India IN: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data was reported at 19.800 NA in 2016. This records a decrease from the previous number of 20.000 NA for 2015. India IN: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data is updated yearly, averaging 21.200 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 23.400 NA in 2000 and a record low of 19.800 NA in 2016. India IN: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s India – Table IN.World Bank.WDI: Health Statistics. Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

Nigeria NG: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Male data was reported at 20.900 NA in 2016. This records an increase from the previous number of 20.800 NA for 2015. Nigeria NG: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Male data is updated yearly, averaging 21.000 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 22.600 NA in 2000 and a record low of 20.800 NA in 2015. Nigeria NG: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nigeria – Table NG.World Bank.WDI: Health Statistics. Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

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.

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.

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.

Ivory Coast CI: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Male data was reported at 28.200 NA in 2016. This records a decrease from the previous number of 28.500 NA for 2015. Ivory Coast CI: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Male data is updated yearly, averaging 27.700 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 28.500 NA in 2015 and a record low of 25.200 NA in 2000. Ivory Coast CI: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ivory Coast – Table CI.World Bank.WDI: Health Statistics. Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

Among many types of cancers, to date, lung cancer remains one of the deadliest cancers around the world. Many researchers, scientists, doctors, and people from other fields continuously contribute to this subject regarding early prediction and diagnosis. One of the significant problems in prediction is the black-box nature of machine learning models. Though the detection rate is comparatively satisfactory, people have yet to learn how a model came to that decision, causing trust issues among patients and healthcare workers. This work uses multiple machine learning models on a numerical dataset of lung cancer-relevant parameters and compares performance and accuracy. After comparison, each model has been explained using different methods. The main contribution of this research is to give logical explanations of why the model reached a particular decision to achieve trust. This research has also been compared with a previous study that worked with a similar dataset and took expert opinions regarding their proposed model. We also showed that our research achieved better results than their proposed model and specialist opinion using hyperparameter tuning, having an improved accuracy of almost 100% in all four models.

Saudi Arabia SA: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data was reported at 16.400 % in 2016. This records a decrease from the previous number of 16.500 % for 2015. Saudi Arabia SA: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data is updated yearly, averaging 17.900 % from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 18.900 % in 2000 and a record low of 16.400 % in 2016. Saudi Arabia SA: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Saudi Arabia – Table SA.World Bank: Health Statistics. Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted Average;

This dataset corresponds to a collection of images and/or image-derived data available from National Cancer Institute Imaging Data Commons (IDC) [1]. This dataset was converted into DICOM representation and ingested by the IDC team. You can explore and visualize the corresponding images using IDC Portal here: TCGA-PCPG. You can use the manifests included in this Zenodo record to download the content of the collection following the Download instructions below.

Collection description

Paraganglioma is a rare cancer that originates in the nerve cells of the adrenal glands, organs on top of each kidney that produce important hormones. Paraganglioma that develops in the center of the adrenal gland is called pheochromocytoma. Paraganglioma that forms outside of the adrenal gland, often along blood vessels and nerves in the head and neck, is called extra-adrenal paraganglioma, or simply paraganglioma. Each year, between 2 and 8 people per million worldwide are diagnosed with paraganglioma and pheochromocytoma. 10% of all cases occur in children. In both adults and children, pheochromocytoma is more common than paraganglioma. No known environmental, dietary, or lifestyle risk factors have been associated with these cancers.

Please see the TCGA-PCPG information page to learn more about the images and to obtain any supporting metadata for this collection.

Citation guidelines can be found on the Citing TCGA in Publications and Presentations information page.

Files included

A manifest file's name indicates the IDC data release in which a version of collection data was first introduced. For example, collection_id-idc_v8-aws.s5cmd corresponds to the contents of the collection_id collection introduced in IDC data release v8. If there is a subsequent version of this Zenodo page, it will indicate when a subsequent version of the corresponding collection was introduced.

1. tcga_pcpg-idc_v8-aws.s5cmd: manifest of files available for download from public IDC Amazon Web Services buckets
2. tcga_pcpg-idc_v8-gcs.s5cmd: manifest of files available for download from public IDC Google Cloud Storage buckets
3. tcga_pcpg-idc_v8-dcf.dcf: Gen3 manifest (for details see https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids)

Note that manifest files that end in -aws.s5cmd reference files stored in Amazon Web Services (AWS) buckets, while -gcs.s5cmd reference files in Google Cloud Storage. The actual files are identical and are mirrored between AWS and GCP.

Download instructions

Each of the manifests include instructions in the header on how to download the included files.

To download the files using .s5cmd manifests:

1. install idc-index package: pip install --upgrade idc-index
2. download the files referenced by manifests included in this dataset by passing the .s5cmd manifest file: idc download manifest.s5cmd.

To download the files using .dcf manifest, see manifest header.

Acknowledgments

Imaging Data Commons team has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Task Order No. HHSN26110071 under Contract No. HHSN261201500003l.

References

[1] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H. J. W., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. & Kikinis, R. National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence. RadioGraphics (2023). https://doi.org/10.1148/rg.230180

The PROFILES registry offers scientific data in the area of medical psychology and more specific on quality of life of cancer survivors. The PROFILES registry is open to academics anywhere in the world for scientific purposes, free of charge. The archive can be found on the following website: www.profilesregistry.nl.This study is part of the project: Building an infrastructure for multidisciplinary and longitudinal data collection of the physical and psychosocial impact of cancer and its treatment: Patient Reported Outcomes Following Initial treatment and Long term Survivorship (PROFILES).The current research project studied the quality of life in the normative population, in this case a Dutch population consisting of people who were not diagnosed with cancer. The questionnaire used was the EORTC QLQ-C30.

BackgroundBladder cancer (BCa) is a malignant tumor that usually forms cancer cells in the inner lining of the bladder. Hundreds of thousands of people worldwide have BCa diagnosed each year. The purpose of this study was to construct a prognostic model by differential expression of genes between muscular and non-muscular invasive BCa, and to investigate the prognosis of BCa patients.MethodsThe data of BCa patients was sourced from the GEO and TCGA database. Single-cell sequencing data was obtained from three patients in the GSE135337 database, and microarray data for verification was obtained from GSE32894. Univariate, Lasso and multivariate cox regression analyses were performed to construct the prognostic model. The prognostic features, immune features and drug sensitivity of the model were further evaluated. Single-cell data and microarray data were used to validate the differential expression of model genes between muscle-invasive and non-muscle-invasive BCa. The invasion and migration of BCa cells were evaluated using the transwell assay and wound-healing assay. The cell proliferation capacity was simultaneously evaluated using Colony formation experiments. The protein expression of the specific gene was detected by western blot analysis.ResultsWe identified 183 differentially expressed muscle-invasive-related differential genes (MIRDGs), among which four were selected to establish a prognostic model. Based on our signature, patients in different groups displayed varying levels of immune infiltration and immunotherapy profiles. Single-cell sequencing data and microarray data confirmed that four invasion-related genes were expressed at higher levels in muscle-invasive BCa. Given the critical role of S100A9 in the progression of BCa, we performed further analysis. The results showed that protein expression of S100A9 was high in muscle-invasive BCa, and S100A9 knockdown could inhibit the proliferation, migration and invasion of BCa.ConclusionThese findings demonstrated that the prognostic model for BCa patients was reasonably accurate and valid, and it may prove to be of considerable value for the treatment and prognosis of BCa patients in the future. S100A9 may become a better prognostic marker and potential therapeutic target to further guide clinical treatment decisions.

Kenya KE: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data was reported at 13.400 % in 2016. This records an increase from the previous number of 13.300 % for 2015. Kenya KE: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data is updated yearly, averaging 13.400 % from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 17.300 % in 2000 and a record low of 13.300 % in 2015. Kenya KE: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank: Health Statistics. Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted Average;

Background: Low educational attainment has been reported as a risk factor for many diseases. However, conclusion on the association between educational attainment and endometrial cancer (EC) are inconsistent in previous observational studies. This study aims to explore the potential causal association between educational attainment and EC.Methods: A Mendelian Randomization analysis was performed using publicly summary-level data sets of genome-wide association studies (GWAS). A total of 306 single-nucleotide polymorphisms (SNPs) were extracted as instrumental variables for the exposure of educational attainment from the Social Science Genetic Association Consortium GWAS summary data of 1,131,881 participants of European ancestry. SNPs of EC were obtained from the Endometrial Cancer Association Consortium, the Epidemiology of Endometrial Cancer Consortium and the UK Biobank involving 121,885 people. We conducted inverse variance weighted (IVW) to estimate the causal effect as our primary outcome. And we perform several sensitivity analyses, including MR-Egger regression, weighted median method, MR-PRESSO (Mendelian Randomization Pleiotropy Residual Sum and Outlier) global test, and leave-one-out sensitivity analysis, to evaluate the effect of pleiotropism on the causal estimates.Results: Genetic predisposition towards 4.2 years of additional educational attainment was associated with 38% lower risk of EC. (odds ratio 0.72, 95% confidence interval 0.62 to 0.83; p = 1.65*10−5). The consistent results of sensitivity analyses indicated our causal estimates were reliable. Genetic predisposition towards longer educational attainment was associated with lower risk of obesity, high waist-to-hip ratio (WHR), and diabetes.Conclusion: This study indicated that low educational attainment was a causal risk factor for EC, especially for EC with endometrioid histology. Low educational attainment might lead to EC through the mediator of obesity, high WHR, and diabetes.

Zimbabwe ZW: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data was reported at 19.800 NA in 2016. This records a decrease from the previous number of 19.900 NA for 2015. Zimbabwe ZW: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data is updated yearly, averaging 21.500 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 22.900 NA in 2005 and a record low of 19.800 NA in 2016. Zimbabwe ZW: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Zimbabwe – Table ZW.World Bank.WDI: Health Statistics. Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

Singapore SG: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data was reported at 9.300 % in 2016. This records a decrease from the previous number of 10.000 % for 2015. Singapore SG: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data is updated yearly, averaging 11.300 % from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 16.800 % in 2000 and a record low of 9.300 % in 2016. Singapore SG: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Singapore – Table SG.World Bank: Health Statistics. Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted Average;

Bangladesh BD: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data was reported at 18.900 % in 2019. This stayed constant from the previous number of 18.900 % for 2018. Bangladesh BD: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data is updated yearly, averaging 21.800 % from Dec 2000 (Median) to 2019, with 20 observations. The data reached an all-time high of 23.700 % in 2006 and a record low of 18.500 % in 2016. Bangladesh BD: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bangladesh – Table BD.World Bank.WDI: Social: Health Statistics. Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).;World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).;Weighted average;This is the Sustainable Development Goal indicator 3.4.1 [https://unstats.un.org/sdgs/metadata/].