الولايات المتحدة الأمريكية

Causes of Death data refer to the underlying cause which - according to the World Health Organisation (WHO) - is the disease or injury which initiated the train of morbid events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury"."

Non-expenditure health care data provide information on institutions providing health care in countries, on resources used and on output produced in the framework of health care provision. Data on health care form a major element of public health information as they describe the capacities available for different types of health care provision as well as potential 'bottlenecks' observed. The quantity and quality of health care services provided and the work sharing established between the different institutions are a subject of ongoing debate in all countries. Sustainability - continuously providing the necessary monetary and personal resources needed - and meeting the challenges of ageing societies are the primary perspectives used when analysing and using the data. The output-related data ('activities') refer to contacts between patients and the health care system, and to the treatment thereby received. Data are available for hospital discharges of in-patients and day cases, average length of stay of in-patients and medical procedures performed in hospitals. Annual national and regional data are provided in absolute numbers and in population-standardised rates (per 100 000 inhabitants). Wherever applicable, the definitions and classifications of the System of Health Accounts (SHA) are followed, e.g. International Classification for Health Accounts - Providers of health care (ICHA-HP). For hospital discharges, the International Shortlist for Hospital Morbidity Tabulation (ISHMT) is used. Health care data on activities are largely based on administrative data sources in the countries. Therefore, they reflect the country-specific way of organising health care and may not always be completely comparable.

This data set provides the Estimated numbers of new cancer cases and deaths in 2023. In 2023, 1,958,310 new cancer cases and 609,820 cancer deaths are projected to occur in the United States. 

Data cited: Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2016 (GBD 2016) Cancer Incidence, Mortality, Years of Life Lost, Years Lived with Disability, and Disability-Adjusted Life Years 1990-2016. Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2018.   The Global Burden of Disease Study 2016 (GBD 2016), coordinated by the Institute for Health Metrics and Evaluation (IHME), estimated the burden of diseases, injuries, and risk factors for 195 countries and territories and at the subnational level for a subset of countries. Estimates for deaths, disability-adjusted life years (DALYs), years lived with disability (YLDs), years of life lost (YLLs), prevalence, and incidence for 29 cancer groups by age and sex for 1990-2016 are available from the GBD Results Tool. Files available in this record are the web tables published in JAMA Oncology in June 2018 in "Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-years for 29 Cancer Groups, 1990 to 2016."

United States Cancer Statistics (USCS)

The Global Burden of Disease Study 2017 (GBD 2017), coordinated by the Institute for Health Metrics and Evaluation (IHME), estimated the burden of diseases, injuries, and risk factors for 195 countries and territories, and at the subnational level for a subset of countries.

Global and regional deaths of children under 5 years of age by cause. Estimates generated by the WHO and Maternal and Child Epidemiology Estimation Group (MCEE) 2018.

Note: Effective September 27, 2023, this dataset will no longer be updated.  This dataset shows health conditions and contributing causes mentioned in conjunction with deaths involving coronavirus disease 2019 (COVID-19).   Number of conditions reported in this table are tabulated from deaths received and coded as of the date of analysis and do not represent all deaths that occurred in that period. Data during this period are incomplete because of the lag in time between when the death occurred and when the death certificate is completed, submitted to NCHS and processed for reporting purposes. This delay can range from 1 week to 8 weeks or more. Conditions contributing to the death were identified using the International Classification of Diseases, Tenth Revision (ICD-10). Deaths involving more than one condition (e.g., deaths involving both diabetes and respiratory arrest) were counted in both totals. To avoid counting the same death multiple times, the numbers for different conditions should not be summated. Deaths with confirmed or presumed COVID-19, coded to ICD–10 code U07.1  

Data cited at: Our World in data-https://ourworldindata.org/coronavirus-source-data 

Note: For Measures-"Lower Credible (50%), Upper Credible(50%),Probability of Control", the data is available as of 25th June 2020 and there will be no further update for these measures as discontinued at source level. Latest estimates of the number of confirmed cases by date of infection, the effective reproduction number, and the doubling time (when negative this corresponds to the halving time) in each region. The mean and 90% credible interval is shown. Data cited at https://epiforecasts.io/covid/posts/global/

Data cited at: COVID-19 Vaccine Projections https://covid19-projections.com/path-to-herd-immunity/

The indicator measures the standardised death rate of chronic diseases. Chronic diseases included in the indicator are malignant neoplasms, diabetes mellitus, ischaemic heart diseases, cerebrovascular diseases, chronic lower respiratory diseases and chronic liver diseases (International Classification of Diseases (ICD) codes C00 to C97, E10 to E14, I20 to I25, I60 to I69 and J40 to J47). Death due to chronic diseases is considered premature if it occurs before the age of 65. The rate is calculated by dividing the number of people under 65 dying due to a chronic disease by the total population under 65. Data on causes of death (COD) refer to the underlying cause which - according to the World Health Organisation (WHO) - is "the disease or injury which initiated the train of morbid events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury". COD data are derived from death certificates. The medical certification of death is an obligation in all Member States. The data are presented as standardised death rates, meaning they are adjusted to a standard age distribution in order to measure death rates independently of different age structures of populations. This approach improves comparability over time and between countries. The standardised death rates used here are calculated on the basis of the standard European population referring to the residents of the countries.

Deaths involving coronavirus disease 2019 (COVID-19), pneumonia, and influenza reported to NCHS by race, age, and state.   Number of deaths reported in this dataset are the total number of deaths received and coded as of the date of analysis, and do not represent all deaths that occurred in that period. Data during this period are incomplete because of the lag in time between when the death occurred and when the death certificate is completed, submitted to NCHS and processed for reporting purposes. This delay can range from 1 week to 8 weeks or more.

Measles cases are defined as laboratory confirmed, epidemiologically linked, and clinical cases as reported to the World Health Organization.

Data cited at: Rt.live-https://rt.live/ These are up-to-date values for Rt, a key measure of how fast the virus is growing. It’s the average number of people who become infected by an infectious person. If Rt is above 1.0, the virus will spread quickly. When Rt is below 1.0, the virus will stop spreading. The reason historical values change is that source is not producing a single point each day, but rather a single curve. One of the constraints of the model is that this curve be connected and smooth. So, if new data suggests that R~t~ should be higher, it will pull up previous values so that the newest point is connected. Imagine a rope lying on the ground. If you pick up the end of that rope, the rope needs to slope up to your hand. The same thing is roughly happening with the model. If all of a sudden a testing center releases far more tests than were expected, the R~t~ curve increases which drags up previous values of R~t~. Since case data is staggered in its arrival, a bunch of new cases will sometimes rewrite its view of history given the new data. 

Estimates of excess deaths can provide information about the burden of mortality potentially related to COVID-19, beyond the number of deaths that are directly attributed to COVID-19. Excess deaths are typically defined as the difference between observed numbers of deaths and expected numbers. This visualization provides weekly data on excess deaths by jurisdiction of occurrence. Counts of deaths in more recent weeks are compared with historical trends to determine whether the number of deaths is significantly higher than expected. Estimates of excess deaths can be calculated in a variety of ways, and will vary depending on the methodology and assumptions about how many deaths are expected to occur. Estimates of excess deaths presented in this web page were calculated using Farrington surveillance algorithms (1). For each jurisdiction, a model is used to generate a set of expected counts, and the upper bound of the 95% Confidence Intervals (95% CI) of these expected counts is used as a threshold to estimate excess deaths. Observed counts are compared to these upper bound estimates to determine whether a significant increase in deaths has occurred. Provisional counts are weighted to account for potential under reporting in the most recent weeks. However, data for the most recent week(s) are still likely to be incomplete. Only about 60% of deaths are reported within 10 days of the date of death, and there is considerable variation by jurisdiction.

The Global Burden of Disease Study 2015 (GBD 2015), coordinated by the Institute for Health Metrics and Evaluation (IHME), estimated the burden of diseases, injuries, and risk factors at the global, regional, national, territorial, and, for a subset of countries, subnational level. As part of this study, estimates for obesity and overweight prevalence and the disease burden attributable to high body mass index (BMI) were produced by sex, age group, and year for 195 countries and territories. Estimates for high BMI-attributable deaths, DALYs, and other measures (1990-2015) are available from the GBD Results Tool. Files available in this record include obesity and overweight prevalence estimates for 1980-2015. Study results were published in The New England Journal of Medicine in June 2017 in "Health Effects of Overweight and Obesity in 195 Countries over 25 Years."

Citation: Global Health Observatory (GHO) Data: https://www.who.int/gho/en/: World Health Organization; 2019. Licence: CC BY-NC-SA 3.0 IGO   The GHO data provides access to indicators on priority health topics including mortality and burden of diseases, the Millennium Development Goals (child nutrition, child health, maternal and reproductive health, immunization, HIV/AIDS, tuberculosis, malaria, neglected diseases, water and sanitation), non communicable diseases and risk factors, epidemic-prone diseases, health systems, environmental health, violence and injuries, equity among others.

Note: Daily data is getting released with a time gap of T-2. For example, 16-March-2020 data is released on 18-March-2020   This dataset gives Google Web Search Trend for search item - "coronavirus" for past 90 days. Interest over time: Numbers represent search interest relative to the highest point on the chart for the given region and time. A value of 100 is the peak popularity for the term. A value of 50 means that the term is half as popular. A score of 0 means there was not enough data for this term.

This dataset presents HNP data by wealth quintile since 1990s to present. It covers more than 70 indicators, including childhood diseases and interventions, nutrition, sexual and reproductive health, mortality, and other determinants of health, for more than 90 low- and middle-income countries. The data sources are Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS).

Disconnected youth, also referred to as opportunity youth, are teenagers and young adults between the ages of 16 and 24 who are neither in school nor working Research reveals that being disconnected as a young person has long-term consequences; it’s associated with lower earnings, less education, worse health, and even less happiness in later adulthood. The duration i.e. how long a young person is disconnected also matters, with longer spells of disconnection associated with worse outcomes. This dataset shows the impact of Covid-19 on disconnected youth rate in the USA.

Data Citation: FluNet: https://apps.who.int/flumart/Default?ReportNo=2: World Health Organization; [2021]. License: CC BY-NC-SA 3.0 IGO WHO- FluMart is a platform that has been developed to facilitate data exchange, harmonization, consolidation and storage of influenza related data. FluMart allows the upload of any user defined data files in their own format and transforms them into standard data. Standard format data can be used for analysis purposes and to produce reports. FluMart does not replace already existing applications such as FluNet and FluID, but combines the data from different applications and/or data sources in one common platform to enable integrated analysis and reporting. Note: Date has been taken as Starting Date of range date for the week  

Data cited at: Prof.Prof. Lauren Gardner; Center for Systems Science and Engineering at John Hopkins University, blog Post -  https://systems.jhu.edu/research/public-health/ncov/   On December 31, 2019, the World Health Organization (WHO) was informed of an outbreak of “pneumonia of unknown cause” detected in Wuhan City, Hubei Province, China – the seventh-largest city in China with 11 million residents. As of February 04, 2020, there are over 24,502 cases confirmed globally, including cases in at least 30 regions in China and 30 countries.  Interests: In-Market Segments Knoema All Users   Knoema modified the original dataset to include calculations per million.   https://knoema.com/WBPEP2018Oct https://knoema.com/USICUBDS2020 https://knoema.com/NBSCN_P_A_A0301 https://knoema.com/IMFIFSS2017Nov https://knoema.com/AUDSS2019 https://knoema.com/UNAIDSS2017 https://knoema.com/UNCTADPOPOCT2019Nov https://knoema.com/WHOWSS2018 https://knoema.com/KPMGDHC2019

NCHS - Leading Causes of Death: United States This dataset presents the age-adjusted death rates for the 10 leading causes of death in the United States beginning in 1999. Data are based on information from all resident death certificates filed in the 50 states and the District of Columbia using demographic and medical characteristics. Age-adjusted death rates (per 100,000 population) are based on the 2000 U.S. standard population. Populations used for computing death rates after 2010 are postcensal estimates based on the 2010 census, estimated as of July 1, 2010. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for non-census years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Causes of death classified by the International Classification of Diseases, Tenth Revision (ICD–10) are ranked according to the number of deaths assigned to rankable causes. Cause of death statistics are based on the underlying cause of death. 

(CDC ID's: y6uv-t34t ,7pb7-w9us ,93k9-hy54) NNDSS - Table II. Lyme disease to Meningococcal - 2016. In this Table, provisional* cases of selected notifiable diseases (1,000 cases reported during the preceding year), and selected low frequency diseases are displayed. The Table includes total number of cases reported in the United States, by region and by states, in accordance with the current method of displaying MMWR data. Data on United States exclude counts from US territories. Note: These are provisional cases of selected national notifiable diseases, from the National Notifiable Diseases Surveillance System (NNDSS). NNDSS data reported by the 50 states, New York City, the District of Columbia, and the U.S. territories are collated and published weekly as numbered tables printed in the back of the Morbidity and Mortality Weekly Report (MMWR). Cases reported by state health departments to CDC for weekly publication are provisional because of ongoing revision of information and delayed reporting. Case counts in this table are presented as they were published in the MMWR issues. Therefore, numbers listed in later MMWR weeks may reflect changes made to these counts as additional information becomes available. Footnotes: C.N.M.I.: Commonwealth of Northern Mariana Islands. U: Unavailable. -: No reported cases. N: Not reportable. NN: Not Nationally Notifiable. NP: Nationally notifiable but not published. Cum: Cumulative year-to-date counts. Med: Median. Max: Maximum. * Case counts for reporting year 2015 and 2016 are provisional and subject to change. For further information on interpretation of these data, see http://wwwn.cdc.gov/nndss/document/ ProvisionalNationaNotifiableDiseasesSurveillanceData20100927.pdf. Data for TB are displayed in Table IV, which appears quarterly. Three low incidence conditions, rubella, rubella congenital, and tetanus, are in Table II to facilitate case count verification with reporting jurisdictions. Data for meningococcal disease, invasive caused by serogroups ACWY; serogroup B; other serogroup; and unknown serogroup are available in Table I.

The Access Ranking indicates how much access to mental health care exists within a state. The access measures include access to insurance, access to treatment, quality and cost of insurance, access to special education, and workforce availability. A high Access Ranking indicates that a state provides relatively more access to insurance and mental health treatment.

Midyear population for selected Countries

2022 Monkeypox Outbreak: Global Trends. Geneva: World Health Organization, 2022. Available online: https://worldhealthorg.shinyapps.io/mpx_global/ (last cited: 29 August 2022).

Data cited at: Global Fund

 Case counts for reporting years 2020 and 2021 are provisional and subject to change. Cases are assigned to the reporting jurisdiction submitting the case to e National Notifiable Diseases Surveillance System (NNDSS), if the case's country of usual residence is the U.S., a U.S. territory, unknown, or null (i.e. country not reported); otherwise, the case is assigned to the 'Non-U.S. Residents' category. Country of usual residence is currently not reported by all jurisdictions or for all conditions NNDSS data reported by the 50 states, New York City, the District of Columbia, and the U.S. territories are collated and published weekly asnumbered tables  TRANSLATE with xEnglishArabicHebrewPolishBulgarianHindiPortugueseCatalanHmong DawRomanianChinese SimplifiedHungarianRussianChinese TraditionalIndonesianSlovakCzechItalianSlovenianDanishJapaneseSpanishDutchKlingonSwedishEnglishKoreanThaiEstonianLatvianTurkishFinnishLithuanianUkrainianFrenchMalayUrduGermanMalteseVietnameseGreekNorwegianWelshHaitian CreolePersian  TRANSLATE with COPY THE URL BELOW BackEMBED THE SNIPPET BELOW IN YOUR SITEEnable collaborative features and customize widget: Bing Webmaster PortalBack

Table 2o - Typhoid fever; Vancomycin-intermediate Staphylococcus aureus; Vancomycin-resistant Staphylococcus aureus; Varicella morbidity; Varicella mortality; Vibriosis; Suggested Citation:  Centers for Disease Control and Prevention. National Notifiable Diseases Surveillance System, 2018 Annual Tables of Infectious Disease Data.  Atlanta, GA. CDC Division of Health Informatics and Surveillance, 2019.  Available at: https://www.cdc.gov/nndss/infectious-tables.html, https://www.cdc.gov/nndss/infectious-tables.html.

(CDC ID's: s5s8-d82d ,4y34-2pku) NNDSS - Table II. Babesiosis to Campylobacteriosis - 2016. In this Table, provisional* cases of selected notifiable diseases (1,000 cases reported during the preceding year), and selected low frequency diseases are displayed. The Table includes total number of cases reported in the United States, by region and by states, in accordance with the current method of displaying MMWR data. Data on United States exclude counts from US territories. Note: These are provisional cases of selected national notifiable diseases, from the National Notifiable Diseases Surveillance System (NNDSS). NNDSS data reported by the 50 states, New York City, the District of Columbia, and the U.S. territories are collated and published weekly as numbered tables printed in the back of the Morbidity and Mortality Weekly Report (MMWR). Cases reported by state health departments to CDC for weekly publication are provisional because of ongoing revision of information and delayed reporting. Case counts in this table are presented as they were published in the MMWR issues. Therefore, numbers listed in later MMWR weeks may reflect changes made to these counts as additional information becomes available. Footnotes: C.N.M.I.: Commonwealth of Northern Mariana Islands. U: Unavailable. -: No reported cases. N: Not reportable. NN: Not Nationally Notifiable. NP: Nationally notifiable but not published. Cum: Cumulative year-to-date counts. Med: Median. Max: Maximum. * Case counts for reporting years 2015 and 2016 are provisional and subject to change. For further information on interpretation of these data, see http://wwwn.cdc.gov/nndss/document/ProvisionalNationaNotifiableDiseasesSurveillanceData20100927.pdf. Data for TB are displayed in Table IV, which appears quarterly. Three low incidence conditions, rubella, rubella congenital, and tetanus, are in Table II to facilitate case count verification with reporting jurisdictions.

(CDC ID's: 9ix3-ryt6 ,mpdg-hf57 ,afja-b25e) NNDSS - Table II. Giardiasis to Haemophilus influenza - 2016. In this Table, provisional* cases of selected notifiable diseases (1,000 cases reported during the preceding year), and selected low frequency diseases are displayed. The Table includes total number of cases reported in the United States, by region and by states, in accordance with the current method of displaying MMWR data. Data on United States exclude counts from US territories. Note: These are provisional cases of selected national notifiable diseases, from the National Notifiable Diseases Surveillance System (NNDSS). NNDSS data reported by the 50 states, New York City, the District of Columbia, and the U.S. territories are collated and published weekly as numbered tables printed in the back of the Morbidity and Mortality Weekly Report (MMWR). Cases reported by state health departments to CDC for weekly publication are provisional because of ongoing revision of information and delayed reporting. Case counts in this table are presented as they were published in the MMWR issues. Therefore, numbers listed in later MMWR weeks may reflect changes made to these counts as additional information becomes available. Footnotes: C.N.M.I.: Commonwealth of Northern Mariana Islands. U: Unavailable. -: No reported cases. N: Not reportable. NN: Not Nationally Notifiable. NP: Nationally notifiable but not published. Cum: Cumulative year-to-date counts. Med: Median. Max: Maximum. * Case counts for reporting year 2015 and 2016 are provisional and subject to change. For further information on interpretation of these data, see http://wwwn.cdc.gov/nndss/document/ ProvisionalNationaNotifiableDiseasesSurveillanceData20100927.pdf. Data for TB are displayed in Table IV, which appears quarterly. Three low incidence conditions, rubella, rubella congenital, and tetanus, are in Table II to facilitate case count verification with reporting jurisdictions. Data for H. influenzae (age <5 years for serotype b, nonserotype b, and unknown serotype) are available in Table I.

NNDSS - Table II. Salmonellosis to Shigellosis - 2014.In this Table, all conditions with a 5-year average annual national total of more than or equals 1,000 cases but less than or equals 10,000 cases will be displayed ( 1,000 and _ 10,000). The Table includes total number of cases reported in the United States, by region and by states, in accordance with the current method of displaying MMWR data. Data on United States exclude counts from US territories. Note:These are provisional cases of selected national notifiable diseases, from the National Notifiable Diseases Surveillance System (NNDSS). NNDSS data reported by the 50 states, New York City, the District of Columbia, and the U.S. territories are collated and published weekly as numbered tables printed in the back of the Morbidity and Mortality Weekly Report (MMWR). Cases reported by state health departments to CDC for weekly publication are provisional because of ongoing revision of information and delayed reporting. Case counts in this table are presented as they were published in the MMWR issues. Therefore, numbers listed in later MMWR weeks may reflect changes made to these counts as additional information becomes available. Footnotes:C.N.M.I.: Commonwealth of Northern Mariana Islands. U: Unavailable. -: No reported cases. N: Not reportable. NN: Not Nationally Notifiable Cum: Cumulative year-to-date counts. Med: Median. Max: Maximum. * Case counts for reporting years 2013 and 2014 are provisional and subject to change. For further information on interpretation of these data, see http://wwwn.cdc.gov/nndss/document/ProvisionalNationaNotifiableDiseasesSurveillanceData20100927.pdf. Data for TB are displayed in Table IV, which appears quarterly. Includes E. coli O157:H7; Shiga toxin positive, serogroup non-O157; and Shiga toxin positive, not serogrouped.More information on NNDSS is available at http://wwwn.cdc.gov/nndss/.

These are weekly cases of selected infectious national notifiable diseases, from the National Notifiable Diseases Surveillance System (NNDSS). NNDSS data reported by the 50 states, New York City, the District of Columbia, and the U.S. territories are collated and published weekly as numbered tables. Cases reported by state health departments to CDC for weekly publication are subject to ongoing revision of information and delayed reporting. Therefore, numbers listed in later weeks may reflect changes made to these counts as additional information becomes available. Case counts in the tables are presented as published each week.  Note- https://wonder.cdc.gov/nndss/static/2021/31/2021-31-table1ii.html https://wonder.cdc.gov/nndss/static/2021/31/2021-31-table1kk.html  https://wonder.cdc.gov/nndss/static/2021/31/2021-31-table1ll.html   

Non-medical determinants of health: Unhealthy lifestyles and poor environments cause millions of people to die prematurely. Smoking, harmful alcohol use, physical inactivity and obesity are the root cause of many chronic conditions. This dataset presents the latest data for tobacco consumption (including daily smokers by age and sex), vaping (by age and sex), alcohol consumption, fruits and vegetables consumption, as well as measured and self-reported data on overweight and obesity.

Data cited at: AllClear.App-https://home.allclear.app/  

The domain "Income and living conditions" covers four topics: people at risk of poverty or social exclusion, income distribution and monetary poverty, living conditions and material deprivation, which are again structured into collections of indicators on specific topics. The collection "People at risk of poverty or social exclusion" houses main indicator on risk of poverty or social inclusion included in the Europe 2020 strategy as well as the intersections between sub-populations of all Europe 2020 indicators on poverty and social exclusion. The collection "Income distribution and monetary poverty" houses collections of indicators relating to poverty risk, poverty risk of working individuals as well as the distribution of income. The collection "Living conditions" hosts indicators relating to characteristics and living conditions of households, characteristics of the population according to different breakdowns, health and labour conditions, housing conditions as well as childcare related indicators. The collection "Material deprivation" covers indicators relating to economic strain, durables, housing deprivation and environment of the dwelling.

The European Health Interview Survey (EHIS) aims at measuring on a harmonised basis and with a high degree of comparability among MS the health status, lifestyle (health determinants) and health care services use of the EU citizens. The European Health Interview Survey (EHIS) was developed between 2003 and 2006, during a process in which all the EU Member States (MS) were largely involved. It consists of four modules on health status, health care, health determinants, and background variables. Those modules may be implemented at the national level either as one specific survey or as elements of existing surveys (i.e. national health interview survey, labour force survey, other household surveys). The final version of the questionnaire for the first wave of EHIS was adopted by the MS at the Working Group on Public Health Statistics in November 2006. The survey contained around 130 questions split among the four modules covering the following topics: Background variables on demography and socio-economic status Health status: Minimum European Health Module (MEHM): self-perceived health, chronic health problems and activity limitationDisease specific morbidityAccidents and injuriesWork-related health problemsHealth related absenteeism from workPhysical and sensory functional limitationsActivities of daily living (ADL - feeding, bathing, etc.) and help receivedInstrumental activities of daily living (IADL - preparing meals, shopping, etc.) and help receivedPainAspect of mental health (psychological distress and mental well-being) Health care: Hospitalisation (inpatient and day care)Consultations with doctors and dentistsUnmet needs for hospitalization and for consultation with a specialistVisits to specific non-medical health professionalsVisits to specific categories of alternative medicine practitionersUse of home care and home help servicesSatisfaction with services provided by health care providersUse of medicines (prescribed and non-prescribed)Health care preventive actions (influenza vaccination, breast examination, cervical smear test, blood tests, etc.)Out-of-pocket payments for medical care (self-completion form) Health determinants: Height and weightPhysical activityConsumption of fruits, vegetables and juiceEnvironnent (home and workplace exposures, criminality exposure, social support)Smoking behaviour and exposure to tobacco smoke (self-completion form)Alcohol consumption (self-completion form)Illicit drug use (self-completion form). The first wave of the EHIS was implemented during the period 2006-2009 under a gentlemen's agreement. Nineteen countries have carried out it: 2006: AT, EE2007: SI, CH2008: BE, BG, CZ, CY, FR, LV, MT, RO, TR2009: DE, EL, ES, HU, PL, SK. Germany provided aggregated data and for breakdowns with a strata size less than 20, the values were marked as confidential (flag ~c). No data have been received for Switzerland. In total, 26 indicators based on DG SANCO and DG EMPL needs and covering health status, health determinants and health care are disseminated on Eurostat website. For more information on indicators see document EHIS indicators guidelines.   The indicators present distribution percentages and are calculated with different breakdown according to the indicator: sex, age group (10-years intervals, 15 – 24, 25 – 34, …, 75 – 84, 85 or over) and educational attainment levels (ISCED0-2, ISCED3-4, ISCED5-6);sex, age group (18-44, 45-54, 55-64, 65-74, 75 or over) and income quintiles. For example: 4.5 % of Latvian women aged 25-34 are obese (BMI is equal or greater than 30). Records with missing values on age and sex were excluded from the calculation of indicators.   Most of the indicators are worked out for the population aged 15 or over. Nevertheless, for some specific indicators, frequencies are calculated on different populations: the Body Mass Index (BMI) (tables hlth_ehis_de1 and hlth_ehis_de2) is calculated for adults only (18+);the self-reported prevalence of high blood pressure (table hlth_ehis_st1) is computed for people aged 25+;the self-reported vaccination against influenza (table hlth_ehis_hc1) is computed for people aged 65+;the self-reported breast examination by X-ray (table hlth_ehis_hc2) is computed for women aged 50-69;the self-reported cervical smear test (table hlth_ehis_hc3) is computed for women aged 20-69;the self-reported colorectal cancer screening test (table hlth_ehis_hc4) is computed for people aged 50-74.

Deaths involving coronavirus disease 2019 (COVID-19), pneumonia and influenza reported to NCHS by place of death and state, United States.

Deaths involving coronavirus disease 2019 (COVID-19), pneumonia, and influenza reported to NCHS by sex and age group and state.   Number of deaths reported in this table are the total number of deaths received and coded as of the date of analysis, and do not represent all deaths that occurred in that period. Data during this period are incomplete because of the lag in time between when the death occurred and when the death certificate is completed, submitted to NCHS and processed for reporting purposes. This delay can range from 1 week to 8 weeks or more.

Provisional count of deaths involving coronavirus disease 2019 (COVID-19) by United States county.   Deaths with confirmed or presumed COVID-19, coded to ICD–10 code U07.1. Counties included in this table have 10 or more COVID-19 deaths at the time of analysis. Number of deaths reported in this table are the total number of deaths received and coded as of the date of analysis and do not represent all deaths that occurred in that period. Data during this period are incomplete because of the lag in time between when the death occurred and when the death certificate is completed, submitted to NCHS and processed for reporting purposes.

This data file contains the following indicators that can be used to illustrate potential differences in the burden of deaths due to COVID-19 according to race and ethnicity: •Count of COVID-19 deaths: Number of deaths due to COVID-19 reported for each race and Hispanic origin group •Distribution of COVID-19 deaths (%): Deaths for each group as a percent of the total number of COVID-19 deaths reported •Unweighted distribution of population (%): Population of each group as a percent of the total population •Weighted distribution of population (%): Population of each group as percent of the total population after accounting for how the race and Hispanic origin population is distributed in relation to the geographic areas impacted by COVID-19

Rates of Laboratory-Confirmed RSV, COVID-19, and Flu Hospitalizations from the RESP-NET Surveillance Systems

In the United States, the seasonality of respiratory syncytial virus (RSV) has traditionally been defined on the basis of weeks during which antigen-based tests detect RSV in >10% of specimens. Because molecular testing has become more widely used, we explored the extent of polymerase chain reaction (PCR)–based RSV testing and its impact on determining the seasonality of RSV.

Reporting of new Aggregate Case and Death Count data was discontinued on May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. According to the source, this dataset will receive a final update on June 1, 2023, to reconcile historical data.

These are  weekly  cases of selected infectious national notifiable diseases, from the National Notifiable Diseases Surveillance System (NNDSS). NNDSS data reported by the 50 states, New York City, the District of Columbia, and the US territories are collated and published weekly as numbered tables. Cases reported by state health departments to CDC for weekly publication are subject to the ongoing revision of information and delayed reporting. Therefore, numbers listed in later weeks may reflect changes made to these counts as additional information becomes available.    TRANSLATE with xEnglishArabicHebrewPolishBulgarianHindiPortugueseCatalanHmong DawRomanianChinese SimplifiedHungarianRussianChinese TraditionalIndonesianSlovakCzechItalianSlovenianDanishJapaneseSpanishDutchKlingonSwedishEnglishKoreanThaiEstonianLatvianTurkishFinnishLithuanianUkrainianFrenchMalayUrduGermanMalteseVietnameseGreekNorwegianWelshHaitian CreolePersian  TRANSLATE with COPY THE URL BELOW BackEMBED THE SNIPPET BELOW IN YOUR SITEEnable collaborative features and customize widget: Bing Webmaster PortalBack

Avian influenza is caused by influenza Type A virus (influenza A). Avian-origin influenza viruses are broadly categorized based on a combination of two groups of proteins on the surface of the influenza A virus: hemagglutinin or “H” proteins, of which there are 16 (H1-H16), and neuraminidase or “N” proteins, of which there are 9 (N1-N9). Many different combinations of “H” and “N” proteins are possible. Each combination is considered a different subtype, and related viruses within a subtype may be referred to as a lineage. Avian influenza viruses are classified as either “low pathogenic” or “highly pathogenic” based on their genetic features and the severity of the disease they cause in poultry. Most viruses are of low pathogenicity, meaning that they causes no signs or only minor clinical signs of infection in poultry.

Non-expenditure health care data provide information on institutions providing health care in countries, on resources used and on output produced in the framework of health care provision. Data on health care form a major element of public health information as they describe the capacities available for different types of health care provision as well as potential 'bottlenecks' observed. The quantity and quality of health care services provided and the work sharing established between the different institutions are a subject of ongoing debate in all countries. Sustainability - continuously providing the necessary monetary and personal resources needed - and meeting the challenges of ageing societies are the primary perspectives used when analysing and using the data. The output-related data ('activities') refer to contacts between patients and the health care system, and to the treatment thereby received. Data are available for hospital discharges of in-patients and day cases, average length of stay of in-patients and medical procedures performed in hospitals. Annual national and regional data are provided in absolute numbers and in population-standardised rates (per 100 000 inhabitants). Wherever applicable, the definitions and classifications of the System of Health Accounts (SHA) are followed, e.g. International Classification for Health Accounts - Providers of health care (ICHA-HP). For hospital discharges, the International Shortlist for Hospital Morbidity Tabulation (ISHMT) is used. Health care data on activities are largely based on administrative data sources in the countries. Therefore, they reflect the country-specific way of organising health care and may not always be completely comparable.

Data cited at:  Ahmad FB, Rossen LM, Sutton P. Provisional drug overdose death counts. National Center for Health Statistics. 2020. Designed by LM Rossen, A Lipphardt, FB Ahmad, JM Keralis, and Y Chong: National Center for Health Statistics. This data contains provisional counts for drug overdose deaths based on a current flow of mortality data in the National Vital Statistics System. Counts for the most recent final annual data are provided for comparison. National provisional counts include deaths occurring within the 50 states and the District of Columbia as of the date specified and may not include all deaths that occurred during a given time period. Provisional counts are often incomplete and causes of death may be pending investigation (see Technical notes) resulting in an underestimate relative to final counts. To address this, methods were developed to adjust provisional counts for reporting delays by generating a set of predicted provisional counts (see Technical notes). Starting in June 2018, this monthly data release will include both reported and predicted provisional counts.

Data cited at: Ahmad FB, Bastian B. Quarterly provisional estimates for selected indicators of mortality, 2018-Quarter 3, 2019. National Center for Health Statistics. National Vital Statistics System, Vital Statistics Rapid Release Program. 2020. Provisional estimates of death rates. Estimates are presented for each of the 15 leading causes of death plus estimates for deaths attributed to drug overdose, falls (for persons aged 65 and over), human immunodeficiency virus (HIV) disease, homicide, and firearms-related deaths.

Note: Effective September 27, 2023, this dataset will no longer be updated. Provisional counts of deaths by the week the deaths occurred, by state of occurrence, and by select underlying causes of death. The dataset also includes weekly provisional counts of death for COVID-19, coded to ICD-10 code U07.1 as an underlying or multiple cause of death.