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).

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

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 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. 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. 6. 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. 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.

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.

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

In 2021, the average mortality rate across OECD countries from preventable causes stood at 158 deaths per 100,000 population. This varied widely from just 83 deaths in Israel to 435 preventable deaths in Mexico per 100,000 population. The OECD defines preventable mortality as causes of death amongst people aged under 75 years that can be mainly avoided through effective public health and primary prevention interventions (i.e. before the onset of disease/injury, to reduce incidence). Treatable (or amenable mortality is defined as causes of death that can be mainly avoided through timely and effective health care interventions including secondary prevention and treatment (i.e. after the onset of disease, to reduce case fatality). This statistic presents the mortality rates from preventable causes worldwide in 2021, by country.

Mortality among people, aged 20 years or over, with bipolar disorder compared to mortality in the population. The value of 1.0 means that there is no excess mortality. Age-standard values. The measurement period is 5 years. The year reported is the last of these. Numerator: The age-standardised mortality rate in all causes of death for people aged 20 years and over who, over a period of 5 years, are diagnosed with bipolar disorder in the patient register. Denominator: The age-standardised mortality rate in the same reference year for all persons aged 20 years and older in the total population. Data is available according to gender breakdown.

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. On 1st January 2025, North Middlesex University Hospital NHS Trust (trust code RAP) was acquired by Royal Free London NHS Foundation Trust (trust code RAL). This new organisation structure is reflected from this publication onwards. 2. There is a shortfall in the number of records for Northumbria Healthcare NHS Foundation Trust (trust code RTF), The Rotherham NHS Foundation Trust (trust code RFR), The Shrewsbury and Telford Hospital NHS Trust (trust code RXW), and Wirral University Teaching Hospital NHS Foundation Trust (trust code RBL). Values for these trusts are based on incomplete data and should therefore be interpreted with caution. 3. There is a high percentage of invalid diagnosis codes for Chesterfield Royal Hospital NHS Foundation Trust (trust code RFS), East Lancashire Hospitals NHS Trust (trust code RXR), Great Western Hospitals NHS Foundation Trust (trust code RN3), Harrogate and District NHS Foundation Trust (trust code RCD), Milton Keynes University Hospital NHS Foundation Trust (trust code RD8), Portsmouth Hospitals University NHS Trust (trust code RHU), Royal United Hospitals Bath NHS Foundation Trust (trust code RD1), University Hospitals Birmingham NHS Foundation Trust (trust code RRK), University Hospitals of North Midlands NHS Trust (trust code RJE), and University Hospitals Plymouth NHS Trust (trust code RK9). Values for these trusts should therefore be interpreted with caution. 4. 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. 5. 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.

Odds ratios of relationship between competition and 30-day mortality using patient flow methods.

Infant mortality has been falling in Bangladesh in the past decade, from 32.7 deaths per 1,000 live births in 2013 to 24.4 in 2023. This figure helps to assess the overall healthcare system’s efficacy, because childbirth and infant care require more direct patient care than any other period of life. Similarly, measures taken to combat infant mortality often have spillover effects, improving the entire healthcare system. Population in Bangladesh Bangladesh has one of the highest population densities in the world. While the economy is growing at a fair rate, gross domestic product (GDP) per capita is still low. This points to Bangladesh’s status as a developing nation. However, these indicators also suggest that the country continues to flourish. This development can benefit a significant number of people. Other development indicators As health outcomes improve, life expectancy should follow. This will lead to an upward shift in the population pyramid, which measures the age structure in a country. Such a change means that there are more workers in the medium term, increasing the country’s productivity. Productivity growth then enables more expenditure on health care, creating a virtuous cycle. For this reason, experts follow infant mortality closely.

IntroductionContradictions remain on the impact of interhospital competition on the quality of care, mainly the mortality. The aim of the study is to evaluate the impact of interhospital competition on postoperative mortality after surgery for colorectal cancer in France.MethodsWe conducted a retrospective cross-sectional study from 2015 to 2019. Data were collected from a National Health Database. Patients operated on for colorectal cancer in a hospital in mainland France were included. Competition was measured using number of competitors by distance-based approach. A mixed-effect model was carried out to test the link between competition and mortality.ResultsNinety-five percent (n = 152,235) of the 160,909 people operated on for colorectal cancer were included in our study. The mean age of patients was 70.4 ±12.2 years old, and female were more represented (55%). A total of 726 hospitals met the criteria for inclusion in our study. Mortality at 30 days was 3.6% and we found that the mortality decreases with increasing of the hospital activity. Using the number of competitors per distance method, our study showed that a “highly competitive” and “moderately competitive” markets decreased mortality by 31% [OR: 0.69 (0.59, 0.80); p

Analysis of ‘In Hospital Mortality Prediction’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/saurabhshahane/in-hospital-mortality-prediction on 28 January 2022.

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

Context

The predictors of in-hospital mortality for intensive care units (ICU)-admitted HF patients remain poorly characterized. We aimed to develop and validate a prediction model for all-cause in-hospital mortality among ICU-admitted HF patients.

Content

Using Structured Query Language queries (PostgreSQL, version 9.6), demographic characteristics, vital signs, and laboratory values data were extracted from the following tables in the MIMIC III dataset: ADMISSIONS, PATIENTS, ICUSTAYS, D_ICD DIAGNOSIS, DIAGNOSIS_ICD, LABEVENTS, D_LABIEVENTS, CHARTEVENTS, D_ITEMS, NOTEEVENTS, and OUTPUTEVENTS. Based on previous studies 7-9 13-15, clinical relevance, and general availability at the time of presentation, we extracted the following data: demographic characteristics (age at the time of hospital admission, sex, ethnicity, weight, and height); vital signs (heart rate, (HR), systolic blood pressure [SBP], diastolic blood pressure [DBP], mean blood pressure, respiratory rate, body temperature, saturation pulse oxygen [SPO2], urine output [first 24 h]); comorbidities (hypertension, atrial fibrillation, ischemic heart disease, diabetes mellitus, depression, hypoferric anemia, hyperlipidemia, chronic kidney disease (CKD), and chronic obstructive pulmonary disease [COPD]); and laboratory variables (hematocrit, red blood cells, mean corpuscular hemoglobin [MCH], mean corpuscular hemoglobin concentration [MCHC], mean corpuscular volume [MCV], red blood cell distribution width [RDW], platelet count, white blood cells, neutrophils, basophils, lymphocytes, prothrombin time [PT], international normalized ratio [INR], NT-proBNP, creatine kinase, creatinine, blood urea nitrogen [BUN] glucose, potassium, sodium, calcium, chloride, magnesium, the anion gap, bicarbonate, lactate, hydrogen ion concentration [pH], partial pressure of CO2 in arterial blood, and LVEF), using Structured Query Language (SQL) with PostgreSQL (version 9.6). Demographic characteristics and vital signs extracted were recorded during the first 24 hours of each admission and laboratory variables were measured during the entire ICU stay. Comorbidities were identified using ICD-9 codes. For variable data with multiple measurements, the calculated mean value was included for analysis. The primary outcome of the study was in-hospital mortality, defined as the vital status at the time of hospital discharge in survivors and non-survivors.

Acknowledgements

Zhou, Jingmin et al. (2021), Prediction model of in-hospital mortality in intensive care unit patients with heart failure: machine learning-based, retrospective analysis of the MIMIC-III database, Dryad, Dataset, https://doi.org/10.5061/dryad.0p2ngf1zd

LICENSE - CC0 1.0 Universal (CC0 1.0) Public Domain Dedication

Target Variable - Outcome 0 - Alive 1 - Death

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

Purpose: To evaluate the association of prior to intensive care unit (ICU) statin use with the clinical outcomes in critically ill patients with acute kidney injury (AKI).Materials and Methods: Patients with AKI were selected from the Medical Information Mart for Intensive Care IV (version 1.0) database for this retrospective observational study. The primary outcome was 30-day intensive care unit (ICU) mortality. A 30-day in-hospital mortality and ICU length of stay (LOS) were considered as secondary outcomes. Comparison of mortality between pre-ICU statin users with non-users was conducted by the multivariate Cox proportional hazards model. Comparison of ICU LOS between two groups was implemented by multivariate linear model. Three propensity score methods were used to verify the results as sensitivity analyses. Stratification analyses were conducted to explore whether the association between pre-ICU statin use and mortality differed across various subgroups classified by sex and different AKI stages.Results: We identified 3,821 pre-ICU statin users and 9,690 non-users. In multivariate model, pre-ICU statin use was associated with reduced 30-day ICU mortality rate [hazard ratio (HR) 0.68 (0.59, 0.79); p < 0.001], 30-day in-hospital mortality rate [HR 0.64 (0.57, 0.72); p < 0.001] and ICU LOS [mean difference −0.51(−0.79, −0.24); p < 0.001]. The results were consistent in three propensity score methods. In subgroup analyses, pre-ICU statin use was associated with decreased 30-day ICU mortality and 30-day in-hospital mortality in both sexes and AKI stages, except for 30-day ICU mortality in AKI stage 1.Conclusion: Patients with AKI who were administered statins prior to ICU admission might have lower mortality during ICU and hospital stay and shorter ICU LOS.

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

ObjectivesAn increased risk of mortality in patients with hyperuricemia has been reported. We examined (1) the risk of all-cause and cardiovascular disease (CVD) mortality in untreated hyperuricemic patients who did not receive urate-lowering therapy (ULT), and (2) the impact of ULT on mortality risk in patients with hyperuricemia.MethodsIn this retrospective case-matched cohort study during a mean follow-up of 6.4 years, 40,118 Taiwanese individuals aged ≥17 years who had never used ULT and who had never had gout were examined. The mortality rate was compared between 3,088 hyperuricemic patients who did not receive ULT and reference subjects (no hyperuricemia, no gout, no ULT) matched for age and sex (1:3 hyperuricemic patients/reference subjects), and between 1,024 hyperuricemic patients who received ULT and 1,024 hyperuricemic patients who did not receive ULT (matched 1:1 based on their propensity score and the index date of ULT prescription). Cox proportional hazard modeling was used to estimate the respective risk of all-cause and CVD (ICD-9 code 390–459) mortality.ResultsAfter adjustment, hyperuricemic patients who did not receive ULT had increased risks of all-cause (hazard ratio, 1.24; 95% confidence interval, 0.97–1.59) and CVD (2.13; 1.34–3.39) mortality relative to the matched reference subjects. Hyperuricemic patients treated with ULT had a lower risk of all-cause death (0.60; 0.41–0.88) relative to hyperuricemic patients who did not receive ULT.ConclusionUnder-treatment of hyperuricemia has serious negative consequences. Hyperuricemic patients who received ULT had potentially better survival than patients who did not.

The deadliest energy source worldwide is coal. It is estimated that there are roughly 33 deaths from brown coal (also known as Lignite) and 25 deaths from coal per terawatt-hour (TWh) of electricity produced from these fossil fuels. While figures take into account accidents, the majority of deaths associated with coal come from air pollution.

Air pollution deaths from fossil fuels

Air pollution from coal-fired plants has been of growing concern as it has been linked to asthma, cancer, and heart disease. Burning coal can release toxic airborne pollutants such as mercury, sulfur dioxide, nitrogen oxides, and particulate matter. Eastern Asia accounts for roughly 31 percent of global deaths attributable to exposure to fine particulate matter (PM2.5) generated by fossil fuel combustion, which is perhaps unsurprising given the fact China and India are the two largest coal consumers in the world.

Safest energy source

Clean and renewable energy sources are unsurprisingly the least deadly energy sources, with 0.04 and 0.02 deaths associated with wind and solar per unit of electricity, respectively. Nuclear energy also has a low death rate, even after the inclusion of nuclear catastrophes like Chernobyl and Fukushima.

The 2019 newfound Coronavirus (COVID-19) still remains as a threatening disease of which new cases are being reported daily from all over the world. The present study aimed at estimating the related rates of morbidity, growth, and mortality for COVID-19 over a three-month period starting from Feb, 19, 2020 to May 18, 2020 in Iran. In addition, it revealed the effect of the mean age, changes in weather temperature and country’s executive policies including social distancing, restrictions on travel, closing public places, shops and educational centers. We have developed a combined neural network to estimate basic reproduction number, growth, and mortality rates of COVID-19. Required data was obtained from daily reports of World Health Organization (WHO), Iran Meteorological Organization (IRIMO) and the Statistics Center of Iran. The technique used in the study encompassed the use of Artificial Neural Network (ANN) combined with Swarm Optimization (PSO) and Bus Transportation Algorithms (BTA). The results of the present study showed that the related mortality rate of COVID-19 is in the range of [0.1], and the point 0.275 as the mortality rate provided the best results in terms of the total training and test squared errors of the network. Furthermore, the value of basic reproduction number for ANN-BTA and ANN-PSO was 1.045 and 1.065, respectively. In the present study, regarding the closest number to the regression line (0.275), the number of patients was equal to 2566200 cases (with and without clinical symptoms) and the growth rate based on arithmetic means was estimated to be 1.0411 and 1.06911, respectively. Reviewing the growth and mortality rates over the course of 90 days, after 45 days of first case detection, the highest increase in mortality rate was reported 158 cases. Also, the highest growth rate was related to the eighth and the eighteenth days after the first case report (2.33). In the present study, the weather variant in relationship to the basic reproduction number and mortality rate was estimated ineffective. In addition, the role of quarantine policies implemented by the Iranian government was estimated to be insignificant concerning the mortality rate. However, the age range was an ifluential factor in mortality rate. Finally, the method proposed in the present study cofirmed the role of the mean age of the country in the mortality rate related to COVID-19 patients at the time of research conduction. The results indicated that if sever quarantine restrictions are not applied and Iranian government does not impose effective interventions, about 60% to 70% of the population (it means around 49 to 58 million people) would be afflicted by COVID-19 during June to September 2021.

BackgroundThis meta-analysis aimed at investigating the pooled evidence regarding the effects of intravenous vitamin C (IVVC) on mortality rate in critically ill patients.MethodsDatabases including Medline, Embase, and Cochrane Library were searched from inception to October, 2022 to identify RCTs. The primary outcome was the risk of overall mortality. Subgroup analyses were performed based on IVVC dosage (i.e., cut-off value: 100 mg/kg/day or 10000 mg/day). Trial sequential analysis (TSA) was used to examine the robustness of evidence.ResultsA total of 12 trials including 1,712 patients were analyzed. Although meta-analysis demonstrated a lower risk of mortality in patients with IVVC treatment compared to those without [risk ratio (RR): 0.76, 95% CI: 0.6 to 0.97, p = 0.02, I2 = 36%, 1,711 patients), TSA suggested the need for more studies for verification. Moreover, subgroup analyses revealed a reduced mortality risk associated with a low IVVC dosage (RR = 0.72, p = 0.03, 546 patients), while no beneficial effect was noted with high IVVC dosage (RR = 0.74, p = 0.13, I2 = 60%, 1,165 patients). The durations of vasopressor [mean difference (MD): −37.75 h, 404 patients) and mechanical ventilation (MD: −47.29 h, 388 patients) use were shorter in the IVVC group than those in the controls, while there was no significant difference in other prognostic outcomes (e.g., length of stay in intensive care unit/hospital) between the two groups.ConclusionAlthough intravenous vitamin C as a monotherapy reduced pooled mortality, durations of vasopressor use and mechanical ventilation, further research is required to support our findings and to identify the optimal dosage of vitamin C in the critical care setting.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42022371090.

With global estimates of 15 million cases of sepsis annually, together with a 24% in-hospital mortality rate, this condition comes at a high cost to both the patient and to the health services delivering care. This translational research determined the cost-effectiveness of state-wide implementation of a whole of hospital Sepsis Pathway in reducing mortality and/or hospital admission costs from a healthcare sector perspective, and report the cost of implementation over 12-months. A non-randomised stepped wedge cluster implementation study design was used to implement an existing Sepsis Pathway (“Think sepsis. Act fast”) across 10 of Victoria’s public health services, comprising 23 hospitals, which provide hospital care to 63% of the State’s population, or 15% of the Australian population. The pathway utilised a nurse led model with early warning and severity criteria, and actions to be initiated within 60 minutes of sepsis recognition. Pathway elements included oxygen administration; blood cultures (x2); venous blood lactate; fluid resuscitation; intravenous antibiotics, and increased monitoring. At baseline there were 876 participants (392 female (44.7%), mean 68.4 years); and during the intervention, there were 1,476 participants (684 female (46.3%), mean 66.8 years). Mortality significantly reduced from 11.4% (100/876) at baseline to 5.8% (85/1,476) during implementation (p>0.001). Respectively, at baseline and intervention the average length of stay was 9.1 (SD 10.3) and 6.2 (SD 7.9) days, and cost was $AUD22,107 (SD $26,937) and $14,203 (SD $17,611) per patient, with a significant 2.9 day reduction in length of stay (-2.9; 95%CI -3.7 to -2.2, p

Competing risk analysis of patients with cirrhosis identifying predictors of all-cause mortality and liver-related death.