This dataset contains counts of deaths for California as a whole 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 California regardless of the place of residence (by occurrence) and deaths to California residents (by residence), whereas the provisional data table only includes deaths that occurred in California 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 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.

Background: It is not known how the number of deaths due to COVID-19 compare to the number of deaths due to "unsafe water, sanitation, and handwashing" during the COVID-19 global health emergency. Methods: A dataset of deaths due to COVID-19 was downloaded from the World Health Organization. A dataset summarizing deaths due to unsafe water, sanitation, and handwashing was obtained from the Institute for Health Metrics and Evaluation (IHME).
Results indicate that COVID-19 deaths in Africa and South East Asia regions exceeded those due to unsafe water, sanitation, and hygiene. Methods Two raw datasets were obtained and processed.
To construct the dataset, "Estimates of mortality due to inadequate water, sanitation, and hygiene (WASH) during the COVID-19 Global Health Emergency" raw data were downloaded from the Institute for Health Metrics and Evaluation (IHME). The raw dataset was reduced, eliminating variables. The original IHME dataset was for the year 2019. IMHE does not yet have data for 2020 or beyond. The final data contains calculations that project into 2020-2023 the estimated number of WASH-related deaths, by region. That was done by multiplying the 2019 estimated deaths by regions, by a factor of the duration of the pandemic period/the number of days in 2019, assuming a constant rate. To construct the dataset "Estimates of COVID-19 mortality, by region January 3 2020-May 5, 2023, with assumptions about undercounting" raw data were downloaded from the public WHO Coronavirus (COVID-19) Dashboard. The raw dataset contains COVID-19 mortality data by country, by date. The raw data were tabulated by region (rather than by country) and for the period of January 3, 2020-May 5, 2023 (rather than by date).

Machine learning (ML) and deep learning (DL) models are being increasingly employed for medical imagery analyses, with both approaches used to enhance the accuracy of classification/prediction in the diagnoses of various cancers, tumors and bloodborne diseases. To date however, no review of these techniques and their application(s) within the domain of white blood cell (WBC) classification in blood smear images has been undertaken, representing a notable knowledge gap with respect to model selection and comparison. Accordingly, the current study sought to comprehensively identify, explore and contrast ML and DL methods for classifying WBCs. Following development and implementation of a formalized review protocol, a cohort of 136 primary studies published between January 2006 and May 2023 were identified from the global literature, with the most widely used techniques and best-performing WBC classification methods subsequently ascertained. Studies derived from 26 countries, with highest numbers from high-income countries including the United States (n = 32) and The Netherlands (n = 26). While WBC classification was originally rooted in conventional ML, there has been a notable shift toward the use of DL, and particularly convolutional neural networks (CNN), with 54.4% of identified studies (n = 74) including the use of CNNs, and particularly in concurrence with larger datasets and bespoke features e.g., parallel data pre-processing, feature selection, and extraction. While some conventional ML models achieved up to 99% accuracy, accuracy was shown to decrease in concurrence with decreasing dataset size. Deep learning models exhibited improved performance for more extensive datasets and exhibited higher levels of accuracy in concurrence with increasingly large datasets. Availability of appropriate datasets remains a primary challenge, potentially resolvable using data augmentation techniques. Moreover, medical training of computer science researchers is recommended to improve current understanding of leucocyte structure and subsequent selection of appropriate classification models. Likewise, it is critical that future health professionals be made aware of the power, efficacy, precision and applicability of computer science, soft computing and artificial intelligence contributions to medicine, and particularly in areas like medical imaging.

As part of the Pacific Dataviz Challenge 2024,the New Caledonian government's Department of Health and Social Affairs (DASS) is making available the list of medical causes of death in New Caledonia between 2011 and 2023.

This dataset includes medical causes of death in New Caledonia from 2011 to 2023.

For reasons of confidentiality linked to medical secrecy, people's ages have been classified by broad category.

Medical causes are coded in ICD-10 (International Classification of Diseases established by the World Health Organization).

The study of medical causes of death in New Caledonia, whether or not the person is domiciled in the territory, is essential for public health. It enables us to identify the most pressing health problems, as well as vulnerable populations, in order to guide public policy and set up appropriate health programs.

Medical causes of death are recorded on medical certificates. Their analysis focuses on the initial cause mentioned by the physician as being at the origin of the health event leading to death.

It should be noted that this dataset is subject to certain biases, as medical certificates are completed on the basis of the information available at the time of death, which means that the doctor does not systematically have all the information needed to identify the initial cause of death.

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.

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.

BackgroundGlobally, HIV infection remains a leading cause of morbidity and mortality. Despite reducing new infections, the global response to advanced HIV disease (AHD) remains ineffective, leaving HIV epidemics a significant public health threat worldwide. In Ethiopia, evidence regarding AHD is scarce. Therefore, this study aimed to assess the prevalence and predictors of AHD among newly diagnosed people living with HIV (PLHIV) initiating antiretroviral therapy in the Gedeo zone, southern Ethiopia.MethodsA facility-based cross-sectional study was conducted from May 29, 2023, to February 06, 2024, at health facilities providing HIV care in the Gedeo zone, southern Ethiopia. A total of 427 PLHIV-initiating antiretroviral therapy (ART) were recruited for the study. The data were collected through face-to-face interviews and record reviews using KoboCollect version 2.4 and analyzed using R version 4.3.3. The Akaike information criterion (AIC) model selection was used to evaluate and choose the best-fitting model to describe the relationship between AHD and predictors. Finally, variables with a p-value less than 0.05 were considered independent predictors in the multivariable regression analysis.ResultsThe study participants’ mean (±SD) age was 31.3 (±8.7) years. The overall prevalence of AHD among newly diagnosed PLHIV-initiating ART was 34.4% (95% CI: 29.8%, 39.1%). Rural residence (AOR = 3.48, 95% CI: 2.24, 5.47), alcohol consumption (AOR = 2.48, 95% CI: 1.59, 3.90), and being identified through community-based index case testing (ICT) (AOR = 0.26, 95% CI: 0.13, 0.51) were found to be independent predictors of AHD.ConclusionsThe prevalence of AHD among newly diagnosed individuals initiating ART was high. PLHIV who consume alcohol should receive detailed counseling on how it can negatively impact their progress with antiretroviral treatment. HIV testing should be enhanced in rural communities by strengthening community health campaigns. Furthermore, community-based index case testing should be strengthened for early identification of PLHIV.