The leading cause of death in low-income countries worldwide in 2021 was lower respiratory infections, followed by stroke and ischemic heart disease. The death rate from lower respiratory infections that year was 59.4 deaths per 100,000 people. While the death rate from stroke was around 51.6 per 100,000 people. Many low-income countries suffer from health issues not seen in high-income countries, including infectious diseases, malnutrition and neonatal deaths, to name a few. Low-income countries worldwide Low-income countries are defined as those with per gross national incomes (GNI) per capita of 1,045 U.S. dollars or less. A majority of the world’s low-income countries are located in sub-Saharan Africa and South East Asia. Some of the lowest-income countries as of 2023 include Burundi, Sierra Leone, and South Sudan. Low-income countries have different health problems that lead to worse health outcomes. For example, Chad, Lesotho, and Nigeria have some of the lowest life expectancies on the planet. Health issues in low-income countries Low-income countries also tend to have higher rates of HIV/AIDS and other infectious diseases as a consequence of poor health infrastructure and a lack of qualified health workers. Eswatini, Lesotho, and South Africa have some of the highest rates of new HIV infections worldwide. Likewise, tuberculosis, a treatable condition that affects the respiratory system, has high incident rates in lower income countries. Other health issues can be affected by the income of a country as well, including maternal and infant mortality. In 2023, Afghanistan had one of the highest rates of infant mortality rates in the world.

Background: Information on patient’s death is a major outcome of health-related research, but it is not always available in claim-based databases. Herein, we suggested the operational definition of death as an optimal indicator of real death and aim to examine its validity and application in patients with cancer.Materials and methods: Data of newly diagnosed patients with cancer between 2006 and 2015 from the Korean National Health Insurance Service—National Sample Cohort data were used. Death indicators were operationally defined as follows: 1) in-hospital death (the result of treatment or disease diagnosis code from claims data), or 2) case wherein there are no claims within 365 days of the last claim. We estimated true-positive rates (TPR) and false-positive rates (FPR) for real death and operational definition of death in patients with high-, middle-, and low-mortality cancers. Kaplan−Meier survival curves and log-rank tests were conducted to determine whether real death and operational definition of death rates were consistent.Results: A total of 40,970 patients with cancer were recruited for this study. Among them, 12,604 patients were officially reported as dead. These patients were stratified into high- (lung, liver, and pancreatic), middle- (stomach, skin, and kidney), and low- (thyroid) mortality groups consisting of 6,626 (death: 4,287), 7,282 (1,858), and 6,316 (93) patients, respectively. The TPR was 97.08% and the FPR was 0.98% in the high mortality group. In the case of the middle and low mortality groups, the TPR (FPR) was 95.86% (1.77%) and 97.85% (0.58%), respectively. The overall TPR and FPR were 96.68 and 1.27%. There was no significant difference between the real and operational definition of death in the log-rank test for all types of cancers except for thyroid cancer.Conclusion: Defining deaths operationally using in-hospital death data and periods after the last claim is a robust alternative to identifying mortality in patients with cancer. This optimal indicator of death will promote research using claim-based data lacking death information.

The dataset comprises - 90 000 records from inventories in 54 strict forest reserves in Switzerland and Lower Saxony / Germany along a considerable environmental gradient. It was used to develop parsimonious, species-specific mortality models for 18 European tree species based on tree size and growth as well as additional covariates on stand structure and climate. Inventory data Measurements had been conducted repeatedly on up to 14 permanent plots per reserve for up to 60 years with re-measurement intervals of 4 - 27 years. The permanent plots vary in size between 0.03 and 3.47 ha. The inventories provide diameter measurements at breast height (DBH) and information on the species and status (alive or dead) of trees with DBH ≥ 4 cm for Switzerland and ≥ 7 cm for Germany. Data selection We excluded three permanent plots where at least 80 % of the trees died during an interval of 10 years, and mortality could be clearly assigned to a disturbance agent. Mortality in the remaining stands was rather low, with a mean annual mortality rate of 1.5 % and strong variation between plots from 0 to 6.5 % (assessed for trees of all species with DBH ≥ 7 cm). We only used data from permanent plots with at least 20 trees per species to obtain reliable plot-level mortality rates even for species with low mortality rates (about 5 % during 10 years), and selected tree species occurring on at least 10 plots to cover sufficient ecological gradients. This led to a dataset of 197 permanent plots and 18 tree or shrub species: Abies alba Mill., Acer campestre L., Acer pseudoplatanus L., Alnus incana Moench., Betula pendula Roth, Carpinus betulus L., Cornus mas L., Corylus avellana L., Fagus sylvatica L., Fraxinus excelsior L., Picea abies (L.) Karst, Pinus mugo Turra, Pinus sylvestris L., Quercus pubescens Willd., Quercus spp. (Q. petraea Liebl. and Q. robur L.; not properly differentiated in the Swiss inventories), Sorbus aria Crantz, Tilia cordata Mill. and Ulmus glabra Huds.. Predictors of tree mortality We considered tree size and growth as key indicators for mortality risk. Radial stem growth between the first and second inventory and DBH at the second inventory were used to predict tree status (alive or dead) at the third inventory. To this end, the annual relative basal area increment (relBAI) was calculated as the compound annual growth rate of the trees basal area. Additional covariates on stand structure and climate comprise mean annual precipitation sum (P), mean annual air temperature (mT), the mean and the interquartile range of DBH (mDBH, iqrDBH), basal area (BA) and the number of trees (N) per hectare. Further information For further information, refer to Hülsmann et al. (in press) How to kill a tree – Empirical mortality models for eighteen species and their performance in a dynamic forest model. Ecological Applications.

COVID-19 rate of death, or the known deaths divided by confirmed cases, was over ten percent in Yemen, the only country that has 1,000 or more cases. This according to a calculation that combines coronavirus stats on both deaths and registered cases for 221 different countries. Note that death rates are not the same as the chance of dying from an infection or the number of deaths based on an at-risk population. By April 26, 2022, the virus had infected over 510.2 million people worldwide, and led to a loss of 6.2 million. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. Note that Statista aims to also provide domestic source material for a more complete picture, and not to just look at one particular source. Examples are these statistics on the confirmed coronavirus cases in Russia or the COVID-19 cases in Italy, both of which are from domestic sources. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

A word on the flaws of numbers like this

People are right to ask whether these numbers are at all representative or not for several reasons. First, countries worldwide decide differently on who gets tested for the virus, meaning that comparing case numbers or death rates could to some extent be misleading. Germany, for example, started testing relatively early once the country’s first case was confirmed in Bavaria in January 2020, whereas Italy tests for the coronavirus postmortem. Second, not all people go to see (or can see, due to testing capacity) a doctor when they have mild symptoms. Countries like Norway and the Netherlands, for example, recommend people with non-severe symptoms to just stay at home. This means not all cases are known all the time, which could significantly alter the death rate as it is presented here. Third and finally, numbers like this change very frequently depending on how the pandemic spreads or the national healthcare capacity. It is therefore recommended to look at other (freely accessible) content that dives more into specifics, such as the coronavirus testing capacity in India or the number of hospital beds in the UK. Only with additional pieces of information can you get the full picture, something that this statistic in its current state simply cannot provide.

Number of deaths and mortality rates, by age group, sex, and place of residence, 1991 to most recent year.

MMWR Surveillance Summary 66 (No. SS-1):1-8 found that nonmetropolitan areas have significant numbers of potentially excess deaths from the five leading causes of death. These figures accompany this report by presenting information on potentially excess deaths in nonmetropolitan and metropolitan areas at the state level. They also add additional years of data and options for selecting different age ranges and benchmarks. Potentially excess deaths are defined in MMWR Surveillance Summary 66(No. SS-1):1-8 as deaths that exceed the numbers that would be expected if the death rates of states with the lowest rates (benchmarks) occurred across all states. They are calculated by subtracting expected deaths for specific benchmarks from observed deaths. Not all potentially excess deaths can be prevented; some areas might have characteristics that predispose them to higher rates of death. However, many potentially excess deaths might represent deaths that could be prevented through improved public health programs that support healthier behaviors and neighborhoods or better access to health care services. Mortality data for U.S. residents come from the National Vital Statistics System. Estimates based on fewer than 10 observed deaths are not shown and shaded yellow on the map. Underlying cause of death is based on the International Classification of Diseases, 10th Revision (ICD-10) Heart disease (I00-I09, I11, I13, and I20–I51) Cancer (C00–C97) Unintentional injury (V01–X59 and Y85–Y86) Chronic lower respiratory disease (J40–J47) Stroke (I60–I69) Locality (nonmetropolitan vs. metropolitan) is based on the Office of Management and Budget’s 2013 county-based classification scheme. Benchmarks are based on the three states with the lowest age and cause-specific mortality rates. Potentially excess deaths for each state are calculated by subtracting deaths at the benchmark rates (expected deaths) from observed deaths. Users can explore three benchmarks: “2010 Fixed” is a fixed benchmark based on the best performing States in 2010. “2005 Fixed” is a fixed benchmark based on the best performing States in 2005. “Floating” is based on the best performing States in each year so change from year to year. SOURCES CDC/NCHS, National Vital Statistics System, mortality data (see http://www.cdc.gov/nchs/deaths.htm); and CDC WONDER (see http://wonder.cdc.gov). REFERENCES Moy E, Garcia MC, Bastian B, Rossen LM, Ingram DD, Faul M, Massetti GM, Thomas CC, Hong Y, Yoon PW, Iademarco MF. Leading Causes of Death in Nonmetropolitan and Metropolitan Areas – United States, 1999-2014. MMWR Surveillance Summary 2017; 66(No. SS-1):1-8. Garcia MC, Faul M, Massetti G, Thomas CC, Hong Y, Bauer UE, Iademarco MF. Reducing Potentially Excess Deaths from the Five Leading Causes of Death in the Rural United States. MMWR Surveillance Summary 2017; 66(No. SS-2):1–7.

This statistic shows the 20 countries* with the highest infant mortality rate in 2024. An estimated 101.3 infants per 1,000 live births died in the first year of life in Afghanistan in 2024. Infant and child mortality Infant mortality usually refers to the death of children younger than one year. Child mortality, which is often used synonymously with infant mortality, is the death of children younger than five. Among the main causes are pneumonia, diarrhea – which causes dehydration – and infections in newborns, with malnutrition also posing a severe problem. As can be seen above, most countries with a high infant mortality rate are developing countries or emerging countries, most of which are located in Africa. Good health care and hygiene are crucial in reducing child mortality; among the countries with the lowest infant mortality rate are exclusively developed countries, whose inhabitants usually have access to clean water and comprehensive health care. Access to vaccinations, antibiotics and a balanced nutrition also help reducing child mortality in these regions. In some countries, infants are killed if they turn out to be of a certain gender. India, for example, is known as a country where a lot of girls are aborted or killed right after birth, as they are considered to be too expensive for poorer families, who traditionally have to pay a costly dowry on the girl’s wedding day. Interestingly, the global mortality rate among boys is higher than that for girls, which could be due to the fact that more male infants are actually born than female ones. Other theories include a stronger immune system in girls, or more premature births among boys.

Analysis of ‘NCHS - Potentially Excess Deaths from the Five Leading Causes of Death’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/3d1da62a-9f1c-47e8-b5a1-b473f57d7fdc on 28 January 2022.

--- Dataset description provided by original source is as follows ---

MMWR Surveillance Summary 66 (No. SS-1):1-8 found that nonmetropolitan areas have significant numbers of potentially excess deaths from the five leading causes of death. These figures accompany this report by presenting information on potentially excess deaths in nonmetropolitan and metropolitan areas at the state level. They also add additional years of data and options for selecting different age ranges and benchmarks.

Potentially excess deaths are defined in MMWR Surveillance Summary 66(No. SS-1):1-8 as deaths that exceed the numbers that would be expected if the death rates of states with the lowest rates (benchmarks) occurred across all states. They are calculated by subtracting expected deaths for specific benchmarks from observed deaths.

Not all potentially excess deaths can be prevented; some areas might have characteristics that predispose them to higher rates of death. However, many potentially excess deaths might represent deaths that could be prevented through improved public health programs that support healthier behaviors and neighborhoods or better access to health care services.

Mortality data for U.S. residents come from the National Vital Statistics System. Estimates based on fewer than 10 observed deaths are not shown and shaded yellow on the map.

Underlying cause of death is based on the International Classification of Diseases, 10th Revision (ICD-10)

Heart disease (I00-I09, I11, I13, and I20–I51) Cancer (C00–C97) Unintentional injury (V01–X59 and Y85–Y86) Chronic lower respiratory disease (J40–J47) Stroke (I60–I69) Locality (nonmetropolitan vs. metropolitan) is based on the Office of Management and Budget’s 2013 county-based classification scheme.

Benchmarks are based on the three states with the lowest age and cause-specific mortality rates.

Potentially excess deaths for each state are calculated by subtracting deaths at the benchmark rates (expected deaths) from observed deaths.

Users can explore three benchmarks:

“2010 Fixed” is a fixed benchmark based on the best performing States in 2010. “2005 Fixed” is a fixed benchmark based on the best performing States in 2005. “Floating” is based on the best performing States in each year so change from year to year.

SOURCES

CDC/NCHS, National Vital Statistics System, mortality data (see http://www.cdc.gov/nchs/deaths.htm); and CDC WONDER (see http://wonder.cdc.gov).

REFERENCES

1. Moy E, Garcia MC, Bastian B, Rossen LM, Ingram DD, Faul M, Massetti GM, Thomas CC, Hong Y, Yoon PW, Iademarco MF. Leading Causes of Death in Nonmetropolitan and Metropolitan Areas – United States, 1999-2014. MMWR Surveillance Summary 2017; 66(No. SS-1):1-8.

2. Garcia MC, Faul M, Massetti G, Thomas CC, Hong Y, Bauer UE, Iademarco MF. Reducing Potentially Excess Deaths from the Five Leading Causes of Death in the Rural United States. MMWR Surveillance Summary 2017; 66(No. SS-2):1–7.

--- Original source retains full ownership of the source dataset ---

This publication of the SHMI relates to discharges in the reporting period March 2023 - February 2024. The SHMI is the ratio between the actual number of patients who die following hospitalisation at the trust and the number that would be expected to die on the basis of average England figures, given the characteristics of the patients treated there. The SHMI covers patients admitted to hospitals in England who died either while in hospital or within 30 days of being discharged. To help users of the data understand the SHMI, trusts have been categorised into bandings indicating whether a trust's SHMI is 'higher than expected', 'as expected' or 'lower than expected'. For any given number of expected deaths, a range of observed deaths is considered to be 'as expected'. If the observed number of deaths falls outside of this range, the trust in question is considered to have a higher or lower SHMI than expected. The expected number of deaths is a statistical construct and is not a count of patients. The difference between the number of observed deaths and the number of expected deaths cannot be interpreted as the number of avoidable deaths or excess deaths for the trust. The SHMI is not a measure of quality of care. A higher than expected number of deaths should not immediately be interpreted as indicating poor performance and instead should be viewed as a 'smoke alarm' which requires further investigation. Similarly, an 'as expected' or 'lower than expected' SHMI should not immediately be interpreted as indicating satisfactory or good performance. Trusts may be located at multiple sites and may be responsible for 1 or more hospitals. A breakdown of the data by site of treatment is also provided, as well as a breakdown of the data by diagnosis group. Further background information and supporting documents, including information on how to interpret the SHMI, are available on the SHMI homepage (see Related Links).

Statistical significance of differences in the distribution (χ2 test) and mean (Tukey test) of Ovarian Categories for consecutive months.

IntroductionA common quality indicator for monitoring and comparing hospitals is based on death within 30 days of admission. An important use is to determine whether a hospital has higher or lower mortality than other hospitals. Thus, the ability to identify such outliers correctly is essential. Two approaches for detection are: 1) calculating the ratio of observed to expected number of deaths (OE) per hospital and 2) including all hospitals in a logistic regression (LR) comparing each hospital to a form of average over all hospitals. The aim of this study was to compare OE and LR with respect to correctly identifying 30-day mortality outliers. Modifications of the methods, i.e., variance corrected approach of OE (OE-Faris), bias corrected LR (LR-Firth), and trimmed mean variants of LR and LR-Firth were also studied.Materials and methodsTo study the properties of OE and LR and their variants, we performed a simulation study by generating patient data from hospitals with known outlier status (low mortality, high mortality, non-outlier). Data from simulated scenarios with varying number of hospitals, hospital volume, and mortality outlier status, were analysed by the different methods and compared by level of significance (ability to falsely claim an outlier) and power (ability to reveal an outlier). Moreover, administrative data for patients with acute myocardial infarction (AMI), stroke, and hip fracture from Norwegian hospitals for 2012–2014 were analysed.ResultsNone of the methods achieved the nominal (test) level of significance for both low and high mortality outliers. For low mortality outliers, the levels of significance were increased four- to fivefold for OE and OE-Faris. For high mortality outliers, OE and OE-Faris, LR 25% trimmed and LR-Firth 10% and 25% trimmed maintained approximately the nominal level. The methods agreed with respect to outlier status for 94.1% of the AMI hospitals, 98.0% of the stroke, and 97.8% of the hip fracture hospitals.ConclusionWe recommend, on the balance, LR-Firth 10% or 25% trimmed for detection of both low and high mortality outliers.

Background: More than 1 year after the beginning of the international spread of coronavirus 2019 (COVID-19), the reasons explaining its apparently lower reported burden in Africa are still to be fully elucidated. Few studies previously investigated the potential reasons explaining this epidemiological observation using data at the level of a few African countries. However, an updated analysis considering the various epidemiological waves and variables across an array of categories, with a focus on African countries might help to better understand the COVID-19 pandemic on the continent. Thus, we investigated the potential reasons for the persistently lower transmission and mortality rates of COVID-19 in Africa.Methods: Data were collected from publicly available and well-known online sources. The cumulative numbers of COVID-19 cases and deaths per 1 million population reported by the African countries up to February 2021 were used to estimate the transmission and mortality rates of COVID-19, respectively. The covariates were collected across several data sources: clinical/diseases data, health system performance, demographic parameters, economic indicators, climatic, pollution, and radiation variables, and use of social media. The collinearities were corrected using variance inflation factor (VIF) and selected variables were fitted to a multiple regression model using the R statistical package.Results: Our model (adjusted R-squared: 0.7) found that the number of COVID-19 tests per 1 million population, GINI index, global health security (GHS) index, and mean body mass index (BMI) were significantly associated (P < 0.05) with COVID-19 cases per 1 million population. No association was found between the median life expectancy, the proportion of the rural population, and Bacillus Calmette–Guérin (BCG) coverage rate. On the other hand, diabetes prevalence, number of nurses, and GHS index were found to be significantly associated with COVID-19 deaths per 1 million population (adjusted R-squared of 0.5). Moreover, the median life expectancy and lower respiratory infections rate showed a trend towards significance. No association was found with the BCG coverage or communicable disease burden.Conclusions: Low health system capacity, together with some clinical and socio-economic factors were the predictors of the reported burden of COVID-19 in Africa. Our results emphasize the need for Africa to strengthen its overall health system capacity to efficiently detect and respond to public health crises.

Mortality from causes considered amenable to health care (see “Numerator data” in the indicator specification for definition). As from the November 2005 Compendium release this indicator is one of three indicators that replace the ‘mortality from potentially avoidable causes’ indicator published in previous Compendia. To help reduce deaths from causes considered amenable to health care. Causes of death are included if there is evidence that they are amenable to healthcare interventions and – given timely, appropriate, and high quality care – death rates should be low among the age groups specified. Healthcare intervention includes preventing disease onset as well as treating disease. Two additional indicators are provided: ‘mortality from causes considered amenable to health care (exc Ischaemic heart disease)’ and ‘mortality from causes other than those considered amenable to health care’. The difference between amenable and non-amenable causes in their trends over time may provide evidence of the increasing (or decreasing) effectiveness of health care. Legacy unique identifier: P00361

The indicator measures the standardised death rate of chronic diseases. Chronic diseases included in the indicator are malignant neoplasms, diabetes mellitus, ischaemic heart diseases, cerebrovascular diseases, chronic lower respiratory diseases and chronic liver diseases (International Classification of Diseases (ICD) codes C00 to C97, E10 to E14, I20 to I25, I60 to I69 and J40 to J47). Death due to chronic diseases is considered premature if it occurs before the age of 65. The rate is calculated by dividing the number of people under 65 dying due to a chronic disease by the total population under 65. Data on causes of death (COD) refer to the underlying cause which - according to the World Health Organisation (WHO) - is "the disease or injury which initiated the train of morbid events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury". COD data are derived from death certificates. The medical certification of death is an obligation in all Member States. The data are presented as standardised death rates, meaning they are adjusted to a standard age distribution in order to measure death rates independently of different age structures of populations. This approach improves comparability over time and between countries. The standardised death rates used here are calculated on the basis of the standard European population referring to the residents of the countries.

This table contains 33048 series, with data for years 2000/2002 - 2010/2012 (not all combinations necessarily have data for all years), and was last released on 2016-03-16. This table contains data described by the following dimensions (Not all combinations are available): Geography (36 items: Total, census metropolitan areas; St. John's, Newfoundland and Labrador; Halifax, Nova Scotia;Moncton, New Brunswick; ...), Sex (3 items: Both sexes; Males; Females), Indicators (2 items: Mortality; Potential years of life lost), Selected causes of death (ICD-10) (17 items: Total, all causes of death; All malignant neoplasms (cancers); Colorectal cancer; Lung cancer; ...), Characteristics (9 items: Number; Low 95% confidence interval, number; High 95% confidence interval, number; Rate; ...).

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.

These indicators are designed to accompany the SHMI publication. The SHMI methodology includes an adjustment for admission method. This is because crude mortality rates for elective admissions tend to be lower than crude mortality rates for non-elective admissions. Contextual indicators on the crude percentage mortality rates for elective and non-elective admissions where a death occurred either in hospital or within 30 days (inclusive) of being discharged from hospital are produced to support the interpretation of the SHMI. Notes: 1. As of the July 2020 publication, COVID-19 activity has been excluded from the SHMI. The SHMI is not designed for this type of pandemic activity and the statistical modelling used to calculate the SHMI may not be as robust if such activity were included. Activity that is being coded as COVID-19, and therefore excluded, is monitored in the contextual indicator 'Percentage of provider spells with COVID-19 coding' which is part of this publication. 2. Please note that there was a fall in the overall number of spells from March 2020 due to COVID-19 impacting on activity for England and the number has not returned to pre-pandemic levels. Further information at Trust level is available in the contextual indicator ‘Provider spells compared to the pre-pandemic period’ which is part of this publication. 3. There is a shortfall in the number of records for East Lancashire Hospitals NHS Trust (trust code RXR) and The Princess Alexandra Hospital NHS Trust (trust code RQW). Values for these trusts are based on incomplete data and should therefore be interpreted with caution. 4. Frimley Health NHS Foundation Trust (trust code RDU) stopped submitting data to the Secondary Uses Service (SUS) during June 2022 and did not start submitting data again until April 2023 due to an issue with their patient records system. This is causing a large shortfall in records and values for this trust should be viewed in the context of this issue. 5. Due to a problem with the process which links Hospital Episode Statistics (HES) data to the Office for National Statistics (ONS) death registrations data, some in-hospital deaths have been counted as survivals in a small number of trusts. This affects 80 spells in the current time period for Mid and South Essex NHS Foundation Trust (trust code RAJ) meaning that the number of observed deaths has been underestimated and so the results for this trust should be interpreted with caution. For the other trusts, the number of affected spells is 5 or fewer and so the impact will be small. 6. A number of trusts are now submitting Same Day Emergency Care (SDEC) data to the Emergency Care Data Set (ECDS) rather than the Admitted Patient Care (APC) dataset. The SHMI is calculated using APC data. Removal of SDEC activity from the APC data may impact a trust’s SHMI value and may increase it. More information about this is available in the Background Quality Report. 7. Further information on data quality can be found in the SHMI background quality report, which can be downloaded from the 'Resources' section of this page.

Number of deaths and age-specific mortality rates for selected grouped causes, by age group and sex, 2000 to most recent year.

These indicators are designed to accompany the SHMI publication. As well as information on the main condition the patient is in hospital for (the primary diagnosis), the SHMI data contain up to 19 secondary diagnosis codes for other conditions the patient is suffering from. This information is used to calculate the expected number of deaths. 'Depth of coding' is defined as the number of secondary diagnosis codes for each record in the data. A higher mean depth of coding may indicate a higher proportion of patients with multiple conditions and/or comorbidities, but may also be due to differences in coding practices between trusts. Contextual indicators on the mean depth of coding for elective and non-elective admissions are produced to support the interpretation of the SHMI. Notes: 1. As of the July 2020 publication, COVID-19 activity has been excluded from the SHMI. The SHMI is not designed for this type of pandemic activity and the statistical modelling used to calculate the SHMI may not be as robust if such activity were included. Activity that is being coded as COVID-19, and therefore excluded, is monitored in the contextual indicator 'Percentage of provider spells with COVID-19 coding' which is part of this publication. 2. Please note that there was a fall in the overall number of spells from March 2020 due to COVID-19 impacting on activity for England and the number has not returned to pre-pandemic levels. Further information at Trust level is available in the contextual indicator ‘Provider spells compared to the pre-pandemic period’ which is part of this publication. 3. There is a shortfall in the number of records for The Princess Alexandra Hospital NHS Trust (trust code RQW). Values for this trust are based on incomplete data and should therefore be interpreted with caution. 4. Frimley Health NHS Foundation Trust (trust code RDU) stopped submitting data to the Secondary Uses Service (SUS) during June 2022 and did not start submitting data again until April 2023 due to an issue with their patient records system. This is causing a large shortfall in records and values for this trust should be viewed in the context of this issue. 5. There is a high percentage of invalid diagnosis codes for Chesterfield Royal Hospital NHS Foundation Trust (trust code RFS), Milton Keynes University Hospital NHS Foundation Trust (trust code RD8), and West Suffolk NHS Foundation Trust (trust code RGR). Values for these trusts should therefore be interpreted with caution. 6. A number of trusts are now submitting Same Day Emergency Care (SDEC) data to the Emergency Care Data Set (ECDS) rather than the Admitted Patient Care (APC) dataset. The SHMI is calculated using APC data. Removal of SDEC activity from the APC data may impact a trust’s SHMI value and may increase it. More information about this is available in the Background Quality Report. 7. East Kent Hospitals University NHS Foundation Trust (trust code RVV) has a submission issue which is causing many of their patient spells to be duplicated in the HES Admitted Patient Care data. This means that the number of spells for this trust in this dataset are overstated by approximately 60,000, and the trust’s SHMI value will be lower as a result. Values for this trust should therefore be interpreted with caution. 8. Further information on data quality can be found in the SHMI background quality report, which can be downloaded from the 'Resources' section of this page.

Analysis of ‘NCHS - Drug Poisoning Mortality by County: United States’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/3452f1d5-5a52-4f78-8ff8-02a7f7bff7fc on 12 February 2022.

--- Dataset description provided by original source is as follows ---

This dataset contains model-based county estimates for drug-poisoning mortality.

Deaths are classified using the International Classification of Diseases, Tenth Revision (ICD–10). Drug-poisoning deaths are defined as having ICD–10 underlying cause-of-death codes X40–X44 (unintentional), X60–X64 (suicide), X85 (homicide), or Y10–Y14 (undetermined intent).

Estimates are based on the National Vital Statistics System multiple cause-of-death mortality files (1). Age-adjusted death rates (deaths per 100,000 U.S. standard population for 2000) are calculated using the direct method. Populations used for computing death rates for 2011–2016 are postcensal estimates based on the 2010 U.S. census. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published.

Death rates for some states and years may be low due to a high number of unresolved pending cases or misclassification of ICD–10 codes for unintentional poisoning as R99, “Other ill-defined and unspecified causes of mortality” (2). For example, this issue is known to affect New Jersey in 2009 and West Virginia in 2005 and 2009 but also may affect other years and other states. Drug poisoning death rates may be underestimated in those instances.

Smoothed county age-adjusted death rates (deaths per 100,000 population) were obtained according to methods described elsewhere (3–5). Briefly, two-stage hierarchical models were used to generate empirical Bayes estimates of county age-adjusted death rates due to drug poisoning for each year. These annual county-level estimates “borrow strength” across counties to generate stable estimates of death rates where data are sparse due to small population size (3,5). Estimates for 1999-2015 have been updated, and may differ slightly from previously published estimates. Differences are expected to be minimal, and may result from different county boundaries used in this release (see below) and from the inclusion of an additional year of data. Previously published estimates can be found here for comparison.(6) Estimates are unavailable for Broomfield County, Colorado, and Denali County, Alaska, before 2003 (7,8). Additionally, Clifton Forge County, Virginia only appears on the mortality files prior to 2003, while Bedford City, Virginia was added to Bedford County in 2015 and no longer appears in the mortality file in 2015. These counties were therefore merged with adjacent counties where necessary to create a consistent set of geographic units across the time period. County boundaries are largely consistent with the vintage 2005-2007 bridged-race population file geographies, with the modifications noted previously (7,8).

REFERENCES 1. National Center for Health Statistics. National Vital Statistics System: Mortality data. Available from: http://www.cdc.gov/nchs/deaths.htm.

1. CDC. CDC Wonder: Underlying cause of death 1999–2016. Available from: http://wonder.cdc.gov/wonder/help/ucd.html.

2. Rossen LM, Khan D, Warner M. Trends and geographic patterns in drug-poisoning death rates in the U.S., 1999–2009. Am J Prev Med 45(6):e19–25. 2013.

3. Rossen LM, Khan D, Warner M. Hot spots in mortality from drug poisoning in the United States, 2007–2009. Health Place 26:14–20. 2014.

4. Rossen LM, Khan D, Hamilton B, Warner M. Spatiotemporal variation in selected health outcomes from the National Vital Statistics System. Presented at: 2015 National Conference on Health Statistics, August 25, 2015, Bethesda, MD. Available from: http://www.cdc.gov/nchs/ppt/nchs2015/Rossen_Tuesday_WhiteOak_BB3.pdf.

5. Rossen LM, Bastian B, Warner M, and Khan D. NCHS – Drug Poisoning Mortality by County: United States, 1999-2015. Available from: https://data.cdc.gov/NCHS/NCHS-Drug-Poisoning-Mortality-by-County-United-Sta/pbkm-d27e.

6. National Center for Health Statistics. County geog

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