Between 2022 and 2023, there were nearly 20 thousand alcohol related hospital admissions for men and approximately 9 thousand for women in Scotland. The number of admissions due to alcohol consumption in Scotland has been consistently higher among men compared to women, although the number of admissions for both genders has generally decreased since 2005. This statistic depicts the number of stays in hospital due to all alcohol related conditions in Scotland from 2005/06 to 2022/23, by gender.

ObjectiveAlcohol misuse is a well-known risk factor for suicide however, the relationship between alcohol-related hospital admission and subsequent risk of death from suicide is unknown. We aimed to determine the risk of death from suicide following emergency admission to hospital with an alcohol-related cause.MethodsWe established an electronic cohort study of all 2,803,457 residents of Wales, UK, aged from 10 to under 100 years on 1 January 2006 with six years’ follow-up. The outcome event was death from suicide defined as intentional self-harm (ICD-10 X60-84) or undetermined intent (Y10-34). The main exposure was an alcohol-related admission defined as a ‘wholly attributable’ ICD-10 alcohol code in the admission record. Admissions were coded for the presence or absence of co-existing psychiatric morbidity. The analysis was by Cox regression with adjustments for confounding variables within the dataset.ResultsDuring the study follow-up period, there were 15,546,355 person years at risk with 28,425 alcohol-related emergency admissions and 1562 suicides. 125 suicides followed an admission (144.6 per 100,000 person years), of which 11 (9%) occurred within 4 weeks of discharge. The overall adjusted hazard ratio (HR) for suicide following admission was 26.8 (95% confidence interval (CI) 18.8 to 38.3), in men HR 9.83 (95% CI 7.91 to 12.2) and women HR 28.5 (95% CI 19.9 to 41.0). The risk of suicide remained substantial in subjects without known co-existing psychiatric morbidity: HR men 8.11 (95% CI 6.30 to 10.4) and women HR 24.0 (95% CI 15.5 to 37.3). The analysis was limited by the absence in datasets of potentially important confounding variables and the lack of information on alcohol-related harm and psychiatric morbidity in subjects not admitted to hospital.ConclusionEmergency alcohol-related hospital admission is associated with an increased risk of suicide. Identifying individuals in hospital provides an opportunity for psychosocial assessment and suicide prevention of a targeted at-risk group before their discharge to the community.

Indirectly age and sex standardised ratio of emergency readmissions with a primary diagnosis or an external cause code of an alcohol-specific condition within 30 days of a previous discharge following an alcohol-specific admission, with 95% confidence intervals (CI). As of the October 2020 release, the 4 provisional periods, July 2016 to June 2019, October 2016 - September 2019, January 2017 - December 2019 and April 2017 to March 2020 have been replaced by the final data April 2017 to March 2020. As of the March 2020 release, the July 2016 to June 2019 (Provisional) data has been revised. This is due to a revision of a large proportion of records for East Sussex Healthcare NHS Trust (RXC) which had missing information for the condition the patient was in hospital for and other conditions the patients suffer from. The revised data for this reporting period also differs from that originally published in September 2019 in that the HES database is routinely updated (overwritten) on a monthly basis for the year in progress. Data presented for July 2016 to June 2019 remains provisional, but is now more complete than it was when the September 2019 publication was released. This effect cannot be readily separated from the effect of the East Sussex Healthcare NHS Trust (RXC) resubmission which took place after processing for the September 2019 publication. Legacy unique identifier: P01862

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.

Analysis of ‘Hospital Admissions Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/ashishsahani/hospital-admissions-data on 13 February 2022.

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

This dataset is being provided under creative commons License (Attribution-Non-Commercial-Share Alike 4.0 International (CC BY-NC-SA 4.0)) https://creativecommons.org/licenses/by-nc-sa/4.0/

Context

This data was collected from patients admitted over a period of two years (1 April 2017 to 31 March 2019) at Hero DMC Heart Institute, Unit of Dayanand Medical College and Hospital, Ludhiana, Punjab, India. This is a tertiary care medical college and hospital. During the study period, the cardiology unit had 14,845 admissions corresponding to 12,238 patients. 1921 patients who had multiple admissions.

Specifically, data were related to patients ; date of admission; date of discharge; demographics, such as age, sex, locality (rural or urban); type of admission (emergency or outpatient); patient history, including smoking, alcohol, diabetes mellitus (DM), hypertension (HTN), prior coronary artery disease (CAD), prior cardiomyopathy (CMP), and chronic kidney disease (CKD); and lab parameters corresponding to hemoglobin (HB), total lymphocyte count (TLC), platelets, glucose, urea, creatinine, brain natriuretic peptide (BNP), raised cardiac enzymes (RCE) and ejection fraction (EF). Other comorbidities and features (28 features), including heart failure, STEMI, and pulmonary embolism, were recorded and analyzed.

Shock was defined as systolic blood pressure < 90 mmHg, and when the cause for shock was any reason other than cardiac. Patients in shock due to cardiac reasons were classified into cardiogenic shock. Patients in shock due to multifactorial pathophysiology (cardiac and non-cardiac) were considered for both categories. The outcomes indicating whether the patient was discharged or expired in the hospital were also recorded.

Further details about this dataset can be found here: https://doi.org/10.3390/diagnostics12020241

If you use this dataset in academic research all publications arising out of it must cite the following paper: Bollepalli, S.C.; Sahani, A.K.; Aslam, N.; Mohan, B.; Kulkarni, K.; Goyal, A.; Singh, B.; Singh, G.; Mittal, A.; Tandon, R.; Chhabra, S.T.; Wander, G.S.; Armoundas, A.A. An Optimized Machine Learning Model Accurately Predicts In-Hospital Outcomes at Admission to a Cardiac Unit. Diagnostics 2022, 12, 241. https://doi.org/10.3390/diagnostics12020241

If you intend to use this data for commercial purpose explicit written permission is required from data providers.

Content

table_headings.csv has explanatory names of all columns.

Acknowledgements

Data was collected from Hero Dayanand Medical College Heart Institute Unit of Dayanand Medical College and Hospital, Ludhiana, Punjab, India.

Inspiration

For any questions about the data or collaborations please contact ashish.sahani@iitrpr.ac.in

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

• Area of greatest interest to citizens: defence, education, health, housing, pensions, transport, citizen security or social services.
• Opinion on the Spanish health system.
• Satisfaction scale with the functioning of the Spanish health system.
• Agreement with sentences on the consequences of the application of the Tobacco Law.
• Possession of children or grandchildren under 18 years of age, and between 11 and 18 years of age.
• Agreement with a law limiting the consumption of alcohol to those under 18 years of age.
• Agreement with different phrases on a law that would limit the consumption of alcohol.
• Agreement with different phrases on alcohol consumption.
• Type of health service, public or private, that would be used in case of needing to go to a consultation, in the event of suffering from a serious illness.
• Need for healthcare outside the Autonomous Community of residence. Type of identification document used and problems when using the health card. Health services used outside the autonomous community of residence. Origin of medical history information. Benefit from the computerization of the medical history and authorization of your consultation in health care outside the autonomous community of residence.
• Assistance to the doctor of general medicine or paediatrics and type of medical service, public or private, that he has used.
• Consultations to the GP in the last year.
• Satisfaction scale with various aspects of the care received in public health in the general medical/pediatric consultation, in specialized care and in hospitals.
• Assistance to the emergency service in the last year, number of visits and type of service ultilized. Main cause for going to a public hospital emergency department and reason for choosing a hospital.
• Speed and assessment of the care received in emergencies.
• Assistance to the specialist doctor in the last year, number of visits and type of ultilized service. Time to consultation.
• Assessment of the care received in the consultation of the public health specialist and comparison with the expected care. Specialty she went to. Communication between the GP and the public health specialist.
• Assessment of aspects related to the care provided in specialized care consultations: time dedicated to the user, not specialties with access, waiting time,...
• Hospital admission in the last year and type of Hospital, public or private.
• In case of hospitalization in a public hospital in the last year: main cause of admission and assignment during the hospital stay of a responsible doctor.
• Information on the time it would take to enter for operation.
• Assessment of the care received in Public Hospital and comparison with the expected care.
• Rating scale on the information provided by public health services.
• Opinion on whether the health authorities are carrying out measures to improve waiting lists and on the evolution of waiting lists in the last year.
• Gender differentiation in health. Reason for better health of men versus women.
• Opinion on equal health benefits for all citizens regardless of: the autonomous community of residence, the age and gender, the area (rural or urban) in which you reside and the social level, country of origin, residence status.
• Developments in the last five years of health care services: primary, specialised and hospital care.
• Comparison of the health services received in the Community of residence with respect to others.
• Comparison between the health service provided by the Community or by the State.
• Opinion on the desirability of the Autonomous Communities agreeing to provide new health services.
• Frequency of monitoring news about errors in healthcare.
• Errors in healthcare in Spain and their importance.
• Confidence in the work of various healthcare professionals: doctors, nurses and other health personnel.
• Errors in health care to the interviewee or family and severity thereof.
• Nationality of the interviewee.
• Fixed telephony holding.

• Area of greatest interest to citizens: defence, education, health, housing, pensions, transport, citizen security or social services.
• Opinion on the Spanish health system.
• Satisfaction scale with the functioning of the Spanish health system.
• Agreement with sentences on the consequences of the application of the Tobacco Law.
• Possession of children or grandchildren under 18 years of age, and between 11 and 18 years of age.
• Agreement with a law limiting the consumption of alcohol to those under 18 years of age.
• Agreement with different phrases on a law that would limit the consumption of alcohol.
• Reasons for choosing a public or private health service.
• Type of health service, public or private, that would be used in case of needing to go to a consultation, in the event of suffering from a serious illness.
• Need for healthcare outside the Autonomous Community of residence. Type of identification document used and problems when using the health card. Health services used outside the autonomous community of residence.
• Submission of a claim for not being satisfied with a health service. Person who made the claim. Obtaining response and result of the claim.
• Consultations to the GP in the last year. Number of times.
• Assessment of the care received in general medicine and comparison with the expected care.
• Appointment request at the health center (coincidence between the day of the request and the appointment. Waiting time between the request and the day of the appointment at the health center.
• Agreement with some phrases referring to different aspects of your health center: the center informs about the offer of services, manages the health card, solves doubts,...
• Satisfaction scale with various aspects of the care received in public health in the general medical/pediatric consultation, in specialized care and in hospitals.
• Assistance to the emergency service in the last year, number of visits and type of service ultilized. Main cause for going to a public hospital emergency department and reason for choosing a hospital.
• Speed and assessment of the care received in emergencies.
• Assistance to the specialist doctor in the last year, number of visits and type of ultilized service. Time to consultation.
• Assessment of the care received in the consultation of the public health specialist and comparison with the expected care. Specialty she went to. Communication between the GP and the public health specialist.
• Assessment of aspects related to the care provided in specialized care consultations: time dedicated to the user, not specialties with access, waiting time,...
• Hospital admission in the last year and type of Hospital, public or private.
• In case of hospitalization in a public hospital in the last year: main cause of admission and assignment during the hospital stay of a responsible doctor.
• Information on the time it would take to enter for operation.
• Assessment of the care received in Public Hospital and comparison with the expected care.
• Rating scale on the information provided by public health services.
• Opinion on whether the health authorities are carrying out measures to improve waiting lists and on the evolution of waiting lists in the last year.
• Gender differentiation in health. Reason for better health of men versus women.
• Opinion on equal health benefits for all citizens regardless of: the autonomous community of residence, the age and gender, the area (rural or urban) in which you reside and the social level, country of origin, residence status.
• Developments in the last five years of health care services: primary, specialised and hospital care.
• Comparison of the health services received in the Community of residence with respect to others.
• Comparison between the health service provided by the Community or by the State.
• Opinion on the desirability of the Autonomous Communities agreeing to provide new health services.
• Frequency of monitoring news about errors in healthcare.
• Errors in healthcare in Spain and their importance.
• Confidence in the work of various healthcare professionals: doctors, nurses and other health personnel.
• Errors in health care to the interviewee or family and severity thereof.
• Nationality of the interviewee.
• Fixed telephony holding.

On average, annually 55 children under the age of 5 die due to an unintentional injury, 370,000 children attend the emergency departments and 40,000 children are admitted to hospital as an emergency. Each year over 300 infants die suddenly and unexpectedly, many in circumstances with recognised risk factors such as unsafe sleeping arrangements.Illnesses such as gastroenteritis and upper respiratory tract infections, along with injuries caused by accidents in the home, and poor oral health are the leading causes of attendances at emergency departments and hospitalisation amongst under-5s.Unintentional injuries for the under-5s tend to happen in and around the home. Five causes account for 90% of unintentional injury hospital admissions for this age group and are a significant cause of preventable death and serious long-term harm. These are:

Logistic regression analyses for factors associated with complicated hospital stay.

This table shows the wide variety of long-term series in the field of health, lifestyle and healthcare. Figures on birth and mortality, some causes of death and the occurrence of certain infectious diseases have been available since 1900. Other series will start at a later date. In addition to perceived health, the table includes figures on infectious diseases, hospital admissions by diagnosis, life expectancy, lifestyle factors such as smoking, alcohol and obesity, and causes of death. Various aspects of health care such as the number of professionals, the number of hospital beds available, the average duration of care and the expenditure on care are also included in the table. Many topics are also, in more detail, in other StatLine tables, but sometimes with a shorter runtime. Data on notifiable infectious diseases and AIDS/HIV are not included in other tables.

Data available from: 1900

Status of figures: 2024: The available figures are final. 2023: Most of the available figures are final. Figures are provisional for: - notifiable infectious diseases, HIV, AIDS; - absenteeism due to illness. 2022: Most of the available figures are final. Figures are provisional for: - notifiable infectious diseases, HIV, AIDS; - diagnoses at hospitalisation; - hospitalisations, days of nursing, duration of nursing; - health professions; - volume index expenditure care. Figures are provisional for: Expenditure on care. 2021: Most of the available figures are final. Figures are provisional for: - notifiable infectious diseases, HIV, AIDS; - number of hospital beds. Figures are provisional for: - expenditure on care; - volume index expenditure care. 2020 and earlier: Most of the available figures are final. Due to the dynamic nature of the registration, figures for all years are provisional for notifiable infectious diseases, HIV, AIDS.

Changes as of 5 June 2024: Supplement with the latest available figures: - population on 1 January; - experienced health; - notifiable infectious diseases, HIV, AIDS; - diagnoses at hospitalisation; - use of medicines; - sick leave; - lifestyle; - use of care, contact with caregivers; - hospitalisations, days of nursing, duration of nursing; - health professions; - expenditure on care; - volume index expenditure care.

When will there be new figures?
The latest available figures will be published in December 2024.

The Drug Abuse Warning Network (DAWN) is a nationally representative public health surveillance system that has monitored drug related emergency department (ED) visits to hospitals since the early 1970s. First administered by the Drug Enforcement Administration (DEA) and the National Institute on Drug Abuse (NIDA), the responsibility for DAWN now rests with the Substance Abuse and Mental Health Services Administration's (SAMHSA) Center for Behavioral Health Statistics and Quality (CBHSQ). Over the years, the exact survey methodology has been adjusted to improve the quality, reliability, and generalizability of the information produced by DAWN. The current approach was first fully implemented in the 2004 data collection year. DAWN relies on a longitudinal probability sample of hospitals located throughout the United States. To be eligible for selection into the DAWN sample, a hospital must be a non-Federal, short-stay, general surgical and medical hospital located in the United States, with at least one 24-hour ED. DAWN cases are identified by the systematic review of ED medical records in participating hospitals. The unit of analysis is any ED visit involving recent drug use. DAWN captures both ED visits that are directly caused by drugs and those in which drugs are a contributing factor but not the direct cause of the ED visit. The reason a patient used a drug is not part of the criteria for considering a visit to be drug-related. Therefore, all types of drug-related events are included: drug misuse or abuse, accidental drug ingestion, drug-related suicide attempts, malicious drug poisonings, and adverse reactions. DAWN does not report medications that are unrelated to the visit. The DAWN public-use dataset provides information for all types of drugs, including illegal drugs, prescription drugs, over-the-counter medications, dietary supplements, anesthetic gases, substances that have psychoactive effects when inhaled, alcohol when used in combination with other drugs (all ages), and alcohol alone (only for patients aged 20 or younger). Public-use dataset variables describe and categorize up to 22 drugs contributing to the ED visit, including toxicology confirmation and route of administration. Administrative variables specify the type of case, case disposition, categorized episode time of day, and quarter of year. Metropolitan area is included for represented metropolitan areas. Created variables include the number of unique drugs reported and case-level indicators for alcohol, non-alcohol illicit substances, any pharmaceutical, non-medical use of pharmaceuticals, and all misuse and abuse of drugs. Demographic items include age category, sex, and race/ethnicity. Complex sample design and weighting variables are included to calculate various estimates of drug-related ED visits for the Nation as a whole, as well as for specific metropolitan areas, from the ED visits classified as DAWN cases in the selected hospitals.This study has 1 Data Set.

Clinical features of AWS and pharmacological management.

ABSTRACT Background: Although alcohol is the most common cause for chronic pancreatitis worldwide, idiopathic type is prevalent in India. Natural history and disease progression are different between these two groups. There is paucity of data comparing surgical outcome and quality of life in these patients. Aim: To evaluate clinical features, surgical outcome and quality of life between these two groups of patients. Method: All patients with chronic pancreatitis who underwent surgery were prospectively reviewed. Results: From 98 patients, 42 were alcoholic. Number of male and the mean age at the time of operation was significantly more in alcoholic patients. Smoking, preoperative hospital admission rate and the prevalence of local complications like inflammatory pancreatic head mass, biliary stricture and left sided portal hypertension were distinctly more common in alcoholic group. Frey procedure was required more commonly in alcoholic group. Mean postoperative hospital stay and overall postoperative complication rate were comparable between the two groups. Over a median follow up of 18 months there was significant improvement in quality of life and pain score in both the groups. Improvement of physical functioning score at follow-up was significantly more in alcoholic group but the requirement for analgesic medications were significantly more in alcoholic group. However, appetite loss was more perceived by non-alcoholic group. Conclusion: Alcoholic chronic pancreatitis presents with more local complications associated with chronic pancreatitis. Frey procedure is a safe and well accepted surgery in this group. Though they required more analgesic requirement in short term follow up, other aspects of quality of life are similar to non-alcoholic group.

Deaths covering Smoking only to 2019.

The Drug Abuse Warning Network (DAWN) is a nationally representative public health surveillance system that has monitored drug related emergency department (ED) visits to hospitals since the early 1970s. First administered by the Drug Enforcement Administration (DEA) and the National Institute on Drug Abuse (NIDA), the responsibility for DAWN now rests with the Substance Abuse and Mental Health Services Administration's (SAMHSA) Center for Behavioral Health Statistics and Quality (CBHSQ). Over the years, the exact survey methodology has been adjusted to improve the quality, reliability, and generalizability of the information produced by DAWN. The current approach was first fully implemented in the 2004 data collection year. DAWN relies on a longitudinal probability sample of hospitals located throughout the United States. To be eligible for selection into the DAWN sample, a hospital must be a non-Federal, short-stay, general surgical and medical hospital located in the United States, with at least one 24-hour ED. DAWN cases are identified by the systematic review of ED medical records in participating hospitals. The unit of analysis is any ED visit involving recent drug use. DAWN captures both ED visits that are directly caused by drugs and those in which drugs are a contributing factor but not the direct cause of the ED visit. The reason a patient used a drug is not part of the criteria for considering a visit to be drug-related. Therefore, all types of drug-related events are included: drug misuse or abuse, accidental drug ingestion, drug-related suicide attempts, malicious drug poisonings, and adverse reactions. DAWN does not report medications that are unrelated to the visit. The DAWN public-use dataset provides information for all types of drugs, including illegal drugs, prescription drugs, over-the-counter medications, dietary supplements, anesthetic gases, substances that have psychoactive effects when inhaled, alcohol when used in combination with other drugs (all ages), and alcohol alone (only for patients aged 20 or younger). Public-use dataset variables describe and categorize up to 22 drugs contributing to the ED visit, including toxicology confirmation and route of administration. Administrative variables specify the type of case, case disposition, categorized episode time of day, and quarter of year. Metropolitan area is included for represented metropolitan areas. Created variables include the number of unique drugs reported and case-level indicators for alcohol, non-alcohol illicit substances, any pharmaceutical, non-medical use of pharmaceuticals, and all misuse and abuse of drugs. Demographic items include age category, sex, and race/ethnicity. Complex sample design and weighting variables are included to calculate various estimates of drug-related ED visits for the Nation as a whole, as well as for specific metropolitan areas, from the ED visits classified as DAWN cases in the selected hospitals.This study has 1 Data Set.

Risk factors at phase 2 associated with having an admission to hospital for an accident or injury.