For more than three decades UCSUR has documented the status of older adults in the County along multiple life domains. Every decade we issue a comprehensive report on aging in Allegheny County and this report represents our most recent effort. It documents important shifts in the demographic profile of the population in the last three decades, characterizes the current status of the elderly in multiple life domains, and looks ahead to the future of aging in the County. This report is unique in that we examine not only those aged 65 and older, but also the next generation old persons, the Baby Boomers. Collaborators on this project include the Allegheny County Area Agency on Aging, the United Way of Allegheny County, and the Aging Institute of UPMC Senior Services and the University of Pittsburgh. The purpose of this report is to provide a comprehensive analysis of aging in Allegheny County. To this end, we integrate survey data collected from a representative sample of older county residents with secondary data available from Federal, State, and County agencies to characterize older individuals on multiple dimensions, including demographic change and population projections, income, work and retirement, neighborhoods and housing, health, senior service use, transportation, volunteering, happiness and life satisfaction, among others. Since baby boomers represent the future of aging in the County we include data for those aged 55-64 as well as those aged 65 and older.

Inspiration

Which countries have the most social contacts in the world? In particular, do countries with more social contacts among the elderly report more deaths caused by a pandemic caused by a respiratory virus?

Context

With the emergence of the COVID-19 pandemic, reports have shown that the elderly are at a higher risk of dying than any other age groups. 8 out of 10 deaths reported in the U.S. have been in adults 65 years old and older. Countries have also began to enforce 2km social distancing to contain the pandemic.

To this end, I wanted to explore the relationship between social contacts among the elderly and its relationship with the number of COVID-19 deaths across countries.

Content

This dataset includes a subset of the projected social contact matrices in 152 countries from surveys Prem et al. 2020. It was based on the POLYMOD study where information on social contacts was obtained using cross-sectional surveys in Belgium (BE), Germany (DE), Finland (FI), Great Britain (GB), Italy (IT), Luxembourg (LU), The Netherlands (NL), and Poland (PL) between May 2005 and September 2006.

This dataset includes contact rates from study participants ages 65+ for all countries from all sources of contact (work, home, school and others).

I used this R code to extract this data:

load('../input/contacts.Rdata') # https://github.com/kieshaprem/covid19-agestructureSEIR-wuhan-social-distancing/blob/master/data/contacts.Rdata
View(contacts)
contacts[["ALB"]][["home"]]
contacts[["ITA"]][["all"]]
rowSums(contacts[["ALB"]][["all"]])
out1 = data.frame(); for (n in names(contacts)) { x = (contacts[[n]][["all"]])[16,]; out <- rbind(out, data.frame(x)) }
out2 = data.frame(); for (n in names(contacts)) { x = (contacts[[n]][["all"]])[15,]; out <- rbind(out, data.frame(x)) }
out3 = data.frame(); for (n in names(contacts)) { x = (contacts[[n]][["all"]])[14,]; out <- rbind(out, data.frame(x)) }
m1 = data.frame(t(matrix(unlist(out1), nrow=16)))
m2 = data.frame(t(matrix(unlist(out2), nrow=16)))
m3 = data.frame(t(matrix(unlist(out3), nrow=16)))
rownames(m1) = names(contacts)
colnames(m1) = c("00_04", "05_09", "10_14", "15_19", "20_24", "25_29", "30_34", "35_39", "40_44", "45_49", "50_54", "55_59", "60_64", "65_69", "70_74", "75_79")
rownames(m2) = rownames(m1)
rownames(m3) = rownames(m1)
colnames(m2) = colnames(m1)
colnames(m3) = colnames(m1)
write.csv(zapsmall(m1),"contacts_75_79.csv", row.names = TRUE)
write.csv(zapsmall(m2),"contacts_70_74.csv", row.names = TRUE)
write.csv(zapsmall(m3),"contacts_65_69.csv", row.names = TRUE)

Rows names correspond to the 3 letter country ISO code, e.g. ITA represents Italy. Column names are the age groups of the individuals contacted in 5 year intervals from 0 to 80 years old. Cell values are the projected mean social contact rate.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1139998%2Ffa3ddc065ea46009e345f24ab0d905d2%2Fcontact_distribution.png?generation=1588258740223812&alt=media" alt="">

Acknowledgements

Thanks goes to Dr. Kiesha Prem for her correspondence and her team for publishing their work on social contact matrices.

References

• The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study
• Projecting social contact matrices in 152 countries using contact surveys and demographic data
• Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases (POLYMOD study)

Related resources

• My starter notebook
• http://www.socialcontactdata.org/
• https://www.kaggle.com/tsubasatwi/close-contact-status-of-corona-in-japan
• Facebook Data for Good Mobility Dashboard

About this dataset

Context

The dataset tabulates the data for the Loma Linda, CA population pyramid, which represents the Loma Linda population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Loma Linda, CA, is 23.0.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Loma Linda, CA, is 26.0.
• Total dependency ratio for Loma Linda, CA is 49.0.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Loma Linda, CA is 3.9.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Loma Linda population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Loma Linda for the selected age group is shown in the following column.
• Population (Female): The female population in the Loma Linda for the selected age group is shown in the following column.
• Total Population: The total population of the Loma Linda for the selected age group is shown in the following column.

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/.

Recommended for further research

This dataset is a part of the main dataset for Loma Linda Population by Age. You can refer the same here

Abstract (en): The Research on Early Life and Aging Trends and Effects (RELATE) study compiles cross-national data that contain information that can be used to examine the effects of early life conditions on older adult health conditions, including heart disease, diabetes, obesity, functionality, mortality, and self-reported health. The complete cross sectional/longitudinal dataset (n=147,278) was compiled from major studies of older adults or households across the world that in most instances are representative of the older adult population either nationally, in major urban centers, or in provinces. It includes over 180 variables with information on demographic and geographic variables along with information about early life conditions and life course events for older adults in low, middle and high income countries. Selected variables were harmonized to facilitate cross national comparisons. In this first public release of the RELATE data, a subset of the data (n=88,273) is being released. The subset includes harmonized data of older adults from the following regions of the world: Africa (Ghana and South Africa), Asia (China, India), Latin America (Costa Rica, major cities in Latin America), and the United States (Puerto Rico, Wisconsin). This first release of the data collection is composed of 19 downloadable parts: Part 1 includes the harmonized cross-national RELATE dataset, which harmonizes data from parts 2 through 19. Specifically, parts 2 through 19 include data from Costa Rica (Part 2), Puerto Rico (Part 3), the United States (Wisconsin) (Part 4), Argentina (Part 5), Barbados (Part 6), Brazil (Part 7), Chile (Part 8), Cuba (Part 9), Mexico (Parts 10 and 15), Uruguay (Part 11), China (Parts 12, 18, and 19), Ghana (Part 13), India (Part 14), Russia (Part 16), and South Africa (Part 17). The Health and Retirement Study (HRS) was also used in the compilation of the larger RELATE data set (HRS) (N=12,527), and these data are now available for public release on the HRS data products page. To access the HRS data that are part of the RELATE data set, please see the collection notes below. The purpose of this study was to compile and harmonize cross-national data from both the developing and developed world to allow for the examination of how early life conditions are related to older adult health and well being. The selection of countries for this study was based on their diversity but also on the availability of comprehensive cross sectional/panel survey data for older adults born in the early to mid 20th century in low, middle and high income countries. These data were then utilized to create the harmonized cross-national RELATE data (Part 1). Specifically, data that are being released in this version of the RELATE study come from the following studies: CHNS (China Health and Nutrition Study) CLHLS (Chinese Longitudinal Healthy Longevity Survey) CRELES (Costa Rican Study of Longevity and Healthy Aging) PREHCO (Puerto Rican Elderly: Health Conditions) SABE (Study of Aging Survey on Health and Well Being of Elders) SAGE (WHO Study on Global Ageing and Adult Health) WLS (Wisconsin Longitudinal Study) Note that the countries selected represent a diverse range in national income levels: Barbados and the United States (including Puerto Rico) represent high income countries; Argentina, Cuba, Uruguay, Chile, Costa Rica, Brazil, Mexico, and Russia represent upper middle income countries; China and India represent lower middle income countries; and Ghana represents a low income country. Users should refer to the technical report that accompanies the RELATE data for more detailed information regarding the study design of the surveys used in the construction of the cross-national data. The Research on Early Life and Aging Trends and Effects (RELATE) data includes an array of variables, including basic demographic variables (age, gender, education), variables relating to early life conditions (height, knee height, rural/urban birthplace, childhood health, childhood socioeconomic status), adult socioeconomic status (income, wealth), adult lifestyle (smoking, drinking, exercising, diet), and health outcomes (self-reported health, chronic conditions, difficulty with functionality, obesity, mortality). Not all countries have the same variables. Please refer to the technical report that is part of the documentation for more detail regarding the variables available across countries. Sample weights are applicable to all countries exc...

About this dataset

Context

The dataset tabulates the data for the Country Club Heights, IN population pyramid, which represents the Country Club Heights population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey 5-Year estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Country Club Heights, IN, is 20.2.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Country Club Heights, IN, is 61.5.
• Total dependency ratio for Country Club Heights, IN is 81.7.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Country Club Heights, IN is 1.6.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Country Club Heights population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Country Club Heights for the selected age group is shown in the following column.
• Population (Female): The female population in the Country Club Heights for the selected age group is shown in the following column.
• Total Population: The total population of the Country Club Heights for the selected age group is shown in the following column.

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/.

Recommended for further research

This dataset is a part of the main dataset for Country Club Heights Population by Age. You can refer the same here

this graph was created in R,PowerBi and Tableau:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F1ff6f4c9909fbc1f9823a40b599a42e1%2Fgraph1.gif?generation=1725724753823963&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F2fe80fc1639fd390ce2b3da72bc9686c%2Fgraph2.jpg?generation=1725724760373919&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2Fe621d0a637c3d5c83825a69de684d8c5%2Fgraph3.png?generation=1725724765816050&alt=media" alt="">

The elderly population refers to the portion of a country's inhabitants who are aged 65 and older. This demographic plays a crucial role in various economic and social analyses, especially when it comes to determining the dependent population. The dependent population consists of those individuals who do not actively participate in the workforce and, as a result, rely on others for essential goods and services. This group primarily includes both the elderly and the youth (typically under 15 years of age).

The concept of the elderly dependency ratio is a significant measure used to understand the burden on the working-age population, which consists of those between the ages of 15 and 64. This ratio is calculated by comparing the number of elderly people to those of working age. A higher elderly dependency ratio indicates a larger proportion of elderly individuals relative to those who are contributing economically, leading to increased demands on social systems such as healthcare, pensions, and other support services.

These demographic shifts have widespread implications for both government policies and private sectors. As the elderly population increases, so too does the pressure on pension systems and healthcare services, necessitating reforms to ensure sustainability. Additionally, the aging population affects broader economic growth and welfare, as fewer people of working age contribute to economic productivity, potentially slowing overall economic expansion.

This indicator, often measured as a percentage of the total population, provides valuable insights into the aging trends within a society and their potential impact on the economy, welfare, and social structures. Understanding these trends is essential for shaping future policies that address the needs of an aging population while maintaining economic stability and growth.

Introduction : The worldwide increase in the elderly population has highlighted the importance of accidental falls and their consequences.Objective: To perform time-trend analysis of the mortality rate from accidental falls in (1) the city of Florianópolis (2) the state of Santa Catarina and (3) Brazil. Method : A time-series study of data from the Sistema de Informação sobre Mortalidade ("the Mortality Information System") was performed. The variation in mortality caused by accidental falls was estimated using the joinpoint regression method, based on the International Disease Classification (ICD-10), chapter XX, codes W00 to W15 and W17 to W19, from 1997 to 2010. Results : It was observed that in the most recent periods (2005/2008; 2002/2008; 2003/2008), there was a significant increase in mortality rates related to accidental falls in all three regions, and that these rates increased with advancing age. Conclusion : Strategies to prevent accidental falls among the elderly should be aimed, mainly, at those who are 80 and over, the age in which accidental falls result in higher death rates.

Global population aging poses an unprecedented challenge and calls for a rising effort in eldercare and healthcare. Steady-state visual evoked potential based brain-computer interface (SSVEP-BCI) boasts its high transfer rate and shows great promise in real-world applications to support aging. Public database is critically important for designing the SSVEP-BCI systems. However, the SSVEP-BCI database tailored for the elder is scarce in existing studies. Therefore, in this study, we present a large eldercare-oriented BEnchmark database of SSVEP-BCI for The Aging population (eldBETA). The eldBETA database consisted of the 64-channel electroencephalogram (EEG) from 100 elder subjects, each of whom performed seven blocks of 9-target SSVEP-BCI task. We expect that the eldBETA database would provide a substrate for the design and optimization of the BCI systems intended for the elders.

Alzheimer's Disease and Healthy Aging Data 2015-2020. This data set contains data from BRFSS.

YearStart - Year Start YearEnd - Year End LocationAbbr - Location Abbreviation LocationDesc - Location Description Datasource - Data Source Class - Class description Topic - Topic description Question - Question Data_Value_Unit - The unit, such as "%" for percentage DataValueTypeID - Identifier for the Data Value Type Data_Value_Type - The data value type, such as age-adjusted prevalence or crude prevalence Data_Value - Data Value, such as 14.7 Data_Value_Alt - Equal to data value, but format is numeric Data_Value_Footnote_Symbol - Footnote Symbol Data_Value_Footnote - Footnote Text Low_Confidence_Limit -Low Confidence Limit High_Confidence_Limit - High Confidence Limit StratificationCategory1 - Stratification grouping e.g. Age group, Race/ethnicity group Stratification1 - Stratification value e.g. 18-24yrs StratificationCategory2 - Stratification grouping e.g. Age group, Race/ethnicity group Stratification2 - Stratification value e.g. 18-24yrs Geolocation - ClassID - Identifier for Class TopicID - Topic Identifier QuestionID - Question or Indicator Identifier LocationID - Location number value corresponding to geographic location like state StratificationCategoryID1 - Identifier for the first category stratification StratificationID1 - Identifier for the first stratification StratificationCategoryID2 - Identifier for the second category stratification StratificationID2 - Identifier for the second stratification

About this dataset

Context

The dataset tabulates the data for the Country Club Hills, IL population pyramid, which represents the Country Club Hills population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Country Club Hills, IL, is 24.7.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Country Club Hills, IL, is 21.3.
• Total dependency ratio for Country Club Hills, IL is 46.0.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Country Club Hills, IL is 4.7.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Country Club Hills population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Country Club Hills for the selected age group is shown in the following column.
• Population (Female): The female population in the Country Club Hills for the selected age group is shown in the following column.
• Total Population: The total population of the Country Club Hills for the selected age group is shown in the following column.

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/.

Recommended for further research

This dataset is a part of the main dataset for Country Club Hills Population by Age. You can refer the same here

BackgroundPOD places a heavy burden on the healthcare system as the number of elderly people undergoing surgery is increasing annually because of the aging population. As a large country with a severely aging population, China's elderly population has reached 267 million. There has been no summary analysis of the pooled incidence of POD in the elderly Chinese population.MethodsSystematic search databases included PubMed, Web of Science, EMBASE, Cochrane Library Databases, China Knowledge Resource Integrated Database (CNKI), Chinese Biomedical Database (CBM), WanFang Database, and Chinese Science and Technology Periodicals (VIP). The retrieval time ranged from the database's establishment to February 8, 2023. The pooled incidence of delirium after non-cardiac surgery was calculated using a random effects model. Meta-regression, subgroup, and sensitivity analyses were used to explore the source of heterogeneity.ResultsA total of 52 studies met the inclusion criteria, involving 18,410 participants. The pooled incidence of delirium after non-cardiac surgery in the elderly Chinese population was 18.6% (95% CI: 16.4–20.8%). The meta-regression results revealed anesthesia method and year of publication as a source of heterogeneity. In the subgroup analysis, the gender subgroup revealed a POD incidence of 19.6% (95% CI: 16.9–22.3%) in males and 18.3% (95% CI: 15.7–20.9%) in females. The year of publication subgroup analysis revealed a POD incidence of 20.3% (95% CI: 17.4–23.3%) after 2018 and 14.6 (95% CI: 11.6–17.6%) in 2018 and before. In the subgroup of surgical types, the incidence of hip fracture surgery POD was 20.7% (95% CI: 17.6–24.3%), the incidence of non-cardiac surgery POD was 18.4% (95% CI: 11.8–25.1%), the incidence of orthopedic surgery POD was 16.6% (95% CI: 11.8–21.5%), the incidence of abdominal neoplasms surgery POD was 14.3% (95% CI: 7.6–21.1%); the incidence of abdominal surgery POD was 13.9% (95% CI: 6.4–21.4%). The anesthesia methods subgroup revealed a POD incidence of 21.5% (95% CI: 17.9–25.1%) for general anesthesia, 15.0% (95% CI: 10.6–19.3%) for intraspinal anesthesia, and 8.3% (95% CI: 10.6–19.3%) for regional anesthesia. The measurement tool subgroup revealed a POD incidence of 19.3% (95% CI: 16.7–21.9%) with CAM and 16.8% (95% CI: 12.6–21.0%) with DSM. The sample size subgroup revealed a POD incidence of 19.4% (95% CI: 16.8–22.1%) for patients ≤ 500 and 15.3% (95% CI: 11.0–19.7%) for patients > 500. The sensitivity analysis suggested that the pooled incidence of postoperative delirium in this study was stable.ConclusionOur systematic review of the incidence of delirium after non-cardiac surgery in elderly Chinese patients revealed a high incidence of postoperative delirium. Except for cardiac surgery, the incidence of postoperative delirium was higher for hip fracture surgery than for other types of surgery. However, this finding must be further explored in future large-sample studies.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier: PROSPERO CRD42023397883.

IntroductionThe aging population and the rise in chronic diseases are linked to a higher number of elderly individuals with impairments. These individuals often depend on family caregivers for basic daily activities, which can impose a significant burden and increase the risk of violence against them.ObjectiveTo assess the effectiveness of itinerant community caregivers (ICC) in reducing burden, depression and risk of violence among family caregivers of impaired elderly (FCIE), while also increasing their social support.MethodsRandomized controlled trial with 38 pairs of elderly people and their caregivers. For six months, twice a week, the ICC spent three hours with the elderly, completing tasks given by the FCIE. The primary outcomes were reduction of at least one level in the burden, and or in the risk of violence against the elderly. The secondary outcomes were a decrease in depressive symptoms and/or an increase in social support. Multiple log binomial regression models were used to assess the relationship between the predictors and the response variables.ResultsIn the FCIE group, most individuals providing care were women who spent over 16 hours each day in the task of caring for the impaired elderly, with most falling between the ages of 41 and 60. Over half of them were children of the elderly participants. In the intervention group, there was a significant decrease in the likelihood of violence against the elderly, with a 10-fold reduction. However, other endpoints did not present significant changes.ConclusionThe involvement of an ICC in the care of impaired elderly can contribute to reducing domestic violence by FCIE.

Abstract Background In several countries, prevalence studies demonstrate that chronic use of BZD in the elderly population is very high. This scenario has reached pandemic proportions for decades and is an important public health problem. Objectives To examine the independent association between chronic benzodiazepine use in depression, anxiety and bipolar disorder, as well as other clinical and sociodemographic factors. Methods This cross-sectional study was developed from a population-based survey and conducted from March, 2011 to December, 2012 using a random sample of 550 elderly people who were enrolled in the Family Health Strategy in Porto Alegre, Brazil. Data was collected from identifying epidemiological and health data (sociodemographic, self-perception health, self-reported diseases, smoking, alcohol and pharmacotherapeutic evaluation) and from the diagnoses of mood and anxiety disorders. Results Elderly patients diagnosed with depression, anxiety, concomitant depression/anxiety and bipolar disorders, and those who were using antidepressants have a higher risk of benzodiazepine use. Individuals who self-reported drinking alcohol had a lower risk of benzodiazepine use. Discussion Benzodiazepines are often used by the elderly for long periods, which has a direct impact on the treatment of mood and anxiety disorders and on vulnerable groups such as the elderly, who may be unnecessarily taking these drugs.

This dataset presents a comprehensive overview of Alzheimer’s disease. Alzheimer’s is the most common type of dementia and is a progressive disease affecting nearly 6 million people. Alzheimer’s disease involves parts of the brain that control thought, memory, and language. It can seriously affect a person’s ability to carry out daily activities. It begins with mild memory loss and can lead to loss of ability to carry a conversation and respond to the environment.

Here are several potential analyses that can be performed:

Prevalence Analysis: Explore the overall prevalence of Alzheimer's disease across different years and locations.

Demographic Trends: Examine the distribution of Alzheimer's cases by age, gender, and ethnicity. Analyze how the prevalence varies across different demographic groups.

Geospatial Mapping: Create maps to visualize the geographic distribution of Alzheimer's cases. Identify regions with higher or lower prevalence rates.

Temporal Trends: Investigate how the prevalence of Alzheimer's has changed over the years. Identify any significant temporal patterns or trends. Confidence Interval Analysis:

Age-specific Analysis: Analyze how Alzheimer's prevalence varies across different age groups. Identify any age-specific trends or patterns.

Gender and Ethnicity Insights: Investigate how Alzheimer's prevalence differs among genders and ethnicities.

Ethnicity-specific Analysis: Explore variations in Alzheimer's prevalence within different ethnic groups.

About this dataset

Context

The dataset tabulates the data for the Brazos Country, TX population pyramid, which represents the Brazos Country population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey 5-Year estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Brazos Country, TX, is 11.2.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Brazos Country, TX, is 25.8.
• Total dependency ratio for Brazos Country, TX is 37.0.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Brazos Country, TX is 3.9.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Brazos Country population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Brazos Country for the selected age group is shown in the following column.
• Population (Female): The female population in the Brazos Country for the selected age group is shown in the following column.
• Total Population: The total population of the Brazos Country for the selected age group is shown in the following column.

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/.

Recommended for further research

This dataset is a part of the main dataset for Brazos Country Population by Age. You can refer the same here

Top Covid19 Countries and Health Demographic Trend

Context

This is a time-series trend data collection with a series of json files primarily focused on countries most impacted by Covid-19. The tree formatted time series data should be able to enable various different kinds of analysis to answer questions about what may make a country's health system vulnerable to Covid-19 and what health demographics may help reducing the impact.

Demographic metrics

Healthcare GDP Expenditure 
Healthcare Employment
Hospital Bed Capacity
Air Pollution and Death Rate
Chronic illnesses and DALYs(Disability-Adjusted Life Years)
Body Weight 
Elderly(Aged 65+) Population
CT Scanner Density
Tobacco Consumption(Smoker population %)

More metrics can be added upon request.

Data Normalization

The raw CSV includes many different types of measurements such as number, percentage and per 1 million population. This data normalizes the time_series data by selecting data that is more about density, and number per capita data rather than absolute numbers. This could help doing comparison among nations since they may vary significantly on population.

Content

Most of the JSON files contain time_series data. For people who want to use the data as country metadata, the most-recent data attribute is collected in top_countries_latest_fact_summary.json

The JSON data focuses on the above mentioned demographic areas in a simple tree schema { Country_name: { metric_name:[ List of {year, value, unit} ] } }

Data source & License

The data is sourced from OECD(https://stats.oecd.org/) and GDHX(http://ghdx.healthdata.org/). The json files with prefix "gbd_" are from GDHX

Following citation is needed for using GDHX data:

GBD Results tool: Use the following to cite data included in this download: Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2017 (GBD 2017) Results. Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2018. Available from http://ghdx.healthdata.org/gbd-results-tool.

Inspiration

• Where does US rank in term of Healthcare/Preventive spending in GDP, hospital bed/ICU bed/physician density and long-term illness? In which areas can US do more to prevent future Cov-19 crisis?

• Is there correlation in a nation's medical preparedness and the rate of growth in confirmation, death rate and recovery rate? From GBD data graphs, it seems that Dalys(DALYs (Disability-Adjusted Life Years), rate per 100k) can divided nations into different camps.

• How does death rate from Cov-19 correlate with Death rate related to Cardiovascular diseases and Chronic respiratory diseases?

• What trends can we discover in various nation's health demographics over time? Are some areas getting better while others getting worse?

• With time span from 2010 to 2018, this dataset can also correlate with data related to recent outbreaks such as seasonal flus, Avian influenza, etc.

Example Notebook

With some quick analysis, it shows that the US actually ranks higher than China for DALYs(Disability-adjusted life years) caused by Chronic Respiratory conditions, which could be due to seasonal allergies. It seems counter-intuitive that this may suggest that countries with cleaner air may have higher burden of people with Chronic Respiratory conditions that may have made them more vulnerable in the Covid-19 crisis.

Example Kernel: https://www.kaggle.com/timxia/bar-chart-comparison-of-countries https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4802460%2F2fce05195108856422b437316f34e837%2FTobacco.png?generation=1585936274243838&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4802460%2Fe8db14764a47a8bce48fa79bdfdfb0f1%2FChronicDisease.png?generation=1585936274372639&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4802460%2Fc534d40af042b9a503325f41c49b83cb%2FAirPollution.png?generation=1585936274337626&alt=media" alt="">

About this dataset

Context

The dataset tabulates the data for the Country Life Acres, MO population pyramid, which represents the Country Life Acres population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Country Life Acres, MO, is 26.2.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Country Life Acres, MO, is 64.3.
• Total dependency ratio for Country Life Acres, MO is 90.5.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Country Life Acres, MO is 1.6.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Country Life Acres population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Country Life Acres for the selected age group is shown in the following column.
• Population (Female): The female population in the Country Life Acres for the selected age group is shown in the following column.
• Total Population: The total population of the Country Life Acres for the selected age group is shown in the following column.

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/.

Recommended for further research

This dataset is a part of the main dataset for Country Life Acres Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the Town And Country, MO population pyramid, which represents the Town And Country population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey 5-Year estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Town And Country, MO, is 27.8.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Town And Country, MO, is 51.7.
• Total dependency ratio for Town And Country, MO is 79.5.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Town And Country, MO is 1.9.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Town And Country population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Town And Country for the selected age group is shown in the following column.
• Population (Female): The female population in the Town And Country for the selected age group is shown in the following column.
• Total Population: The total population of the Town And Country for the selected age group is shown in the following column.

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/.

Recommended for further research

This dataset is a part of the main dataset for Town And Country Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the Hill Country Village, TX population pyramid, which represents the Hill Country Village population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Hill Country Village, TX, is 28.0.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Hill Country Village, TX, is 43.4.
• Total dependency ratio for Hill Country Village, TX is 71.4.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Hill Country Village, TX is 2.3.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Hill Country Village population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Hill Country Village for the selected age group is shown in the following column.
• Population (Female): The female population in the Hill Country Village for the selected age group is shown in the following column.
• Total Population: The total population of the Hill Country Village for the selected age group is shown in the following column.

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/.

Recommended for further research

This dataset is a part of the main dataset for Hill Country Village Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the Country Club, MO population pyramid, which represents the Country Club population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Country Club, MO, is 38.2.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Country Club, MO, is 23.6.
• Total dependency ratio for Country Club, MO is 61.8.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Country Club, MO is 4.2.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Country Club population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Country Club for the selected age group is shown in the following column.
• Population (Female): The female population in the Country Club for the selected age group is shown in the following column.
• Total Population: The total population of the Country Club for the selected age group is shown in the following column.

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/.

Recommended for further research

This dataset is a part of the main dataset for Country Club Population by Age. You can refer the same here