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 ** and *** 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 *** percent, but in five-star hospitals, mortality rates improved by **** percent.

This publication of the SHMI relates to discharges in the reporting period March 2024 - February 2025. 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).

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

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:

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 **** percent between 2013 and 2016.

This dataset contains the number of cases, number of in hospital/30 day deaths, observed, expected and risk- adjusted mortality rates for cardiac surgery and percutaneous coronary interventions (PCI) by hospital. Regions represent where the hospitals are located. The initial Health Data NY dataset includes patients discharged between January 1, 2008, and December 31, 2010. Analyses of risk-adjusted mortality rates and associated risk factors are provided for 2010 and for the three-year period from 2008 through 2010. For PCI, analyses of all cases, non-emergency cases (which represent the majority of procedures) and emergency cases are included. Subsequent year reports data will be appended to this dataset. For more information check out: http://www.health.ny.gov/health_care/consumer_information/cardiac_surgery/ or go to the “About” tab.

Notes:

This dataset contains Mortality Statistics for years 2015 and 2016 from Quality Based Reimbursement (QBR) Program for hospitals in Maryland. It includes Hospital ID, Hospital Name, Mortality Rate, Ratio of Observed to Predicted Mortality Rate, Risk Adjusted Mortality and Survival Rates, Number of Dead and time period covered for the data collected.

In the United States from 2022 to 2024, the 30-day mortality rate in hospital at home programs for patients with respiratory infections and inflammations with MCC was around ** deaths per 1,000. In comparison, the mortality rate in comparable hospitals for the same diagnosis related groups was almost *** deaths per 1,000.

This is a database (parquet format) containing publicly available multiple cause mortality data from the US (CDC/NCHS) for 2014-2022. Not all variables are included on this export. Please see below for restrictions on the use of these data imposed by NCHS. You can use the arrow package in R to open the file. See here for example analysis; https://github.com/DanWeinberger/pneumococcal_mortality/blob/main/analysis_nongeo.Rmd . For instance, save this file in a folder called "parquet3":

library(arrow)

library(dplyr)

pneumo.deaths.in <- open_dataset("R:/parquet3", format = "parquet") %>% #open the dataset
filter(grepl("J13|A39|J181|A403|B953|G001", all_icd)) %>% #filter to records that have the selected ICD codes
collect() #call the dataset into memory. Note you should do any operations you canbefore calling 'collect()" due to memory issues

The variables included are named: (see full dictionary:https://www.cdc.gov/nchs/nvss/mortality_public_use_data.htm)

year: Calendar year of death

month: Calendar month of death

age_detail_number: number indicating year or part of year; can't be interpreted itself here. see agey variable instead

sex: M/F

place_of_death:

Place of Death and Decedent’s Status
Place of Death and Decedent’s Status
1 ... Hospital, Clinic or Medical Center
- Inpatient
2 ... Hospital, Clinic or Medical Center
- Outpatient or admitted to Emergency Room
3 ... Hospital, Clinic or Medical Center
- Dead on Arrival
4 ... Decedent’s home
5 ... Hospice facility
6 ... Nursing home/long term care
7 ... Other
9 ... Place of death unknown

all_icd: Cause of death coded as ICD10 codes. ICD1-ICD21 pasted into a single string, with separation of codes by an underscore

hisp_recode: 0=Non-Hispanic; 1=Hispanic; 999= Not specified

race_recode: race coding prior to 2018 (reconciled in race_recode_new)

race_recode_alt: race coding after 2018 (reconciled in race_recode_new)

race_recode_new:

1='White'

2= 'Black'

3='Hispanic'

4='American Indian'

5='Asian/Pacific Islanders'

agey:

age in years (or partial years for kids <12months)

https://www.cdc.gov/nchs/data_access/restrictions.htm

Please Read Carefully Before Using NCHS Public Use Survey Data

The National Center for Health Statistics (NCHS), Centers for Disease Control and Prevention (CDC), conducts statistical and epidemiological activities under the authority granted by the Public Health Service Act (42 U.S.C. § 242k). NCHS survey data are protected by Federal confidentiality laws including Section 308(d) Public Health Service Act [42 U.S.C. 242m(d)] and the Confidential Information Protection and Statistical Efficiency Act or CIPSEA [Pub. L. No. 115-435, 132 Stat. 5529 § 302]. These confidentiality laws state the data collected by NCHS may be used only for statistical reporting and analysis. Any effort to determine the identity of individuals and establishments violates the assurances of confidentiality provided by federal law.

Terms and Conditions

NCHS does all it can to assure that the identity of individuals and establishments cannot be disclosed. All direct identifiers, as well as any characteristics that might lead to identification, are omitted from the dataset. Any intentional identification or disclosure of an individual or establishment violates the assurances of confidentiality given to the providers of the information. Therefore, users will:

1. Use the data in this dataset for statistical reporting and analysis only.

1. Make no attempt to learn the identity of any person or establishment included in these data.

1. Not link this dataset with individually identifiable data from other NCHS or non-NCHS datasets.

1. Not engage in any efforts to assess disclosure methodologies applied to protect individuals and establishments or any research on methods of re-identification of individuals and establishments.

By using these data you signify your agreement to comply with the above-stated statutorily based requirements.

Sanctions for Violating NCHS Data Use Agreement

Willfully disclosing any information that could identify a person or establishment in any manner to a person or agency not entitled to receive it, shall be guilty of a class E felony and imprisoned for not more than 5 years, or fined not more than $250,000, or both.

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

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

Notes:

All comers.*Between all mortality rates.

Abbreviations: IABP, intra-aortic balloon pumping; ICD-9-CM, International Classification of Diseases, Ninth revision, Clinical Modification.Stratified based on the first two ICD-9-CM discharge diagnosis codes.

Notes:

Comparison between the 30-day stroke in-hospital mortality rates for each year with the Canada average.

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