This page is considered archival. please refer to the new data landing page at Medi-Cal Managed Care Capitation Rates by Managed Care Plan Models. The datasets contain reimbursement rates paid to participating Program of All-Inclusive Care for the Elderly (PACE) organizations for calendar years 2015-2020. To be eligible for the PACE program, a person must be 55 years of age or older and reside in one of the following PACE service areas: Alameda, Contra Costa, Fresno, Humboldt, Los Angeles, Orange, Riverside, Sacramento, San Bernardino, San Diego, San Francisco, San Joaquin, Santa Clara, Stanislaus.

The datasets contain reimbursement rates paid to participating County Organized Health Systems, Geographic Managed Care, Regional, Senior Care Act (SCAN), Single Plan, and Two-Plan model counties as well as Program of All-Inclusive Care for the Elderly (PACE) organizations for calendar years 2021-2024.

Contains data on Community Services Statistics for December 2024 and a provisional data file for January 2025 (note this is intended as an early view until providers submit a refresh of their data).

This page is considered archival. please refer to the new data landing page at Medi-Cal Managed Care Capitation Rates by Managed Care Plan Models.

Medi-Cal Managed Care Capitation Rates for Capitated Rates for Senior Care Action Network (SCAN) – for Calendar Year 2016-2020. Senior Care Action Network (SCAN) Health Plan is a Medicare Advantage Special Needs Plan that contracts with the Department of Health Care Services for dual eligible Medicare/Medi-Cal population subset residing in Los Angeles, San Bernardino, and Riverside counties.

The National Social Life, Health and Aging Project (NSHAP) is the first population-based study of health and social factors on a national scale, aiming to understand the well-being of older, community-dwelling Americans by examining the interactions among physical health, illness, medication use, cognitive function, emotional health, sensory function, health behaviors, and social connectedness. It is designed to provide health providers, policy makers, and individuals with useful information and insights into these factors, particularly on social and intimate relationships. The National Opinion Research Center (NORC), along with Principal Investigators at the University of Chicago, conducted more than 3,000 interviews during 2005 and 2006 with a nationally representative sample of adults aged 57 to 85. Face-to-face interviews and biomeasure collection took place in respondents' homes. Round 2 interviews were conducted from August 2010 through May 2011, during which Round 1 Respondents were re-interviewed. An attempt was also made to interview individuals who were sampled in Round 1 but declined to participate. In addition, spouses or co-resident partners were also interviewed using the same instruments as the main respondents. This process resulted in 3,377 total respondents. The following files constitute Round 2: Core Data, Disposition of Round 1 Partner Data, Social Networks Data, Social Networks Update Data, Partner History Data, Partner History Update Data, Medications Data, Proxy Data, and Sleep Statistics Data. Included in the Core files (Datasets 1 and 2) are demographic characteristics, such as gender, age, education, race, and ethnicity. Other topics covered respondents' social networks, social and cultural activity, physical and mental health including cognition, well-being, illness, history of sexual and intimate partnerships, and patient-physician communication, in addition to bereavement items. Data were also collected from respondents on the following items and modules: social activity items, physical contact module, sexual interest module, get up and go assessment of physical function, and a panel of biomeasures, including weight, waist circumference, height, blood pressure, smell, saliva collection, and taste. The Disposition of Round 1 Partner files (Datasets 3 and 4) detail information derived from Section 6A items regarding the partner from Round 1 within the questionnaire. This provides a complete history for respondent partners across both rounds. The Social Networks files (Datasets 5 and 6) contain one record for each person identified on the network roster. Respondents who refused to participate in the roster or who did not identify anyone are not represented in this file. The Social Networks Update files (Datasets 7 and 8) detail respondents' current relationship status with each person identified on the network roster. The Partner History file (Dataset 9) contains one record for each marriage, cohabitation, or romantic relationship identified in Section 6A of the questionnaire, including a current partner in Round 2 but excluding the partner from Round 1. The Partner History Update file (Dataset 10) details respondents' current sexual partner information, as well as marital and cohabiting status. The Medications Data file (Dataset 11) contains records for items listed in the medications log. The Proxy Data files (Datasets 12 and 13) contain information from proxy interviews administered for Round 1 Respondents who were either deceased or whose health was too poor to participate in Round 2. The Sleep Statistics Data files (Dataset 14 and 15) provide information on actigraphy sleep variables. NACDA also maintains a Colectica portal with the NSHAP Core data across rounds 1-3, which allows users to interact with variables across rounds and create customized subsets. Registration is required.

Information on residential care services by type of homes, number of homes, number of places

For unit level, the value is given as "Yes, for both" = 2, "Yes, for simple" & "Yes, for deepened" = 1 and "No" = 0 while at municipality level it is only given as a proportion (%) of units where the answer is "Yes, for both". Number of units that have achieved the indicator divided by all units that have answered the survey question. Based on the questions: "Have you, on 1 March, written and at management level decided on routines for how simple and in-depth drug reviews", "Does the routine describe how collaboration at drug reviews should take place together with the individual, the doctor, the nurse and the home care staff at the unit? should be carried out in collaboration with the region?" and "Have you at any time during the last year (12 months) followed up the routine?". A simple drug review shall be offered to people 75 years of age and older with at least five drugs in accordance with the National Board of Health and Welfare's regulations and general advice HSLF-FS 2017:37. An in-depth drug review shall be offered to people who, after a simple drug review, have persistent drug-related problems or where there are suspicions of the presence of such problems.

This page is considered archival. please refer to the new data landing page at Medi-Cal Managed Care Capitation Rates by Managed Care Plan Models.

The datasets contain reimbursement rates paid to participating Program of All-Inclusive Care for the Elderly (PACE) organizations for calendar years 2015-2020. To be eligible for the PACE program, a person must be 55 years of age or older and reside in one of the following PACE service areas: Alameda, Contra Costa, Fresno, Humboldt, Los Angeles, Orange, Riverside, Sacramento, San Bernardino, San Diego, San Francisco, San Joaquin, Santa Clara, Stanislaus.

About this dataset

Context

The dataset tabulates the Medical Lake population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Medical Lake. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 3,317 (67.32% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Medical Lake population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Medical Lake is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Medical Lake 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 Medical Lake Population by Age. You can refer the same here

Behavioral Risk Factors: HRQOL

Analyzing Health-Related Quality of Life in the United States

By Health [source]

About this dataset

The Behavioral Risk Factor Surveillance System (BRFSS) is an annual state-based, telephone survey of adults in the United States. It collects a variety of health-related data, including Health Related Quality of Life (HRQOL). This dataset contains results from the HRQOL survey within a range of locations across the US for the year indicated.

This dataset includes 14 columns which summarize and quantify different aspects concerning HRQOL topics. The year, location abbreviation, description and geo-location provide background contextual information which help define each row. The question column indicates the response provided to by respondents, while category classifies it into overarching groupings. Additionally there are columns covering sample size and data value attributes such as standard error, unit and type all evidence chipping away at informative insights into how Americans’ quality of life is changing over time — all cleverly presented in this one concise dataset!

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For more datasets, click here.

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How to use the dataset

In order to analyze this dataset, it is important have a good understanding of the columns included in it. The columns provide various pieces of information about the data such as year collected, location abbreviation, location name and type of data value collected. Furthermore, understanding what each column means is essential for proper interpretation and analysis; for example knowing that ‘Data_Value %’ indicates what percentage responded a certain way or that ‘Sample_Size’ shows how many people were surveyed can help you make better decisions when looking at patterns within the data set.

Once you understand the general structure behind this dataset one should also familiarize themselves with some basic statistical analysis tools such as mean/median/mode calculations comparative/correlative analysis so they can really gain insights into how health-related quality of life affects different populations across countries or regions.. To get even more meaningful results you might also want to consider adding other variables or datasets into your report that correlate with HRQOL - like poverty rate or average income level - so you can make clearer conclusions about potential contributing factors towards certain insights you uncover while using this dataset alone.

Research Ideas

• Identifying trends between geolocation and health-related quality of life indicators to better understand how environmental factors may impact specific communities.
• Visualizing the correlations between health-related quality of life variables across different locations over time to gain insights on potential driving developmental or environmental issues.
• Monitoring the effects of public health initiatives dealing with qualitative health data such as those conducted by CDC, Department of Health and Human Services, and other organizations by tracking changes in different aspects of HRQOL measures over time across multiple locations

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: rows.csv | Column name | Description | |:-------------------------------|:-------------------------------------------------------------------------------------------------------------| | Year | Year when the data was collected. (Integer) | | LocationAbbr | Abbreviations of various locations where data was recorded. (String) | | LocationDesc | Full names of states whose records are included in this survey. (String) | | Category | Particular topic chosen for research such as “Healthy People 2010 Topics” or “Older Adults Issues”. (String) | | Question | Each question corresponds to metrics tracked within each topic. (String) | | DataSource | Source from which survey responses were collected. (String) | | Data_Value_Unit | Units taken for recording survey types...

Ebitda-Per-Share Time Series for SMS Co Ltd. SMS Co., Ltd. provides information infrastructure for the nursing care, medical care, career, healthcare, and elderly care field business areas in Japan and internationally. It operates an online community site for professionals or families engaged in nursing care; a certification course information portal; and a housing information portal for seniors, as well as a home-delivered meal search site. The company also operates various sites that provide online community, mail order, and magazines/book services for nurses; services for pharmacists and pharmacies; hospital management and administrator services; and consulting services for national and local governments. In addition, it provides recruiting agent services, as well as recruiting ads services for professionals and operators in nursing and medical care fields. Further, the company offers medical-related services. SMS Co., Ltd. was incorporated in 2003 and is based in Tokyo, Japan.

This table shows how health(care) expenditure according to the international definition and health and social care expenditure used by Statistics Netherlands are related. Financing has been chosen as the starting point. Healthcare is delineated according to the international definition of the System of Health Accounts. All healthcare activities count, regardless of whether they take place inside or outside the healthcare sector. Only resident health expenditure counts. Health and social care expenditure covers all care activities, including welfare and childcare, regardless of whether these activities take place as a main or secondary activity. Care can be for residents or non-residents. Care provided in the Netherlands for non-residents (such as tourists) is included in the export of services.

In brief: Health and social care expenditure -/- health-related expenditure, such as domestic care within care for the elderly -/- expenditure on other care and welfare, such as childcare -/- expenditure on education, research and development, other services -/- export, foreign-paid activities = Total current expenditure on health

Data available from: 2021

Status of the figures: The figures for 2024 are provisional. The figures for 2022 and 2023 are revised provisional. The figures for 2021 are final.

Changes as of 8 October 2025: The provisional figures for 2024 have been added. In addition, the figures for 2021–2023 have been partly adjusted, based on the most up-to-date data from various sources, including data from the National Health Care Institute and the CBS business and economic statistics on health care.

When will new figures be published? In spring 2026, the figures for 2022–2024 will be revised. At the end of 2026, provisional figures for 2025 will be published.

The dataset contains hospitalization counts and rates, statewide and by county, for 10 ambulatory care sensitive conditions plus 4 composite measures. Hospitalizations due to these medical conditions are potentially preventable through access to high-quality outpatient care. The conditions include: diabetes short-term complications; diabetes long-term complications; chronic obstructive pulmonary disease (COPD) or asthma in older adults (age 40 and over); hypertension; heart failure; community-acquired pneumonia; urinary tract infection; uncontrolled diabetes; asthma in younger adults (age 18-39); and lower-extremity amputation among patients with diabetes. The composite measures include overall, acute conditions, chronic conditions, and diabetes (new, 2016). The data provides a good starting point for assessing quality of health services in the community. The data does not measure hospital quality. Note: In 2015, HCAI (formerly OSHPD) only released the first three quarters of data due to a change in the reporting of diagnoses from ICD-9-CM to ICD-10-CM codes, effective October 1, 2015. Due to the significant differences resulting from the code change, the ICD-9-CM data is distinguished from the ICD-10-CM data in the data file beginning in 2016.

Abstract

The multi-country Study on Global Ageing and Adult Health (SAGE) is run by the World Health Organization's Multi-Country Studies unit in the Health Systems and Innovation Cluster. SAGE is part of the unit's Longitudinal Study Programme which is compiling longitudinal data on the health and well-being of adult populations, and the ageing process, through primary data collection and secondary data analysis. SAGE baseline data (Wave 0, 2002/3) was collected as part of WHO's World Health Survey http://www.who.int/healthinfo/survey/en/index.html (WHS). SAGE Wave 2 (2014/15) provides a comprehensive data set on the health and well-being of adults in six low and middle-income countries: China, Ghana, India, Mexico, Russian Federation and South Africa.

Objectives: To obtain reliable, valid and comparable health, health-related and well-being data over a range of key domains for adult and older adult populations in nationally representative samples To examine patterns and dynamics of age-related changes in health and well-being using longitudinal follow-up of a cohort as they age, and to investigate socio-economic consequences of these health changes To supplement and cross-validate self-reported measures of health and the anchoring vignette approach to improving comparability of self-reported measures, through measured performance tests for selected health domains To collect health examination and biomarker data that improves reliability of morbidity and risk factor data and to objectively monitor the effect of interventions

Additional Objectives: To generate large cohorts of older adult populations and comparison cohorts of younger populations for following-up intermediate outcomes, monitoring trends, examining transitions and life events, and addressing relationships between determinants and health, well-being and health-related outcomes To develop a mechanism to link survey data to demographic surveillance site data To build linkages with other national and multi-country ageing studies To improve the methodologies to enhance the reliability and validity of health outcomes and determinants data To provide a public-access information base to engage all stakeholders, including national policy makers and health systems planners, in planning and decision-making processes about the health and well-being of older adults

Methods: SAGE's first full round of data collection included both follow-up and new respondents in most participating countries. The goal of the sampling design was to obtain a nationally representative cohort of persons aged 50 years and older, with a smaller cohort of persons aged 18 to 49 for comparison purposes. In the older households, all persons aged 50+ years (for example, spouses and siblings) were invited to participate. Proxy respondents were identified for respondents who were unable to respond for themselves. Standardized SAGE survey instruments were used in all countries consisting of five main parts: 1) household questionnaire; 2) individual questionnaire; 3) proxy questionnaire; 4) verbal autopsy questionnaire; and, 5) appendices including showcards. A VAQ was completed for deaths in the household over the last 24 months. The procedures for including country-specific adaptations to the standardized questionnaire and translations into local languages from English follow those developed by and used for the World Health Survey.

Content: - Household questionnaire 0000 Coversheet 0100 Sampling Information 0200 Geocoding and GPS Information 0300 Recontact Information 0350 Contact Record 0400 Household Roster 0450 Kish Tables and Household Consent 0500 Housing 0600 Household and Family Support Networks and Transfers 0700 Assets and Household Income 0800 Household Expenditures 0900 Interviewer Observations

• Verbal Autopsy questionnaire Section 1: Information on the Deceased and Date/Place of Death Section 1A7: Vital Registration and Certification Section 2: Information on the Respondent Section 3A: Medical History Associated with Final Illness Section 3B: General Signs and Symptoms Associated with Final Illness Section 3E: History of Injuries/Accidents Section 3G: Health Service Utilization Section 4: Background Section 5A: Interviewer Observations

• Individual questionnaire 1000 Socio-Demographic Characteristics 1500 Work History and Benefits 2000 Health State Descriptions 2500 Anthropometrics, Performance Tests and Biomarkers 3000 Risk Factors and Preventive Health Behaviours 4000 Chronic Conditions and Health Services Coverage 5000 Health Care Utilisation 6000 Social Networks 7000 Subjective Well-Being and Quality of Life (WHOQoL-8 and Day Reconstruction Method) 8000 Impact of Caregiving 9000 Interviewer Assessment

• Proxy Questionnaire Section1 Respondent Characteristics and IQ CODE Section2 Health State Descriptions Section4 Chronic Conditions and Health Services Coverage Section5 Health Care Utilisation

Geographic coverage

National coverage

Analysis unit

households and individuals

Universe

The household section of the survey covered all households in 31 of the 32 federal states in Mexico. Colima was excluded. Institutionalised populations are excluded. The individual section covered all persons aged 18 years and older residing within individual households. As the focus of SAGE is older adults, a much larger sample of respondents aged 50 years and older was selected with a smaller comparative sample of respondents aged 18-49 years.

Kind of data

Sample survey data [ssd]

Sampling procedure

In Mexico strata were defined by locality (metropolitan, urban, rural). All 211 PSUs selected for wave 1 were included in the wave 2 sample. A sub-sample of 211 PSUs was selected from the 797 WHS PSUs for the wave 1 sample. The Basic Geo-Statistical Areas (AGEB) defined by the National Institute of Statistics (INEGI) constitutes a PSU. PSUs were selected probability proportional to three factors: a) (WHS/SAGE Wave 0 50plus): number of WHS/SAGE Wave 0 50-plus interviewed at the PSU, b) (State Population): population of the state to which the PSU belongs, c) (WHS/SAGE Wave 0 PSU at county): number of PSUs selected from the county to which the PSU belongs for the WHS/SAGE Wave 0 The first and third factors were included to reduce geographic dispersion. Factor two affords states with larger populations a greater chance of selection.

All WHS/SAGE Wave 0 individuals aged 50 years or older in the selected rural or urban PSUs and a random sample 90% of individuals aged 50 years or older in metropolitan PSUs who had been interviewed for the WHS/SAGE Wave 0 were included in the SAGE Wave 1 ''primary'' sample. The remaining 10% of WHS/SAGE Wave 0 individuals aged 50 years or older in metropolitan areas were then allocated as a ''replacement'' sample for individuals who could not be contacted or did not consent to participate in SAGE Wave 1. A systematic sample of 1000 WHS/SAGE Wave 0 individuals aged 18-49 across all selected PSUs was selected as the ''primary'' sample and 500 as a ''replacement'' sample.

This selection process resulted in a sample which had an over-representation of individuals from metropolitan strata; therefore, it was decided to increase the number of individuals aged 50 years or older from rural and urban strata. This was achieved by including individuals who had not been part of WHS/SAGE Wave 0 (which became a ''supplementary'' sample), although the household in which they lived included an individual from WHS/SAGE Wave 0. All individuals aged 50 or over were included from rural and urban ''18-49 households'' (that is, where an individual aged 18-49 was included in WHS/SAGE Wave 0) as part of the ''primary supplementary'' sample. A systematic random sample of individuals aged 50 years or older was then obtained from urban and rural households where an individual had already been selected as part of the 50 years and older or 18-49 samples. These individuals then formed part of the ''primary supplementary'' sample and the remainder (that is, those not systematically selected) were allocated to the ''replacement supplementary'' sample. Thus, all individuals aged 50 years or older who lived in households in urban and rural PSUs obtained for SAGE Wave 1 were selected as either a primary or replacement participant. A final ''replacement'' sample for the 50 and over age group was obtained from a systematic sample of all individuals aged 50 or over from households which included the individuals already selected for either the 50 and over or 18-49. This sampling strategy also provided participants who had not been included in WHS/SAGE Wave 0, but lived in a household where an individual had been part of WHS/SAGE Wave 0 (that is, the ''supplementary'' sample), in addition to follow-up of individuals who had been included in the WHS/SAGE Wave 0 sample.

Strata: Locality = 3 PSU: AGEBs = 211 SSU: Households = 6549 surveyed TSU: Individual = 6342 surveyed

Mode of data collection

Face-to-face [f2f], CAPI

Research instrument

The questionnaires were based on the SAGE Wave 1 Questionnaires with some modification and new additions, except for verbal autopsy. SAGE Wave 2 used the 2012 version of the WHO Verbal Autopsy Questionnare. SAGE Wave 1 used an adapted version of the Sample Vital Registration iwth Verbal Autopsy (SAVVY) questionnaire. A Household questionnaire was administered to all households eligible for the study. A Verbal Autopsy questionnaire was administered to 50 plus households only. In follow-up 50 plus household if the death occured since the last wave of the study and in a new 50 plus household if the death occurred in the

About this dataset

Context

The dataset tabulates the population of Medical Lake by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Medical Lake. The dataset can be utilized to understand the population distribution of Medical Lake by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Medical Lake. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Medical Lake.

Key observations

Largest age group (population): Male # 30-34 years (355) | Female # 35-39 years (308). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Medical Lake population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Medical Lake is shown in the following column.
• Population (Female): The female population in the Medical Lake is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Medical Lake for each age group.

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 Medical Lake Population by Gender. You can refer the same here

ABSTRACT OBJECTIVE To evaluate the adequacy of the Clinical-Functional Vulnerability Index-20, a rapid triage instrument to test vulnerability in Brazilian older adults, for the use in primary health care. METHODS The study included convenience sample of 397 patients aged older than or equal to 60 years attended at Centro de Referência para o Idoso (Reference Center for Older Adults) and of 52 older adults the same age attended at the community. The results of the questionnaire, consisting of 20 questions, were compared with those of the Comprehensive Geriatric Assessment, considered a reference for identifying frail older adults. Spearman’s correlation was evaluated in the Clinical-Functional Vulnerability Index-20 with the Comprehensive Geriatric Assessment; the validity was verified by the area under the ROC curve; reliability was estimated by the percentage of agreement among evaluators and by the kappa coefficient, both with quadratic weighted. The cut-off point was obtained based on the higher accuracy criterion. Cronbach’s alpha, a measure of internal consistency, was estimated. RESULTS The Spearman’s correlation coefficient was high and positive for both groups (0.792 for older adults attended at the Reference Center and 0.305 for older adults from the community [p < 0.001]). The area under the ROC curve for older adults attended at the Reference Center was substantial (0.903). The cut-off point obtained was six, and older adults with scores in Clinical-Functional Vulnerability Index-20 above that value had strong possibility of being frail. For older adults from the community, the quadratic weighted agreement among evaluators was 99.5%, and the global quadratic weighted kappa coefficient was 0.94. Cronbach’s alpha was high for older adults attended at the Reference Center (0.861) and those attended at the community (0.740). CONCLUSIONS The Clinical-Functional Vulnerability Index-20 questionnaire, in the sample examined, turned out to be positively correlated with the Comprehensive Geriatric Assessment, in addition to the results indicating a high degree of validity and reliability. Thus, the Clinical-Functional Vulnerability Index-20 proves to be viable as a triage instrument in the primary health care that identifies frail older adults (older adults at risk of weakening and frail older adults).

San Francisco Flood Health Vulnerability

Socioeconomic, Demographic, Health, and Housing Indicators

By City of San Francisco [source]

About this dataset

This dataset provides a comprehensive composite index that captures the relative vulnerability of San Francisco communities to the health impacts of flooding and extreme storms. Predominantly sourced from local governmental health, housing, and public data sources, this index is constructed from an array of socio-economic factors, exposure indices,Health indicators and housing attributes. Used as a valuable planning tool for both health and climate adaptation initiatives throughout San Francisco, this dataset helps to identify vulnerable populations within the city such as areas with high concentrations of children or elderly individuals. Data points included in this index include: census blockgroup numbers; the percentage of population under 18 years old; percentage of population above 65; percentage non-white; poverty levels; education level; yearly precipitation estimates; diabetes prevalence rate; mental health issues reported in the area; asthma cases by geographic location;; disability rates within each block group measure as well as housing quality metrics. All these components provide a broader understanding on how best to tackle issues faced within SF arising from any form of climate change related weather event such as floods or extreme storms

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How to use the dataset

This dataset can be used to analyze the vulnerability of the population in San Francisco to the health impacts of floods and storms. This dataset includes a number of important indicators such as poverty, education, demographic, exposure and health-related information. These indicators can be useful for developing effective strategies for health and climate adaptation in an urban area.

To get started with this dataset: First, review the data dictionary provided in the attachments section of this metadata to understand each variable that you plan on using in your analysis. Second, see if there are any null or missing values in your columns by checking out ‘Null Value’ column provided in this metadata sheet and look at how they will affect your analysis - use appropriate methods to handle those values based on your goals and objectives. Thirdly begin exploring relationships between different variables using visualizations like pandas scatter_matrix() & pandas .corr() . These tools can help you identify potential strong correlations between certain variables that you may have not seen otherwise through simple inspection of the data.

Lastly if needed use modelling techniques like regression analysis or other quantitative methods like ANOVA’s etc., for further elaboration on understanding relationships between different parameters involved as per need basis

Research Ideas

• Developing targeted public health interventions focused on high-risk areas/populations as identified in the vulnerability index.
• Establishing criteria for insurance premiums and policies within high-risk areas/populations to incentivize adaption to climate change.
• Visual mapping of individual indicators in order to identify trends and correlations between flood risk and socioeconomic indicators, resource availability, and/or healthcare provision levels at a granular level

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: san-francisco-flood-health-vulnerability-1.csv | Column name | Description | |:---------------------------|:----------------------------------------------------------------------------------------| | Census Blockgroup | Unique numerical identifier for each block in the city. (Integer) | | Children | Percentage of population under 18 years of age. (Float) | | Children_wNULLvalues | Percentage of population under 18 years of age with null values. (Float) | | Elderly | Percentage of population over 65 years of age. (Float) | | Elderly_wNULLvalues | Percentage of population over 65 years of age with null values. (Float) | | NonWhite | Percentage of non-white population. (Float) ...

Dataset Description

This dataset serves as a comprehensive repository of global development metrics, consolidating data from multiple international organizations into a single, unified structure. It provides a granular view of the state of health, economy, and nutrition across 193 countries over a 30-year period (1990–2019).

The data is organized by Country, Year, and Gender (Male, Female, and Both Sexes), making it a valuable resource for longitudinal studies, demographic analysis, and socio-economic research. It combines high-level economic indicators (like GDP) with granular health metrics (specific mortality rates) and detailed nutritional breakdowns (diet composition by food group).

Content Overview

The dataset covers a wide spectrum of categories:

• Demographics & Economy: Population stats, GNI, GDP, and poverty rates.
• Mortality & Life Expectancy: Survival rates at various ages, maternal mortality, and life expectancy.
• Public Health: Incidence of infectious diseases (Malaria, Tuberculosis, Hepatitis B) and prevalence of health risks (Tobacco, road traffic accidents).
• Environmental Health: Mortality attributed to air pollution, sanitation access, and clean fuel availability.
• Nutrition: Detailed caloric and quantity breakdown of food consumption (fruits, vegetables, cereals, meats, etc.).
• Healthcare Infrastructure: Coverage of essential health services and density of medical professionals.

Sources

The data was extracted and unified via an ETL process from the following organizations:

• World Bank: World Development Indicators
• WHO: Global Health Observatory Data
• UNICEF: Data Warehouse
• Our World In Data: Statistics and Research
• FAOSTAT: Food and Agriculture Data
• Kaggle: WHO World Health Statistics 2020 Complete

Data Dictionary

Index Columns

• Country: Name of the country.
• Year: The calendar year of the recorded data.
• Gender: The gender category for the data (Female, Male, or Both sexes).

Demographics & Health Metrics

• Life Expectancy: The average number of years a newborn is expected to live.
• Infant Mortality Rate: Number of infants dying before reaching one year of age, per 1,000 live births.
  • Includes Low/High Confidence Interval (CI) columns.
• Under 5 Mortality Rate: Probability of a child dying before reaching age 5, per 1,000 live births.
  • Includes Low/High CI columns.
• Neonatal Mortality Rate: Number of deaths during the first 28 days of life per 1,000 live births.
  • Includes Low/High CI columns.
• Maternal Mortality Ratio: Number of maternal deaths due to childbirth per 100,000 live births.
  • Includes Low/High CI columns.
• Birth Rate: Number of births per 1,000 inhabitants.
• Death Rate: Number of deaths per 1,000 inhabitants.
• Adolescent Birth Rate: Number of births by women aged 15 to 19 per 1,000 women in that age range.
• % Population Aged 0-14 / 15-64 / 65+: Percentage of the total population falling into these specific age brackets.
• % Population Aged 65-69 / 70-74 / 75-79 / 80+: Granular breakdown of the elderly population percentages.
• Total Population: Total number of inhabitants.

Causes of Death & Disease

• % Death Cardiovascular: Probability of dying from cardiovascular diseases, cancer, diabetes, or chronic respiratory diseases between ages 30 and 70.
  • Includes Low/High CI columns.
• Incidence of Malaria: Number of malaria cases per 1,000 inhabitants at risk per year.
• Incidence of Tuberculosis: Estimated cases of tuberculosis per 100,000 inhabitants.
  • Includes Low/High CI columns.
• Hepatitis B Surface Antigen: Prevalence of hepatitis B surface antigen.
  • Includes Low/High CI columns.
• Road Traffic Deaths: Number of deaths due to traffic accidents per 100,000 people.
• Poisoning Mortality Rate: Deaths attributed to unintentional poisoning per 100,000 people.
• Conflict and Terrorism Deaths: Number of deaths due to armed conflicts and terrorism.
• Battle Related Deaths: Number of deaths related to battles in an armed conflict.
• % Injury Deaths: Percentage of deaths caused by injuries.
• Suicides Rate: Number of deliberate deaths per 100,000 inhabitants.
• Homicide Rate: Number of homicides per 100,000 inhabitants.

Air Pollution Mortality

• Air Pollution Death Rate Total: Probability of dying fr...

IntroductionIn 2015, the WHO introduced intrinsic capacity (IC) as a health indicator with five domains to promote healthy aging. Multicomponent exercise programs are recommended to enhance IC, but research in Brazil on their comprehensive impact is limited. This study aimed to evaluate the effects of such a program on IC, functional, and psychosocial aspects in older adults.MethodsThis pre- and post-study assessed older adults in Brazil enrolled in a multicomponent training program, evaluating IC as the main outcome using specific tests for each domain. Inclusion criteria were: aged 60+, completing assessments in five domains, attending the program at least twice a week, and participating in two exercise modalities per session for 90 min. Exclusion criteria included: history of stroke, Parkinson’s or Alzheimer’s, recent hand, hip, or knee surgery, or absence for more than 15 consecutive days. A total of 43 older adults were evaluated, and the score was calculated by summing the results of the five domains, yielding a total score ranging from 0 to 10 points. Subsequently, participants underwent a 12-week intervention involving multicomponent exercises and were reassessed.ResultsAfter 12 weeks of intervention, there was a significant reduction in the proportion of participants with low IC, from 7.0% to 0.0%, and an increase in those with high IC, from 4.7% to 20.0% (p = 0.018). Improvements were seen in cognitive aspects, locomotor dimension (p < 0.001), vitality (p = 0.045) and functional classification (p < 0.001), with the greatest effect in the locomotor domain (es = 1.12). Significant gains were also observed in perceived health, quality of life, and physical activity (p < 0.002; p < 0.004; p < 0.001). Body composition showed improvements, including reduced body fat percentage, increased muscle mass, and better fat classification (p < 0.001), along with reductions in waist and abdominal circumferences (p < 0.001; p = 0.001).ConclusionThe multicomponent exercise program demonstrated a positive influence on composite IC, including functional and psychosocial aspects. These findings highlight the critical role of tailored and supervised exercise interventions in enhancing both physical and psychosocial dimensions of health, contributing to healthier aging trajectories.

this graph was created in R,PowerBi and Tableau:

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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.