This publication of the SHMI relates to discharges in the reporting period July 2022 - June 2023. 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. Deaths related to COVID-19 are excluded from the SHMI. 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). Information about the exclusion of COVID-19 from the SHMI can also be found on the same page. A link to the methodological changes statement which details the exclusion is also available in the Related Links section

This statistic shows the improvement in mortality rates 2007-2009 amongst all hospitals in the United States, sorted by mortality rates for inhospital care as well as 30 and 180 days following hospitalization. In addition to presenting information on improvement in the United States overall, this graph includes further data on hospitals of differing quality ratings. In the United States overall, mortality rates improved by 8.2 percent, but in five-star hospitals, mortality rates improved by 9.81 percent.

Notes:

This statistic depicts the 30-day mortality rate for patients with select conditions in U.S. hospitals who were discharged, between 2010 and 2016. Among heart attack, stroke, heart failure and pneumonia patients, the 30-day mortality rate for discharged patients averaged 14.1 percent between 2013 and 2016.

In 2022, the highest in-hospital mortality rate in Spain was recorded in the Canary Islands, with a total of 6.69 deaths for every 100 discharges. Galicia followed, with 6.56 deaths per 100 discharges. In comparison, the Spanish communities with the lowest in-hospital mortality rates were Madrid and Catalonia.

Introduction: Due to a lack of information on patient mortality, healthcare planners rarely use local data for resource allocation and hospital management. This results in missed opportunities to build hospital capacity to address common causes of death, as well as a poor hospital reputation, fewer patients seeking hospital care, increased medical errors, and increased inpatient mortality. Objective: To determine trends of hospital mortality between 2018 and 2019 at Level Four Kisumu County Hospital, Kenya. Methods: The study was a cross sectional retrospective study design. The study targeted files of patients who died between January 2018 and December 2022. Systematic sampling was used in which every file per ward was given a serial number. Each department formed a stratum. Sample size was determined using Yamane Taro formula (N/1+N(e2) which yielded 203 as sample size from population of 680. The risk of death based on the presence or absence of doctor and nurse was analyzed by odds ratio. Chi-square was used to check association of appropriateness of facility, delay of care and distance and mortality. Variation in ward mortalities was analyzed using ANOVA to assess and data presented as line graphs. Results: According to the current study, the medical ward had the highest 2-year in-hospital mortality rate of 13.86%, while obstetrics and gynecology (reproductive health) had the lowest mortality rate of 0.47 percent. Infections were responsible for 42% of hospital deaths in patients under the age of 35, while noncommunicable diseases were responsible for 41% of hospital deaths in patients over the age of 60. According to the study, 3% of hospital deaths could have been avoided. When a nurse and a doctor were all present, there was a significant difference in the odds of a patient dying (OR=0.697). Comorbidity was a significant risk factor for death among patients who died in 2018 and 2019 (p=0.05). Patient characteristics such as age, education level, and gender were not associated with hospital deaths (p>0.05). Conclusion: Hospital deaths among the elderly are caused by noncommunicable diseases, while deaths among the young are caused by infectious diseases, raising the question of the need to improve the nurse-doctor relationship in order to reduce avoidable deaths among patients admitted.

In 2018, over 1.5 million people died due to poor quality of care in hospitals in the south Asian country of India. Furthermore, over 838 people died due to insufficient access to healthcare in the country during that time.

Complications and deaths - state data. This data set includes state-level data for the hip/knee complication measure, the CMS Patient Safety Indicators, and 30-day death rates.

This dataset includes national-level data for the hospital return days (or excess days in acute care) measures and the 30-day readmission measures, the unplanned readmissions measures, and the rate of unplanned hospital visits after an outpatient colonoscopy.

Notes:

As of 2021, there were 20.5 deaths per 100 hospital admissions for stroke among those aged 45 years and older in Latvia. The statistic shows the thirty-day mortality after admission to hospital for ischaemic stroke in selected OECD countries as of 2021, per 100 admissions among adults aged 45 years and older.

This indicator is designed to accompany the SHMI publication. The SHMI includes all deaths reported of patients who were admitted to non-specialist acute trusts in England and either died while in hospital or within 30 days of discharge. Deaths related to COVID-19 are excluded from the SHMI. A contextual indicator on the percentage of deaths reported in the SHMI which occurred in hospital and the percentage which occurred outside of hospital is 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 Ashford and St Peter's Hospitals NHS Foundation Trust (trust code RTK), County Durham and Darlington NHS Foundation Trust (trust code RXP), Frimley Health NHS Foundation Trust (trust code RDU), Mid Cheshire Hospitals NHS Foundation Trust (trust code RBT), Royal Surrey County Hospital NHS Foundation Trust (trust code RA2), and Wrightington, Wigan and Leigh NHS Foundation Trust (trust code RRF). Values for these trusts are based on incomplete data and should therefore be interpreted with caution. 4. There are a high percentage of invalid diagnosis codes for Wye Valley NHS Trust (trust code RLQ). Values for this trust should therefore be interpreted with caution. 5. A number of trusts are currently engaging in a pilot to submit Same Day Emergency Care (SDEC) data to the Emergency Care Data Set (ECDS), rather than the Admitted Patient Care (APC) dataset. As the SHMI is calculated using APC data, this does have the potential to impact on the SHMI value for these trusts. Trusts with SDEC activity removed from the APC data have generally seen an increase in the SHMI value. This is because the observed number of deaths remains approximately the same as the mortality rate for this cohort is very low; secondly, the expected number of deaths decreases because a large number of spells are removed, all of which would have had a small, non-zero risk of mortality contributing to the expected number of deaths. NHS Digital are working with NHS England to better understand the planned changes to the recording of SDEC activity and the potential impact on the SHMI. The trusts affected in this publication are: Barts Health NHS Trust (trust code R1H), Cambridge University Hospitals NHS Foundation Trust (trust code RGT), Croydon Health Services NHS Trust (trust code RJ6), Epsom and St Helier University Hospitals NHS Trust (trust code RVR), Frimley Health NHS Foundation Trust (trust code RDU), Imperial College Healthcare NHS Trust (trust code RYJ), Norfolk and Norwich University Hospitals NHS Foundation Trust (RM1), and University Hospitals of Derby and Burton NHS Foundation Trust (RTG). 6. Further information on data quality can be found in the SHMI background quality report, which can be downloaded from the 'Resources' section of the publication page.

In 2021, there were around 1.8 million inpatient discharges and deaths from Hospital Authority hospitals. In that year, Hospital Authority hospitals reported the largest number of inpatient discharges and deaths, followed by private hospitals.

Notes:

In 2019, the crude mortality rate per thousand inhabitants in Dubai amounted to 2.98. In the same year, the total number of deaths inside the hospitals in Dubai amounted to about 1.5 thousand deaths taking place in private and governmental hospitals.

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. There is a shortfall in the number of records for East Lancashire Hospitals NHS Trust (trust code RXR), Harrogate and District NHS Foundation Trust (trust code RCD) and Northern Lincolnshire and Goole NHS Foundation Trust (trust code RJL). Values for these trusts are based on incomplete data and should therefore be interpreted with caution. 2. Data for Royal Surrey County Hospital NHS Foundation Trust (trust code RA2) has been suppressed from publication. This trust had submitted in error a high percentage of records with no secondary care diagnosis codes, this has made their SHMI values highly misleading. 3. 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 SHMI background quality report. 4. 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.

This table provides an overview of the key figures on health and care available on StatLine. All figures are taken from other tables on StatLine, either directly or through a simple conversion. In the original tables, breakdowns by characteristics of individuals or other variables are possible. The period after the year of review before data become available differs between the data series. The number of exam passes/graduates in year t is the number of persons who obtained a diploma in school/study year starting in t-1 and ending in t.

Data available from: 2001

Status of the figures: 2024: The available figures are definite. 2023: Most available figures are definite Figures are provisional for: - perinatal mortality at pregnancy duration at least 24 weeks; - diagnoses known to the general practitioner; - supplied drugs; - AWBZ/Wlz-funded long term care; - persons employed in health and welfare; - persons employed in healthcare; - Mbo health care graduates; - Hbo nursing graduates / medicine graduates (university); - expenditures on health and welfare; - average distance to facilities. 2022: Most available figures are definite, figures are provisional for: - hospital admissions by some diagnoses; - physicians and nurses employed in care; - persons employed in health and welfare; - persons employed in healthcare; - expenditures on health and welfare; - profitability and operating results at institutions. 2021: Most available figures are definite, figures are provisional for: - expenditures on health and welfare. 2020 and earlier: All available figures are definite.

Changes as of 18 december 2024: - Distance to facilities: the figures withdrawn on 5 June have been replaced (unchanged). - Youth care: the previously published final results for 2021 and 2022 have been adjusted due to improvements in the processing. - Due to a revision of the statistics Expenditure on health and welfare 2021, figures for expenditure on health and welfare care have been replaced from 2021 onwards. - Due to the revision of the National Accounts, the figures on persons employed in health and welfare have been replaced for all years. - AWBZ/Wlz-funded long term care: from 2015, the series Wlz residential care including total package at home has been replaced by total Wlz care. This series fits better with the chosen demarcation of indications for Wlz care.

More recent figures have been added for: - crude birth rate; - live births to teenage mothers; - causes of death; - perinatal mortality at pregnancy duration at least 24 weeks; - life expectancy in perceived good health; - diagnoses known to the general practitioner; - supplied drugs; - AWBZ/Wlz-funded long term care; - youth care; - persons employed in health and welfare; - persons employed in healthcare; - expenditures on health and welfare; - average distance to facilities.

When will new figures be published? New figures will be published in July 2025.

In 2022, diseases of the circulatory system was the most common cause of death in Malaysian hospitals with a share of around 20.79 percent. In the same year, the country reported that the main reasons for hospitalization were pregnancy related.

In 2021, approximately 2.2 million inpatient discharges and deaths from hospitals were recorded in Hong Kong. Before that year, the number of inpatient discharges and deaths from hospitals had seen an ongoing increase since 2010.

In 2023, the hospital mortality rate totaled 4.1 percent in Hungary, marking a decrease from the previous year. The highest figure was recorded in 2021 at 6.1 percent.