Time series data for the statistic Mortality rate, under-5 (per 1,000 live births) and country Finland. Indicator Definition:Under-five mortality rate is the probability per 1,000 that a newborn baby will die before reaching age five, if subject to age-specific mortality rates of the specified year.The indicator "Mortality rate, under-5 (per 1,000 live births)" stands at 2.30 as of 12/31/2023. Regarding the One-Year-Change of the series, the current value is equal to the value the year prior.The 1 year change in percent is 0.0.The 3 year change in percent is 0.0.The 5 year change in percent is -4.17.The 10 year change in percent is -11.54.The Serie's long term average value is 8.46. It's latest available value, on 12/31/2023, is 72.81 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2019, to it's latest available value, on 12/31/2023, is +0.0%.The Serie's change in percent from it's maximum value, on 12/31/1960, to it's latest available value, on 12/31/2023, is -91.42%.

Time series data for the statistic Mortality rate, under-5 (per 1,000 live births) and country Marshall Islands. Indicator Definition:Under-five mortality rate is the probability per 1,000 that a newborn baby will die before reaching age five, if subject to age-specific mortality rates of the specified year.The indicator "Mortality rate, under-5 (per 1,000 live births)" stands at 28.20 as of 12/31/2023, the lowest value at least since 12/31/1961, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes a decrease of -3.09 percent compared to the value the year prior.The 1 year change in percent is -3.09.The 3 year change in percent is -9.03.The 5 year change in percent is -14.29.The 10 year change in percent is -24.80.The Serie's long term average value is 56.21. It's latest available value, on 12/31/2023, is 49.83 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2023, to it's latest available value, on 12/31/2023, is +0.0%.The Serie's change in percent from it's maximum value, on 12/31/1960, to it's latest available value, on 12/31/2023, is -76.81%.

Under-five mortality rate including country, regional, and global breakdown

Definition:
Probability of dying between birth and exactly 5 years of age, expressed per 1,000 live births

Time series data for the statistic Mortality rate, under-5 (per 1,000 live births) and country Lebanon. Indicator Definition:Under-five mortality rate is the probability per 1,000 that a newborn baby will die before reaching age five, if subject to age-specific mortality rates of the specified year.The indicator "Mortality rate, under-5 (per 1,000 live births)" stands at 18.30 as of 12/31/2023, the highest value since 12/31/2002. Regarding the One-Year-Change of the series, the current value constitutes an increase of 5.78 percent compared to the value the year prior.The 1 year change in percent is 5.78.The 3 year change in percent is 18.06.The 5 year change in percent is 30.71.The 10 year change in percent is 47.58.The Serie's long term average value is 41.60. It's latest available value, on 12/31/2023, is 56.01 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2012, to it's latest available value, on 12/31/2023, is +47.58%.The Serie's change in percent from it's maximum value, on 12/31/1976, to it's latest available value, on 12/31/2023, is -90.17%.

Number of infant deaths and infant mortality rates, by age group (neonatal and post-neonatal), 1991 to most recent year.

Retirement Notice: This item is in mature support as of August 2025 and will be retired in December 2026. Please use this source dataset and follow the steps in the From Vector to Raster blog as a replacement for this service. Esri recommends updating your maps and apps. The annual infant mortality rate in Africa ranges from 99 to 2031 deaths of children less than one-year-old per 10,000 live births. This layer provides access to an approximately 5 km cell sized raster of the Global Subnational Infant Mortality Rates dataset that provides the number of deaths of children less than one-year-old per 10,000 live births in the year 2000. The data cover Africa, Madagascar, and other islands near Africa and were produced by the NASA Socioeconomic Data and Applications Center in 2005. Link to source metadata Dataset SummaryAnalysis: Restricted single source analysis. Maximum size of analysis is 24,000 x 24,000 pixels. What can you do with this layer?This layer has query, identify, and export image services available. The layer is restricted to a 24,000 x 24,000 pixel limit for these services. The source data for this layer are available here. Restricted single source analysis means this layer has size constraints for analysis and it is not recommended for use with other layers in multisource analysis.

Time series data for the statistic Mortality rate, under-5 (per 1,000 live births) and country British Virgin Islands. Indicator Definition:Under-five mortality rate is the probability per 1,000 that a newborn baby will die before reaching age five, if subject to age-specific mortality rates of the specified year.The indicator "Mortality rate, under-5 (per 1,000 live births)" stands at 12.70 as of 12/31/2023, the lowest value at least since 12/31/1961, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes a decrease of -3.05 percent compared to the value the year prior.The 1 year change in percent is -3.05.The 3 year change in percent is -8.63.The 5 year change in percent is -13.01.The 10 year change in percent is -18.59.The Serie's long term average value is 30.55. It's latest available value, on 12/31/2023, is 58.43 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2023, to it's latest available value, on 12/31/2023, is +0.0%.The Serie's change in percent from it's maximum value, on 12/31/1960, to it's latest available value, on 12/31/2023, is -85.94%.

In 2023, the infant mortality rate in India was at about 24.5 deaths per 1,000 live births, a significant decrease from previous years. Infant mortality as an indicatorThe infant mortality rate is the number of deaths of children under one year of age per 1,000 live births. This rate is an important key indicator for a country’s health and standard of living; a low infant mortality rate indicates a high standard of healthcare. Causes of infant mortality include premature birth, sepsis or meningitis, sudden infant death syndrome, and pneumonia. Globally, the infant mortality rate has shrunk from 63 infant deaths per 1,000 live births to 27 since 1990 and is forecast to drop to 8 infant deaths per 1,000 live births by the year 2100. India’s rural problemWith 32 infant deaths per 1,000 live births, India is neither among the countries with the highest nor among those with the lowest infant mortality rate. Its decrease indicates an increase in medical care and hygiene, as well as a decrease in female infanticide. Increasing life expectancy at birth is another indicator that shows that the living conditions of the Indian population are improving. Still, India’s inhabitants predominantly live in rural areas, where standards of living as well as access to medical care and hygiene are traditionally lower and more complicated than in cities. Public health programs are thus put in place by the government to ensure further improvement.

This appendix formed part of the original submission and has been peer reviewed. The journal post it as supplied by the authors.Supplement to: Alkema L, Chao F, You D, Pedersen J, Sawyer CC. National, regional, and global sex ratios of infant, child, and under-5 mortality and identification of countries with outlying ratios: a systematic assessment. Lancet Glob Health 2014; 2: e521–30.

The annual infant mortality rate in Africa ranges from 99 to 2031 deaths of children less than one-year-old per 10,000 live births. This layer provides access to an approximately 5 km cell sized raster of the Global Subnational Infant Mortality Rates dataset that provides the number of deaths of children less than one-year-old per 10,000 live births in the year 2000. The data cover Africa, Madagascar, and other islands near Africa and were produced by the NASA Socioeconomic Data and Applications Center in 2005. Link to source metadata Dataset SummaryAnalysis: Restricted single source analysis. Maximum size of analysis is 24,000 x 24,000 pixels. What can you do with this layer?This layer has query, identify, and export image services available. The layer is restricted to a 24,000 x 24,000 pixel limit for these services. The source data for this layer are available here. Restricted single source analysis means this layer has size constraints for analysis and it is not recommended for use with other layers in multisource analysis. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.

Important Note: This item is in mature support as of August 2025 and will be retired in December 2026. Please use this source dataset and follow the steps in the From Vector to Raster blog as a replacement for this service. Esri recommends updating your maps and apps. The annual infant mortality rate in Africa ranges from 99 to 2031 deaths of children less than one-year-old per 10,000 live births. This layer provides access to an approximately 5 km cell sized raster of the Global Subnational Infant Mortality Rates dataset that provides the number of deaths of children less than one-year-old per 10,000 live births in the year 2000. The data cover Africa, Madagascar, and other islands near Africa and were produced by the NASA Socioeconomic Data and Applications Center in 2005. Link to source metadata Dataset SummaryAnalysis: Restricted single source analysis. Maximum size of analysis is 24,000 x 24,000 pixels. What can you do with this layer?This layer has query, identify, and export image services available. The layer is restricted to a 24,000 x 24,000 pixel limit for these services. The source data for this layer are available here. Restricted single source analysis means this layer has size constraints for analysis and it is not recommended for use with other layers in multisource analysis. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.

Time series data for the statistic Mortality rate, under-5 (per 1,000 live births) and country South Sudan. Indicator Definition:Under-five mortality rate is the probability per 1,000 that a newborn baby will die before reaching age five, if subject to age-specific mortality rates of the specified year.The indicator "Mortality rate, under-5 (per 1,000 live births)" stands at 98.70 as of 12/31/2023. Regarding the One-Year-Change of the series, the current value is equal to the value the year prior.The 1 year change in percent is 0.0.The 3 year change in percent is 0.0.The 5 year change in percent is -25.45.The 10 year change in percent is -0.504.The Serie's long term average value is 253.54. It's latest available value, on 12/31/2023, is 61.07 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2019, to it's latest available value, on 12/31/2023, is +0.0%.The Serie's change in percent from it's maximum value, on 12/31/1988, to it's latest available value, on 12/31/2023, is -87.14%.

HIV MLT refers to the model presented in this paper with three possible input combinations: 1) HIV prevalence with life expectancy at birth [input: ], 2) HIV prevalence with child mortality [input: ], or 3) HIV prevalence with child mortality and adult mortality [input: and ]. MAE for ‘All-ages’, , and expressed per 1,000 and the smallest number in each column is bolded. For HIV MLT model, ‘–’ indicates MAE 0.001 (or 0.01 for ).‘All-ages’ refers to the mean absolute error for the non-logged mortality rates across age groups (0, 1–4, 5–9, 10–14,…, 75) and amongst all life tables ().‘WHO’, ‘CD’, and ‘Log-Quad’ contain blank spaces as these quantities are inputs to these systems and thus have no error.

BackgroundSustainable Development Goal 3 (SDG 3), focusing on ensuring healthy lives and well-being for all, holds global significance and is particularly vital for Bangladesh. Neonatal Mortality Rate (NMR), Under-5 Mortality Rate (U5MR), Maternal Mortality Ratio (MMR) and Death Rate Due to Road Traffic Injuries (RTI) are considered responsible indicators of SDG 3 progress in Bangladesh. The objective of the study is to forecast these indicators of Bangladesh up to 2030 and compare these forecasts with predetermined 2030 targets. The data is obtained from the World Bank’s (WB) website.MethodFor forecasting, time series models were employed, specifically Autoregressive Integrated Moving Average- ARIMA (0,2,1) with Akaike Information Criterion (AIC) 94.6 for NMR and ARIMA (2,1,2) with AIC 423.2 for U5MR, selected based on their lowest AIC values. Additionally, Machine Learning (ML) models, including Bidirectional Recurrent Neural Networks (BRNN) and Elastic Neural Networks (ENET), were employed for all the indicators.ResultsENET demonstrates superior performance compared to both BRNN and ARIMA in the context of NMR, achieving a Root Mean Absolute Error (RMAE) of 0.603446 and a Root Mean Square Error (RMSE) of 0.451162. Furthermore, when considering U5MR, MMR, and Death Rate Due to RTI, ENET consistently exhibits lower error metrics compared to the alternative models. Following the time series and ML analyses, a consistent trend emerges in the forecasted values for NMR and U5MR, which consistently fall below their respective 2030 targets. This promising finding suggests that Bangladesh is making significant progress toward meeting its 2030 targets for NMR and U5MR. However, in the cases of MMR and Death Rate Due to RTI, the forecasted values exceeded 2030 targets. This indicates that Bangladesh faces challenges in meeting the 2030 targets for MMR and Death Rate Due to RTI.ConclusionThe analyses underscore the importance of SDG 3 in Bangladesh and its progress towards ensuring healthy lives and well-being for all. While there is optimism regarding NMR and U5MR, more focused efforts may be needed to address the challenges posed by MMR and Death Rate Due to RTI to align with the 2030 targets. This study contributes valuable insights into Bangladesh’s journey toward sustainable development in the realm of health and well-being.

BackgroundSustainable Development Goal 3 (SDG 3), focusing on ensuring healthy lives and well-being for all, holds global significance and is particularly vital for Bangladesh. Neonatal Mortality Rate (NMR), Under-5 Mortality Rate (U5MR), Maternal Mortality Ratio (MMR) and Death Rate Due to Road Traffic Injuries (RTI) are considered responsible indicators of SDG 3 progress in Bangladesh. The objective of the study is to forecast these indicators of Bangladesh up to 2030 and compare these forecasts with predetermined 2030 targets. The data is obtained from the World Bank’s (WB) website.MethodFor forecasting, time series models were employed, specifically Autoregressive Integrated Moving Average- ARIMA (0,2,1) with Akaike Information Criterion (AIC) 94.6 for NMR and ARIMA (2,1,2) with AIC 423.2 for U5MR, selected based on their lowest AIC values. Additionally, Machine Learning (ML) models, including Bidirectional Recurrent Neural Networks (BRNN) and Elastic Neural Networks (ENET), were employed for all the indicators.ResultsENET demonstrates superior performance compared to both BRNN and ARIMA in the context of NMR, achieving a Root Mean Absolute Error (RMAE) of 0.603446 and a Root Mean Square Error (RMSE) of 0.451162. Furthermore, when considering U5MR, MMR, and Death Rate Due to RTI, ENET consistently exhibits lower error metrics compared to the alternative models. Following the time series and ML analyses, a consistent trend emerges in the forecasted values for NMR and U5MR, which consistently fall below their respective 2030 targets. This promising finding suggests that Bangladesh is making significant progress toward meeting its 2030 targets for NMR and U5MR. However, in the cases of MMR and Death Rate Due to RTI, the forecasted values exceeded 2030 targets. This indicates that Bangladesh faces challenges in meeting the 2030 targets for MMR and Death Rate Due to RTI.ConclusionThe analyses underscore the importance of SDG 3 in Bangladesh and its progress towards ensuring healthy lives and well-being for all. While there is optimism regarding NMR and U5MR, more focused efforts may be needed to address the challenges posed by MMR and Death Rate Due to RTI to align with the 2030 targets. This study contributes valuable insights into Bangladesh’s journey toward sustainable development in the realm of health and well-being.

BackgroundSustainable Development Goal 3 (SDG 3), focusing on ensuring healthy lives and well-being for all, holds global significance and is particularly vital for Bangladesh. Neonatal Mortality Rate (NMR), Under-5 Mortality Rate (U5MR), Maternal Mortality Ratio (MMR) and Death Rate Due to Road Traffic Injuries (RTI) are considered responsible indicators of SDG 3 progress in Bangladesh. The objective of the study is to forecast these indicators of Bangladesh up to 2030 and compare these forecasts with predetermined 2030 targets. The data is obtained from the World Bank’s (WB) website.MethodFor forecasting, time series models were employed, specifically Autoregressive Integrated Moving Average- ARIMA (0,2,1) with Akaike Information Criterion (AIC) 94.6 for NMR and ARIMA (2,1,2) with AIC 423.2 for U5MR, selected based on their lowest AIC values. Additionally, Machine Learning (ML) models, including Bidirectional Recurrent Neural Networks (BRNN) and Elastic Neural Networks (ENET), were employed for all the indicators.ResultsENET demonstrates superior performance compared to both BRNN and ARIMA in the context of NMR, achieving a Root Mean Absolute Error (RMAE) of 0.603446 and a Root Mean Square Error (RMSE) of 0.451162. Furthermore, when considering U5MR, MMR, and Death Rate Due to RTI, ENET consistently exhibits lower error metrics compared to the alternative models. Following the time series and ML analyses, a consistent trend emerges in the forecasted values for NMR and U5MR, which consistently fall below their respective 2030 targets. This promising finding suggests that Bangladesh is making significant progress toward meeting its 2030 targets for NMR and U5MR. However, in the cases of MMR and Death Rate Due to RTI, the forecasted values exceeded 2030 targets. This indicates that Bangladesh faces challenges in meeting the 2030 targets for MMR and Death Rate Due to RTI.ConclusionThe analyses underscore the importance of SDG 3 in Bangladesh and its progress towards ensuring healthy lives and well-being for all. While there is optimism regarding NMR and U5MR, more focused efforts may be needed to address the challenges posed by MMR and Death Rate Due to RTI to align with the 2030 targets. This study contributes valuable insights into Bangladesh’s journey toward sustainable development in the realm of health and well-being.

Time series data for the statistic Mortality rate, under-5 (per 1,000 live births) and country Sudan. Indicator Definition:Under-five mortality rate is the probability per 1,000 that a newborn baby will die before reaching age five, if subject to age-specific mortality rates of the specified year.The indicator "Mortality rate, under-5 (per 1,000 live births)" stands at 50.10 as of 12/31/2023, the lowest value at least since 12/31/1961, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes a decrease of -3.28 percent compared to the value the year prior.The 1 year change in percent is -3.28.The 3 year change in percent is -9.40.The 5 year change in percent is -14.94.The 10 year change in percent is -26.65.The Serie's long term average value is 120.60. It's latest available value, on 12/31/2023, is 58.46 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2023, to it's latest available value, on 12/31/2023, is +0.0%.The Serie's change in percent from it's maximum value, on 12/31/1983, to it's latest available value, on 12/31/2023, is -72.50%.

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.

A. SUMMARY This dataset shows San Francisco COVID-19 deaths by population characteristics. This data may not be immediately available for recently reported deaths. Data updates as more information becomes available. Because of this, death totals may increase or decrease.

Population characteristics are subgroups, or demographic cross-sections, like age, race, or gender. The City tracks how deaths have been distributed among different subgroups. This information can reveal trends and disparities among groups.

B. HOW THE DATASET IS CREATED As of January 1, 2023, COVID-19 deaths are defined as persons who had COVID-19 listed as a cause of death or a significant condition contributing to their death on their death certificate. This definition is in alignment with the California Department of Public Health and the national https://preparedness.cste.org/wp-content/uploads/2022/12/CSTE-Revised-Classification-of-COVID-19-associated-Deaths.Final_.11.22.22.pdf">Council of State and Territorial Epidemiologists. Death certificates are maintained by the California Department of Public Health.

Data on the population characteristics of COVID-19 deaths are from: *Case reports *Medical records *Electronic lab reports *Death certificates

Data are continually updated to maximize completeness of information and reporting on San Francisco COVID-19 deaths.

To protect resident privacy, we summarize COVID-19 data by only one population characteristic at a time. Data are not shown until cumulative citywide deaths reach five or more.

Data notes on select population characteristic types are listed below.

Race/ethnicity * We include all race/ethnicity categories that are collected for COVID-19 cases.

Gender * The City collects information on gender identity using these guidelines.

C. UPDATE PROCESS Updates automatically at 06:30 and 07:30 AM Pacific Time on Wednesday each week.

Dataset will not update on the business day following any federal holiday.

D. HOW TO USE THIS DATASET Population estimates are only available for age groups and race/ethnicity categories. San Francisco population estimates for race/ethnicity and age groups can be found in a dataset based on the San Francisco Population and Demographic Census dataset.These population estimates are from the 2018-2022 5-year American Community Survey (ACS).

This dataset includes several characteristic types. Filter the “Characteristic Type” column to explore a topic area. Then, the “Characteristic Group” column shows each group or category within that topic area and the number of cumulative deaths.

Cumulative deaths are the running total of all San Francisco COVID-19 deaths in that characteristic group up to the date listed.

To explore data on the total number of deaths, use the COVID-19 Deaths Over Time dataset.

E. CHANGE LOG

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, tests, and associated deaths from COVID-19 that have been reported among Connecticut residents. 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 daily COVID-19 update. The case rate per 100,000 includes probable and confirmed cases. Probable and confirmed are defined using the CSTE case definition, which is available online: https://cdn.ymaws.com/www.cste.org/resource/resmgr/2020ps/Interim-20-ID-01_COVID-19.pdf 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. 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 CO