Effective June 28, 2023, this dataset will no longer be updated. Similar data are accessible from CDC WONDER (https://wonder.cdc.gov/mcd-icd10-provisional.html).

Deaths involving coronavirus disease 2019 (COVID-19) with a focus on ages 0-18 years in the United States.

Data for CDC’s COVID Data Tracker site on Rates of COVID-19 Cases and Deaths by Vaccination Status. Click 'More' for important dataset description and footnotes

Dataset and data visualization details: These data were posted on October 21, 2022, archived on November 18, 2022, and revised on February 22, 2023. These data reflect cases among persons with a positive specimen collection date through September 24, 2022, and deaths among persons with a positive specimen collection date through September 3, 2022.

Vaccination status: A person vaccinated with a primary series had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after verifiably completing the primary series of an FDA-authorized or approved COVID-19 vaccine. An unvaccinated person had SARS-CoV-2 RNA or antigen detected on a respiratory specimen and has not been verified to have received COVID-19 vaccine. Excluded were partially vaccinated people who received at least one FDA-authorized vaccine dose but did not complete a primary series ≥14 days before collection of a specimen where SARS-CoV-2 RNA or antigen was detected. Additional or booster dose: A person vaccinated with a primary series and an additional or booster dose had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after receipt of an additional or booster dose of any COVID-19 vaccine on or after August 13, 2021. For people ages 18 years and older, data are graphed starting the week including September 24, 2021, when a COVID-19 booster dose was first recommended by CDC for adults 65+ years old and people in certain populations and high risk occupational and institutional settings. For people ages 12-17 years, data are graphed starting the week of December 26, 2021, 2 weeks after the first recommendation for a booster dose for adolescents ages 16-17 years. For people ages 5-11 years, data are included starting the week of June 5, 2022, 2 weeks after the first recommendation for a booster dose for children aged 5-11 years. For people ages 50 years and older, data on second booster doses are graphed starting the week including March 29, 2022, when the recommendation was made for second boosters. Vertical lines represent dates when changes occurred in U.S. policy for COVID-19 vaccination (details provided above). Reporting is by primary series vaccine type rather than additional or booster dose vaccine type. The booster dose vaccine type may be different than the primary series vaccine type. ** Because data on the immune status of cases and associated deaths are unavailable, an additional dose in an immunocompromised person cannot be distinguished from a booster dose. This is a relevant consideration because vaccines can be less effective in this group. Deaths: A COVID-19–associated death occurred in a person with a documented COVID-19 diagnosis who died; health department staff reviewed to make a determination using vital records, public health investigation, or other data sources. Rates of COVID-19 deaths by vaccination status are reported based on when the patient was tested for COVID-19, not the date they died. Deaths usually occur up to 30 days after COVID-19 diagnosis. Participating jurisdictions: Currently, these 31 health departments that regularly link their case surveillance to immunization information system data are included in these incidence rate estimates: Alabama, Arizona, Arkansas, California, Colorado, Connecticut, District of Columbia, Florida, Georgia, Idaho, Indiana, Kansas, Kentucky, Louisiana, Massachusetts, Michigan, Minnesota, Nebraska, New Jersey, New Mexico, New York, New York City (New York), North Carolina, Philadelphia (Pennsylvania), Rhode Island, South Dakota, Tennessee, Texas, Utah, Washington, and West Virginia; 30 jurisdictions also report deaths among vaccinated and unvaccinated people. These jurisdictions represent 72% of the total U.S. population and all ten of the Health and Human Services Regions. Data on cases

Data for CDC’s COVID Data Tracker site on Rates of COVID-19 Cases and Deaths by Vaccination Status. Click 'More' for important dataset description and footnotes

Dataset and data visualization details: These data were posted on October 21, 2022, archived on November 18, 2022, and revised on February 22, 2023. These data reflect cases among persons with a positive specimen collection date through September 24, 2022, and deaths among persons with a positive specimen collection date through September 3, 2022.

Vaccination status: A person vaccinated with a primary series had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after verifiably completing the primary series of an FDA-authorized or approved COVID-19 vaccine. An unvaccinated person had SARS-CoV-2 RNA or antigen detected on a respiratory specimen and has not been verified to have received COVID-19 vaccine. Excluded were partially vaccinated people who received at least one FDA-authorized vaccine dose but did not complete a primary series ≥14 days before collection of a specimen where SARS-CoV-2 RNA or antigen was detected. Additional or booster dose: A person vaccinated with a primary series and an additional or booster dose had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after receipt of an additional or booster dose of any COVID-19 vaccine on or after August 13, 2021. For people ages 18 years and older, data are graphed starting the week including September 24, 2021, when a COVID-19 booster dose was first recommended by CDC for adults 65+ years old and people in certain populations and high risk occupational and institutional settings. For people ages 12-17 years, data are graphed starting the week of December 26, 2021, 2 weeks after the first recommendation for a booster dose for adolescents ages 16-17 years. For people ages 5-11 years, data are included starting the week of June 5, 2022, 2 weeks after the first recommendation for a booster dose for children aged 5-11 years. For people ages 50 years and older, data on second booster doses are graphed starting the week including March 29, 2022, when the recommendation was made for second boosters. Vertical lines represent dates when changes occurred in U.S. policy for COVID-19 vaccination (details provided above). Reporting is by primary series vaccine type rather than additional or booster dose vaccine type. The booster dose vaccine type may be different than the primary series vaccine type. ** Because data on the immune status of cases and associated deaths are unavailable, an additional dose in an immunocompromised person cannot be distinguished from a booster dose. This is a relevant consideration because vaccines can be less effective in this group. Deaths: A COVID-19–associated death occurred in a person with a documented COVID-19 diagnosis who died; health department staff reviewed to make a determination using vital records, public health investigation, or other data sources. Rates of COVID-19 deaths by vaccination status are reported based on when the patient was tested for COVID-19, not the date they died. Deaths usually occur up to 30 days after COVID-19 diagnosis. Participating jurisdictions: Currently, these 31 health departments that regularly link their case surveillance to immunization information system data are included in these incidence rate estimates: Alabama, Arizona, Arkansas, California, Colorado, Connecticut, District of Columbia, Florida, Georgia, Idaho, Indiana, Kansas, Kentucky, Louisiana, Massachusetts, Michigan, Minnesota, Nebraska, New Jersey, New Mexico, New York, New York City (New York), North Carolina, Philadelphia (Pennsylvania), Rhode Island, South Dakota, Tennessee, Texas, Utah, Washington, and West Virginia; 30 jurisdictions also report deaths among vaccinated and unvaccinated people. These jurisdictions represent 72% of the total U.S. population and all ten of the Health and Human Services Regions. Data on cases

Effective September 27, 2023, this dataset will no longer be updated. Similar data are accessible from wonder.cdc.gov.

Deaths involving COVID-19, pneumonia, and influenza reported to NCHS by sex, age group, and jurisdiction of occurrence.

Note: Data elements were retired from HERDS on 10/6/23 and this dataset was archived.

This dataset includes the cumulative number and percent of healthcare facility-reported fatalities for patients with lab-confirmed COVID-19 disease by reporting date and age group. This dataset does not include fatalities related to COVID-19 disease that did not occur at a hospital, nursing home, or adult care facility. The primary goal of publishing this dataset is to provide users with information about healthcare facility fatalities among patients with lab-confirmed COVID-19 disease.

The information in this dataset is also updated daily on the NYS COVID-19 Tracker at https://www.ny.gov/covid-19tracker.

The data source for this dataset is the daily COVID-19 survey through the New York State Department of Health (NYSDOH) Health Electronic Response Data System (HERDS). Hospitals, nursing homes, and adult care facilities are required to complete this survey daily. The information from the survey is used for statewide surveillance, planning, resource allocation, and emergency response activities. Hospitals began reporting for the HERDS COVID-19 survey in March 2020, while Nursing Homes and Adult Care Facilities began reporting in April 2020. It is important to note that fatalities related to COVID-19 disease that occurred prior to the first publication dates are also included.

The fatality numbers in this dataset are calculated by assigning age groups to each patient based on the patient age, then summing the patient fatalities within each age group, as of each reporting date. The statewide total fatality numbers are calculated by summing the number of fatalities across all age groups, by reporting date. The fatality percentages are calculated by dividing the number of fatalities in each age group by the statewide total number of fatalities, by reporting date. The fatality numbers represent the cumulative number of fatalities that have been reported as of each reporting date.

Age-standardised mortality rates for deaths involving coronavirus (COVID-19), non-COVID-19 deaths and all deaths by vaccination status, broken down by age group.

This dataset was retired on 2/7/2024.

This dataset switched to a weekly M-F cadence on 12/27/2022..

This data set includes the cumulative (total) number of Multisystem Inflammatory Syndrome in Children (MIS-C) cases and deaths in Virginia by report date. This data set was first published on May 24, 2020. When you download the data set, the dates will be sorted in ascending order, meaning that the earliest date will be at the top. To see data for the most recent date, please scroll down to the bottom of the data set. The Virginia Department of Health’s Thomas Jefferson Health District (TJHD) will be renamed to Blue Ridge Health District (BRHD), effective January 2021. More information about this change can be found here: https://www.vdh.virginia.gov/blue-ridge/name-change/

Abstract Objectives: to characterize school-aged children, adolescents, and young people’s profile and their associations with positive COVID-19 test results. Methods: an observational and descriptive study of secondary data from the COVID-19 Panel in Espírito Santo State in February to August 2020. People suspected of COVID-19, in the 0–19-years old age group, were included in order to assess clinical data and demographic and epidemiological factors associated with the disease. Results: in the study period, 27,351 COVID-19 notification were registered in children, adolescents, and young people. The highest COVID-19 test confirmation was found in Caucasians and were 5-14 years age group. It was also observed that headache was the symptom with the highest test confirmation. Infection in people with disabilities was more frequent in the confirmed cases. The confirmation of cases occurred in approximately 80% of the notified registrations and 0.3% of the confirmed cases, died. Conclusion: children with confirmed diagnosis for COVID-19 have lower mortality rates, even though many were asymptomatic. To control the chain of transmission and reduce morbidity and mortality rates, it was necessaryto conduct more comprehensive research and promote extensive testing in the population.

Greetings everyone! I hope you find this dataset valuable for your COVID-19 models. It is aligned with SRK's Novel Corona Virus dataset. Feel free to upvote if you use it!

This dataset contains what I find as essential demographic information for every country specified in the submission COVID-19 competition file. Moreover, there is additional data which is critical in my point of view in order to predict the infection rate and mortality rate per country such as the number of COVID detection tests, detection date of 'patient zero' and initial restrictions dates. Please look at the columns description for the comprehensive explanation.

Major Insights:

1. I've seen that there are some pretty clear distinctions between female and male mortality rate as men tend to develop more severe symptoms. Therefore, I added some variables which represent the sex ratio (amount of males per female) in each country, with separation by age groups & total. Moreover, I added lung disease data (death rate per 100k people) in each country with separation by sex as well.
2. The average amount of children per woman has a quite high p-value when trying to analyze the trend of the confirmed cases. Especially when it comes in interaction with 'density' and school restrictions.

Citations and Data Gathering

1. https://www.worldometers.info/ - Population, Density, Median Age, Urban Population, Fertility Rate, Patient Zero Detection Date, Confirmed Cases, New Cases, Total Deaths, Total Recovered, Critical Cases.
2. @benhamner 's link (see acknowledgements section below) - Restrictions Initial dates.
3. https://worldpopulationreview.com/countries/smoking-rates-by-country/ - % of smokers by country.
4. https://data.worldbank.org/indicator/SH.MED.BEDS.ZS - Hospital beds per 1000 citizens.
5. https://en.wikipedia.org/wiki/List_of_countries_by_sex_ratio - Sex ratio by age.
6. https://www.worldlifeexpectancy.com/cause-of-death/lung-disease/by-country/ - Lung diseases death rate.
7. https://en.wikipedia.org/wiki/COVID-19_testing - COVID-19 Tests
8. https://www.worldbank.org/ - GDP 2019, Health Expenses (Whatever was missing was filled with information from Wikipedia)
9. https://en.climate-data.org/ - Temperature and Humidity raw data.

Acknowledgements

1. Restrictions are taken from here. Thanks to Ben Hamner for sharing this link!
2. Special thanks to @diamondsnake for the idea of collecting the average temperature and humidity.

Good luck trying to learn more about the virus, feel free to comment and collaborate in order to collect more relevant data!

My

IntroductionRecent reviews summarize evidence that some vaccines have heterologous or non-specific effects (NSE), potentially offering protection against multiple pathogens. Numerous economic evaluations examine vaccines' pathogen-specific effects, but less than a handful focus on NSE. This paper addresses that gap by reporting economic evaluations of the NSE of oral polio vaccine (OPV) against under-five mortality and COVID-19.Materials and methodsWe studied two settings: (1) reducing child mortality in a high-mortality setting (Guinea-Bissau) and (2) preventing COVID-19 in India. In the former, the intervention involves three annual campaigns in which children receive OPV incremental to routine immunization. In the latter, a susceptible-exposed-infectious-recovered model was developed to estimate the population benefits of two scenarios, in which OPV would be co-administered alongside COVID-19 vaccines. Incremental cost-effectiveness and benefit-cost ratios were modeled for ranges of intervention effectiveness estimates to supplement the headline numbers and account for heterogeneity and uncertainty.ResultsFor child mortality, headline cost-effectiveness was $650 per child death averted. For COVID-19, assuming OPV had 20% effectiveness, incremental cost per death averted was $23,000–65,000 if it were administered simultaneously with a COVID-19 vaccine <200 days into a wave of the epidemic. If the COVID-19 vaccine availability were delayed, the cost per averted death would decrease to $2600–6100. Estimated benefit-to-cost ratios vary but are consistently high.DiscussionEconomic evaluation suggests the potential of OPV to efficiently reduce child mortality in high mortality environments. Likewise, within a broad range of assumed effect sizes, OPV (or another vaccine with NSE) could play an economically attractive role against COVID-19 in countries facing COVID-19 vaccine delays.FundingThe contribution by DTJ was supported through grants from Trond Mohn Foundation (BFS2019MT02) and Norad (RAF-18/0009) through the Bergen Center for Ethics and Priority Setting.

Background and objectiveEstimating the contribution of risk factors of mortality due to COVID-19 is particularly important in settings with low vaccination coverage and limited public health and clinical resources. Very few studies of risk factors of COVID-19 mortality used high-quality data at an individual level from low- and middle-income countries (LMICs). We examined the contribution of demographic, socioeconomic and clinical risk factors of COVID-19 mortality in Bangladesh, a lower middle-income country in South Asia.MethodsWe used data from 290,488 lab-confirmed COVID-19 patients who participated in a telehealth service in Bangladesh between May 2020 and June 2021, linked with COVID-19 death data from a national database to study the risk factors associated with mortality. Multivariable logistic regression models were used to estimate the association between risk factors and mortality. We used classification and regression trees to identify the risk factors that are the most important for clinical decision-making.FindingsThis study is one of the largest prospective cohort studies of COVID-19 mortality in a LMIC, covering 36% of all lab-confirmed COVID-19 cases in the country during the study period. We found that being male, being very young or elderly, having low socioeconomic status, chronic kidney and liver disease, and being infected during the latter pandemic period were significantly associated with a higher risk of mortality from COVID-19. Males had 1.15 times higher odds (95% Confidence Interval, CI: 1.09, 1.22) of death compared to females. Compared to the reference age group (20–24 years olds), the odds ratio of mortality increased monotonically with age, ranging from an odds ratio of 1.35 (95% CI: 1.05, 1.73) for ages 30–34 to an odds ratio of 21.6 (95% CI: 17.08, 27.38) for ages 75–79 year group. For children 0–4 years old the odds of mortality were 3.93 (95% CI: 2.74, 5.64) times higher than 20–24 years olds. Other significant predictors were severe symptoms of COVID-19 such as breathing difficulty, fever, and diarrhea. Patients who were assessed by a physician as having a severe episode of COVID-19 based on the telehealth interview had 12.43 (95% CI: 11.04, 13.99) times higher odds of mortality compared to those assessed to have a mild episode. The finding that the telehealth doctors’ assessment of disease severity was highly predictive of subsequent COVID-19 mortality, underscores the feasibility and value of the telehealth services.ConclusionsOur findings confirm the universality of certain COVID-19 risk factors—such as gender and age—while highlighting other risk factors that appear to be more (or less) relevant in the context of Bangladesh. These findings on the demographic, socioeconomic, and clinical risk factors for COVID-19 mortality can help guide public health and clinical decision-making. Harnessing the benefits of the telehealth system and optimizing care for those most at risk of mortality, particularly in the context of a LMIC, are the key takeaways from this study.

Since the president of Brazil, in an interview at Flow podcast on August 10, 2022, stated that in COVID, children are asymptomatic, almost never hospitalized, and rarely needed intensive care, which is a huge and dangerous lie. Based on Brazilian Health SUS data provided by the Bolsonaro government itself, we prove the ignorance and risk of mixing ideology and feelings with science and medicine. The most dangerous ignorance is not unknowing, but believing that they have knowledge, being miles away from it. Dataset provided by the opendataSUS platform with all patients notified with a diagnosis of COVID in Brazil between Jan/20 and Aug/22 under 12 years old. Number of cases, hospital admissions, ICU admissions and deaths.

Morbidity and mortality attributable to COVID-19 is devastating global health systems and economies. Bacillus Calmette Guérin (BCG) vaccination has been in use for many decades to prevent severe forms of tuberculosis in children. Studies have also shown a combination of improved long-term innate or trained immunity (through epigenetic reprogramming of myeloid cells) and adaptive responses after BCG vaccination, which leads to non-specific protective effects in adults. Observational studies have shown that countries with routine BCG vaccination programs have significantly less reported cases and deaths of COVID-19, but such studies are prone to significant bias and need confirmation. To date, in the absence of direct evidence, WHO does not recommend BCG for the prevention of COVID-19. This project aims to investigate in a timely manner whether and why BCG-revaccination can reduce infection rate and/or disease severity in health care workers during the SARS-CoV-2 outbreak in South Africa. These objectives will be achieved with a blinded, randomised controlled trial of BCG revaccination versus placebo in exposed front-line staff in hospitals in Cape Town. Observations will include the rate of infection with COVID-19 as well as the occurrence of mild, moderate or severe ambulatory respiratory tract infections, hospitalisation, need for oxygen, mechanical ventilation or death. HIV-positive individuals will be excluded. Safety of the vaccines will be monitored. A secondary endpoint is the occurrence of latent or active tuberculosis. Initial sample size and follow-up duration is at least 500 workers and 52 weeks. Statistical analysis will be model-based and ongoing in real time with frequent interim analyses and optional increases of both sample size or observation time, based on the unforeseeable trajectory of the South African COVID-19 epidemic, available funds and recommendations of an independent data and safety monitoring board. The study will be supported by a novel 3D lung organoid model of SARS-CoV-2 infection system that can mimic the cascade of immunological events after SARS-CoV-2 infection to determine and analyse the contribution of cellular components to the impact of BCG revaccination in this study. Given the immediate threat of the SARS-CoV-2 epidemic the trial has been designed as a pragmatic study with highly feasible endpoints that can be continuously measured. This allows for the most rapid identification of a beneficial outcome that would lead to immediate dissemination of the results, vaccination of the control group and outreach to the health authorities to consider BCG vaccination for all qualifying health care workers. Methods This dataset was collected in a clinical randomised control trial under the TASK008-BCG CORONA protocol. The trial was conducted in South Africa. This trial was registered with ClinicalTrials.gov, NCT04379336.

Background and objectiveEstimating the contribution of risk factors of mortality due to COVID-19 is particularly important in settings with low vaccination coverage and limited public health and clinical resources. Very few studies of risk factors of COVID-19 mortality used high-quality data at an individual level from low- and middle-income countries (LMICs). We examined the contribution of demographic, socioeconomic and clinical risk factors of COVID-19 mortality in Bangladesh, a lower middle-income country in South Asia.MethodsWe used data from 290,488 lab-confirmed COVID-19 patients who participated in a telehealth service in Bangladesh between May 2020 and June 2021, linked with COVID-19 death data from a national database to study the risk factors associated with mortality. Multivariable logistic regression models were used to estimate the association between risk factors and mortality. We used classification and regression trees to identify the risk factors that are the most important for clinical decision-making.FindingsThis study is one of the largest prospective cohort studies of COVID-19 mortality in a LMIC, covering 36% of all lab-confirmed COVID-19 cases in the country during the study period. We found that being male, being very young or elderly, having low socioeconomic status, chronic kidney and liver disease, and being infected during the latter pandemic period were significantly associated with a higher risk of mortality from COVID-19. Males had 1.15 times higher odds (95% Confidence Interval, CI: 1.09, 1.22) of death compared to females. Compared to the reference age group (20–24 years olds), the odds ratio of mortality increased monotonically with age, ranging from an odds ratio of 1.35 (95% CI: 1.05, 1.73) for ages 30–34 to an odds ratio of 21.6 (95% CI: 17.08, 27.38) for ages 75–79 year group. For children 0–4 years old the odds of mortality were 3.93 (95% CI: 2.74, 5.64) times higher than 20–24 years olds. Other significant predictors were severe symptoms of COVID-19 such as breathing difficulty, fever, and diarrhea. Patients who were assessed by a physician as having a severe episode of COVID-19 based on the telehealth interview had 12.43 (95% CI: 11.04, 13.99) times higher odds of mortality compared to those assessed to have a mild episode. The finding that the telehealth doctors’ assessment of disease severity was highly predictive of subsequent COVID-19 mortality, underscores the feasibility and value of the telehealth services.ConclusionsOur findings confirm the universality of certain COVID-19 risk factors—such as gender and age—while highlighting other risk factors that appear to be more (or less) relevant in the context of Bangladesh. These findings on the demographic, socioeconomic, and clinical risk factors for COVID-19 mortality can help guide public health and clinical decision-making. Harnessing the benefits of the telehealth system and optimizing care for those most at risk of mortality, particularly in the context of a LMIC, are the key takeaways from this study.

RECOVERY is a randomised trial investigating whether treatment with Lopinavir-Ritonavir, Hydroxychloroquine, Corticosteroids, Azithromycin, Colchicine, IV Immunoglobulin (children only), Convalescent plasma, Casirivimab+Imdevimab, Tocilizumab, Aspirin, Baricitinib, Infliximab, Empagliflozin, Sotrovimab, Molnupiravir, Paxlovid or Anakinra (children only) prevents death in patients with COVID-19.

Context

In the time of epidemics, what is the status of HIV AIDS across the world, where does each country stands, is it getting any better. The data set should be helpful to explore much more about above mentioned factors.

Content

The data set contains data on

1. No. of people living with HIV AIDS
2. No. of deaths due to HIV AIDS
3. No. of cases among adults (19-45)
4. Prevention of mother-to-child transmission estimates
5. ART (Anti Retro-viral Therapy) coverage among people living with HIV estimates
6. ART (Anti Retro-viral Therapy) coverage among children estimates

Acknowledgements / Data Source

• WHO : https://www.who.int/hiv/data/en/
• UNESCO : https://data.unicef.org/resources/dataset/hiv-aids-statistical-tables/

Collection methodology

https://github.com/imdevskp/hiv_aids_who_unesco_data_cleaning

Cover Photo

Photo by Anna Shvets from Pexels https://www.pexels.com/photo/red-ribbon-on-white-surface-3900425/

Similar Datasets

• COVID-19 - https://www.kaggle.com/imdevskp/corona-virus-report
• MERS - https://www.kaggle.com/imdevskp/mers-outbreak-dataset-20122019
• Ebola Western Africa 2014 Outbreak - https://www.kaggle.com/imdevskp/ebola-outbreak-20142016-complete-dataset
• H1N1 | Swine Flu 2009 Pandemic Dataset - https://www.kaggle.com/imdevskp/h1n1-swine-flu-2009-pandemic-dataset
• SARS 2003 Pandemic - https://www.kaggle.com/imdevskp/sars-outbreak-2003-complete-dataset
• HIV AIDS - https://www.kaggle.com/imdevskp/hiv-aids-dataset

Young Lives research has expanded to explore linking geographical data collected during the rounds to external datasets. Matching Young Lives data with administrative and geographic datasets significantly increases the scope for research in several areas, and may allow researchers to identify sources of exogenous variation for more convincing causal analysis on policy and/or early life circumstances.

Young Lives: Data Matching Series, 1900-2021 includes the following linked datasets:

1. Climate Matched Datasets (four YL study countries): Community-level GPS data has been matched with temperature and precipitation data from the University of Delaware. Climate variables are offered at the community level, with a panel data structure spanning across years and months. Hence, each community has a unique value of precipitation (variable PRCP) and temperature (variable TEMP), for each year and month pairing for the period 1900-2017.

2. COVID-19 Matched Dataset (Peru only): The YL Phone Survey Calls data has been matched with external data sources (The Peruvian Ministry of Health and the National Information System of Deaths in Peru). The matched dataset includes the total number of COVID cases per 1,000 inhabitants, the total number of COVID deaths by district and per 1,000 inhabitants; the total number of excess deaths per 1,000 inhabitants and the number of lockdown days in each Young Lives district in Peru during August 2020 to December 2021.

Further information is available in the PDF reports included in the study documentation.

World health statistics 2023: monitoring health for the SDGs, sustainable development goals

Overview

The World health statistics report is the annual compilation of health and health-related indicators which has been published by the World Health Organization (WHO) since 2005.

The 2023 edition reviews more than 50 health-related indicators from the Sustainable Development Goals (SDGs) and WHO’s Thirteenth General Programme of Work (GPW 13)

The report summarizes the trends in life expectancy and causes of death, and reports on progress towards the health-related Sustainable Development Goals (SDGs) and associated targets.

https://cdn.who.int/media/images/default-source/ddi-department/world-health-statistics-report-2023/01-who_mca-danangkmc-vnm-(22-of-37).tmb-1366v.jpg?sfvrsn=cb53a2df_1" alt="test">

Annual rate of reduction in maternal and child mortality has dropped in recent years

https://cdn.who.int/media/images/default-source/ddi-department/world-health-statistics-report-2023/778-whs-2023-visual-summary_message-1_230505.svg?sfvrsn=f80e927a_4" alt="">

Without faster progress, no regions will achieve the SDG target for NCD mortality by 2030 – and half still won’t by 2048

https://cdn-auth-cms.who.int/media/images/default-source/ddi-department/world-health-statistics-report-2023/km2_western-pacific.svg" alt="">

Total years of life lost due to COVID-19 by age-group

https://cdn-auth-cms.who.int/media/images/default-source/ddi-department/world-health-statistics-report-2023/km4_western-pacific.svg" alt="">

Acknowledgements

This Dataset is created from https://www.who.int/ . If you want to learn more, you can visit the Website.

IntroductionWe compared hospitalization outcomes of young children hospitalized with COVID-19 to those hospitalized with influenza in the United States.MethodsPatients aged 0-<5 years hospitalized with an admission diagnosis of acute COVID-19 (April 2021-March 2022) or influenza (April 2019-March 2020) were selected from the PINC AI Healthcare Database Special Release. Hospitalization outcomes included length of stay (LOS), intensive care unit (ICU) admission, oxygen supplementation, and mechanical ventilation (MV). Inverse probability of treatment weighting was used to adjust for confounders in logistic regression analyses.ResultsAmong children hospitalized with COVID-19 (n = 4,839; median age: 0 years), 21.3% had an ICU admission, 19.6% received oxygen supplementation, 7.9% received MV support, and 0.5% died. Among children hospitalized with influenza (n = 4,349; median age: 1 year), 17.4% were admitted to the ICU, 26.7% received oxygen supplementation, 7.6% received MV support, and 0.3% died. Compared to children hospitalized with influenza, those with COVID-19 were more likely to have an ICU admission (adjusted odds ratio [aOR]: 1.34; 95% confidence interval [CI]: 1.21–1.48). However, children with COVID-19 were less likely to receive oxygen supplementation (aOR: 0.71; 95% CI: 0.64–0.78), have a prolonged LOS (aOR: 0.81; 95% CI: 0.75–0.88), or a prolonged ICU stay (aOR: 0.56; 95% CI: 0.46–0.68). The likelihood of receiving MV was similar (aOR: 0.94; 95% CI: 0.81, 1.1).ConclusionsHospitalized children with either SARS-CoV-2 or influenza had severe complications including ICU admission and oxygen supplementation. Nearly 10% received MV support. Both SARS-CoV-2 and influenza have the potential to cause severe illness in young children.

SARS-CoV-2 infection during pregnancy is not usually associated with significant adverse effects. However, in this study, we report a fetal death associated with mild COVID-19 in a 34-week-pregnant woman. The virus was detected in the placenta and in an unprecedented way in several fetal tissues. Placental abnormalities (MRI and anatomopathological study) were consistent with intense vascular malperfusion, probably the cause of fetal death. Lung histopathology also showed signs of inflammation, which could have been a contributory factor. Monitoring inflammatory response and coagulation in high-risk pregnant women with COVID-19 may prevent unfavorable outcomes, as shown in this case.