The dataset contains risk-adjusted mortality rates, quality ratings, and number of deaths and cases for 6 medical conditions treated (Acute Stroke, Acute Myocardial Infarction, Heart Failure, Gastrointestinal Hemorrhage, Hip Fracture and Pneumonia) and 5 procedures performed (Abdominal Aortic Aneurysm Repair, Unruptured/Open, Abdominal Aortic Aneurysm Repair, Unruptured/Endovascular, Carotid Endarterectomy, Pancreatic Resection, Percutaneous Coronary Intervention) in California hospitals. The 2022 IMIs were generated using AHRQ Version 2023, while previous years' IMIs were generated with older versions of AHRQ software (2021 IMIs by Version 2022, 2020 IMIs by Version 2021, 2019 IMIs by Version 2020, 2016-2018 IMIs by Version 2019, 2014 and 2015 IMIs by Version 5.0, and 2012 and 2013 IMIs by Version 4.5). The differences in the statistical method employed and inclusion and exclusion criteria using different versions can lead to different results. Users should not compare trends of mortality rates over time. However, many hospitals showed consistent performance over years; “better” performing hospitals may perform better and “worse” performing hospitals may perform worse consistently across years. This dataset does not include conditions treated or procedures performed in outpatient settings. Please refer to statewide table for California overall rates: https://data.chhs.ca.gov/dataset/california-hospital-inpatient-mortality-rates-and-quality-ratings/resource/af88090e-b6f5-4f65-a7ea-d613e6569d96

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.

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 and percentage of deaths, by place of death (in hospital or non-hospital), 1991 to most recent year.

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.

In 2016, the hospital death rate in Myanmar was estimated at 1.5 percent, which was the same compared to the previous year. In 2016, there were around 2.75 million hospital admissions.

The dataset contains risk-adjusted mortality rates, and number of deaths and cases for 6 medical conditions treated (Acute Stroke, Acute Myocardial Infarction, Heart Failure, Gastrointestinal Hemorrhage, Hip Fracture and Pneumonia) and 6 procedures performed (Abdominal Aortic Aneurysm Repair, Carotid Endarterectomy, Craniotomy, Esophageal Resection, Pancreatic Resection, Percutaneous Coronary Intervention) in California hospitals. The 2014 and 2015 IMIs were generated using AHRQ Version 5.0, while the 2012 and 2013 IMIs were generated using AHRQ Version 4.5. The differences in the statistical method employed and inclusion and exclusion criteria using different versions can lead to different results. Users should not compare trends of mortality rates over time. However, many hospitals showed consistent performance over years; “better” performing hospitals may perform better and “worse” performing hospitals may perform worse consistently across years. This dataset does not include conditions treated or procedures performed in outpatient settings. Please refer to hospital table for hospital rates: https://data.chhs.ca.gov/dataset/california-hospital-inpatient-mortality-rates-and-quality-ratings

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.

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.

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This scatter chart displays hospital beds (per 1,000 people) against death rate (per 1,000 people) and is filtered where the region is Eastern Africa. The data is about countries per year.

This table presents a wide variety of historical data in the field of health, lifestyle and health care. Figures on births and mortality, causes of death and the occurrence of certain infectious diseases are available from 1900, other series from later dates. In addition to self-perceived health, the table contains figures on infectious diseases, hospitalisations per diagnosis, life expectancy, lifestyle factors such as smoking, alcohol consumption and obesity, and causes of death. The table also gives information on several aspects of health care, such as the number of practising professionals, the number of available hospital beds, nursing day averages and the expenditures on care. Many subjects are also covered in more detail by data in other tables, although sometimes with a shorter history. Data on notifiable infectious diseases and HIV/AIDS are not included in other tables.

Data available from: 1900

Status of the figures:

2024: The available figures are definite. 2023: Most available figures are definite. Figures are provisional for: - occurrence of infectious diseases; - expenditures on health and welfare; - perinatal and infant mortality. 2022: Most available figures are definite. Figures are provisional for: - occurrence of infectious diseases; - diagnoses at hospital admissions; - number of hospital discharges and length of stay; - number of hospital beds; - health professions; - expenditures on health and welfare. 2021: Most available figures are definite. Figures are provisional for: - occurrence of infectious diseases; - expenditures on health and welfare. 2020 and earlier: Most available figures are definite. Due to 'dynamic' registrations, figures for notifiable infectious diseases, HIV, AIDS remain provisional.

Changes as of 18 december 2024: - Due to a revision of the statistics Health and welfare expenditure 2021, figures for expenditure on health and welfare have been replaced from 2021 onwards. - Revised figures on the volume index of healthcare costs are not yet available, these figures have been deleted from 2021 onwards.

The most recent available figures have been added for: - live born children, deaths; - occurrence of infectious diseases; - number of hospital beds; - expenditures on health and welfare; - perinatal and infant mortality; - healthy life expectancy; - causes of death.

When will new figures be published? July 2025.

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 scatter chart displays death rate (per 1,000 people) against hospital beds (per 1,000 people) and is filtered where the country is Tuvalu. The data is about countries per year.

This scatter chart displays hospital beds (per 1,000 people) against death rate (per 1,000 people) and is filtered where the country is Algeria. The data is about countries per year.

This scatter chart displays hospital beds (per 1,000 people) against death rate (per 1,000 people) and is filtered where the country is Serbia. The data is about countries per year.

This scatter chart displays hospital beds (per 1,000 people) against death rate (per 1,000 people) and is filtered where the country is Eritrea. The data is about countries per year.

This dataset is about countries in Burkina Faso per year, featuring 4 columns: country, date, death rate, and hospital beds. The preview is ordered by date (descending).