Dataset replaced by: http://data.europa.eu/euodp/data/dataset/tHJ7RfJO3ZAXvnwP5Jm5kw The indicator Healthy Life Years (HLY) at age 65 measures the number of years that a person at age 65 is still expected to live in a healthy condition. HLY is a health expectancy indicator which combines information on mortality and morbidity. The data required are the age-specific prevalence (proportions) of the population in healthy and unhealthy conditions and age-specific mortality information. A healthy condition is defined by the absence of limitations in functioning/disability. The indicator is calculated separately for males and females. The indicator is also called disability-free life expectancy (DFLE). Life expectancy at age 65 is defined as the mean number of years still to be lived by a person at age 65, if subjected throughout the rest of his or her life to the current mortality conditions.

About this dataset

Context

The dataset tabulates the data for the Live Oak, CA population pyramid, which represents the Live Oak 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 Live Oak, CA, is 33.9.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Live Oak, CA, is 28.8.
• Total dependency ratio for Live Oak, CA is 62.8.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Live Oak, CA is 3.5.

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 Live Oak population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Live Oak for the selected age group is shown in the following column.
• Population (Female): The female population in the Live Oak for the selected age group is shown in the following column.
• Total Population: The total population of the Live Oak 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 Live Oak Population by Age. You can refer the same here

Estimated remaining life expectancy in number of years at age 65, the period T-4 to year T for persons living in the county. Data is available according to gender breakdown.

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

Healthy life years (HLY) at 65 is a composite indicator that measures the number of remaining years that a person aged 65 is expected to live in a healthy condition. It is calculated separately for women and men by combining mortality data from Eurostat's demographic database with data on self-perceived activity limitations from the European Statistics of Income and Living Condition survey. A healthy conditions is defined by the absence of longstanding severe or moderate limitations in usual activities because of a health problem. Longstanding refers to a period of more than 6 months.

This dataset provides values for RETIREMENT AGE MEN reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Life expectancy at birth and at age 65, by sex, on a three-year average basis.

Life expectancy is the number of years a person would be expected to live, starting from birth (for life expectancy at birth) or at age 65 (for life expectancy at age 65), on the basis of the mortality statistics for a given observation period. Life expectancy is a widely used indicator of the health of a population. Life expectancy measures quantity rather than quality of life.

A collection of data of an epidemiological study of chronic disease in the oldest old based on information collected from Kaiser Permanente facilities in Northern California (KPNC). The initial sample was drawn from the Kaiser''s active membership lists for the years 1971 and 1980. The sample was restricted to members that had a Multiphasic Health Checkup examination (MHC) within 7 years of the baseline date. The sample was stratified to attain equal numbers of observations (1,000 in each) in three sex-age cells for each cohort: 65-69, 70-79, and 80+. Each cohort was followed for 9 years through existing medical records and computerized hospitalization tapes. Mortality data was collected by matching the sampled data with state Vital Statistics data for an additional 3 years for a total follow-up time of 12 years. Part 1 of the data collections consists of Master Records, which includes information from the morbidity review, in which over 35 chronic conditions or diagnoses were abstracted from the member charts, as well as detailed diagnostic criteria for the major conditions. A prevalence review was done, which included the 4 years prior to the baseline date for these same conditions. Recurrent disease is included for the following conditions: cancers, myocardial infarction, and various forms of strokes. A detailed account of outpatient health services use, and data from the multiphasic health checkup, which was administered to each participant during the nine yearly follow-ups, are also included in the Master Records file. The labs and procedures included: chemistry, hematology, urinalysis, bacteriology, chest x-ray, GI x-ray, ultrasound, CT/MRI, mammogram, resting ECG, treadmill ECG, echocardiograms, nuclear scans, outpatient breast biopsy, cystoscopy, and cataract surgery. Inpatient utilization includes all hospitalizations, procedures done during a hospital stay, length of stay, admitting/discharge diagnosis. Part 2, Hospitalization, contains records of causes and dates of hospitalizations and discharges and nursing home admissions. There is also a section on incomplete reviews and the reasons for them. Demographic information and some lifestyle information from the multiphasic health checkup (e.g., smoking, alcohol, and Body Mass Index) are also in this file. Data Availability: These datasets have been documented extensively and are available from the ICPSR (Study No. 4219). * Dates of Study: 1971-1992 * Study Features: Longitudinal, Anthropometric Measures * Sample Size: ** 1971 cohort: 2,877 (baseline) ** 1980 cohort: 3,113 (baseline) ** 1971 & 1980: 5,990 ** Hospitalization: 14,730 Links: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/04219 * HSRR: http://wwwcf.nlm.nih.gov/hsrr_search/view_hsrr_record_table.cfm?TITLE_ID=381&PROGRAM_CAME=toc_with_source2.cfm

Lake County, Illinois Demographic Data. Explanation of field attributes: Total Population – The entire population of Lake County. White – Individuals who are of Caucasian race. This is a percent.African American – Individuals who are of African American race. This is a percent.Asian – Individuals who are of Asian race. This is a percent. Hispanic – Individuals who are of Hispanic ethnicity. This is a percent. Does not Speak English- Individuals who speak a language other than English in their household. This is a percent. Under 5 years of age – Individuals who are under 5 years of age. This is a percent. Under 18 years of age – Individuals who are under 18 years of age. This is a percent. 18-64 years of age – Individuals who are between 18 and 64 years of age. This is a percent. 65 years of age and older – Individuals who are 65 years old or older. This is a percent. Male – Individuals who are male in gender. This is a percent. Female – Individuals who are female in gender. This is a percent. High School Degree – Individuals who have obtained a high school degree. This is a percent. Associate Degree – Individuals who have obtained an associate degree. This is a percent. Bachelor’s Degree or Higher – Individuals who have obtained a bachelor’s degree or higher. This is a percent. Utilizes Food Stamps – Households receiving food stamps/ part of SNAP (Supplemental Nutrition Assistance Program). This is a percent. Median Household Income - A median household income refers to the income level earned by a given household where half of the homes in the area earn more and half earn less. This is a dollar amount. No High School – Individuals who have not obtained a high school degree. This is a percent. Poverty – Poverty refers to families and people whose income in the past 12 months is below the poverty level. This is a percent.

Data come from the follow-up of the main study “Inclusive ageing in place” (IN-AGE), regarding frail older people aged 65 years and over (males and females). The main study was a cross-sectional qualitative survey carried out in 2019 by face-to-face interviews to frail older people without cognitive impairment, and living at home, alone or with a private personal care assistant (PCA), in three Italian Regions: Lombardy (North), Marche (Centre) and Calabria (South). Both peripheral/degraded areas of urban sites and fragile rural locations were included, with regard to social and material vulnerability aspects (e.g. high presence of frail older people living alone, poor provision of services). The follow up was carried out in July-September 2020, and it was aimed to explore and compare effects of lockdown, due to the first wave of the COVID-19 pandemic (February-May 2020), on frail older people living alone at home in Brescia and Ancona, two urban cities located respectively in the Northern and Central Italy. This country was the Western epicenter of the first wave of the pandemic, that differently affected the two cities as for infections, with a more severe impact on the former one. The dataset (41 respondents, vs 48 in the main survey) regards available care arrangements, both informal (family members) and formal (public services), to support the performing of daily living activities (ADLs and IADLs), especially in the presence of functional limitations. The use of/access to health services (General Practitioner, Medical Specialist and other health services) was also explored. A semi-structured interview was administered by telephone due to social distancing imposed by the pandemic. Participants were asked to report possible worsening/improving (or no change/not affected) due to the pandemic. A simple quantitative analysis (frequency distribution/bivariate analysis) of closed responses was carried out by using Microsoft Excel software 2019. Analyses suggested how the lockdown and social distancing overall negatively impacted on frail older people living alone, to a different extent in Ancona and Brescia, with a better resilience of home care services in Brescia, and a greater support from the family in Ancona, where however major problems in accessing health services also emerged. Even though the study was exploratory only, also due to the small sample, that cannot be considered as representative of the target population, findings suggested that enhancing home care services, and supporting older people in accessing health services, could allow ageing in place, especially in emergency time. The dataset is provided in open format (xlsx) and includes the following: a “numeric” dataset regarding the unlabelled dimensions used for statistics elaboration; a codebook with both the complete variables list and variables labels we used. The dataset was produced within the framework of the IN-AGE project, funded by Fondazione Cariplo, Grant N. 2017-0941.

Title of DatasetFactors related to quality of life in community-dwelling adults in Sleman Regency, Special Region of Yogyakarta, Indonesia: Results from a cross-sectional studyThis dataset contains the data to explore the association between quality of life among older adults age 50+ years in Indonesia and the exposure variables. The variables consist of 1) quality of life in physical and mental health summary; 2) demographic variables including age, gender, marital status, occupation, education level and living arrangements; 3) socio-economic status; 4) financial management behavior; 5) nutritional status; 6) multimorbidity; 7) cognitive impairment status; 8) depression status; 9) independence in activity daily living (ADLs); and 10) independence in instrumental activity daily living (IADLs).## Description of the data and file structureThe dataset is organized by the variables as follows:a. Age (age_k2) describe the age group of participants, divided into 3 groups: 51 – 65 years; 66 – 80 years and 81+ years.b. Gender (gender_k2) describe the gender of participants, divided into men and womenc. Marital status (marital_k2) describe the marital status of the participants, divided into married and single (not married, divorced or widowed)d. Type of occupation (occupation_k5) describe the current occupation of the participants, divided into 4 groups: 1) working in government or private sector; 2) working as farmer or laborer; 3) retired or working as others (artist, entrepreneur, etc.) and 4) not workinge. Education level (education_k2) describe the level of education of the participants, consists of 3 level: 1) Low (do not have any education or only finished elementary school); 2) middle (finished junior or senior high school); and 3) high (finished higher education)f. Living arrangements (livarr_k5) describe the living arrangements of older adults, consists of living alone or living with family member(s)g. Socioeconomic status (ses_k1) describe the socio-economic status of the participants, divided into upper, middle and lowerh. Quality of life describe the quality of life of the participants in the form of physical health summary and mental health summary. We provided the data extracted from PRO CoRE scoring software, consists of:1) The raw results of the Short Form 12 (SF-12v2) questionnaire (GH01, PF02, PF04, RP02, RP03, RE02, RE03, BP02, MH03, VT02, MH04, SF02)2) The scoring results of each domain of the questionnaire (physical functioning (PF), role-physical (RP), bodily pain (BP), general health (GH), vitality (VT), social functioning (SF), role emotional (RE) and mental health (MH))3) The norm-based score, the scores which were weighted and aggregated using the regression coefficient and intercept from the 1998 General US Population Survey, of each domain of the questionnaire (PF_NBS, RP_NBS, BP_NBS, GH_NBS, VT_NBS, SF_NBS, RE_NBS and MH_NBS)4) The score of physical and mental health summary (PCS and MCS)5) The categorization of physical and mental health summary (PCS_k1 and MCS_k1), which divided into good and poori. Financial management behavior (fmbs_k3) describe the behavior of financial management by the participants, categorized into good and moderate – poorj. Nutrition status describe the status of nutrition of the participants, grouped into: good and moderate - poork. Multimorbidity status (chronic_k1) describe the status of chronic diseases experienced by the participants, grouped into 3 categories: 1) no chronic; 2) 1 chronic; and 3) 2 or more chronic. The calculation was performed from the data of chronic disease experienced by the participants in variable ppok_1 (chronic obstructive pulmonary disease/COPD); asma_k1 (asthma); cancer_k1 (cancer); pjk_1 (coronary heart disease); stroke_k1 (stroke); hiper_k1 (hypertension); and dm_k1 (diabetes mellitus)l. Cognitive impairment status (dementia_k3) describe the status of cognitive impairment among the participants, consists of: moderate – severe and normal – mildm. Depression status (depress_k2) describe participants’ condition regarding depression, categorized into: at risk – depression and not at riskn. Independence in activity daily living (ADL) describe the participants condition regarding their difficulties in performing the following activities: bathing (adl1); dressing (adl2); eating (adl3); getting up from lying down (adl4); and getting to/going to the toilet (adl5). The total score was calculated and grouped using the variable of activity daily living (adl_k3) categorized into independent (had none or mild difficulties in performing all tasks) and dependent (had at least one moderate, hard or extreme difficulties in performing any task)o. Independence in instrumental activity daily living (IADL) describe the participants condition regarding their difficulties in performing the following activities: taking care of household responsibility (iadl1); participating in community activities (iadl2); using public or private transportation (iadl3); and going where they wanted to go (iadl4). The total score was calculated and grouped using the variable of instrumental activity daily living (iadl_k3) categorized into independent (had none or mild difficulties in performing all tasks) and dependent (had at least one moderate, hard or extreme difficulties in performing any task)## Sharing/Access informationThere is no other way to access the data. Any inquiries or further access of the data can be requested through personal correspondence to the corresponding author (Fitrina M Kusumaningrum, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Indonesia, email: fitrina.mahardani.k@mail.ugm.ac.id)

This table contains mortality indicators by sex for Canada and all provinces except Prince Edward Island. These indicators are derived from three-year complete life tables. Mortality indicators derived from single-year life tables are also available (table 13-10-0837). For Prince Edward Island, Yukon, the Northwest Territories and Nunavut, mortality indicators derived from three-year abridged life tables are available (table 13-10-0140).

Spontaneous pneumothorax occurs predominantly in young males and older adults, often as a secondary condition, and can be refractory and fatal. This study aimed to investigate the mortality and prognostic factors for pneumothorax in older patients. We retrospectively cohort studied patients with pneumothorax aged ≥65 years who visited our department from October 2012 to January 2019. Data on sex, age, medical history, smoking history, underlying lung disease, treatment, and prognosis were extracted from medical records. Cox proportional hazards regression analysis was used to investigate pneumothorax mortality and prognostic factors. In total, 239 patients were included. Among them, 36 (15%) died during hospitalization. Respiratory disease was the direct cause of death in 30 patients (83.3%), and 211 (88.3%) patients had underlying lung disease. The incidence of pneumonia in our hospital was 22.6% (54 cases). On admission, the mortality rate was 33% (18/54) in patients with concomitant pneumonia; univariate analysis showed significant differences in the Charlson Comorbidity Index (CCI), activities of daily living (ADL), and concomitant pneumonia. In the Cox proportional hazards analysis of ADL (p = 0.09), CCI (p = 0.05), and concomitant pneumonia on admission (p = 0.02), concomitant pneumonia on admission was found to be an independent predictor of in-hospital mortality. This study suggests that concomitant pneumonia at admission may be a mortality risk factor for pneumothorax.

This report presents the latest figures on male and female health expectancy, at birth and at age 65, for the UK and its four constituent countries. While life expectancy (LE) provides an estimate of average expected life-span, healthy life expectancy (HLE) divides total LE into years spent in good or ‘not good’ health. Disability-free life expectancy (DFLE) divides LE into years lived with and without a chronic illness or disability. These figures are three-year averages. LE is taken from the UK national interim life tables published annually by ONS, and the measures of health and chronic illness from the General Household Survey (GHS) in Great Britain and the Continuous Household Survey (CHS) in Northern Ireland. Source agency: Office for National Statistics Designation: National Statistics Language: English Alternative title: National Health Expectancies

The Japanese Kids Speech database (Upper Grade) contains the total recordings of 232 Japanese Kids speakers (104 males and 128 females), from 9 to 13 years’ old (fourth, fifth and sixth graders in elementary school), recorded in quiet rooms using smartphones. This database may be combined with the Japanese Kids Speech database (Lower Grade) also available in the ELRA Catalogue under reference ELRA-S0411.Number of speakers, utterances and duration, age are as follows :Number of speakers 232 (104 male/128 female)Number of utterances (average):385 utterances per speakerTotal number of utterances:89,454Age: from 9 to 13 years' oldTotal hours of data: 145.41018 sentences were used. Recordings were made through smartphones and audio data stored in .wav files as sequences of 16KHz Mono, 16 bits, Linear PCM.Database:・Audio data: WAV format, 16KHz, 16bit, mono (recorded with smartphone)・Recording scripts: TSV format(tab-delimited), UTF-8 (without BOM)・Transcription data: TSV format(tab-delimited), UTF-8 (without BOM)・Size: 16.2GBNumber of speakers per age:9 years' old: 56 (21 male, 35 female)10 years' old: 71 (30 male, 41 female)11 years' old: 65 (28 male, 37 female)12 years' old: 38 (24 male, 14 female)13 years' old: 2 (1 male, 1 female)Structure of database:├─ readme.txt├─ Japanese Kids Speech Database.pdfDescription document of the database├─ Transcription.tsvTranscription├─ scripts.tsvScript│└─ voices/directory of audio data ├─ high/directory of upper grade └─(speaker_ID/)directory of speaker ID (six digits) └─(audio_file)audio file (WAV format, 16KHz, 16bit, mono)File naming conventions of audio files are as follows:Field number | Contents | Description | Remarks0 | Language ID | “JA” (fixed) | Japanese1 | Speaker ID | Six digit | 5XXXXX2 | Script ID | HXXXX | XXXX: four digits3 | Age | Two digits4 | Gender | M: male, F: femaleFiled separation character is “_”.For example, if the audio file name is “JA_500002_H0001_10_F.wav, this file has the following meaning:JA: Language ID (Japanese)500002: speaker ID H0001: script ID 10: age (ten years old)F: gender (female)

In 1995 a cross-sectional study of the Swedish population between the ages of 20 and 85 was carried out. In this study, three dimensions of gerotranscendence were approximated and operationalized in three measures: cosmic transcendence, coherence and need for solitude. The general purpose of the 2001 study was to obtain a better understanding of the gerotranscendence patterns when the unlimited age span 65+ is studied in detail. As in the previous 1995 study, a series of questions/statements were framed in accordance with dimensions of gerotranscendence. Statements measuring the cosmic transcendence included: 'I feel connected with the entire universe', 'I feel that I am part of everything alive', 'I can feel a strong presence of people who are elsewhere', 'Sometimes I feel like I live in the past and present simultaneously', and 'I feel a strong connection with earlier generations'. Coherence was measured by the respondent's attitude to following statements: 'My life feels chaotic and disrupted' and 'The life I have lived has coherence and meaning'. Statements measuring solitude included: 'I like to be myself better than being with others', 'I like meetings with new people', and 'Being at peace and philosophizing by myself is important for my well-being'. The respondents were also asked if their view on life and existence had changed compared to when they were 50 years old. Respondents were also asked to read a list of common diseases and mark the diseases they suffered from. In the same vein, the respondents were asked if they, during the past two years, had experienced something they regarded as a life crises. Furthermore the respondents were asked how often they: a) participated in activities outside the home (organizational activities, church, cinema, theatre, etc.), b) receive visitors at home (friends, neighbors, children, other relatives), c) themselves visit friends,neighbors, children or other relatives. Response alternatives were: daily, weekly, monthly, every six months, less often. Purpose: The general purpose of the 2001 study was to obtain a better understanding of the gerotranscendence patterns when the unlimited age span 65+ is studied in detail The response rate declined with age from 76 percent in the lowest age category to 53 percent in the highest. År 1995 genomfördes en tvärsnittsstudie av den svenska befolkningen i åldrarna mellan 20 och 85. I studien studerades och mättes tre dimensioner av gerotranscendens; kosmisk transcendens, sammanhang, och behovet av att vara ensam. Det övergripande syftet med studien var att få en bättre förståelse för de gerotranscendentala mönsterna när man studerar åldrarna 65 år och äldre i detalj. Precis som vid studien som gjordes 1995 ställdes frågor och påståenden i enlighet med de olika dimensionerna. Påståenden som mätte den kosmiska transcendensen var exempelvis: "Jag känner samhörighet med hela universum" "Jag känner att jag är en del av allt levande" "Jag kan känna en stark närvaro av personer som inte är med oss längre" "Ibland känns det som att jag lever i det förflutna och i nutid simultant" "Jag känner en stark koppling till tidigare generationer". Sammanhang mättes genom den svarandes attityder till följande påståenden: "Mitt liv känns kaotiskt och splittrat" "Det liv jag har levt har sammanhang och mening". Påståenden som mätte behov av ensamhet var: "Jag tycker mer om att vara ensam än att vara med andra" "Jag gillar möten med nya människor" "Att vara tillfreds och att filosofera ensam är viktigt för mitt välbefinnande". Man frågande även de svarande huruvida deras syn på livet och existens hade förändrats jämfört med när de var 50 år. De svarande tillfrågandes även att läsa från en lista med olika sjukdomar och markera vilka de led av. Samtidigt frågade man om de svarande hade upplevt något som de uppfattade som en livskris under de senaste två åren. Man frågade även hur ofta de; a) deltog i aktiviteter utanför hemmet (organiserade aktiviteter, kyrkaktviteter, bio, teater). b) fick besök hemma (vänner, grannar, barn, andra anhöriga) c) om de själva beskökte vänner, grannar, barn eller andra anhöriga. Svarsalternativen var; dagligen, veckovis, månadsvis, varje halvår, mindre ofta. Syfte: Det övergripande syftet med studien var att få en bättre förståelse för de gerotranscendentala mönsterna när man studerar åldrarna 65 år och äldre i detalj Svarsfrekvensen minskade i förhållande till ålder, 76% svar i den yngsta åldersgruppen och 53% i den äldsta. The sample was age stratified with 200 men and 200 women randomly sampled within each of the age categories 65-69, 70-74, 75-79, 80-84, 85-89, 90-94, 95+.The sample was age stratified with 200 men and 200 women randomly sampled within each of the age categories 65-69, 70-74, 75-79, 80-84, 85-89, 90-94, 95+. Urvalet var åldersstratifierat med 200 män och 200 kvinnor slumpmässigt valda ur respektive ålderskategori 65-69, 70-74, 75-79, 80-84, 85-89, 90-94, 95+.Urvalet var åldersstratifierat med 200 män och 200 kvinnor slumpmässigt valda ur respektive ålderskategori 65-69, 70-74, 75-79, 80-84, 85-89, 90-94, 95+.

Data for CDC’s COVID Data Tracker site on Rates of COVID-19 Cases and Deaths by Vaccination Status. Click 'More' for important dataset description and footnotes

Dataset and data visualization details: These data were posted on October 21, 2022, archived on November 18, 2022, and revised on February 22, 2023. These data reflect cases among persons with a positive specimen collection date through September 24, 2022, and deaths among persons with a positive specimen collection date through September 3, 2022.

Vaccination status: A person vaccinated with a primary series had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after verifiably completing the primary series of an FDA-authorized or approved COVID-19 vaccine. An unvaccinated person had SARS-CoV-2 RNA or antigen detected on a respiratory specimen and has not been verified to have received COVID-19 vaccine. Excluded were partially vaccinated people who received at least one FDA-authorized vaccine dose but did not complete a primary series ≥14 days before collection of a specimen where SARS-CoV-2 RNA or antigen was detected. Additional or booster dose: A person vaccinated with a primary series and an additional or booster dose had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after receipt of an additional or booster dose of any COVID-19 vaccine on or after August 13, 2021. For people ages 18 years and older, data are graphed starting the week including September 24, 2021, when a COVID-19 booster dose was first recommended by CDC for adults 65+ years old and people in certain populations and high risk occupational and institutional settings. For people ages 12-17 years, data are graphed starting the week of December 26, 2021, 2 weeks after the first recommendation for a booster dose for adolescents ages 16-17 years. For people ages 5-11 years, data are included starting the week of June 5, 2022, 2 weeks after the first recommendation for a booster dose for children aged 5-11 years. For people ages 50 years and older, data on second booster doses are graphed starting the week including March 29, 2022, when the recommendation was made for second boosters. Vertical lines represent dates when changes occurred in U.S. policy for COVID-19 vaccination (details provided above). Reporting is by primary series vaccine type rather than additional or booster dose vaccine type. The booster dose vaccine type may be different than the primary series vaccine type. ** Because data on the immune status of cases and associated deaths are unavailable, an additional dose in an immunocompromised person cannot be distinguished from a booster dose. This is a relevant consideration because vaccines can be less effective in this group. Deaths: A COVID-19–associated death occurred in a person with a documented COVID-19 diagnosis who died; health department staff reviewed to make a determination using vital records, public health investigation, or other data sources. Rates of COVID-19 deaths by vaccination status are reported based on when the patient was tested for COVID-19, not the date they died. Deaths usually occur up to 30 days after COVID-19 diagnosis. Participating jurisdictions: Currently, these 31 health departments that regularly link their case surveillance to immunization information system data are included in these incidence rate estimates: Alabama, Arizona, Arkansas, California, Colorado, Connecticut, District of Columbia, Florida, Georgia, Idaho, Indiana, Kansas, Kentucky, Louisiana, Massachusetts, Michigan, Minnesota, Nebraska, New Jersey, New Mexico, New York, New York City (New York), North Carolina, Philadelphia (Pennsylvania), Rhode Island, South Dakota, Tennessee, Texas, Utah, Washington, and West Virginia; 30 jurisdictions also report deaths among vaccinated and unvaccinated people. These jurisdictions represent 72% of the total U.S. population and all ten of the Health and Human Services Regions. Data on cases among people who received additional or booster doses were reported from 31 jurisdictions; 30 jurisdictions also reported data on deaths among people who received one or more additional or booster dose; 28 jurisdictions reported cases among people who received two or more additional or booster doses; and 26 jurisdictions reported deaths among people who received two or more additional or booster doses. This list will be updated as more jurisdictions participate. Incidence rate estimates: Weekly age-specific incidence rates by vaccination status were calculated as the number of cases or deaths divided by the number of people vaccinated with a primary series, overall or with/without a booster dose (cumulative) or unvaccinated (obtained by subtracting the cumulative number of people vaccinated with a primary series and partially vaccinated people from the 2019 U.S. intercensal population estimates) and multiplied by 100,000. Overall incidence rates were age-standardized using the 2000 U.S. Census standard population. To estimate population counts for ages 6 months through 1 year, half of the single-year population counts for ages 0 through 1 year were used. All rates are plotted by positive specimen collection date to reflect when incident infections occurred. For the primary series analysis, age-standardized rates include ages 12 years and older from April 4, 2021 through December 4, 2021, ages 5 years and older from December 5, 2021 through July 30, 2022 and ages 6 months and older from July 31, 2022 onwards. For the booster dose analysis, age-standardized rates include ages 18 years and older from September 19, 2021 through December 25, 2021, ages 12 years and older from December 26, 2021, and ages 5 years and older from June 5, 2022 onwards. Small numbers could contribute to less precision when calculating death rates among some groups. Continuity correction: A continuity correction has been applied to the denominators by capping the percent population coverage at 95%. To do this, we assumed that at least 5% of each age group would always be unvaccinated in each jurisdiction. Adding this correction ensures that there is always a reasonable denominator for the unvaccinated population that would prevent incidence and death rates from growing unrealistically large due to potential overestimates of vaccination coverage. Incidence rate ratios (IRRs): IRRs for the past one month were calculated by dividing the average weekly incidence rates among unvaccinated people by that among people vaccinated with a primary series either overall or with a booster dose. Publications: Scobie HM, Johnson AG, Suthar AB, et al. Monitoring Incidence of COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Status — 13 U.S. Jurisdictions, April 4–July 17, 2021. MMWR Morb Mortal Wkly Rep 2021;70:1284–1290. Johnson AG, Amin AB, Ali AR, et al. COVID-19 Incidence and Death Rates Among Unvaccinated and Fully Vaccinated Adults with and Without Booster Doses During Periods of Delta and Omicron Variant Emergence — 25 U.S. Jurisdictions, April 4–December 25, 2021. MMWR Morb Mortal Wkly Rep 2022;71:132–138. Johnson AG, Linde L, Ali AR, et al. COVID-19 Incidence and Mortality Among Unvaccinated and Vaccinated Persons Aged ≥12 Years by Receipt of Bivalent Booster Doses and Time Since Vaccination — 24 U.S. Jurisdictions, October 3, 2021–December 24, 2022. MMWR Morb Mortal Wkly Rep 2023;72:145–152. Johnson AG, Linde L, Payne AB, et al. Notes from the Field: Comparison of COVID-19 Mortality Rates Among Adults Aged ≥65 Years Who Were Unvaccinated and Those Who Received a Bivalent Booster Dose Within the Preceding 6 Months — 20 U.S. Jurisdictions, September 18, 2022–April 1, 2023. MMWR Morb Mortal Wkly Rep 2023;72:667–669.

Objective: China has the largest population living with dementia, causing a tremendous burden on the aging society. Cardiovascular disease (CVD) may trigger a cascade of pathologies associated with cognitive aging. We aim to investigate the association between cardiovascular risk (CVR) factors and cognitive function in the nationally representative cohort in China.Methods: Participants were recruited from 150 counties in 28 provinces via a four-stage sampling method. The outcomes included several cognitive tasks. The independent variable was a composite score of cardiovascular risk calculated from sex-specific equations. We fitted the time-lagged regression to model the association between CVR and cognition. Besides, we performed cross-group analyses to test for model invariance across sex and age. We thus constrained path coefficients to be equal across each grouping variable (e.g., sex) and compared the fit of this constrained model with an unconstrained model in which the path coefficients were allowed to vary by group.Results: A total of 3,799 participants were included in the final analyses. We found that the CVR had a negative linear association with global cognition (β = −0.1, p < 0.01). Additionally, CVR had inverse linear associations with domain-specific measurements of memory and learning, calculation, orientation, and visual–spatial ability (all values of p < 0.01). Regarding sex and age moderation, males had a more pronounced association between higher CVR and worse general cognition, immediate recall, orientation, calculation, and visual–spatial ability (all values of p < 0.0001). In contrast, females exhibited a slightly larger negative association in delayed recall. Older participants (>65 years old) had a more pronounced association between higher CVR and worse calculation ability (p = 0.003).Conclusion: CVD are risk factors for lower global cognition and cognitive subdomains in middle-aged and older adults in China.

Context

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This dataset collects characteristics of the population in each region (age distribution, unemployment rate, immigration percent and primary economic sector) and cross it with the votes per each political part.

It has 52 fields:

1) Code [String]: Region code of the different Spanish areas. There are 8126 different regions, but the dataset only contains 8119, because some sources were incomplete.

2) RegionName [String]: Name of the region.

3) Population [Int]: Amount of people living in that area (1st January 2015)

4) TotalCensus [Int]: Number of people over 18 years old, which means that can vote.

5) TotalVotes [Int]: Number of total votes.

6) AbstentionPtge [Float]: Percent of the people that have not votes in the election. (TotalCensus-TotalVotes)/TotalCensus*100 %

7) BlankVotesPtge [Float]: Percent of votes that were blank. Calculated as follows: BlankVotes/TotalVotes*100 %

8) NullVotesPtge [Float]: Percent of votes that were null. Calculated as follows: NullVotes/TotalVotes*100 %

9) PP_Ptge [Float]: Percent of the votes given to the political party called “Partido Popular”. (PP_Votes)/TotalVotes*100 %

10) PSOE_Ptge [Float]: Percent of the votes given to the political party called “Partido Socialista Obrero Español” (PSOE_Votes)/TotalVotes*100 %

11) Podemos_Ptge [Float]: Percent of the votes given to the political party called “Podemos” (Podemos_Votes)/TotalVotes*100 %

12) Ciudadanos_Ptge [Float]: Percent of the votes given to the political party called “Ciudadanos” (Ciudadanos_Votes)/TotalVotes*100 %

13) Others_Ptge [Float]: Percent of the votes given to the others political parties (∑▒MinoritaryVotes)/TotalVotes*100 %

14) Age_0-4_Ptge [Float]: Percent of the populations which age is between 0 and 4 years old. It is calculated as follows: (Number of people in (0-4))/TotalPopulation*100 %

15) Age_5-9_Ptge [Float]: Percent of the populations which age is between 5 and 9 year old.

16) Age_10-14_Ptge [Float]: Percent of the populations which age is between 10 and 14 years old

17) Age_15-19_Ptge [Float]: Percent of the populations which age is between 15 and 19 years old

18) Age_20-24_Ptge [Float]: Percent of the populations which age is between 20 and 24 years old

19) Age_25-29_Ptge [Float]: Percent of the populations which age is between 25 and 29 years old

20) Age_30-34_Ptge [Float]: Percent of the populations which age is between 30 and 34 years old

21) Age_35-39_Ptge [Float]: Percent of the populations which age is between 35 and 39 years old

22) Age_40-44_Ptge [Float]: Percent of the populations which age is between 40 and 44 years old

23) Age_45-49_Ptge [Float]: Percent of the populations which age is between 45 and 49 years old

24) Age_50-54_Ptge [Float]: Percent of the populations which age is between 50 and 54 years old

25) Age_55-59_Ptge [Float]: Percent of the populations which age is between 55 and 59 years old

26) Age_60-64_Ptge [Float]: Percent of the populations which age is between 60 and 64 years old

27) Age_65-69_Ptge [Float]: Percent of the populations which age is between 65 and 69 years old

28) Age_70-74_Ptge [Float]: Percent of the populations which age is between 70 and 74 years old

29) Age_75-79_Ptge [Float]: Percent of the populations which age is between 75 and 79 year old

30) Age_80-84_Ptge [Float]: Percent of the populations which age is between 80 and 84 years old

31) Age_85-89_Ptge [Float]: Percent of the populations which age is between 85 and 89 year old

32) Age_90-94_Ptge [Float]: Percent of the populations which age is between 90 and 94 years old

33) Age_95-99_Ptge [Float]: Percent of the populations which age is between 95 and 99 years old

34) Age_100+_Ptge [Float]: Percent of the populations which is older than 100 years old.

35) ManPopulationPtge [Float]: Percentage of masculine population in a region. Calculated as follows: ManPopulation/TotalPopulation*100

36) WomanPopulationPtge [Float]: Percentage of masculine population in a region. Calculated as follows: WomanPopulation/TotalPopulation*100

37) SpanishPtge [Float]: Percentage of people with spanish nationality in a region. Calculated as follows: NativeSpanishPopulation/TotalPopulation*100

38) ForeignersPtge [Float]: Percentage of foreign people in a region. Calculated as follows: ForeignPopulation/TotalPopulation*100

39) SameComAutonPtge [Float]: Percentage of people who live in the same autonomic community (same province) that was born. Calculated as follows: SameComAutonPopulation/TotalPopulation*100

40) SameComAutonDiffProvPtge [Float]: Percentage of people who live in the same autonomic community (different province) that was born. Calculated as follows: SameComAutonDiffProvPopulation/TotalPopulation*100

41) DifComAutonPtge [Float]: Percentage of people who live in different autonomic community that was born. Calculated as follows: SameComAutonDiffProvPopulation/TotalPopulation*100

42) UnemployLess25_Ptge [Float]: Percent of unemployed people that are under 25 years and older than 18. It is calculated over the total amount of unemployment. (UnemploymentLess25_Man+ UnemploymentLess25_Woman)/TotalUnemployment*100

43) Unemploy25_40_Ptge [Float]: Percent of unemployed people that are 25-40 years over the total amount of unemployment. (Unemployment(25-40)_Man+ Unemployment(25-40)_Woman )/TotalUnemployment*100

44) UnemployMore40_Ptge [Float]: Percent of unemployed people that are older that 40 and younger than 69 years over the total amount of unemployment. (Unemployment(40-69)_Man+Unemployment(40-69)_Woman)/TotalUnemployment*100

45) UnemployLess25_population_Ptge [Float]: Percent of unemployed people younger than 25 and older than 18, over the total population of the region. Note that the percent is calculated over the total population and not over the total active population. (UnemploymentLess25_Man+ UnemploymentLess25_Woman)/TotalPopulation*100

46) Unemploy25_40_population_Ptge [Float]: Percent of unemployed people (25-40) years old, over the total population of the region. Note that the percent is calculated over the total population and not over the total active population. (Unemployment(25-40)_Man+ Unemployment(25-40)_Woman )/TotalPopulation*100

47) UnemployMore40_population_Ptge [Float]: Percent of unemployed people (40-69) years old, over the total population of the region. Note that the percent is calculated over the total population and not over the total active population. (UnemploymentLess25_Man+ UnemploymentLess25_Woman)/TotalPopulation*100

48) AgricultureUnemploymentPtge [Float]: Percent of unemployment in the agriculture sector relative to the total amount of unemployment. PeopleUnemployedInAgriculture/TotalUnemployment*100

49) IndustryUnemploymentPtge [Float]: Percent of unemployment in the industry sector relative to the total amount of unemployment. PeopleUnemployedInIndustry/TotalUnemployment*100

50) ConstructionUnemploymentPtge [Float]: Percent of unemployment in the construction sector relative to the total amount of unemployment. PeopleUnemployedInConstruction/TotalUnemployment*100

51) ServicesUnemploymentPtge [Float]: Percent of unemployment in the services sector relative to the total amount of unemployment. PeopleUnemployedInServices/TotalUnemployment*100

52) NotJobBeforeUnemploymentPtge [Float]: Percent of unemployment of people that didn’t have an employ before, over the total amount of unemployment. PeopleUnemployedWithoutEmployBefore/TotalUnemployment*100

References:

[1] Unemployment: www.datos.gob.es/es/catalogo/e00142804-paro-registrado-por-municipios

[2] Age distribution per region Relation between Spanish and foreigners Relation between woman and man Relation between people born in the same area or different areas of Spain http://www.ine.es/dynt3/inebase/index.htm?type=pcaxis&file=pcaxis&path=%2Ft20%2Fe245%2Fp05%2F%2Fa2015

[3] Congress elections result of Spanish election (June 2016) http://www.infoelectoral.interior.es/min/areaDescarga.html?method=inicio