Rank, number of deaths, percentage of deaths, and age-specific mortality rates for the leading causes of death, by age group and sex, 2000 to most recent year.

Context

Heart Disease Dataset (Most comprehensive)

Content

Heart disease is also known as Cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17.9 million lives each year which is about 32% of all deaths globally. CVDs are a group of disorders of the heart and blood vessels and include coronary heart disease, cerebrovascular disease, rheumatic heart disease, and other conditions. Four out of 5CVD deaths are due to heart attacks and strokes, and one-third of these deaths occur prematurely in people under 70 years of age.

We have curated this dataset by combining different datasets already available independently but not combined before. W have combined them over 11 common features which makes it the largest heart disease dataset available for research purposes. The five datasets used for its curation are:

Database: #####of instances:

1. Cleveland: 303
2. Hungarian: 294
3. Switzerland: 123
4. Long Beach VA: 200
5. Stalog (Heart) Data Set: 270 #####Total 1190

Acknowledgements

The dataset is taken from three other research datasets used in different research papers. The Nature article listing heart disease database and names of popular datasets used in various heart disease research is shared below. https://www.nature.com/articles/s41597-019-0206-3

Inspiration

Can you find interesting insight from the largest heart disease dataset available so far and build predictive model which can assist medical practitioners in detecting early-stage heart disease ?

This dataset contains counts of deaths for California counties based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in each California county regardless of the place of residence (by occurrence) and deaths to residents of each California county (by residence), whereas the provisional data table only includes deaths that occurred in each county regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

This dataset reports the daily reported number of the 7-day moving average rates of Deaths involving COVID-19 by vaccination status and by age group.

Learn how the Government of Ontario is helping to keep Ontarians safe during the 2019 Novel Coronavirus outbreak.

Effective November 14, 2024 this page will no longer be updated. Information about COVID-19 and other respiratory viruses is available on Public Health Ontario’s interactive respiratory virus tool: https://www.publichealthontario.ca/en/Data-and-Analysis/Infectious-Disease/Respiratory-Virus-Tool

Data includes:

• Date on which the death occurred
• Age group
• 7-day moving average of the last seven days of the death rate per 100,000 for those not fully vaccinated
• 7-day moving average of the last seven days of the death rate per 100,000 for those fully vaccinated
• 7-day moving average of the last seven days of the death rate per 100,000 for those vaccinated with at least one booster

Additional notes

As of June 16, all COVID-19 datasets will be updated weekly on Thursdays by 2pm.

As of January 12, 2024, data from the date of January 1, 2024 onwards reflect updated population estimates. This update specifically impacts data for the 'not fully vaccinated' category.

On November 30, 2023 the count of COVID-19 deaths was updated to include missing historical deaths from January 15, 2020 to March 31, 2023.

CCM is a dynamic disease reporting system which allows ongoing update to data previously entered. As a result, data extracted from CCM represents a snapshot at the time of extraction and may differ from previous or subsequent results. Public Health Units continually clean up COVID-19 data, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes and current totals being different from previously reported cases and deaths. Observed trends over time should be interpreted with caution for the most recent period due to reporting and/or data entry lags.

The data does not include vaccination data for people who did not provide consent for vaccination records to be entered into the provincial COVaxON system. This includes individual records as well as records from some Indigenous communities where those communities have not consented to including vaccination information in COVaxON.

“Not fully vaccinated” category includes people with no vaccine and one dose of double-dose vaccine. “People with one dose of double-dose vaccine” category has a small and constantly changing number. The combination will stabilize the results.

Spikes, negative numbers and other data anomalies: Due to ongoing data entry and data quality assurance activities in Case and Contact Management system (CCM) file, Public Health Units continually clean up COVID-19, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes, negative numbers and current totals being different from previously reported case and death counts.

Public Health Units report cause of death in the CCM based on information available to them at the time of reporting and in accordance with definitions provided by Public Health Ontario. The medical certificate of death is the official record and the cause of death could be different.

Deaths are defined per the outcome field in CCM marked as “Fatal”. Deaths in COVID-19 cases identified as unrelated to COVID-19 are not included in the Deaths involving COVID-19 reported.

Rates for the most recent days are subject to reporting lags

All data reflects totals from 8 p.m. the previous day.

This dataset is subject to change.

Number of deaths and age-specific mortality rates for selected grouped causes, by age group and sex, 2000 to most recent year.

Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve.

The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj.

The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 .

The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 .

The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed.

COVID-19 cases and associated deaths that have been reported among Connecticut residents, broken down by race and ethnicity. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the COVID-19 update.

The following data show the number of COVID-19 cases and associated deaths per 100,000 population by race and ethnicity. Crude rates represent the total cases or deaths per 100,000 people. Age-adjusted rates consider the age of the person at diagnosis or death when estimating the rate and use a standardized population to provide a fair comparison between population groups with different age distributions. Age-adjustment is important in Connecticut as the median age of among the non-Hispanic white population is 47 years, whereas it is 34 years among non-Hispanic blacks, and 29 years among Hispanics. Because most non-Hispanic white residents who died were over 75 years of age, the age-adjusted rates are lower than the unadjusted rates. In contrast, Hispanic residents who died tend to be younger than 75 years of age which results in higher age-adjusted rates.

The population data used to calculate rates is based on the CT DPH population statistics for 2019, which is available online here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Population-Statistics. Prior to 5/10/2021, the population estimates from 2018 were used.

Rates are standardized to the 2000 US Millions Standard population (data available here: https://seer.cancer.gov/stdpopulations/). Standardization was done using 19 age groups (0, 1-4, 5-9, 10-14, ..., 80-84, 85 years and older). More information about direct standardization for age adjustment is available here: https://www.cdc.gov/nchs/data/statnt/statnt06rv.pdf

Categories are mutually exclusive. The category “multiracial” includes people who answered ‘yes’ to more than one race category. Counts may not add up to total case counts as data on race and ethnicity may be missing. Age adjusted rates calculated only for groups with more than 20 deaths. Abbreviation: NH=Non-Hispanic.

Data on Connecticut deaths were obtained from the Connecticut Deaths Registry maintained by the DPH Office of Vital Records. Cause of death was determined by a death certifier (e.g., physician, APRN, medical examiner) using their best clinical judgment. Additionally, all COVID-19 deaths, including suspected or related, are required to be reported to OCME. On April 4, 2020, CT DPH and OCME released a joint memo to providers and facilities within Connecticut providing guidelines for certifying deaths due to COVID-19 that were consistent with the CDC’s guidelines and a reminder of the required reporting to OCME.25,26 As of July 1, 2021, OCME had reviewed every case reported and performed additional investigation on about one-third of reported deaths to better ascertain if COVID-19 did or did not cause or contribute to the death. Some of these investigations resulted in the OCME performing postmortem swabs for PCR testing on individuals whose deaths were suspected to be due to COVID-19, but antemortem diagnosis was unable to be made.31 The OCME issued or re-issued about 10% of COVID-19 death certificates and, when appropriate, removed COVID-19 from the death certificate. For standardization and tabulation of mortality statistics, written cause of death statements made by the certifiers on death certificates are sent to the National Center for Health Statistics (NCHS) at the CDC which assigns cause of death codes according to the International Causes of Disease 10th Revision (ICD-10) classification system.25,26 COVID-19 deaths in this report are defined as those for which the death certificate has an ICD-10 code of U07.1 as either a primary (underlying) or a contributing cause of death. More information on COVID-19 mortality can be found at the following link: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Mortality/Mortality-Statistics

Data are subject to future revision as reporting changes.

Starting in July 2020, this dataset will be updated every weekday.

Additional notes: A delay in the data pull schedule occurred on 06/23/2020. Data from 06/22/2020 was processed on 06/23/2020 at 3:30 PM. The normal data cycle resumed with the data for 06/23/2020.

A network outage on 05/19/2020 resulted in a change in the data pull schedule. Data from 5/19/2020 was processed on 05/20/2020 at 12:00 PM. Data from 5/20/2020 was processed on 5/20/2020 8:30 PM. The normal data cycle resumed on 05/20/2020 with the 8:30 PM data pull. As a result of the network outage, the timestamp on the datasets on the Open Data Portal differ from the timestamp in DPH's daily PDF reports.

Starting 5/10/2021, the date field will represent the date this data was updated on data.ct.gov. Previously the date the data was pulled by DPH was listed, which typically coincided with the date before the data was published on data.ct.gov. This change was made to standardize the COVID-19 data sets on data.ct.gov.

Analysis of ‘Stroke Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/redwan1010/stroke-data on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths. This dataset is used to predict whether a patient is likely to get stroke based on the input parameters like gender, age, various diseases, and smoking status. Each row in the data provides relevant information about the patient.

Content

1) id: unique identifier 2) gender: "Male", "Female" or "Other" 3) age: age of the patient 4) hypertension: 0 if the patient doesn't have hypertension, 1 if the patient has hypertension 5) heart_disease: 0 if the patient doesn't have any heart diseases, 1 if the patient has a heart disease 6) ever_married: "No" or "Yes" 7) work_type: "children", "Govt_jov", "Never_worked", "Private" or "Self-employed" 8) Residence_type: "Rural" or "Urban" 9) avg_glucose_level: average glucose level in blood 10) bmi: body mass index 11) smoking_status: "formerly smoked", "never smoked", "smokes" or "Unknown"* 12) stroke: 1 if the patient had a stroke or 0 if not *Note: "Unknown" in smoking_status means that the information is unavailable for this patient

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

--- Original source retains full ownership of the source dataset ---

NOTE: This dataset has been retired and marked as historical-only. Weekly rates of COVID-19 cases, hospitalizations, and deaths among people living in Chicago by vaccination status and age. Rates for fully vaccinated and unvaccinated begin the week ending April 3, 2021 when COVID-19 vaccines became widely available in Chicago. Rates for boosted begin the week ending October 23, 2021 after booster shots were recommended by the Centers for Disease Control and Prevention (CDC) for adults 65+ years old and adults in certain populations and high risk occupational and institutional settings who received Pfizer or Moderna for their primary series or anyone who received the Johnson & Johnson vaccine. Chicago residency is based on home address, as reported in the Illinois Comprehensive Automated Immunization Registry Exchange (I-CARE) and Illinois National Electronic Disease Surveillance System (I-NEDSS). Outcomes: • Cases: People with a positive molecular (PCR) or antigen COVID-19 test result from an FDA-authorized COVID-19 test that was reported into I-NEDSS. A person can become re-infected with SARS-CoV-2 over time and so may be counted more than once in this dataset. Cases are counted by week the test specimen was collected. • Hospitalizations: COVID-19 cases who are hospitalized due to a documented COVID-19 related illness or who are admitted for any reason within 14 days of a positive SARS-CoV-2 test. Hospitalizations are counted by week of hospital admission. • Deaths: COVID-19 cases who died from COVID-19-related health complications as determined by vital records or a public health investigation. Deaths are counted by week of death. Vaccination status: • Fully vaccinated: Completion of primary series of a U.S. Food and Drug Administration (FDA)-authorized or approved COVID-19 vaccine at least 14 days prior to a positive test (with no other positive tests in the previous 45 days). • Boosted: Fully vaccinated with an additional or booster dose of any FDA-authorized or approved COVID-19 vaccine received at least 14 days prior to a positive test (with no other positive tests in the previous 45 days). • Unvaccinated: No evidence of having received a dose of an FDA-authorized or approved vaccine prior to a positive test. CLARIFYING NOTE: Those who started but did not complete all recommended doses of an FDA-authorized or approved vaccine prior to a positive test (i.e., partially vaccinated) are excluded from this dataset. Incidence rates for fully vaccinated but not boosted people (Vaccinated columns) are calculated as total fully vaccinated but not boosted with outcome divided by cumulative fully vaccinated but not boosted at the end of each week. Incidence rates for boosted (Boosted columns) are calculated as total boosted with outcome divided by cumulative boosted at the end of each week. Incidence rates for unvaccinated (Unvaccinated columns) are calculated as total unvaccinated with outcome divided by total population minus cumulative boosted, fully, and partially vaccinated at the end of each week. All rates are multiplied by 100,000. Incidence rate ratios (IRRs) are calculated by dividing the weekly incidence rates among unvaccinated people by those among fully vaccinated but not boosted and boosted people. Overall age-adjusted incidence rates and IRRs are standardized using the 2000 U.S. Census standard population. Population totals are from U.S. Census Bureau American Community Survey 1-year estimates for 2019. All data are provisional and subject to change. Information is updated as additional details are received and it is, in fact, very common for recent dates to be incomplete and to be updated as time goes on. This dataset reflects data known to CDPH at the time when the dataset is updated each week. Numbers in this dataset may differ from other public sources due to when data are reported and how City of Chicago boundaries are defined. For all datasets related to COVID-19, see https://data.cityofchic

This data visualization presents county-level provisional counts for drug overdose deaths based on a current flow of mortality data in the National Vital Statistics System. County-level 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 resulting in an underestimate relative to final counts (see Technical Notes).

The provisional data presented on the dashboard below include reported 12 month-ending provisional counts of death due to drug overdose by the decedent’s county of residence and the month in which death occurred.

Percentages of deaths with a cause of death pending further investigation and a note on historical completeness (e.g. if the percent completeness was under 90% after 6 months) are included to aid in interpretation of provisional data as these measures are related to the accuracy of provisional counts (see Technical Notes). Counts between 1-9 are suppressed in accordance with NCHS confidentiality standards. Provisional data presented on this page will be updated on a quarterly basis as additional records are received.

Technical Notes

Nature and Sources of Data

Provisional drug overdose death counts are based on death records received and processed by the National Center for Health Statistics (NCHS) as of a specified cutoff date. The cutoff date is generally the first Sunday of each month. National provisional estimates include deaths occurring within the 50 states and the District of Columbia. NCHS receives the death records from the state vital registration offices through the Vital Statistics Cooperative Program (VSCP).

The timeliness of provisional mortality surveillance data in the National Vital Statistics System (NVSS) database varies by cause of death and jurisdiction in which the death occurred. The lag time (i.e., the time between when the death occurred and when the data are available for analysis) is longer for drug overdose deaths compared with other causes of death due to the time often needed to investigate these deaths (1). Thus, provisional estimates of drug overdose deaths are reported 6 months after the date of death.

Provisional death counts presented in this data visualization are for “12 month-ending periods,” defined as the number of deaths occurring in the 12 month period ending in the month indicated. For example, the 12 month-ending period in June 2020 would include deaths occurring from July 1, 2019 through June 30, 2020. The 12 month-ending period counts include all seasons of the year and are insensitive to reporting variations by seasonality. These provisional counts of drug overdose deaths and related data quality metrics are provided for public health surveillance and monitoring of emerging trends. Provisional drug overdose death data are often incomplete, and the degree of completeness varies by jurisdiction and 12 month-ending period. Consequently, the numbers of drug overdose deaths are underestimated based on provisional data relative to final data and are subject to random variation.

Cause of Death Classification and Definition of Drug Deaths

Mortality statistics are compiled in accordance with the World Health Organizations (WHO) regulations specifying that WHO member nations classify and code causes of death with the current revision of the International Statistical Classification of Diseases and Related Health Problems (ICD). ICD provides the basic guidance used in virtually all countries to code and classify causes of death. It provides not only disease, injury, and poisoning categories but also the rules used to select the single underlying cause of death for tabulation from the several diagnoses that may be reported on a single death certificate, as well as definitions, tabulation lists, the format of the death certificate, and regulations on use of the classification. Causes of death for data presented on this report were coded according to ICD guidelines described in annual issues of Part 2a of the NCHS Instruction Manual (2). Drug overdose deaths are identified using underlying cause-of-death codes from the Tenth Revision of ICD (ICD–10): X40–X44 (unintentional), X60–X64 (suicide), X85 (homicide), and Y10–Y14 (undetermined).

Selection of Specific Jurisdictions to Report

Provisional counts are presented by the jurisdiction where the decedent resides (e.g. county of residence). Data quality and timeliness for drug overdose deaths vary by reporting jurisdiction. Provisional counts are presented, along with measures of data quality: the percentage of records where the manner of death is listed as “pending investigation”, and a note for specific jurisdictions with historically lower levels of data completeness (where provisional 2019 data were less than 90% complete after 6 months).

Percentage of Records Pending Investigation

Drug overdose deaths often require lengthy investigations, and death certificates may be initially filed with a manner of death “pending investigation” and/or with a preliminary or unknown cause of death. When the percentage of records reported as “pending investigation” is high for a given jurisdiction, the number of drug overdose deaths is likely to be underestimated. Counts of drug overdose deaths may be underestimated to a greater extent in jurisdictions or counties where more records in NVSS are reported as “pending investigation” for the six most recent 12 month-ending periods.

Historical Completeness

The historical percent completeness of provisional data is obtained by dividing the number of death records in the NVSS database for each jurisdiction and county after a 6-month lag for deaths occurring in 2019 by the number of deaths eventually included in the final data files. Counties with historically lower levels of provisional data completeness are flagged with a note to indicate that the data may be incomplete in these areas. However, the completeness of provisional data may change over time, and therefore the degree of underestimation will not be known until data are finalized (typically 11-12 months after the end of the data year).

Differences between Final and Provisional Data

There may be differences between provisional and final data for a given data year (e.g., 2020). Final drug overdose death data published annually through NCHS statistical reports (3) and CDC WONDER undergo additional data quality checks and processing. Provisional counts reported here are subject to change as additional data are received.

Source

NCHS, National Vital Statistics System. Estimates for 2020 and 2021 are based on provisional data. Estimates for 2019 are based on final data (available from: https://www.cdc.gov/nchs/nvss/mortality_public_use_data.htm).

References

1. Spencer MR, Ahmad F. Timeliness of death certificate data for mortality surveillance and provisional estimates. National Center for Health Statistics. 2016. Available from: https://www.cdc.gov/nchs/data/vsrr/report001.pdf
2. National Vital Statistics System. Instructions for classifying the underlying cause of death. In: NCHS instruction manual; Part 2a. Published annually.
3. Hedegaard H, Miniño AM, Warner M. Drug overdose deaths in the United States, 1999–2018. NCHS Data Brief, no 356. Hyattsville, MD: National Center for Health Statistics. 2020. Available from: https://www.cdc.gov/nchs/products/databriefs/db356.htm

Suggested Citation

Ahmad FB, Anderson RN, Cisewski JA, Rossen LM, Warner M, Sutton P. County-level provisional drug overdose death counts. National Center for Health Statistics. 2021.

Designed by MirLogic Solutions Corp: National Center for Health Statistics.

Rank, number of deaths, percentage of deaths, and mortality rates for the leading causes of infant death (under one year of age), by sex, 2000 to most recent year.

Background - We have previously estimated that respiratory syncytial virus (RSV) was associated with 22% of all episodes of (severe) acute lower respiratory infection (ALRI) resulting in 55000 to 199000 deaths in children younger than 5 years in 2005. In the past 5 years, major research activity on RSV has yielded substantial new data from developing countries. With a considerably expanded dataset from a large international collaboration, we aimed to estimate the global incidence, hospital admission rate, and mortality from RSV-ALRI episodes in young children in 2015. Methods - We estimated the incidence and hospital admission rate of RSV-associated ALRI (RSV-ALRI) in children younger than 5 years stratified by age and World Bank income regions from a systematic review of studies published between Jan 1, 1995, and Dec 31, 2016, and unpublished data from 76 high quality population-based studies. We estimated the RSV-ALRI incidence for 132 developing countries using a risk factor-based model and 2015 population estimates. We estimated the in-hospital RSV-ALRI mortality by combining in-hospital case fatality ratios with hospital admission estimates from hospital-based (published and unpublished) studies. We also estimated overall RSV-ALRI mortality by identifying studies reporting monthly data for ALRI mortality in the community and RSV activity. Findings - We estimated that globally in 2015, 33*1 million (uncertainty range [UR] 21*6-50*3) episodes of RSV-ALRI, resulted in about 3*2 million (2*7-3*8) hospital admissions, and 59 600 (48 000-74 500) in-hospital deaths in children younger than 5 years. In children younger than 6 months, 1*4 million (UR 1*2-1*7) hospital admissions, and 27 300 (UR 20 700-36 200) in-hospital deaths were due to RSV-ALRI. We also estimated that the overall RSV-ALRI mortality could be as high as 118 200 (UR 94 600-149 400). Incidence and mortality varied substantially from year to year in any given population. Interpretation Globally, RSV is a common cause of childhood ALRI and a major cause of hospital admissions in young children, resulting in a substantial burden on health-care services. About 45% of hospital admissions and in- hospital deaths due to RSV-ALRI occur in children younger than 6 months. An effective maternal RSV vaccine or monoclonal antibody could have a substantial effect on disease burden in this age group.

ObjectiveSystemic arterial hypertension (HT) is a major modifiable risk factor for cardiovascular disease (CVDs), associated with all-cause death (ACD). Understanding its progression from the early state to late complications should lead to more timely intensification of treatment. This study aimed to construct a real-world cohort profile of HT and to estimate transition probabilities from the uncomplicated state to any of these long-term complications; chronic kidney disease (CKD), coronary artery disease (CAD), stroke, and ACD.MethodsThis real-world cohort study used routine clinical practice data for all adult patients diagnosed with HT in the Ramathibodi Hospital, Thailand from 2010 to 2022. A multi-state model was developed based on the following: state 1-uncomplicated HT, 2-CKD, 3-CAD, 4-stroke, and 5-ACD. Transition probabilities were estimated using Kaplan-Meier method.ResultsA total of 144,149 patients were initially classified as having uncomplicated HT. The transition probabilities (95% CI) from the initial state to CKD, CAD, stroke, and ACD at 10-years were 19.6% (19.3%, 20.0%), 18.2% (17.9%, 18.6%), 7.4% (7.1%, 7.6%), and 1.7% (1.5%, 1.8%), respectively. Once in the intermediate-states of CKD, CAD, and stroke, 10-year transition probabilities to death were 7.5% (6.8%, 8.4%), 9.0% (8.2%, 9.9%), and 10.8% (9.3%, 12.5%).ConclusionsIn this 13-year cohort, CKD was observed as the most common complication, followed by CAD and stroke. Among these, stroke carried the highest risk of ACD, followed by CAD and CKD. These findings provide improved understanding of disease progression to guide appropriate prevention measures. Further investigations of prognostic factors and treatment effectiveness are warranted.

The 3.5-million-word Victorian Anti-Vaccination Discourse Corpus (hereon VicVaDis) is intended to provide a (freely accessible) historical resource for the investigation of the earliest public concerns and arguments against vaccination in England, which revolved around compulsory vaccination against smallpox in the second half of the 19th century. It consists of 133 anti-vaccination pamphlets and publications gathered from 1854 to 1906, a span of 53 years that loosely coincides with the Victorian era (1837-1901). This timeframe was chosen to capture the period between the 1853 Vaccination Act, which made smallpox vaccination for babies compulsory, and the 1907 Act which effectively ended the mandatory nature of vaccination.

The Quo VaDis project applies the latest techniques for large-scale computer-aided linguistic analysis to discussions about vaccinations in public discourse, and specifically in: social media discussions in English, UK Parliamentary debates and UK national press reports. The goal is to arrive at a better understanding of pro- and anti-vaccination views, as well as undecided views, which will inform future public health campaigns.

The project will be based in the world-renowned ESRC Centre for Corpus Approaches to Social Science (CASS) at Lancaster University, which was awarded a Queen's Anniversary Prize for Higher and Further Education in 2015. An interdisciplinary project team will work in interaction with three main project partners: Public Health England, the Department of Health and Social Care and the Department for Digital, Culture, Media & Sport.

The World Health Organization's (WHO) list of top ten global health threats includes 'vaccine hesitancy' - 'a delay in acceptance or refusal of vaccines despite availability of vaccination services'. Vaccination programmes are currently estimated to prevent between 2 and 3 million deaths a year worldwide. However, uptake of vaccinations in 90% of countries has been reported to be affected by vaccine hesitancy. In England, coverage for all routine childhood vaccinations is in decline, resulting in the resurgence of communicable diseases that had previously been eradicated. In August 2019, the UK lost its WHO measles elimination status.

The reasons for vaccine hesitancy are complex, but they need to be understood in order to be addressed effectively. This project focuses on discourse because the ways in which controversial topics such as vaccinations are talked about both reflect and shape beliefs and attitudes, which may in turn influence behaviour. More specifically, vaccinations have been the topic of UK parliamentary debates since before the first Vaccination Act of 1840; they have been increasingly discussed in the UK press since the early 1990s; and anti-vaccination views in particular have been described as part of a complex network of 'anti-public discourses' which, in recent years, are known to be both spread and contested on social media.

This project will involve the analysis of three multi-million-word datasets: (1) English-language contributions to three social media platforms: Mumsnet, Reddit and Twitter since the inception of each platform - respectively, 2000, 2005 and 2006; (2) UK national newspapers since 1990; and (3) UK parliamentary debates since 1830. These datasets will be analysed in a data-driven fashion by means of the computer-aided methods associated with Corpus Linguistics - a branch of Linguistics that involves the construction of large digital collections of naturally-occurring texts (known as 'corpora') and their analysis through tailor-made software. A corpus linguistic approach makes it possible to combine in a principled way the quantitative analysis of corpora containing millions of words with the qualitative analysis of individual texts, patterns and interactions. In this way, we will identify and investigate the different ways in which views about vaccinations are expressed in our data, for example, through patterns in choices of vocabulary, pronouns, negation, evaluation, metaphors, narratives, sources of evidence, and argumentation. We will reveal both differences and similarities in pro- and anti-vaccination views over time and across different groups of people, particularly as they form and interact on social media.

Our findings will make a major contribution to an understanding of views about vaccinations both in the UK (via our parliamentary and news datasets) and internationally (via our social media datasets). Through the involvement of our Project Partners, as well as more general engagement activities, these findings will be used as evidence for the design of future public health campaigns about vaccinations.

Abstract

Noncommunicable diseases are the top cause of deaths. In 2008, more than 36 million people worldwide died of such diseases. Ninety per cent of those lived in low-income and middle-income countries.WHO Maps Noncommunicable Disease Trends in All Countries The STEPS Noncommunicable Disease Risk Factor Survey, part of the STEPwise approach to surveillance (STEPS) Adult Risk Factor Surveillance project by the World Health Organization (WHO), is a survey methodology to help countries begin to develop their own surveillance system to monitor and fight against noncommunicable diseases. The methodology prescribes three steps—questionnaire, physical measurements, and biochemical measurements. The steps consist of core items, core variables, and optional modules. Core topics covered by most surveys are demographics, health status, and health behaviors. These provide data on socioeconomic risk factors and metabolic, nutritional, and lifestyle risk factors. Details may differ from country to country and from year to year.

The general objective of the Zimbabwe NCD STEPS survey was to assess the risk factors of selected NCDs in the adult population of Zimbabwe using the WHO STEPwise approach to non-communicable diseases surveillance. The specific objectives were: - To assess the distribution of life-style factors (physical activity, tobacco and alcohol use), and anthropometric measurements (body mass index and central obesity) which may impact on diabetes and cardiovascular risk factors. - To identify dietary practices that are risk factors for selected NCDs. - To determine the prevalence and determinants of hypertension - To determine the prevalence and determinants of diabetes. - To determine the prevalence and determinants of serum lipid profile.

Geographic coverage

Mashonaland Central, Midlands and Matebeleland South Provinces.

Analysis unit

Household Individual

Universe

The survey comprised of individuals aged 25 years and over.

Kind of data

Sample survey data [ssd]

Sampling procedure

A multistage sampling strategy with 3 stages consisting of province, district and health centre was employed. The World Health Organization STEPwise Approach (STEPS) was used as the design basis for the survey. The 3 randomly selected provinces for the survey were Mashonaland Central, Midlands and Matebeleland South. In each Province four districts were chosen and four health centres were surveyed per district. The survey comprised of individuals aged 25 years and over.The survey was carried out on 3,081 respondents consisting of 1,189 from Midlands,944 from Mashonaland Central and 948 from Matebeleland South. A detailed description of the sampling process is provided in sections 3.8 -3.9. if the survey report provided under the related materials tab.

Sampling deviation

Designing a community-based survey such as this one is fraught with difficulties in ensuring representativeness of the sample chosen. In this survey there was a preponderance of female respondents because of the pattern of employment of males and females which also influences urban rural migration.

The response rate in Midlands was lower than the other two provinces in both STEP 2 and 3. This notable difference was due to the fact that Midlands had more respondents sampled from the urban communities. A higher proportion of urban respondents was formally employed and therefore did not complete STEP 2 and 3 due to conflict with work schedules.

Mode of data collection

Face-to-face [f2f]

Research instrument

In this survey all the core and selected expanded and optional variables were collected. In addition a food frequency questionnaire and a UNICEF developed questionnaire, the Fortification Rapid Assessment Tool (FRAT) were administered to elicit relevant dietary information.

Cleaning operations

Data entry for Step 1 and Step 2 data was carried out as soon as data became available to the data management team. Step 3 data became available in October and data entry was carried out when data quality checks were completed in November. Report writing started in September and a preliminary report became available in December 2005.

Training of data entry clerks Five data entry clerks were recruited and trained for one week. The selection of data entry clerks was based on their performance during previous research carried out by the MOH&CW. The training of the data entry clerks involved the following: - Familiarization with the NCD, FRAT and FFQ questionnaires. - Familiarization with the data entry template. - Development of codes for open-ended questions. - Statistical package (EPI Info 6). - Development of a data entry template using EPI6. - Development of check files for each template - Trial runs (mock runs) to check whether template was complete and user friendly for data entry. - Double entry (what it involves and how to do it and why it should be done). - Pre-primary data cleaning (check whether denominators are tallying) of the data entry template was done.

Data Entry for NCD, FRAT and FFQ questionnaires The questionnaires were sequentially numbered and were then divided among the five data entry clerks. Each one of the data entry clerks had a unique identifier for quality control purposes. Hence, the data was entered into five separate files using the statistical package EPI Info version 6.0. The data entry clerks inter-changed their files for double entry and validation of the data. Preliminary data cleaning was done for each of the five files. The five files were then merged to give a single file. The merged file was then transferred to STATA Version 7.0 using Stat Transfer version 5.0.

Data Cleaning A data-cleaning workshop was held with the core research team members. The objectives of the workshop were: 1. To check all data entry errors. 2. To assess any inconsistencies in data filling. 3. To assess any inconsistencies in data entry. 4. To assess completeness of the data entered.

Data Merging There were two datasets (NCD questionnaire dataset and laboratory dataset) after the data entry process. The two files were merged by joining corresponding observations from the NCD questionnaire dataset with those from the laboratory dataset into single observations using a unique identifier. The ID number was chosen as the unique identifier since it appeared in both data sets. The main aim of merging was to combine the two datasets containing information on behaviour of individuals and the NCD laboratory parameters. When the two data sets were merged, a new merge variable was created. The merge variable took values 1, 2 and 3. Merge variable==1 Observation appeared in the NCD questionnaire data set but a corresponding observation was not in the laboratory data set Merge variable==2 Observation appeared in the laboratory data set but a corresponding observation did not appear in the questionnaire data set Merge variable==3 Observation appeared in both data sets and reflects a complete merge of the two data sets.

Data Cleaning After Merging Data cleaning involved identifying the observations where the merge variable values were either 1 or 2. Merge status for each observation was also changed after effecting any corrections. The other two unique variables that were used in the cleaning were Province, district and health centre since they also appeared in both data sets.

Objectives of cleaning: 1. Match common variables in both data sets and identify inconsistencies in other matching variables e.g. province, district and health centre. 2. To check for any data entry errors.

Response rate

A total of 3,081 respondents were included in the survey against an estimated sample size of 3,000. The response rate for Step 1 was 80% for and for Step 2 70% taking Step 1 accrual as being 100%.

Directly age-sex standardised rate for patients with oral cavity cancer per 100,000 population.

Rationale

Tobacco is a known risk factor for oral cancers1. In England, 65% of hospital admissions (2014–15) for oral cancer and 64% of deaths (2014) due to oral cancer were attributed to smoking2. Oral cancer registration is therefore a direct measure of smoking-related harm. Given the high proportion of these registrations that are due to smoking, a reduction in the prevalence of smoking would reduce the incidence of oral cancer.

Towards a Smokefree Generation: A Tobacco Control Plan for England states that tobacco use remains one of our most significant public health challenges and that smoking is the single biggest cause of inequalities in death rates between the richest and poorest in our communities3. In January 2012 the Public Health Outcomes Framework was published, then updated in 2016. Smoking and smoking-related death play a key role in two of the four domains: Health Improvement and Preventing premature mortality4.

Definition of numerator

The number of diagnoses of patients with oral cavity cancer (ICD-10 code C00.3 to C00.5, C02 to C04 (excluding C02.4), C05.0, C06)

National Cancer Registry Dataset from the National Disease Registration Service

Definition of denominator

ONS mid-year population estimates aggregated into quinary age bands.

Office for National Statistics (ONS), Mid-year population estimates (based on 2011 census)

Methodology

Numerator data for each age sex band are divided by the respective denominator population data to give age sex specific admission rates for the area. These age sex specific rates are multiplied by the standard population for each age sex group respectively and aggregated across all the age sex groups to give the age sex adjusted count of admissions for the area. This age sex adjusted count of admissions is divided by the total standard population for the whole age sex range included in the indicator, and multiplied by 100,000 to give the age sex standardised admission rate for the area.

Caveats Age sex standardised rates based on numbers lower than 10 are not included

References

GBD 2013 Risk Factors Collaborators. Global, regional and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risk factors in 188 countries, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. The Lancet 2015; 386:10010 2287–2323. Statistics on smoking, England 2016, May 2016; http://content.digital.nhs.uk/catalogue/PUB20781 Towards a Smokefree Generation: A Tobacco Control Plan for England, July 2017 Public Health Outcomes Framework 2016 to 2019, August 2016 Huggett C (1995). Review of the Quality and Comparability of Data held by Regional Cancer Registries. Bristol: Bristol Cancer Epidemiology Unit incorporating the South West Cancer Registry. Seddon DJ, Williams EMI (1997). Data quality in population-based cancer registration. British Journal of Cancer 76: 667–674.

This table summarizes the cost assumptions made for Ulaanbaatar, Dakar, and São Paulo. PPP = Purchasing power parity.

Tuberculosis is an infectious air-borne disease and one of the leading causes of death globally among all infectious diseases. There is an urgent need to develop antitubercular drugs that would be highly efficient and less toxic than the presently available marketed drugs. Mycobacterium membrane protein large 3 (MmpL3) is an emerging drug target in tuberculosis with various classes of molecules that have been known to inhibit it. In this study, a dataset of indole-2-carboxamides showing antitubercular activity by inhibiting MmpL3 was utilized. Initially, a chimera-based homology model was developed and docking was performed with the filtered dataset to analyse the interactions. Thereafter, molecular dynamics simulations were run with representative molecules to gain a better insight on the binding patterns. To attain a more quantitative correlation, an atom-based 3D QSAR model was developed which complemented the results from the previous models. A library of novel indole-2-carboxamides was then generated using core hopping-based ligand enumeration and upon screening on our workflow model it predicted three molecules as potent antitubercular compounds. This work not only helps to gain new insights on the interactions at the MmpL3 binding site but also provides novel indole-2-carboxamides having the potential to become antitubercular drugs in future.

The share of the population with overweight in the United States was forecast to continuously increase between 2024 and 2029 by in total 1.6 percentage points. After the fifteenth consecutive increasing year, the overweight population share is estimated to reach 77.43 percent and therefore a new peak in 2029. Notably, the share of the population with overweight of was continuously increasing over the past years.Overweight is defined as a body mass index (BMI) of more than 25.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the share of the population with overweight in countries like Canada and Mexico.

Measles is a highly contagious and severe disease. Despite mass vaccination, it remains a leading cause of death in children in developing regions, killing 114,900 globally in 2014. In 2006, China committed to eliminating measles by 2012; to this end, the country enhanced its mandatory vaccination programs and achieved vaccination rates reported above 95% by 2008. However, in spite of these efforts, during the last 3 years (2013–2015) China documented 27,695, 52,656, and 42,874 confirmed measles cases. How measles manages to spread in China—the world’s largest population—in the mass vaccination era remains poorly understood. To address this conundrum and provide insights for future public health efforts, we analyze the geospatial pattern of measles transmission across China during 2005–2014. We map measles incidence and incidence rates for each of the 344 cities in mainland China, identify the key socioeconomic and demographic features associated with high disease burden, and identify transmission clusters based on the synchrony of outbreak cycles. Using hierarchical cluster analysis, we identify 21 epidemic clusters, of which 12 were cross-regional. The cross-regional clusters included more underdeveloped cities with large numbers of emigrants than would be expected by chance (p = 0.011; bootstrap sampling), indicating that cities in these clusters were likely linked by internal worker migration in response to uneven economic development. In contrast, cities in regional clusters were more likely to have high rates of minorities and high natural growth rates than would be expected by chance (p = 0.074; bootstrap sampling). Our findings suggest that multiple highly connected foci of measles transmission coexist in China and that migrant workers likely facilitate the transmission of measles across regions. This complex connection renders eradication of measles challenging in China despite its high overall vaccination coverage. Future immunization programs should therefore target these transmission foci simultaneously.