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. SOURCES CDC/NCHS, National Vital Statistics System, mortality data (see http://www.cdc.gov/nchs/deaths.htm); and CDC WONDER (see http://wonder.cdc.gov). REFERENCES National Center for Health Statistics. Vital statistics data available. Mortality multiple cause files. Hyattsville, MD: National Center for Health Statistics. Available from: https://www.cdc.gov/nchs/data_access/vitalstatsonline.htm. Murphy SL, Xu JQ, Kochanek KD, Curtin SC, and Arias E. Deaths: Final data for 2015. National vital statistics reports; vol 66. no. 6. Hyattsville, MD: National Center for Health Statistics. 2017. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr66/nvsr66_06.pdf.

These data contain case counts and rates for selected communicable diseases—listed in the data dictionary—that met the surveillance case definition for that disease and was reported for California residents, by disease, county, year, and sex. The data represent cases with an estimated illness onset date from 2001 through the last year indicated from California Confidential Morbidity Reports and/or Laboratory Reports. Data captured represent reportable case counts as of the date indicated in the “Temporal Coverage” section below, so the data presented may differ from previous publications due to delays inherent to case reporting, laboratory reporting, and epidemiologic investigation.

JP: Prevalence of Severe Wasting: Weight for Height: Male: % of Children under 5 data was reported at 0.300 % in 2010. JP: Prevalence of Severe Wasting: Weight for Height: Male: % of Children under 5 data is updated yearly, averaging 0.300 % from Dec 2010 (Median) to 2010, with 1 observations. JP: Prevalence of Severe Wasting: Weight for Height: Male: % of Children under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Japan – Table JP.World Bank: Health Statistics. Prevalence of severe wasting, male, is the proportion of boys under age 5 whose weight for height is more than three standard deviations below the median for the international reference population ages 0-59.; ; World Health Organization, Global Database on Child Growth and Malnutrition. Country-level data are unadjusted data from national surveys, and thus may not be comparable across countries.; Linear mixed-effect model estimates; Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF, www.childinfo.org). Estimates of child malnutrition, based on prevalence of underweight and stunting, are from national survey data. The proportion of underweight children is the most common malnutrition indicator. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.

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.

BackgroundIn recent decades, the escalating prevalence of obesity has contributed to a significant increase in the global burden of disease, with cardiovascular diseases (CVDs) emerging as the leading cause among all diseases attributable to high body-mass index (BMI). Utilizing global burden of disease (GBD) dataset from 1990 to 2021, we conducted a comprehensive analysis of the global, regional, and national trends in deaths and disability-adjusted life years (DALYs) attributable to CVDs caused by high BMI. Age-standardized mortality rate (ASMR) and age-standardized DALY rate (ASDR) were also investigated. Furthermore, we examined the associations of gender, age, and socio-demographic index (SDI) with the burden of CVDs attributable to high BMI. Finally, we assessed the evolution of health inequalities across countries and projected the global deaths and DALYs due to high BMI-related CVDs over the next two decades.MethodsThe absolute numbers and the rates of age-standardized death, Disability-Adjusted Life Years (DALYs) per 100,000 people due to high BMI-related CVDs between 1990 and 2021 were extracted from GBD 2021. The estimated annual percentage changes (EAPCs) of high BMI-related CVDs disease burdens were calculated under the GBD's comparative risk assessment framework. Additionally, the disease burden prediction of the high BMI-related CVDs from 2022 to 2041 was performed using the bayesian age-period-cohort (BAPC) statistical model.ResultsIn 2021, high BMI-related CVDs accounted for 1.90 million deaths globally, representing an increase of 120.63% compared to 1990, with DALYs rising by 115.47% over the same period. Notably, while ASMR and ASDR among male showed no decline, female experienced 11.30% reduction in ASMRs and 6.12% reduction in ASDR. The burden was disproportionately borne by middle-aged and older populations across all age groups. Global health inequalities related to high BMI-related CVDs demonstrated a narrowing trend from 1990 to 2010, followed by a reversal into a negative correlation and continued to widen until 2021. Looking ahead, the burden of high BMI-related CVDs is projected to rise significantly due to population growth, the increasing prevalence of obesity, and aging populations.ConclusionThe results indicate that from 1990 to 2021, the burden of CVDs caused by high BMI has significantly increased. Particular attention should be directed toward middle and low-middle SDI regions. To mitigate this burden, it is imperative to implement public health strategies that emphasize education and awareness regarding the correlation between high BMI and CVDs. Policies promoting healthy dietary habits and regular physical activity are essential for reducing the future impact of high BMI-related cardiovascular morbidity and mortality. Such measures are not only urgently needed but also offer substantial long-term benefits for global health.

Mali ML: Prevalence of Overweight: Weight for Height: % of Children Under 5 data was reported at 1.900 % in 2015. This records an increase from the previous number of 1.000 % for 2010. Mali ML: Prevalence of Overweight: Weight for Height: % of Children Under 5 data is updated yearly, averaging 2.100 % from Dec 1987 (Median) to 2015, with 6 observations. The data reached an all-time high of 4.700 % in 2006 and a record low of 0.500 % in 1987. Mali ML: Prevalence of Overweight: Weight for Height: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mali – Table ML.World Bank: Health Statistics. Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's new child growth standards released in 2006.; ; UNICEF, WHO, World Bank: Joint child malnutrition estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.; Linear mixed-effect model estimates; Estimates of overweight children are also from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues

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.

Updates

• Notice of data discontinuation: Since the start of the pandemic, AP has reported case and death counts from data provided by Johns Hopkins University. Johns Hopkins University has announced that they will stop their daily data collection efforts after March 10. As Johns Hopkins stops providing data, the AP will also stop collecting daily numbers for COVID cases and deaths. The HHS and CDC now collect and visualize key metrics for the pandemic. AP advises using those resources when reporting on the pandemic going forward.

  • CDC Weekly case and death counts (national and state level)
  • CDC County level cases and deaths
  • HHS New hospital admissions
  • CDC NowCast COVID variant proportions (national and regional level)

• April 9, 2020

  • The population estimate data for New York County, NY has been updated to include all five New York City counties (Kings County, Queens County, Bronx County, Richmond County and New York County). This has been done to match the Johns Hopkins COVID-19 data, which aggregates counts for the five New York City counties to New York County.

• April 20, 2020

  • Johns Hopkins death totals in the US now include confirmed and probable deaths in accordance with CDC guidelines as of April 14. One significant result of this change was an increase of more than 3,700 deaths in the New York City count. This change will likely result in increases for death counts elsewhere as well. The AP does not alter the Johns Hopkins source data, so probable deaths are included in this dataset as well.

• April 29, 2020

  • The AP is now providing timeseries data for counts of COVID-19 cases and deaths. The raw counts are provided here unaltered, along with a population column with Census ACS-5 estimates and calculated daily case and death rates per 100,000 people. Please read the updated caveats section for more information.

• September 1st, 2020

  • Johns Hopkins is now providing counts for the five New York City counties individually.

• February 12, 2021

  • The Ohio Department of Health recently announced that as many as 4,000 COVID-19 deaths may have been underreported through the state’s reporting system, and that the "daily reported death counts will be high for a two to three-day period."
  • Because deaths data will be anomalous for consecutive days, we have chosen to freeze Ohio's rolling average for daily deaths at the last valid measure until Johns Hopkins is able to back-distribute the data. The raw daily death counts, as reported by Johns Hopkins and including the backlogged death data, will still be present in the new_deaths column.

• February 16, 2021

  - Johns Hopkins has reconciled Ohio's historical deaths data with the state.

  Overview

The AP is using data collected by the Johns Hopkins University Center for Systems Science and Engineering as our source for outbreak caseloads and death counts for the United States and globally.

The Hopkins data is available at the county level in the United States. The AP has paired this data with population figures and county rural/urban designations, and has calculated caseload and death rates per 100,000 people. Be aware that caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.

This data is from the Hopkins dashboard that is updated regularly throughout the day. Like all organizations dealing with data, Hopkins is constantly refining and cleaning up their feed, so there may be brief moments where data does not appear correctly. At this link, you’ll find the Hopkins daily data reports, and a clean version of their feed.

The AP is updating this dataset hourly at 45 minutes past the hour.

To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

Queries

Use AP's queries to filter the data or to join to other datasets we've made available to help cover the coronavirus pandemic

• Filter cases by state here

• Rank states by their status as current hotspots. Calculates the 7-day rolling average of new cases per capita in each state: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=481e82a4-1b2f-41c2-9ea1-d91aa4b3b1ac

• Find recent hotspots within your state by running a query to calculate the 7-day rolling average of new cases by capita in each county: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=b566f1db-3231-40fe-8099-311909b7b687&showTemplatePreview=true

• Join county-level case data to an earlier dataset released by AP on local hospital capacity here. To find out more about the hospital capacity dataset, see the full details.

• Pull the 100 counties with the highest per-capita confirmed cases here

• Rank all the counties by the highest per-capita rate of new cases in the past 7 days here. Be aware that because this ranks per-capita caseloads, very small counties may rise to the very top, so take into account raw caseload figures as well.

Interactive

The AP has designed an interactive map to track COVID-19 cases reported by Johns Hopkins.

@(https://datawrapper.dwcdn.net/nRyaf/15/)

Interactive Embed Code

<iframe title="USA counties (2018) choropleth map Mapping COVID-19 cases by county" aria-describedby="" id="datawrapper-chart-nRyaf" src="https://datawrapper.dwcdn.net/nRyaf/10/" scrolling="no" frameborder="0" style="width: 0; min-width: 100% !important;" height="400"></iframe><script type="text/javascript">(function() {'use strict';window.addEventListener('message', function(event) {if (typeof event.data['datawrapper-height'] !== 'undefined') {for (var chartId in event.data['datawrapper-height']) {var iframe = document.getElementById('datawrapper-chart-' + chartId) || document.querySelector("iframe[src*='" + chartId + "']");if (!iframe) {continue;}iframe.style.height = event.data['datawrapper-height'][chartId] + 'px';}}});})();</script>

Caveats

• This data represents the number of cases and deaths reported by each state and has been collected by Johns Hopkins from a number of sources cited on their website.
• In some cases, deaths or cases of people who've crossed state lines -- either to receive treatment or because they became sick and couldn't return home while traveling -- are reported in a state they aren't currently in, because of state reporting rules.
• In some states, there are a number of cases not assigned to a specific county -- for those cases, the county name is "unassigned to a single county"
• This data should be credited to Johns Hopkins University's COVID-19 tracking project. The AP is simply making it available here for ease of use for reporters and members.
• Caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.
• Population estimates at the county level are drawn from 2014-18 5-year estimates from the American Community Survey.
• The Urban/Rural classification scheme is from the Center for Disease Control and Preventions's National Center for Health Statistics. It puts each county into one of six categories -- from Large Central Metro to Non-Core -- according to population and other characteristics. More details about the classifications can be found here.

Johns Hopkins timeseries data - Johns Hopkins pulls data regularly to update their dashboard. Once a day, around 8pm EDT, Johns Hopkins adds the counts for all areas they cover to the timeseries file. These counts are snapshots of the latest cumulative counts provided by the source on that day. This can lead to inconsistencies if a source updates their historical data for accuracy, either increasing or decreasing the latest cumulative count. - Johns Hopkins periodically edits their historical timeseries data for accuracy. They provide a file documenting all errors in their timeseries files that they have identified and fixed here

Attribution

This data should be credited to Johns Hopkins University COVID-19 tracking project

BackgroundOur study examined the global, national, and regional trends in the incidence, mortality, and disability-adjusted life years (DALYs) associated with older people’s acute myeloid leukemia (AML) over a 30 years period. AML, which predominantly affects individuals aged 60–89, is known for its severity and unfavorable prognosis. By providing insights into the growing burden of AML, our research highlights the urgent need for effective interventions and support at various levels.MethodsIn this study, we analyzed older people with AML aged 60–89 using the Global Burden of Disease (GBD) database for 2019. Our goal was to assess trends and characteristics by examining the incidence rate, mortality rate, DALYs, and estimated annual percentage change (EAPC). We aimed to provide a comprehensive understanding of the disease’s trajectory and development.ResultsIn 2019, the older age group of 60 to 89 years reported 61,559 new cases of AML, with the corresponding number of deaths being 53,620, and the estimated DALYs standing at 990,656. Over the last 30 years, the incidence rate of AML in this age bracket increased by 1.67 per 100,000 people, the mortality rate rose by 1.57 per 100,000 people, and the rate of DALYs, indicative of disease burden, climbed by 1.42 per 100,000 people. High Socio-demographic Index (SDI) regions, particularly high-income North America and Australia, had the highest incidence rates. Germany had the highest incidence rate among the 204 countries analyzed, while Monaco reported the highest mortality and DALY rates. Smoking, high body mass index, occupational exposure to benzene, and formaldehyde were identified as significant risk factors associated with mortality from older people with AML in 2019.ConclusionOur study showed that the incidence, mortality, and DALY rates of AML in the older population were strongly correlated with the SDI, and these rates have been steadily increasing. This had become an increasingly serious global health issue, particularly in areas with a high SDI. We highlighted the urgency to focus more on this disease and called for the prompt implementation of appropriate preventive and control measures.

BackgroundNumerous studies have already identified an association between excessive consumption of red meat and colorectal cancer (CRC). However, there has been a lack of detailed understanding regarding the disease burden linked to diet high in red meat and CRC.ObjectiveWe aim to offer evidence-based guidance for developing effective strategies that can mitigate the elevated CRC burden in certain countries.MethodsWe used the data from the Global Burden of Disease (GBD) Study 2019 to evaluate global, regional, and national mortality rates and disability-adjusted Life years (DALYs) related to diet high in red meat. We also considered factors such as sex, age, the socio-demographic index (SDI), and evaluated the cross-national inequalities. Furthermore, we utilized DALYs data from 204 countries and regions to measure cross-country inequalities of CRC by calculating the slope index of inequality and concentration index as standard indicators of absolute and relative inequalities.DiscussionThe results show that globally, the age-standardized mortality rate (ASMR) and age-standardized disability adjusted life year rate (ASDR) related to CRC due to diet high in red meat have decreased, with estimated annual percent change (EAPCs) of −0.32% (95% CI −0.37 to −0.28) and-0.18% (95% CI −0.25 to −0.11). Notably, the burden was higher among males and the elderly. The slope index of inequality rose from 22.0 (95% CI 18.1 to 25.9) in 1990 to 32.9 (95% CI 28.3 to 37.5) in 2019 and the concentration index fell from 59.5 (95% CI 46.4 to 72.6) in 1990 to 48.9 (95% CI 34.6 to 63.1) in 2019. Also, according to our projections, global ASDR and ASMR might tend to increase up to 2030.ConclusionASMR and ASDR for CRC associated with high red meat diets declined globally from 1990 to 2019, but the absolute number of cases is still rising, with men and the elderly being more affected. CRC associated with diets high in red meat exhibits significant income inequality, placing a disproportionate burden on wealthier countries. Moreover, according to our projections, ASMR and ASDR are likely to increase globally by 2030. In order to address this intractable disease problem, understanding changes in global and regional epidemiologic trends is critical for policy makers and others.

BackgroundThe scarcity of knowledge regarding the epidemiology and temporal patterns of viral skin diseases worldwide poses significant challenges to their control and management.MethodsWe analyzed the global incidence, prevalence, and age-standardized rates (ASR) of disability-adjusted life years (DALYs) for viral skin diseases in 2021. To examine temporal trends from 1990 to 2021, we employed the EPAC model, assessing changes by country, gender, age, Socio-demographic Index (SDI), and GBD regions. Additionally, we utilized the age-period-cohort (APC) model and the Bayesian age-period-cohort (BAPC) model to forecast the burden of viral skin diseases for the next 25 years.ResultsIn 2021, the global burden of viral skin diseases was estimated at 84.7 million incident cases, with a prevalence of over 130 million cases and 4.2 million DALYs. Males experienced a slightly higher ASR burden than females. The highest burden was observed among individuals aged 10 to 19, with significant geographical variations in cases and ASR, particularly in high SDI regions. Unexpected rises in incidence were noted in East Asia and Sub-Saharan Africa in the detected period. Despite modest declines in ASPR and ASDR, the global ASIR displayed a significant upward trend.ConclusionOur study provides detailed data on the global impact of viral skin diseases from 1990 to 2021, highlighting the need for continuous surveillance and tailored interventions to manage and reduce the effects of these diseases. Targeted public health measures are essential to address and mitigate the global health burden of viral skin diseases.

Purpose: This dataset provides age-standardized prevalence and case data for severe periodontitis among individuals aged 15 years and older, along with a time series analysis and a forecast of the global and regional burden of the disease from 2020 to 2025.Methods: Data were extracted from the WHO Global Oral Health Status Report (2022), covering the period from 1990 to 2019. The dataset is stratified by World Bank income groups and WHO regions. The analysis was performed using autoregressive integrated moving average (ARIMA) models, and future case numbers were estimated by applying prevalence forecasts to United Nations population projections.Findings: The analysis reveals a significant global burden of severe periodontitis, which is projected to increase from a global prevalence of 19.0% (~1.216 billion cases) in 2019 to 19.8% (~1.36 billion cases) by 2025. The data highlights a disproportionately higher burden in lower-middle-income countries, which are forecasted to reach 498 million cases (21.7% prevalence) by 2025. The dataset can be used by public health professionals and policymakers to inform resource allocation and strategy development for managing and preventing periodontal disease.

Air Pollution Health Impacts in London Boroughs

Incidence and 95% Confidence intervals

By data.world's Admin [source]

About this dataset

This dataset reveals the long-term health impacts of air pollution in London's boroughs. Home to over 8 million people, London's air pollution is a growing health concern and this study provides invaluable insights into the devastating effects of exposure.

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How to use the dataset

How to Use this Dataset:

This dataset provides detailed analysis of the long-term health impacts of air pollution. It includes estimated cases and costs associated with each borough, as well as projections for each scenario used in modelling the effects. This dataset is useful for learners who want to learn about how various factors, such as population growth or new technologies, may affect future health outcomes related to air pollution in London.

The columns included are ‘Scenario’ (the scenario used), ‘Year’ (the year modelled), ‘Disease’ (the type of disease modelled), ‘AgeGroup’ (the age group of the population modelled) and ‘95% CL’ (confidence level).

To understand these columns further we recommend looking at the original source report. This will provide additional detail about each element considered when modelling.

To get started with analysing this data set we recommend exploring how estimates differ between scenarios and considering which ages benefit most from different interventions proposed by London Environment Strategy for reducing diseases caused by air pollution. Additionally you could look at different diseases separately, or consider disease costs versus number of cases across different age groups and scenarios

Research Ideas

• Analyzing the long-term impact of air pollution on London's NHS and social care system by borough.
• Comparing the health impacts of different scenarios related to air pollution in different years and age groups to inform effective policymaking.
• Modeling how changes in air pollution levels might affect different diseases or health outcomes over time in a particular area or community

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: newham-no2-xlsm-18.csv | Column name | Description | |:--------------|:-----------------------------------------------------------------------------------------| | Scenario | The scenario used to model potential long-term health impacts of air pollution. (String) | | Year | Year of modelling which ranges from 2016 - 2050. (Integer) | | Disease | The type of disease attributable to air pollution. (String) | | AgeGroup | Age range which data relates to. (String) | | 95% CL | 95% Confidence Level based on modeling techniques used in study. (Float) |

File: bromley-pm25-xlsm-35.csv | Column name | Description | |:--------------|:-----------------------------------------------------------------------------------------| | Scenario | The scenario used to model potential long-term health impacts of air pollution. (String) | | Year | Year of modelling which ranges from 2016 - 2050. (Integer) | | Disease | The type of disease attributable to air pollution. (String) | | AgeGroup | Age range which data relates to. (String) | | 95% CL | 95% Confidence Level based on modeling techniques used in study. (Float) |

Acknowledgements

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BackgroundSyphilis in pregnancy imposes a significant global health and economic burden. More than half of cases result in serious adverse events, including infant mortality and infection. The annual global burden from mother-to-child transmission (MTCT) of syphilis is estimated at 3.6 million disability-adjusted life years (DALYs) and $309 million in medical costs. Syphilis screening and treatment is simple, effective, and affordable, yet, worldwide, most pregnant women do not receive these services. We assessed cost-effectiveness of scaling-up syphilis screening and treatment in existing antenatal care (ANC) programs in various programmatic, epidemiologic, and economic contexts.Methods and FindingsWe modeled the cost, health impact, and cost-effectiveness of expanded syphilis screening and treatment in ANC, compared to current services, for 1,000,000 pregnancies per year over four years. We defined eight generic country scenarios by systematically varying three factors: current maternal syphilis testing and treatment coverage, syphilis prevalence in pregnant women, and the cost of healthcare. We calculated program and net costs, DALYs averted, and net costs per DALY averted over four years in each scenario. Program costs are estimated at $4,142,287 – $8,235,796 per million pregnant women (2010 USD). Net costs, adjusted for averted medical care and current services, range from net savings of $12,261,250 to net costs of $1,736,807. The program averts an estimated 5,754 – 93,484 DALYs, yielding net savings in four scenarios, and a cost per DALY averted of $24 – $111 in the four scenarios with net costs. Results were robust in sensitivity analyses.ConclusionsEliminating MTCT of syphilis through expanded screening and treatment in ANC is likely to be highly cost-effective by WHO-defined thresholds in a wide range of settings. Countries with high prevalence, low current service coverage, and high healthcare cost would benefit most. Future analyses can be tailored to countries using local epidemiologic and programmatic data.

US: People Using Safely Managed Sanitation Services: % of Population data was reported at 89.499 % in 2015. This records an increase from the previous number of 89.448 % for 2014. US: People Using Safely Managed Sanitation Services: % of Population data is updated yearly, averaging 89.281 % from Dec 2000 (Median) to 2015, with 16 observations. The data reached an all-time high of 89.499 % in 2015 and a record low of 89.074 % in 2000. US: People Using Safely Managed Sanitation Services: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Health Statistics. The percentage of people using improved sanitation facilities that are not shared with other households and where excreta are safely disposed of in situ or transported and treated offsite. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines: ventilated improved pit latrines, compositing toilets or pit latrines with slabs.; ; WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).; Weighted Average;

BackgroundColorectal cancer (CRC) is a major global health issue, with rising incidence and mortality rates. Dietary factors, especially whole grains consumption, are critical in determining CRC risk. Understanding CRC deaths and disability-adjusted life years (DALYs) related to low whole grains diets is important for prevention. The purpose of the study is to investigate temporal and geographic trends in CRC deaths and DALYs attributable to diet low in whole grains at the global, regional, and national levels from 1990 to 2021.MethodsThe data on CRC burden attributable to diet low in whole grains from 1990 to 2021 were extracted from the Global Burden of Diseases (GBD) 2021 database. We described the CRC burden attributable to diet low in whole grains across various years, genders, age groups (5-year age groups from 25 to 94 years and 95+ years), different Socio-demographic Index (SDI) regions and countries. To illustrate the temporal trends in the burden of CRC, we calculated the estimated annual percentage change (EAPC) from 1990 to 2021.ResultsFrom 1990 to 2021, the global number of CRC deaths attributable to diet low in whole grains increased from 101,813 (95% UI: 42,588 to 151,170) to 186,257 (95% UI: 76,127 to 284,803), representing a 82.94% growth. Similarly, the number of DALYs increased from 2,540,867 (95% UI: 1,050,794 to 3,754,416) to 4,327,219 (95% UI: 1,754,865 to 6,578,232), representing a 70.30% growth. However, both the age-standardized mortality rate (ASMR) and age-standardized DALY rate (ASDR) exhibited a decline, with an EAPC of −0.82 (95% CI: −0.85 to −0.78) and − 0.84 (95% CI: −0.87 to −0.81), respectively. The disease burden is heavier in high SDI and high-middle SDI regions. However, between 1990 and 2021, the only region where both ASMR and ASDR increased was low-middle SDI, while in all other regions, they showed a declining trend. In 2021, East Asia had the highest number of CRC deaths and DALYs attributable to diet low in whole grains at the regional level, followed by Western Europe and High-income North America. Additionally, the burden is greater among males and the elderly. Between 1990 and 2021, the number of CRC deaths attributable to diet low in whole grains rose by 102.13% among males and by 63.20% among females. Generally, both the global age-specific mortality rate and the DALYs rate tend to increase with age. SDI demonstrates a nonlinear “S”-shaped correlation with both ASMR and ASDR of CRC attributable to diet low in whole grains. In 2021, the EAPC in ASMR of CRC attributable to diet low in whole grains was negatively associated with SDI (R = −0.402, p 

Chronic kidney disease has become an increasingly significant clinical and public health issue, accounting for 1.1 million deaths worldwide. Information on the epidemiology of chronic kidney disease and associated risk factors is limited in the United Arab Emirates. Therefore, this study aimed to evaluate the incidence and causes of chronic kidney disease stages 3–5 in adult United Arab Emirates nationals with or at high risk of cardiovascular disease. This retrospective study included 491 adults with or at high risk of cardiovascular disease (diabetes mellitus or associated clinical disease) who attended outpatient clinics at a tertiary care hospital in Al-Ain, United Arab Emirates. Estimated glomerular filtration rate was assessed every 3 months from baseline to June 30, 2017. Chronic kidney disease stages 3–5 were defined as an estimated glomerular filtration rate < 60 mL/min/1.73 m2 for ≥ 3 months. Multivariable Cox's proportional hazards analysis was used to determine the independent risk factors associated with developing chronic kidney disease stages 3–5. The cumulative incidence of chronic kidney disease stages 3–5 over a 9-year period was 11.4% (95% confidence interval 8.6, 14.0). The incidence rate of these disease stages was 164.8 (95% confidence interval 121.6, 207.9) per 10,000 person-years. The independent risk factors for developing chronic kidney disease stages 3–5 were older age, history of coronary heart disease, history of diabetes mellitus, and history of smoking. These data may be useful to develop effective strategies to prevent chronic kidney disease development in high-risk United Arab Emirates nationals.

BackgroundAcute viral hepatitis (AVH) remains a global health concern, with significant variations in incidence, mortality, and DALYs across different regions, age groups, genders, and socioeconomic levels. This study explores AVH trends using data from the Global Burden of Disease (GBD) database (1990–2021).MethodsWe analyzed age-standardized incidence, mortality, and DALY rates across 204 countries and 27 super-regions. Trends were quantified using the estimated annual percentage change (EAPC) and decomposition analysis to assess the contributions of population growth, aging, and epidemiological shifts. Autoregressive integrated moving average (ARIMA) and age-period-cohort (APC) models were also applied, while global health inequalities were evaluated via the Slope Index of Inequality (SII) and Concentration Index (CI).ResultsIncidence and mortality rates were notably higher in individuals under 20 and over 80 years, with males consistently at greater risk than females. Acute Hepatitis A predominates in low-SDI regions, whereas Acute Hepatitis B is more prevalent in high-SDI regions. Projections indicate a continued decline in AVH burden by 2031, driven by population dynamics and epidemiological changes.ConclusionWhile global AVH burden is decreasing, significant disparities persist, warranting tailored interventions to enhance resource equity in high-SDI regions and strengthen healthcare infrastructure in low-SDI areas.

Aim: We aimed to estimate the disease burden and risk factors attributable to ovarian cancer, and epidemiological trends at global, regional, and national levels.Methods: We described ovarian cancer data on incidence, mortality, and disability-adjusted life-years as well as age-standardized rates from 1990 to 2017 from the Global Health Data Exchange database. We also estimated the risk factors attributable to ovarian cancer deaths and disability-adjusted life-years. Measures were stratified by region, country, age, and socio-demographic index. The estimated annual percentage changes and age-standardized rates were calculated to evaluate temporal trends.Results: Globally, ovarian cancer incident, death cases, and disability-adjusted life-years increased by 88.01, 84.20, and 78.00%, respectively. However, all the corresponding age-standardized rates showed downward trends with an estimated annual percentage change of −0.10 (−0.03 to 0.16), −0.33 (−0.38 to −0.27), and −0.38 (−0.32 to 0.25), respectively. South and East Asia and Western Europe carried the heaviest disease burden. The highest incidence, deaths, and disability-adjusted life-years were mainly in people aged 50–69 years from 1990 to 2017. High fasting plasma glucose level was the greatest contributor in age-standardized disability-adjusted life-years rate globally as well as in all socio-demographic index quintiles and most Global Disease Burden regions. Other important factors were high body mass index and occupational exposure to asbestos.Conclusion: Our study provides valuable information on patterns and trends of disease burden and risk factors attributable to ovarian cancer across age, socio-demographic index, region, and country, which may help improve the rational allocation of health resources as well as inform health policies.

BackgroundFlavivirus pose a continued threat to global health, yet their worldwide burden and trends remain poorly quantified. We aimed to evaluate the global, regional, and national incidence of three common flavivirus infections (Dengue, yellow fever, and Zika) from 2011 to 2021.MethodsData on the number and rate of incidence for the three common flavivirus infection in 204 countries and territories were retrieved from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021. The estimated annual percent change (EAPC) was calculated to quantify the temporal trend during 2011–2016, 2016–2019, and 2019–2021, respectively.ResultsIn 2021, an estimated 59,220,428 individuals were infected globally, comprising 58,964,185 cases of dengue, 86,509 cases of yellow fever, and 169,734 cases of Zika virus infection. The age-standardized incidence rate (ASIR) of the three common flavivirus infections increased by an annual average of 5.08% (95% CI 4.12 to 6.05) globally from 2011 to 2016, whereas decreased by an annual average of −8.37% (95% CI −12.46 to −4.08) per year between 2016 to 2019. The ASIR remained stable during 2019–2021, with an average change of 0.69% (95% CI −0.96 to 2.37) per year globally for the three common flavivirus infections. Regionally, the burden of the three common flavivirus infections was primarily concentrated in those regions with middle income, such as South Asia, Southeast Asia, and Tropical Latin America. Additionally, at the country level, there was an inverted “U” relationship between the SDI level and the ASI. Notably, an increase in the average age of infected cases has been observed worldwide, particularly in higher-income regions.ConclusionFlavivirus infections are an expanding public health concern worldwide, with considerable regional and demographic variation in the incidence. Policymakers and healthcare providers must stay vigilant regarding the impact of COVID-19 and other environmental factors on the risk of flavivirus infection and be prepared for potential future outbreaks.