, originally designed as an online showroom for brands. Nowadays it provides the perfect environment for users to easily display their life to a larger audience and follow people/brands all over the world. Social media in Singapore Instagram has thus emerged as an important promotional platform for brands. In Singapore, brands could reach a target audience of up to *** million people or about ** percent of the population. Unlike traditional advertising channels such as in print media or television, social media advertising, especially Instagram, can be tailored to reach the intended audience. The private information that users share on the platform helps companies to address the right target group for their branding and advertising campaigns, therefore further enhancing their impact.

This table contains 2394 series, with data for years 1991 - 1991 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Population group (19 items: Entire cohort; Income adequacy quintile 1 (lowest);Income adequacy quintile 2;Income adequacy quintile 3 ...), Age (14 items: At 25 years; At 30 years; At 40 years; At 35 years ...), Sex (3 items: Both sexes; Females; Males ...), Characteristics (3 items: Life expectancy; High 95% confidence interval; life expectancy; Low 95% confidence interval; life expectancy ...).

Health literacy is a modifiable determinant of health with the potential to enhance public health. An association between health literacy and health-related quality of life has been reported. Although each country has developed their own original health literacy scales, the assessment of adolescent health literacy using the Health Literacy Scale for School-Aged Children has not yet been studied in Japan. In this study, we aimed to clarify the factors associated with adolescents’ health literacy and examine the relationship between health literacy, health-related behaviors, and health-related quality of life in Japan. Participants were recruited by a research company using registered monitors (1st- to 3rd-year junior high school students and their mothers living in Japan in August 2023). Multivariate regression analysis was performed using the total EuroQoL Five Dimensions, Youth Version scores. SAS software was used for data analysis. Overall, 1,854 adolescents and their mothers participated in the online survey. Factors associated with Health Literacy Scale for School-Aged Children included physical activity, sleep conditions in health-related behaviors, parental communication, parental health literacy, and health-related quality of life. Furthermore, parental health literacy was associated to children’s quality of life. Our study showed the influence of family variables, highlighting the need for tailored approaches that consider parents’ health literacy levels.

Conserving species requires knowledge of demographic rates (survival, recruitment) that govern population dynamics to allow the allocation of limited resources to the most vulnerable stages of target species' life cycles. Additionally, quantifying drivers of demographic change facilitates the enactment of specific remediation strategies. However, knowledge gaps persist in how similar environmental changes lead to contrasting population dynamics through demographic rates. For sympatric hummingbird species, the population of urban-associated partial-migrant Anna's hummigbird (Calypte anna) has increased, yet the populations of Neotropical migrants including rufous, calliope, and black-chinned hummingbirds have decreased. Here, we developed an integrated population model to jointly analyze 25 years of mark-recapture data and population survey data for these four species. We examined the contributions of demographic rates on population growth and evaluated the effects of anthropogenic stressors including human population density and crop cover on demographic change in relation to species' life histories. While recruitment appeared to drive the population increase of urban-associated Anna's hummingbirds, decreases in juvenile survival contributed most strongly to population declines of Neotropical migrants and highlight a potentially vulnerable phase in their life-history. Moreover, rufous hummingbird adult and juvenile survival rates were negatively impacted by human population density. Mitigating threats associated with intensively modified anthropogenic environments is a promising avenue for slowing further hummingbird population loss. Overall, our model grants critical insight into how anthropogenic modification of habitat affects the population dynamics of species of conservation concern. Methods This R data file contains a named list for each species in our study. It has been processed to remove covariates and data that are not public domain but are available for download at the links provided (indicated with * in the readme file). Each species list contains mark-recapture records (y), the known-state records (z), number of years spanned by the analysis (n.years), numbers banded individuals (n.ind), banding station membership (sta), number of banding stations (n.sta), year of first encounter for each individual (first), year of last possible encounter of each individual if it were to be alive (last), first and last years of mark recapture data (first_yr / last_yr), sex (1 = male, 2 = female) and age (1 = juvenile, 2 = adult) membership for each individual, the observed residency information for each individual in each year (r), the partially observed residency state information for each individual (u), the standardized human population density and crop data in the 3 kilometers around each banding station (HPD / crop), the unstandardized HPD and crop data (HPD_raw / crop_raw), the number of days of operational banding activity at each station each year (effort), and indicator for each station and year signifying whether banding occurred on at least two occasions separated by more than 5 days that year (kappa_shrink), the BBS survey year (year), an indicator of whether the BBS surveyor was suveying on their first year or not (firstyr), the number of BBS surveys (ncounts), the species tally on a given survey (count), the number of individual transects surveyed over the study period (nrte), the BBS transect membership for each count (rte), the number of observers contributing data over the study period (nobserver), the anonymized observer ID on a given transect for each count (rte.obser), and the initial abundance estimate given as the mean count across all transects and years, inflated by 100 for precise estimation of demographic rates (lam0).

Demographic data are essential to assessments of the status of endangered species. However, establishing an integrated monitoring program to obtain useful data on contemporary and future population trends requires both the identification of priority areas and populations and realistic evaluations of the kinds of data that can be obtained under different monitoring regimes. We analyzed all known populations of a critically endangered primate, the muriqui (genus: Brachyteles) using population size, genetic uniqueness, geographic importance (including potential importance in corridor programs) and implementability scores to define monitoring priorities. Our analyses revealed nine priority populations for the northern muriqui (B. hypoxanthus) and nine for the southern muriqui (B. arachnoides). In addition, we employed knowledge of muriqui developmental and life history characteristics to define the minimum monitoring intensity needed to evaluate demographic trends along a continuum ranging from simple descriptive changes in population size to predictions of population changes derived from individual based life histories. Our study, stimulated by the Brazilian government’s National Action Plan for the Conservation of Muriquis, is fundamental to meeting the conservation goals for this genus, and also provides a model for defining priorities and methods for the implementation of integrated demographic monitoring programs for other endangered and critically endangered species of primates.

BackgroundFor people with intellectual and developmental disabilities, other’s perceptions of them based on their condition often begin before birth and go on to impact relationships, opportunities, and self perception across the life course. Search engine results and news media, which may portray these conditions stereotypically or in poor light, are often a key source in these perceptions. Our purpose was to understand how search engine results and available news media can shape perceptions on certain intellectual and developmental disabilities.MethodsWe developed an online Likert-scale survey to measure differences in perceptions based off first available search engine results, images, and news headlines of four intellectual and developmental disabilities: cerebral palsy, Down syndrome, Prader-Willi syndrome, and Angelman syndrome. These four conditions were selected to compare less prevalent (Prader-Willi and Angelman) and more prevalent conditions (Down syndrome and cerebral palsy). Perception questions addressed general impression and aspects of the disability experience expected to be impacted by perception from others. We recruited via multiple social media platforms, flyers posted in the Boston area, and word of mouth to local communities and friends.Findings229 individuals opened the survey, and 125 responses were used in analysis. Mean responses to Prader-Willi syndrome were significantly more negative than responses to cerebral palsy, Down syndrome, and Angelman syndrome across all variables. Responses to Angelman syndrome were also more negative than responses to Down syndrome. Significant differences between conditions found when treating the data as continuous were confirmed when treating the data as ordinal.ConclusionLesser-known intellectual and developmental disabilities, such as Prader-Willi syndrome and Angelman syndrome, are subject to more negative portrayal in media, leading to more negative perception, which may impact social opportunity and quality of life. Combined with our finding that the perception of Prader-Willi syndrome follows the ideals of the medical model of disability more closely than the social model, a need for social model of disability training and education for physicians and other medical providers is clear.

Malaysia Life Expectancy at Birth: Female data was reported at 77.700 Year in 2016. This records an increase from the previous number of 77.543 Year for 2015. Malaysia Life Expectancy at Birth: Female data is updated yearly, averaging 72.093 Year from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 77.700 Year in 2016 and a record low of 60.322 Year in 1960. Malaysia Life Expectancy at Birth: Female data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Malaysia – Table MY.World Bank.WDI: Health Statistics. Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average;

Undergraduate research experiences (UREs) have the potential to benefit undergraduates and longer UREs have been shown to lead to greater benefits for students. However, no studies have examined what causes students to stay in or consider leaving their UREs. In this study, we examined what factors cause students to stay in their UREs, what factors cause students to consider leaving their UREs, and what factors cause students to leave their UREs. We sampled from 25 research-intensive (R1) public universities across the United States and surveyed 768 life sciences undergraduates who were currently participating in or had previously participated in a URE. Students answered closed-ended and open-ended questions about factors that they perceived influenced their persistence in UREs. We used logistic regression to explore to what extent student demographics predicted what factors influenced students to stay in or consider leaving their UREs. We applied open-coding methods to probe the student-reported reasons why students chose to stay in and leave their UREs. Fifty percent of survey respondents considered leaving their URE, and 53.1% of those students actually left their URE. Students who reported having a positive lab environment and students who indicated enjoying their everyday research tasks were more likely to not consider leaving their UREs. In contrast, students who reported a negative lab environment or that they were not gaining important knowledge or skills were more likely to leave their UREs. Further, we identified that gender, race/ethnicity, college generation status, and GPA predicted which factors influenced students’ decisions to persist in their UREs. This research provides important insight into how research mentors can create UREs that undergraduates are willing and able to participate in for as long as possible.

Demographic data was collected in two census years 2009 and 2016. During both censuses, girth at breast height or collar girth was measured for each individual. Girth was converted to diameter for analysis. This file contains information of plant sizes (diameter), survival, transition of tree to sprout and presence of harvesting for individuals of Acacia chundra, Chloroxylon swietenia and Gardenia gummifera.

Individuals differ in many ways. Most produce few offspring; a handful produce many. Some die early; others live to old age. It is tempting to attribute these differences in outcomes to differences in individual traits, and thus in the demographic rates experienced. However, there is more to individual variation than meets the eye of the biologist. Even among individuals sharing identical traits, life history outcomes (life expectancy and lifetime reproduction) will vary due to individual stochasticity, i.e., to chance. Quantifying the contributions of heterogeneity and chance is essential to understanding natural variability. Inter-individual differences vary across environmental conditions, hence heterogeneity and stochasticity depend on environmental conditions. We show that favorable conditions increase the contributions of individual stochasticity, and reduce the contributions of heterogeneity, to variance in demographic outcomes in a seabird population. The opposite is true under poor conditions. This result has important consequence for understanding the ecology and evolution of life history strategies.

Specifically, three life-history complexes exist in a population of southern fulmar (defined as sets of life-history characteristics that occur together through the lifetime of an individual). They are reminiscent of the gradient of life- history strategy observed among species:

1. Group 1 (14% of offspring at fledging) is a slow-paced life history where individuals tend to delay recruitment, recruit successfully, and extend their reproductive lifespan.
2. Group 2 (67% of offspring at fledging) consists of individuals that are less likely to recruit, have high adult survival, and skip breeding often.
3. Group 3 (19% of offspring at fledging) is a fast-paced life history where individuals recruit early and attempt to breed often but have a short lifespan.

Individuals in groups 1 and 3 are considered “high-quality” individuals because they produce, on average, more offspring over their lives than do individuals in group 2. But group 2 is made-up of individuals that experience the highest levels of adult survival.

Differences between these groups, i.e. individual heterogeneity, only explains a small fraction of variance in life expectancy (5.9%) and lifetime reproduction (22%) when environmental conditions are ordinary. We expect that the environmental context experienced, especially when environmental conditions get extreme, is key to characterizing individual heterogeneity and its contribution to life history outcomes. Here, we build on previous studies to quantify the impact of extreme environmental conditions on the relative contributions of individual heterogeneity and stochasticity to variance in life history outcomes. We found that the differences in vital rates and demographic outcomes among complexes depend on the sea ice conditions individuals experience. Importantly, differences across life history complexes are amplified when sea ice concentration get extremely low. Sea ice conditions did not only affect patterns of life history traits, but also the variance of life history outcomes and the relative proportion of individual unobserved heterogeneity to the total variance. These new results advance the current debate on the relative importance heterogeneity (i.e. potentially adaptive) and stochasticity (i.e. enhances genetic drift) in shaping potentially neutral vs. adaptive changes in life histories.

It is estimated that the Second World War was responsible for the deaths of approximately 3.76 percent of the world's population between 1939 and 1945. In 2022, where the world's population reached eight billion, this would be equal to the death of around 300 million people.

The region that experienced the largest loss of life relative to its population was the South Seas Mandate - these were former-German territories given to the Empire of Japan through the Treaty of Versailles following WWI, and they make up much of the present-day countries of the Marshall Islands, Micronesia, the Northern Mariana Islands (U.S. territory), and Palau. Due to the location and strategic importance of these islands, they were used by the Japanese as launching pads for their attacks on Pearl Harbor and in the South Pacific, while they were also taken as part of the Allies' island-hopping strategy in their counteroffensive against Japan. This came at a heavy cost for the local populations, a large share of whom were Japanese settlers who had moved there in the 1920s and 1930s. Exact figures for both pre-war populations and wartime losses fluctuate by source, however civilian losses in these islands were extremely high as the Japanese defenses resorted to more extreme measures in the war's final phase.

About this dataset

Context

The dataset tabulates the Country Life Acres population by gender and age. The dataset can be utilized to understand the gender distribution and demographics of Country Life Acres.

Content

The dataset constitues the following two datasets across these two themes

• Country Life Acres, MO Population Breakdown by Gender
• Country Life Acres, MO Population Breakdown by Gender and Age

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

In 2024, about 60.9 percent of the Chinese population was between 16 and 59 years old. Apart from the information given on broad age groups in this statistic, some more information is provided by a timeline for the age distribution and a population breakdown by smaller age groups. Demographic development in China China ranked as the second most populous country in the world with a population of nearly 1.41 billion as of mid 2024, surpassed only by India. As the world population reached more than eight billion in mid 2024, China represented almost one fifth of the global population. China's population increased exponentially between the 1950s and the early 1980s due to Mao Zedong's population policy. To tackle the problem of overpopulation, a one-child policy was implemented in 1979. Since then, China's population growth has slowed from more than two percent per annum in the 1970s to around 0.5 percent per annum in the 2000s, and finally turned negative in 2022. China's aging population One outcome of the strict population policy is the acceleration of demographic aging trends. According to the United Nations, China's population median age has more than doubled over the last five decades, from 18 years in 1970 to 37.5 years in 2020. Few countries have aged faster than China. The dramatic aging of the population is matched by slower growth. The total fertility rate, measuring the number of children a woman can expect to have in her life, stood at just around 1.2 children. This incremental decline in labor force could lead to future challenges for the Chinese government, causing instability in current health care and social insurance mechanisms. To learn more about demographic development of the rural and urban population in China, please take a look at our reports on population in China and aging population in China.