The Nonemployer Statistics by Demographics (NES-D): Company Summary estimates provide economic data classified by sex, ethnicity, race, and veteran status of nonemployer firms. The NES-D is not a survey; rather, it leverages existing administrative records to assign demographic characteristics to the universe of nonemployer businesses. The nonemployer universe is comprised of businesses with no paid employment or payroll, annual receipts of $1,000 or more ($1 or more in the construction industries), and filing IRS tax forms for sole proprietorships (Form 1040, Schedule C), partnerships (Form 1065), or corporations (the Form 1120 series). Data for all firms are also presented. These estimates are produced by combining estimates for nonemployer firms from the Nonemployer Statistics by Demographics (NESD) and employer firms from the Annual Business Survey (ABS).

Medicare provides access to medical and hospital services for all Australian residents and certain categories of visitors to Australia. The Medicare Benefits Schedule (MBS) lists services that are subsidised by the Australian Government under Medicare. These reports provide patient age range and gender, number of services and total benefit amount per State/ Territory on Items in the MBS Schedule. An Item is a number that references a Medicare service. Item numbers are subject to change. Data is provided in the following formats: Excel/ xlxs: the human readable data for the current year is provided in individual excel files according to the relevant quarter. Historical data (1993-2015) may be found in the excel zipped file. CSV: the machine readable data for the current year is provided in individual csv files according to the relevant quarter. Historical data (1993-2015) may be found in the csv zipped file. Additional Medicare statistics may be found on the Department of Human Services website. Disclaimer: The information and data contained in the reports and tables have been provided by Medicare Australia for general information purposes only. While Medicare Australia takes care in the compilation and provision of the information and data, it does not assume or accept liability for the accuracy, quality, suitability and currency of the information or data, or for any reliance on the information and data. Medicare Australia recommends that users exercise their own care, skill and diligence with respect to the use and interpretation of the information and data.

The global aseptic necrosis treatment market is projected to reach a value of $674.52 million by 2033, expanding at a CAGR of 5.64% during the forecast period of 2025-2033. The market growth is driven by increasing prevalence of osteoarthritis, rising geriatric population, advancements in treatment techniques, and growing awareness about the disease. The market is segmented based on treatment type, diagnosis method, patient demographics, indication, company, and region. Medication, surgical intervention, rehabilitation therapy, and physical therapy are the major treatment types. MRI, CT scan, X-ray, and bone scintigraphy are the commonly used diagnosis methods. Adult, pediatric, and geriatric patients are the target patient demographics. Osteonecrosis, avascular necrosis, and post-traumatic necrosis are the main indications for treatment. Key market players include Sanofi, AstraZeneca, AbbVie, Bristol Myers Squibb, Johnson&Johnson, Celgene, Bayer, Teva Pharmaceutical, Gilead Sciences, Pfizer, Eli Lilly, Merck, Roche, Novartis, and Amgen. The market is geographically distributed across North America, South America, Europe, Middle East & Africa, and Asia Pacific. Recent developments include: Recent developments in the Aseptic Necrosis Treatment Market have shown significant activity among key players such as Sanofi, AstraZeneca, and AbbVie. Notably, these companies are increasingly focusing on the research and development of innovative treatment options to address emerging medical needs, especially in the context of joint ailments. Current affairs indicate a strengthening collaboration between pharmaceutical giants, as evidenced by strategic partnerships to accelerate drug discoveries. In recent weeks, several companies have reported notable growth in their market valuations, with Pfizer and Merck showing impressive gains driven by their advanced therapies in clinical trials. Additionally, the acquisition landscape has seen movements involving prominent entities like Johnson & Johnson and Roche, who are actively pursuing acquisitions to enhance their capabilities in aseptic necrosis treatment. These expansions are aimed at broadening their product portfolios and improving patient outcomes. Furthermore, ongoing regulatory approvals for novel therapies are creating a competitive environment that is expected to shape the market landscape significantly. The influx of investment into research initiatives is also anticipated to drive innovations and diversify available treatment options, thereby boosting the overall market dynamics.. Key drivers for this market are: Growing elderly population demand, Advancements in minimally invasive techniques; Expansion of telemedicine services; Increased awareness of treatment options; Rising prevalence of obesity-related conditions. Potential restraints include: Rising prevalence of osteonecrosis, Advances in surgical techniques; Growing awareness of treatment options; Increasing healthcare expenditure; Demand for effective pain management.

US Healthcare Readmissions and Mortality

Evaluating Hospital Performance

By Health [source]

About this dataset

This dataset contains detailed information about 30-day readmission and mortality rates of U.S. hospitals. It is an essential tool for stakeholders aiming to identify opportunities for improving healthcare quality and performance across the country. Providers benefit by having access to comprehensive data regarding readmission, mortality rate, score, measure start/end dates, compared average to national as well as other pertinent metrics like zip codes, phone numbers and county names. Use this data set to conduct evaluations of how hospitals are meeting industry standards from a quality and outcomes perspective in order to make more informed decisions when designing patient care strategies and policies

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How to use the dataset

This dataset provides data on 30-day readmission and mortality rates of U.S. hospitals, useful in understanding the quality of healthcare being provided. This data can provide insight into the effectiveness of treatments, patient care, and staff performance at different healthcare facilities throughout the country.

In order to use this dataset effectively, it is important to understand each column and how best to interpret them. The ‘Hospital Name’ column displays the name of the facility; ‘Address’ lists a street address for the hospital; ‘City’ indicates its geographic location; ‘State’ specifies a two-letter abbreviation for that state; ‘ZIP Code’ provides each facility's 5 digit zip code address; 'County Name' specifies what county that particular hospital resides in; 'Phone number' lists a phone contact for any given facility ;'Measure Name' identifies which measure is being recorded (for instance: Elective Delivery Before 39 Weeks); 'Score' value reflects an average score based on patient feedback surveys taken over time frame listed under ' Measure Start Date.' Then there are also columns tracking both lower estimates ('Lower Estimate') as well as higher estimates ('Higher Estimate'); these create variability that can be tracked by researchers seeking further answers or formulating future studies on this topic or field.; Lastly there is one more measure oissociated with this set: ' Footnote,' which may highlight any addional important details pertinent to analysis such as numbers outlying National averages etc..

This data set can be used by hospitals, research facilities and other interested parties in providing inciteful information when making decisions about patient care standards throughout America . It can help find patterns about readmitis/mortality along county lines or answer questions about preformance fluctuations between different hospital locations over an extended amount of time. So if you are ever curious about 30 days readmitted within US Hospitals don't hesitate to dive into this insightful dataset!

Research Ideas

• Comparing hospitals on a regional or national basis to measure the quality of care provided for readmission and mortality rates.
• Analyzing the effects of technological advancements such as telemedicine, virtual visits, and AI on readmission and mortality rates at different hospitals.
• Using measures such as Lower Estimate Higher Estimate scores to identify systematic problems in readmissions or mortality rate management at hospitals and informing public health care policy

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Readmissions_and_Deaths_-_Hospital.csv | Column name | Description | |:-------------------------|:---------------------------------------------------------------------------------------------------| | Hospital Name ...

OMOP dataset: Hospital COVID patients: severity, acuity, therapies, outcomes Dataset number 2.0

Coronavirus disease 2019 (COVID-19) was identified in January 2020. Currently, there have been more than 6 million cases & more than 1.5 million deaths worldwide. Some individuals experience severe manifestations of infection, including viral pneumonia, adult respiratory distress syndrome (ARDS) & death. There is a pressing need for tools to stratify patients, to identify those at greatest risk. Acuity scores are composite scores which help identify patients who are more unwell to support & prioritise clinical care. There are no validated acuity scores for COVID-19 & it is unclear whether standard tools are accurate enough to provide this support. This secondary care COVID OMOP dataset contains granular demographic, morbidity, serial acuity and outcome data to inform risk prediction tools in COVID-19.

PIONEER geography The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. There is a higher than average percentage of minority ethnic groups. WM has a large number of elderly residents but is the youngest population in the UK. Each day >100,000 people are treated in hospital, see their GP or are cared for by the NHS. The West Midlands was one of the hardest hit regions for COVID admissions in both wave 1 & 2.

EHR. University Hospitals Birmingham NHS Foundation Trust (UHB) is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & 100 ITU beds. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. UHB has cared for >5000 COVID admissions to date. This is a subset of data in OMOP format.

Scope: All COVID swab confirmed hospitalised patients to UHB from January – August 2020. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to care process (timings, staff grades, specialty review, wards), presenting complaint, acuity, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, microbiology, all prescribed & administered treatments (fluids, antibiotics, inotropes, vasopressors, organ support), all outcomes.

Available supplementary data: Health data preceding & following admission event. Matched “non-COVID” controls; ambulance, 111, 999 data, synthetic data. Further OMOP data available as an additional service.

Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

During a survey conducted in 2024, it was revealed that millennials represented the biggest share of pet owners in the United States (** percent), followed in second place by Generation X (** percent). Baby Boomer's came in third, representing some ** percent of pet owners. Pet ownership in the United States Despite some fluctuations, household penetration rates for pet ownership in the United States have generally increased over the years, going from ** percent in 1988 to approximately ** percent in 2023. With millennials constituting the largest group of pet owners in the United States, they also constituted the generational group that planned to spend the most on their pets during the holidays in 2020, with an average spending of ** U.S. dollars, compared to only ** U.S. dollars of average planned spending on pets for Baby Boomers. Pet expenditure in the U.S. Pet food and treats constituted the highest selling category for pet products in the United States, with total food and treats sales reaching **** billion U.S. dollars in 2022. Vet care and product sales were the second biggest pet market category that year, generating around **** billion U.S. dollars in sales. Generally, average annual pet expenditure was higher for dog owners than for cat owners across all pet market categories in 2020.

Summary:

This dataset is a synthetic hospital dataset designed for data science and machine learning practice. It contains detailed information about patient admissions, hospital departments, ward types, bed grades, and hospital resources, making it ideal for predictive modeling, exploratory data analysis, and feature engineering exercises.

Key highlights:

5000 patient records with 18 columns covering patient demographics, admission details, severity, and stay length.

Fully cleaned and preprocessed: duplicates removed, missing values handled, categorical features encoded, and numeric columns ready for ML.

Suitable for regression tasks (predicting length of stay or admission deposit) and classification tasks (predicting severity of illness, type of admission, or department).

Synthetic and safe: no real patient data included, perfect for learning and experimentation.

This dataset is perfect for students, beginners, and practitioners who want to practice healthcare analytics, ML model building, or data preprocessing.

Clinical Trial Site Selection Software Market Outlook

According to our latest research, the global Clinical Trial Site Selection Software market size reached USD 1.24 billion in 2024, reflecting the robust integration of digital solutions in clinical research operations worldwide. The market is expected to grow at a healthy CAGR of 13.2% from 2025 to 2033, reaching a forecasted value of USD 3.61 billion by 2033. This impressive growth is primarily driven by the increasing complexity of clinical trials, the need for data-driven site selection, and the rising adoption of technology to enhance trial efficiency and regulatory compliance.

One of the key growth factors propelling the Clinical Trial Site Selection Software market is the growing demand for accelerated drug development processes. Pharmaceutical and biotechnology companies are under immense pressure to bring new therapies to market faster, particularly in the wake of recent global health challenges. Site selection software leverages advanced analytics, real-time data integration, and predictive modeling to identify optimal trial sites, which significantly reduces study start-up times and boosts overall trial success rates. The software's ability to analyze vast datasets, including investigator performance histories, patient demographics, and site infrastructure, enables sponsors to make informed decisions, thereby optimizing resource allocation and minimizing costly trial delays.

Another significant driver is the increasing emphasis on regulatory compliance and risk mitigation in clinical research. With stringent guidelines enforced by regulatory bodies such as the FDA and EMA, sponsors must ensure that selected trial sites meet all necessary criteria for quality, patient safety, and data integrity. Clinical Trial Site Selection Software streamlines the vetting process by automating compliance checks and providing comprehensive audit trails, which not only reduces administrative burdens but also minimizes the risk of regulatory setbacks. The software's integration with electronic health records (EHRs) and other clinical data sources further enhances its value proposition, supporting seamless collaboration among stakeholders and ensuring that all regulatory requirements are met efficiently.

Furthermore, the increasing adoption of decentralized and hybrid clinical trial models is reshaping the landscape of site selection. As sponsors seek to reach wider and more diverse patient populations, the ability to evaluate non-traditional sites, including community clinics and telemedicine-enabled locations, becomes critical. Clinical Trial Site Selection Software facilitates this shift by offering robust site feasibility assessments, real-time site performance monitoring, and advanced patient recruitment analytics. This adaptability not only improves trial accessibility and patient engagement but also enables sponsors to navigate the complexities of multi-site, multi-region studies with greater agility and precision.

From a regional perspective, North America continues to dominate the Clinical Trial Site Selection Software market, accounting for the largest share in 2024, followed closely by Europe and the rapidly expanding Asia Pacific region. The United States, in particular, benefits from a strong ecosystem of pharmaceutical innovation, advanced healthcare infrastructure, and a high concentration of clinical research organizations (CROs). Meanwhile, Asia Pacific is experiencing the fastest growth, driven by increasing R&D investments, government support for clinical research, and the rise of contract research in emerging markets such as China and India. Europe maintains a robust presence due to its harmonized regulatory environment and active clinical trial networks, while Latin America and the Middle East & Africa are witnessing gradual adoption as local research ecosystems mature.

In the realm of clinical research, managing financial resources efficiently is crucial for the success of any trial. Clinical Trial Budgeting Software has emerged as a vital tool for sponsors and CROs, enabling them to plan, track, and optimize trial budgets with precision. By automating budget forecasting, expense tracking, and financial reporting, this software helps organizations allocate resources effectively and avoid budget overruns. It also faci

Approximately 10% of the population have a penicillin allergy label which is mostly self-reported. The symptoms causing patients to label themselves as being allergic arise from symptoms such as gastrointestinal upset, rash or altered taste. It is widely believed that only 10% of those with a SRPA have a true, immune-mediated penicillin allergy. 

The reporting of a penicillin allergy, irrespective of whether this is true allergy or not, can alter antibiotic prescribing. Alternative prescriptions of non-beta-lactam antibiotics may be less efficacious in serious infections and patients may be at increased risk of secondary infections and poor outcomes.  

To explore this, PIONEER, in collaboration with the HDRUK Medicines in Acute and Chronic Care Driver programme, has curated a dataset of over 37,000 admissions to intensive care, with self-reported allergies documented, as well as serial and highly granular data on presentation, acuity, physiology, investigations and all treatments and outcomes.

Geography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.

Data set availability: Data access is available via the PIONEER Hub for projects which will benefit the public or patients. This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes. Data access can be provided to NHS, academic, commercial, policy and third sector organisations. Applications from SMEs are welcome. There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee. Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.

Available supplementary data: Matched controls; ambulance and community data. Unstructured data (images). We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements.

Available supplementary support: Analytics, model build, validation & refinement; A.I. support. Data partner support for ETL (extract, transform & load) processes. Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run. Consultancy with clinical, patient & end-user and purchaser access/ support. Support for regulatory requirements. Cohort discovery. Data-driven trials and “fast screen” services to assess population size.

PurposeThe pharmacokinetics of voriconazole have been studied across various populations but data specific to the Pakistani cancer population has not yet been reported. The aim of present study was to explore and identify covariates that affect pharmacokinetics of intravenous voriconazole in Pakistani cancer population.MethodsThe therapeutic drug monitoring data from January1st, 2023 to December 31st, 2023 of cancer patients receiving intravenous voriconazole for systemic fungal infections were taken from electronic medical record of the hospital. The data were used for the development of population pharmacokinetic model using NONMEM. Impact of various covariates such as age, weight, sex, liver function test, serum creatinine, creatinine clearance, type of cancer (primary diagnosis) and type of fungal infection were assessed through stepwise covariate modeling. Bootstrap analysis and goodness of fit plots were used to evaluate robustness and predictive performance of final model.ResultsOne compartment model best described the included data with first order elimination. The value of voriconazole clearance was 6.17 L/h with interindividual variability of 83.7% while volume of distribution was 55.9 L. The clearance of voriconazole was significantly influenced by renal status of patients. Creatinine clearance and primary diagnosis were significant covariates affecting clearance of voriconazole in covariate analysis.ConclusionThe findings suggest that this model can be used for dosage adjustment based on creatinine clearance and primary diagnosis as they impact significantly on voriconazole clearance in cancer patients. This approach is especially valuable in resource-limited settings like Pakistan, where individualized therapy can enhance the safety and efficacy of antifungal treatment, addressing the unique clinical and demographic challenges in vulnerable populations.

Hospital readmission prediction is a crucial area of research due to its impact on healthcare expenditure, patient care quality, and policy formulation. Accurate prediction of patient readmissions within 30 days post-discharge remains a considerable challenging, given the complexity of healthcare data, which includes both structured (e.g., demographic, clinical) and unstructured (e.g., clinical notes, medical images) data. Consequently, there is an increasing need for hybrid approaches that effectively integrate these two data types to enhance all-cause readmission prediction performance. Despite notable advancements in machine learning, existing predictive models often struggle to achieve both high precision and balanced predictions, mainly due to the variability in patients’ outcome and the complex factors influencing readmissions. This study seeks to address these challenges by developing a hybrid predictive model that combines structured data with unstructured text representations derived from ClinicalT5, a transformer-based large language model. The performance of these hybrid models is evaluated against text-only models, such as PubMedBERT, using multiple metrics including accuracy, precision, recall, and AUROC score. The results demonstrate that the hybrid models, which integrate both structured and unstructured data, outperform text-only models trained on the same dataset. Specifically, hybrid models achieve higher precision and balanced recall, reducing false positives and providing more reliable predictions. This research underscores the potential of hybrid data integration, using ClinicalT5, to improve hospital readmission prediction, thereby improving healthcare outcomes through more accurate predictions that can support better clinical decision making and reduce unnecessary readmissions.

Being able to work during and after breast cancer treatments is important for patients to have a sense of normalcy, financial security, and improved quality of life, and for society due to the economic burden of sick leave. Factors influencing the length of sick leave can be sociodemographic factors, workplace adaptations, recurrences, symptoms, and type of treatment. The aim of this study is to analyse factors associated with prolonged sick leave after adjuvant breast cancer treatments. The population of this registry study consists of 1333 early breast cancer patients diagnosed and treated in Helsinki University Hospital between 2016 and 2018. Data on patient demographics, disease characteristics, treatment, and healthcare resource utilization were obtained from Helsinki University Hospital and data on income level and sick leave were obtained from Kela sickness benefits registry. Prolonged sick leave was determined as the patient accumulating 30 or more reimbursed sick leave days during a 60-day follow-up period after the end of active oncological treatment. Univariate analysis and multivariate analysis were conducted. A total of 26% of the patients in this study were on sick leave for 30 or more days after the active treatments ended. Study findings show that chemotherapy, triple-negative breast cancer, reconstructive surgery, amount of outpatient visits, and income are associated with prolonged sick leave. Independent predictors of prolonged sick leave were treatment line, number of outpatient contacts, reconstruction, and triple-negative breast cancer. Our study shows that prolonged sick leave affects a substantial number of working-age women with early breast cancer. Independent predictors for prolonged sick leave were all treatment-related. Targeted support for treatment-related side-effects already during the treatment period could lead to better recovery and earlier return to work.

ObjectiveLimited research has evaluated the utility of machine learning models and longitudinal data from electronic health records (EHR) to forecast mental health outcomes following a traumatic brain injury (TBI). The objective of this study is to assess various data science and machine learning techniques and determine their efficacy in forecasting mental health (MH) conditions among active duty Service Members (SMs) following a first diagnosis of mild traumatic brain injury (mTBI).Materials and MethodsPatient demographics and encounter metadata of 35,451 active duty SMs who have sustained an initial mTBI, as documented within the EHR, were obtained. All encounter records from a year prior and post index mTBI date were collected. Patient demographics, ICD-9-CM and ICD-10 codes, enhanced diagnostic related groups, and other risk factors estimated from the year prior to index mTBI were utilized to develop a feature vector representative of each patient. To embed temporal information into the feature vector, various window configurations were devised. Finally, the presence or absence of mental health conditions post mTBI index date were used as the outcomes variable for the models.ResultsWhen evaluating the machine learning models, neural network techniques showed the best overall performance in identifying patients with new or persistent mental health conditions post mTBI. Various window configurations were tested and results show that dividing the observation window into three distinct date windows [−365:−30, −30:0, 0:14] provided the best performance. Overall, the models described in this paper identified the likelihood of developing MH conditions at [14:90] days post-mTBI with an accuracy of 88.2%, an AUC of 0.82, and AUC-PR of 0.66.DiscussionThrough the development and evaluation of different machine learning models we have validated the feasibility of designing algorithms to forecast the likelihood of developing mental health conditions after the first mTBI. Patient attributes including demographics, symptomatology, and other known risk factors proved to be effective features to employ when training ML models for mTBI patients. When patient attributes and features are estimated at different time window, the overall performance increase illustrating the importance of embedding temporal information into the models. The addition of temporal information not only improved model performance, but also increased interpretability and clinical utility.ConclusionPredictive analytics can be a valuable tool for understanding the effects of mTBI, particularly when identifying those individuals at risk of negative outcomes. The translation of these models from retrospective study into real-world validation models is imperative in the mitigation of negative outcomes with appropriate and timely interventions.

In northern Tanzania, alcohol use disorders (AUD) are under-diagnosed and under-treated, and current services are mostly limited to men in clinical settings despite significant alcohol-related harm in the community. The study objective was to identify sex differences in alcohol use and alcohol-related harms within and across community and clinical settings. This was a congruent triangulation mixed methods study consisting of focus group discussions (FGDs) and cross-sectional surveys. Quantitative analysis was conducted via Drinker Inventory of Consequences (DrInC) and Alcohol Use Disorders Identification Test (AUDIT) data from injury patients presenting for care at the Kilimanjaro Christian Medical Center Emergency Department and community participants. Differences in scores by sex were assessed using unpaired t-tests. K-means algorithms were run independently in both samples. Deductive thematic analysis was conducted on FGDs with community members, injury patients, and injury patient relatives. Differences in mean scores between sexes in the community and patient samples were statistically significant (p<0.05). Men showed higher AUDIT and DrInC mean scores in both samples. K-means separated the community and patient samples into two clusters, one with and one without harmful alcohol use. Of those indicating harmful alcohol use, the community cluster (n = 77, AUDIT = 14.29±9.22, DrInC = 22.67±6.80) was 27% women; the patient cluster (n = 57, AUDIT = 15.00±9.48, DrInC = 27.00±7.76) was 5% women. FGD transcripts revealed sex differences in four themes: alcohol initiation, consumption patterns, risk behaviors, and social stigma. This study identified important sex differences in the manifestation of AUD in northern Tanzania with respect to alcohol initiation, consumption patterns, risk behavior, and stigma. These findings indicate that women may need to be encouraged to seek injury care at the Emergency Department. Future research, prevention, and treatment efforts intended to reduce alcohol-related harms need to account for sex differences to optimize reach and effectiveness.

Comprehensive demographic dataset for La Jolla, CA, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.