Data in this layer represents demographic data from the American Community Survey 5 yr estimates, 2015-2019 for Age, Disability, Education, Female Population, Limited English Proficiency, Low Income, Place of Birth, Race, and Zero Vehicle Households. Each layer contains a number of attributes pertaining to the specific topic. For additional information about the data, definitions, and source please contact NJTPA (gfausel@njtpa.org).

An information system based on data from the healthcare sector and related areas. The online portal gives researchers the opportunity to research various health topics including population, socio-economic factors, health insurance, health laws.

• Laws

• Population

  • Births
  • Deaths
  • Population Numbers
  • other

• Pregnancy, Births

• Terminations of Pregnancy

• Households, Families, Living Communities

• Social Conditions

  • Education
  • Habitation
  • Employment
  • Unemployment
  • Poverty and Disparity
  • Single parent
  • Income
  • Social Welfare
  • other

• Training of the Health Personnel

• Economy

• Research

• Private Health Insurance

• Statutory Health Insurance

  • Statutory Health Insurance: Income, Expenditure
  • Statutory Health Insurance: Members, Insured Persons
  • Statutory Health Insurance: Services
  • Statutory Health Insurance: Services (Pharmaceuticals)
  • Statutory Health Insurance: other

• Nursing Care Insurance

• Pension Insurance

  • Insured Persons
  • Rehabilitation Services
  • Pensions because of old age
  • Ppensions because of diminution of ability to wrk
  • Pensions because of death (widow/widower pensions)
  • other

• Accident Insurance, employers' liability insurance association

This statistic shows the demographic and socio-economic factors most likely to shape global industries according to executive respondents from large companies worldwide, as of July 2015. 44% of executives believe that the changing nature of work or flexible work will cause major change in their industry by 2020.

This dataset contains statistics regarding the population exposed to tobacco, foods or overweight/obesity for country members and partners of OECD (The Organization for Economic Co-operation and Development) and for countries in accession negotiations with OECD. The exposure levels to health risk or protection factors statistics cover the period 1960-2018.

Compilation of trends underlying the non-demographic factors.

The U.S. Population Grids (Summary File 3), 2000: New Orleans Metropolitan Statistical Area, Alpha Version data set contains an ARC/INFO Workspace with grids of demographic data from the year 2000 census. The grids have a resolution of 30 arc-seconds (0.0083 decimal degrees), or approximately 1 square km. The gridded variables are based on census block geography from Census 2000 TIGER/Line Files and census variables (income, poverty, education, housing age). This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).

The data relates to the paper that analyses the determinants or factors that best explain student research skills and success in the honours research report module during the COVID-19 pandemic in 2021. The data used have been gathered through an online survey created on the Qualtrics software package. The research questions were developed from demographic factors and subject knowledge including assignments to supervisor influence and other factors in terms of experience or belonging that played a role (see anonymous link at https://unisa.qualtrics.com/jfe/form/SV_86OZZOdyA5sBurY. An SMS was sent to all students of the 2021 module group to make them aware of the survey. They were under no obligation to complete it and all information was regarded as anonymous. We received 39 responses. The raw data from the survey was processed through the SPSS statistical, software package. The data file contains the demographics, frequencies, descriptives, and open questions processed.     The study...

Reporting of Aggregate Case and Death Count data was discontinued on May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. Although these data will continue to be publicly available, this dataset will no longer be updated.

The surveillance case definition for COVID-19, a nationally notifiable disease, was first described in a position statement from the Council for State and Territorial Epidemiologists, which was later revised. However, there is some variation in how jurisdictions implemented these case definitions. More information on how CDC collects COVID-19 case surveillance data can be found at FAQ: COVID-19 Data and Surveillance.

Aggregate Data Collection Process Since the beginning of the COVID-19 pandemic, data were reported from state and local health departments through a robust process with the following steps:

• Aggregate county-level counts were obtained indirectly, via automated overnight web collection, or directly, via a data submission process.
• If more than one official county data source existed, CDC used a comprehensive data selection process comparing each official county data source to retrieve the highest case and death counts, unless otherwise specified by the state.
• A CDC data team reviewed counts for congruency prior to integration and set up alerts to monitor for discrepancies in the data.
• CDC routinely compiled these data and post the finalized information on COVID Data Tracker.
• County level data were aggregated to obtain state- and territory- specific totals.
• Counting of cases and deaths is based on date of report and not on the date of symptom onset. CDC calculates rates in these data by using population estimates provided by the US Census Bureau Population Estimates Program (2019 Vintage).
• COVID-19 aggregate case and death data are organized in a time series that includes cumulative number of cases and deaths as reported by a jurisdiction on a given date. New case and death counts are calculated as the week-to-week change in cumulative counts of cases and deaths reported (i.e., newly reported cases and deaths = cumulative number of cases/deaths reported this week minus the cumulative total reported the prior week.

This process was collaborative, with CDC and jurisdictions working together to ensure the accuracy of COVID-19 case and death numbers. County counts provided the most up-to-date numbers on cases and deaths by report date. Throughout data collection, CDC retrospectively updated counts to correct known data quality issues.

Description This archived public use dataset focuses on the cumulative and weekly case and death rates per 100,000 persons within various sociodemographic factors across all states and their counties. All resulting data are expressed as rates calculated as the number of cases or deaths per 100,000 persons in counties meeting various classification criteria using the US Census Bureau Population Estimates Program (2019 Vintage).

Each county within jurisdictions is classified into multiple categories for each factor. All rates in this dataset are based on classification of counties by the characteristics of their population, not individual-level factors. This applies to each of the available factors observed in this dataset. Specific factors and their corresponding categories are detailed below.

Population-level factors Each unique population factor is detailed below. Please note that the “Classification” column describes each of the 12 factors in the dataset, including a data dict

This data includes analysis of data from the downer cow scenario portion of the National Dairy Study conducted in 2015 (further information regarding the national dairy study can be found in Bauman et al., 2018). Multivariable linear and logistic regression were used to analyze associations between demographic and farm level factors and how producers responded to questions in the downer cow scenario. This dataset includes tables summarizing all multivariable, univariable and descriptive models beyond those reported in the manuscript.

According to learning and development and HR professionals, challenging and impactful work was the most important factor when considering a new job position for those age 50 and older. The most important factor for the 18 to 34 demographic was career growth.

Demographics of systemic factors in patients.

Changes in demographics will fundamentally shift the types of consumers that insurers need to target, as well as the types of products they need to provide. An aging population will put increased strain on state pensions and social services like public healthcare. A declining middle class due to median incomes not increasing as fast as other core goods and services means young people are buying a house, getting married, and starting families at later points in life. And a larger proportion of the population living in urban areas leads to increased health risk due to pollution, poor hygiene, and other urban lifestyle factors. These three factors will help shape the insurance industry going forward. Read More

Objective: Our objective was to develop and test a new concept (affinity) analogous to multimorbidity of chronic conditions for individuals at census tract level in Memphis, TN. The use of affinity will improve the surveillance of multiple chronic conditions and facilitate the design of effective interventions. Methods: We used publicly available chronic condition data (Center for Disease Control and Prevention (CDC) 500 Cities project), socio-demographic data (U.S. Census Bureau) and demographics data (Environmental Systems Research Institute; ESRI). We examined the geographic pattern of the affinity of chronic conditions using global Moran’s I and Getis-Ord Gi statistics and its association with socio-economic disadvantage (poverty, unemployment, and crime) using robust regression models. We also used the most common behavioral factor, smoking, and other demographic factors (percent of the male population, percent of the population 67 years and over and total population size) as control variables in the model. Results: A geo-distinctive pattern of clustered chronic affinity associated with socio-economic deprivation was observed. Statistical results confirmed that neighborhoods with higher rates of crime, poverty, and unemployment were associated with an increased likelihood of having a higher affinity among major chronic conditions. With the inclusion of smoking in the model, however, only the crime prevalence was statistically significantly associated with the chronic affinity. Conclusion: Chronic affinity disadvantages were disproportionately accumulated in socially disadvantaged areas. We showed links between commonly co-observed chronic diseases at the population level and systematically explored the complexity of affinity and socio-economic disparities. Our affinity score, based on publicly available datasets, served as a surrogate for multimorbidity at the population level, which may assist policymakers and public health planners to identify urgent hot spots for chronic disease and allocate clinical, medical and healthcare resources efficiently.

1. How demographic factors lead to variation or change in growth rates can be investigated using life table response experiments (LTRE) based on structured population models. Traditionally, LTREs focused on decomposing the asymptotic growth rate, but more recently decompositions of annual 'realized' growth rates have gained in popularity.
2. Realized LTREs have been used particularly to understand how variation in vital rates translates into variation in growth for populations under long-term study. For these, complete population models may be constructed by combining data in an integrated population model (IPM). IPMs are also used to investigate how temporal variation in environmental drivers affect vital rates. Such investigations have usually come down to estimating covariate coefficients for the effects of environmental variables on vital rates, but formal ways of assessing how they lead to variation in growth rates have been lacking.
3. We extend realized LTREs in two ways. First, we further partition the contributions from vital rates into contributions from temporally varying factors that affect them. The decomposition allows us to compare the resultant effect on the growth rate of different environmental factors that may each act via multiple vital rates. Second, we show how realized growth rates can be decomposed into separate components from environmental and demographic stochasticity. The latter is typically omitted in LTRE analyses.
4. We illustrate how to use the approach in an IPM for data from a 26-year study on northern wheatears (Oenanthe oenanthe), a migratory passerine bird breeding in an agricultural landscape. For this population, consisting of around 50–120 breeding pairs per year, we partition variation in realized growth rates into environmental contributions from temperature, rainfall, population density, and unexplained random variation via multiple vital rates, and from demographic stochasticity.
5. The case study suggests that variation in first-year survival via the random component, and adult survival via temperature are two main factors behind environmental variation in growth rates. More than half of the variation in growth rates is suggested to come from demographic stochasticity, demonstrating the importance of this factor for populations of moderate size.

Since December 2019 when COVID-19 was detected, it took the world by surprise in terms of spread and morbidity/mortality. The high rate of spread and casualties recorded from COVID-19 called for research in all directions to find ways to contain and reverse the incidences. It is against this background that this paper sought to measure the association of the socio-demographic factors in the hard-hit districts in Greater Accra and Ashanti to analyze its relationship with the novel COVID-19 virus. Data on COVID-19 cases from 35 Districts in both Greater Accra and Ashanti Regions were collected from the Ghana Health Service and population data from Ghana Statistical Service. Descriptive statistics and regression analysis were generated using R. We found that some socio-demographic variables have an association with COVID-19 infections. For example, age and religion especially Christianity and Islam pose risk to COVID-19. The population aged 15–64 was particularly at high risk of infections due to the high level of movement of this age group. We, therefore, recommend that places of congregation such as Churches and Mosques be targeted for vigorous sensitization on COVID-19 protocols and prevention. Also, districts with a high population between the ages of 15–64 should step sensitization efforts to educate their inhabitants on the need to reduce travel and related activities to curb the spread of the virus.

Comparison of linked and unlinked transmission pairs.tCalculated for each 3 month period of observation.

In nature species react to a variety of endogenous and exogenous ecological factors. Understanding the mechanisms by which these factors interact and drive population dynamics is a need for understanding and managing ecosystems. In this study we assess, using laboratory experiments, the effects that the combinations of two exogenous factors exert on the endogenous structure of the population dynamics of a size-structured population of Daphnia. One exogenous factor was size-selective predation, which was applied on experimental populations through simulating: (a) selective predation on small prey, (b) selective predation on large prey and (c) non-selective predation. The second exogenous factor was pesticide exposure, applied experimentally in a quasi-continuous regime. Our analysis combined theoretical models and statistical testing of experimental data for analyzing how the density dependence structure of the population dynamics was shifted by the different exogenous factors. Our results showed that pesticide exposure interacted with the mode of predation in determining the endogenous dynamics. Populations exposed to the pesticide and to either selective predation on newborns or selective predation on adults exhibited marked nonlinear effects of pesticide exposure. However, the specific mechanisms behind such nonlinear effects were dependent on the mode of size-selectivity. In populations under non-selective predation the pesticide exposure exerted a weak lateral effect. The ways in which endogenous process and exogenous factors may interact determine population dynamics. Increases in equilibrium density results in higher variance of population fluctuations but do not modify the stability properties of the system, while changes in the maximum growth rate induce changes in the dynamic regimes and stability properties of the population. Future consideration for research includes the consequences of the seasonal variation in the composition and activity of the predator assembly in interaction with the seasonal variation in exposure to agrochemicals on freshwater population dynamics.

Dataset

This dataset was created by Figo

Released under Apache 2.0

Contents

The world's population first reached one billion people in 1803, and reach eight billion in 2023, and will peak at almost 11 billion by the end of the century. Although it took thousands of years to reach one billion people, it did so at the beginning of a phenomenon known as the demographic transition; from this point onwards, population growth has skyrocketed, and since the 1960s the population has increased by one billion people every 12 to 15 years. The demographic transition sees a sharp drop in mortality due to factors such as vaccination, sanitation, and improved food supply; the population boom that follows is due to increased survival rates among children and higher life expectancy among the general population; and fertility then drops in response to this population growth. Regional differences The demographic transition is a global phenomenon, but it has taken place at different times across the world. The industrialized countries of Europe and North America were the first to go through this process, followed by some states in the Western Pacific. Latin America's population then began growing at the turn of the 20th century, but the most significant period of global population growth occurred as Asia progressed in the late-1900s. As of the early 21st century, almost two thirds of the world's population live in Asia, although this is set to change significantly in the coming decades. Future growth The growth of Africa's population, particularly in Sub-Saharan Africa, will have the largest impact on global demographics in this century. From 2000 to 2100, it is expected that Africa's population will have increased by a factor of almost five. It overtook Europe in size in the late 1990s, and overtook the Americas a decade later. In contrast to Africa, Europe's population is now in decline, as birth rates are consistently below death rates in many countries, especially in the south and east, resulting in natural population decline. Similarly, the population of the Americas and Asia are expected to go into decline in the second half of this century, and only Oceania's population will still be growing alongside Africa. By 2100, the world's population will have over three billion more than today, with the vast majority of this concentrated in Africa. Demographers predict that climate change is exacerbating many of the challenges that currently hinder progress in Africa, such as political and food instability; if Africa's transition is prolonged, then it may result in further population growth that would place a strain on the region's resources, however, curbing this growth earlier would alleviate some of the pressure created by climate change.

These datasets are for a cohort of n=1540 anonymised hospitalised COVID-19 patients, and the data provide information on outcomes (i.e. patient death or discharge), demographics and biomarker measurements for two New York hospitals: State University of New York (SUNY) Downstate Health Sciences University and Maimonides Medical Center.

The file "demographics_both_hospitals.csv" contains the ultimate outcomes of hospitalisation (whether a patient was discharged or died), demographic information and known comorbidities for each of the patients.

The file "dynamics_clean_both_hospitals.csv" contains cleaned dynamic biomarker measurements for the n=1233 patients where this information was available and the data passed our various checks (see https://doi.org/10.1101/2021.11.12.21266248 for information of these checks and the cleaning process). Patients can be matched to demographic data via the "id" column.

Study approval and data collection

Study approval was obtained from the State University of New York (SUNY) Downstate Health Sciences University Institutional Review Board (IRB#1595271-1) and Maimonides Medical Center Institutional Review Board/Research Committee (IRB#2020-05-07). A retrospective query was performed among the patients who were admitted to SUNY Downstate Medical Center and Maimonides Medical Center with COVID-19-related symptoms, which was subsequently confirmed by RT PCR, from the beginning of February 2020 until the end of May 2020. Stratified randomization was used to select at least 500 patients who were discharged and 500 patients who died due to the complications of COVID-19. Patient outcome was recorded as a binary choice of “discharged” versus “COVID-19 related mortality”. Patients whose outcome was unknown were excluded. Demographic, clinical history and laboratory data was extracted from the hospital’s electronic health records.