This dataset shows the the world's best hospital in 2023 issued by the Newsweek and Statista.

This dataset provides values for HOSPITAL BEDS reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Success.ai’s Healthcare Professionals Data for Healthcare & Hospital Executives in Europe provides a reliable and comprehensive dataset tailored for businesses aiming to connect with decision-makers in the European healthcare and hospital sectors. Covering healthcare executives, hospital administrators, and medical directors, this dataset offers verified contact details, professional insights, and leadership profiles.

With access to over 700 million verified global profiles and data from 70 million businesses, Success.ai ensures your outreach, market research, and partnership strategies are powered by accurate, continuously updated, and GDPR-compliant data. Backed by our Best Price Guarantee, this solution is indispensable for navigating and thriving in Europe’s healthcare industry.

Why Choose Success.ai’s Healthcare Professionals Data?

1. Verified Contact Data for Targeted Engagement

   • Access verified work emails, phone numbers, and LinkedIn profiles of healthcare executives, hospital administrators, and medical directors.
   • AI-driven validation ensures 99% accuracy, reducing data gaps and improving communication effectiveness.

2. Comprehensive Coverage of European Healthcare Professionals

   • Includes profiles of professionals from top hospitals, healthcare organizations, and medical institutions across Europe.
   • Gain insights into regional healthcare trends, operational challenges, and emerging technologies.

3. Continuously Updated Datasets

   • Real-time updates capture changes in leadership roles, organizational structures, and market dynamics.
   • Stay aligned with the fast-evolving healthcare landscape to identify emerging opportunities.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible and lawful data usage.

Data Highlights:

• 700M+ Verified Global Profiles: Connect with healthcare professionals and decision-makers in Europe’s hospital and healthcare sectors.
• 70M+ Business Profiles: Access detailed firmographic data, including hospital sizes, revenue ranges, and geographic footprints.
• Leadership Insights: Engage with CEOs, medical directors, and administrative leaders shaping healthcare strategies.
• Regional Healthcare Trends: Understand trends in digital healthcare adoption, operational efficiency, and patient care management.

Key Features of the Dataset:

1. Comprehensive Professional Profiles

   • Identify and connect with key players, including hospital executives, medical directors, and department heads in the healthcare industry.
   • Access data on professional histories, certifications, and areas of expertise for precise targeting.

2. Advanced Filters for Precision Campaigns

   • Filter professionals by hospital size, geographic location, or job function (administrative, medical, or operational).
   • Tailor campaigns to align with specific needs such as digital transformation, patient care solutions, or regulatory compliance.

3. Healthcare Industry Insights

   • Leverage data on operational trends, hospital management practices, and regional healthcare needs.
   • Refine product offerings and outreach strategies to address pressing challenges in the European healthcare market.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes with healthcare professionals.

Strategic Use Cases:

1. Marketing and Outreach to Healthcare Executives

   • Promote healthcare IT solutions, medical devices, or operational efficiency tools to executives managing hospitals and clinics.
   • Use verified contact data for multi-channel outreach, including email, phone, and digital marketing.

2. Partnership Development and Collaboration

   • Build relationships with hospitals, healthcare providers, and medical institutions exploring strategic partnerships or new technology adoption.
   • Foster alliances that drive patient care improvements, cost savings, or operational efficiency.

3. Market Research and Competitive Analysis

   • Analyze trends in European healthcare to refine product development, marketing strategies, and engagement plans.
   • Benchmark against competitors to identify growth opportunities, underserved segments, and innovative solutions.

4. Recruitment and Workforce Solutions

   • Target HR professionals and hiring managers in healthcare institutions recruiting for administrative, medical, or operational roles.
   • Provide workforce optimization platforms, training solutions, or staffing services tailored to the healthcare sector.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality healthcare professional data at competitive prices, ensuring strong ROI for your marketing, sales, and strategic initiatives.

2. Seamless Integration
   ...

📝 Dataset Overview: This enhanced dataset captures the real-world operational and clinical performance data from a major hospital in Nigeria — Lagos University Teaching Hospital (LUTH). It includes detailed information on admissions, patient care, medical services, billing, and staff activities, ideal for healthcare analytics, hospital management dashboards, and machine learning projects.

🔍 Dataset Features (Suggested Columns): Column Name Description Patient_ID Unique anonymized patient ID Admission_Date Date of admission Discharge_Date Date of discharge Gender Patient’s gender Age Patient’s age Department Medical department involved Diagnosis Primary diagnosis Doctor Attending physician (anonymized) Treatment_Provided Type of treatment/procedure Lab_Tests Count of lab tests conducted Medications_Administered Total medications given Surgery_Cost (₦) If applicable, cost of surgery Bill_Amount (₦) Total bill charged to patient Ward Hospital ward assigned Length_of_Stay (days) Duration of hospitalization

🎯 Use Cases: Build hospital operations dashboards in Power BI

Analyze billing and cost patterns across departments

Predict length of stay or discharge outcomes

Explore departmental workload and performance

Use as a base for AI in hospital management systems

🏥 Clinical & Operational Value: This dataset empowers analysts and healthcare professionals to:

Track patient outcomes and billing efficiency

Reduce operational bottlenecks

Improve patient care with data-driven recommendations

Benchmark departmental performance

Train predictive models for resource allocation

👤 Created By: Fatolu Peter (Emperor Analytics) Dedicated to transforming public healthcare using analytics and real-world data across Nigerian hospitals. This is Project 14 in my growing health-tech analytics journey.

✅ LinkedIn Post: 🚑 New Kaggle Dataset: LUTH Hospital Enhanced Clinical & Operations Data 📊 Real hospital data on admissions, billing, treatments, and care metrics 🔗 Access the dataset now on Kaggle

This dataset gives you: ✅ Real hospital operations data ✅ Billing and medication insights ✅ Doctor and ward-level activity ✅ A perfect base for building Power BI dashboards or training ML models

Whether you're a data scientist, health analyst, or Power BI pro — this is real-world data to make real impact. Let’s build something powerful together. 💡

HealthcareAnalytics #LUTH #HospitalData #KaggleDataset #PowerBI #FatoluPeter #EmperorAnalytics #DataForGood #Project14 #PublicHealth #NigeriaHealthData

📝 Dataset Overview: This dataset focuses on early warning detection for sepsis, a critical and potentially fatal medical condition. It includes anonymized vital signs, lab results, and clinical indicators of patients admitted to the hospital, structured for real-time monitoring and predictive modeling.

It’s ideal for clinical data analysts, healthcare data scientists, and AI practitioners aiming to develop decision support tools, early warning dashboards, or predictive health models.

🔍 Dataset Features: Column Name Description Patient_ID Unique anonymized identifier Admission_Date Patient’s hospital admission date Temperature_C Body temperature in degrees Celsius BP_Systolic Systolic blood pressure (mmHg) BP_Diastolic Diastolic blood pressure (mmHg) Heart_Rate Beats per minute WBC_Count White blood cell count (x10⁹/L) Lactate_mmol_L Lactate level in mmol/L Sepsis_Flag Binary indicator (1 = Suspected Sepsis, 0 = Normal) Ward Hospital ward/unit Doctor_On_Duty Attending physician name (anonymized)

🎯 Use Cases: Build Power BI dashboards for hospital early warning systems

Train ML classification models to detect early signs of sepsis

Create patient monitoring tools with Python or R

Explore the relationship between vitals & sepsis onset

Perform feature engineering for risk scoring systems

📌 Clinical Relevance: Sepsis is one of the leading causes of in-hospital mortality worldwide. Early detection is crucial to reducing death rates and improving outcomes. This dataset empowers developers and analysts to make a meaningful impact in the healthcare sector.

👤 Created By: Fatolu Peter (Emperor Analytics) A passionate healthcare analyst leveraging data to drive innovation in public health across Nigeria. This is Project 12 in my data-for-good series.

✅ LinkedIn Post: 🚨 New Dataset: Sepsis Early Warning System Data – Now on Kaggle 📊 Clinical vital signs + lab markers + sepsis risk flags 🔗 Explore the dataset here

This dataset enables healthcare data scientists to: ✅ Build real-time hospital dashboards ✅ Predict sepsis risk with machine learning ✅ Explore vitals like BP, lactate, WBC, and temperature ✅ Support early intervention using data insights

Whether you're into: 🧠 Predictive modeling 📈 Power BI clinical dashboards 📉 Risk analytics in healthcare This is for you.

Join me in using data to save lives — one insight at a time. If you build something, tag me. I’ll gladly share it! 💡

HealthcareAnalytics #SepsisAwareness #EarlyWarningSystems #KaggleDataset #PowerBI #DataForGood #FatoluPeter #EmperorAnalytics #PublicHealth #Project12 #RealWorldData

Let me know if you’d like help starting a Power BI or Python model for this!

You said: whic license is the best to choose that is global

ChatGPT said: For globally recognized and widely accepted dataset licensing on Kaggle, the best license to choose depends on your intent — especially whether you want others to reuse and build upon your dataset freely.

✅ Recommended License: Creative Commons Attribution 4.0 (CC BY 4.0) Why CC BY 4.0 is best globally: 🌍 Internationally recognized and legally valid worldwide.

✅ Allows others to use, share, adapt, and build on your dataset, even for commercial purposes.

📢 Requires only attribution to you as the creator.

🔐 You still retain copyright while maximizing openness.

When to choose it: If your goal is to:

Share freely with the global community,

Allow use in academic, commercial, or public projects,

Gain credit and visibility as the original creator.

⚠️ Other license types (if needed): CC BY-NC 4.0 (Attribution-NonCommercial): Only for non-commercial use.

CC0 (Public Domain Dedication): Freest use; no attribution required — but not always ideal if you want credit.

GPL / Open Data Commons: More for software or structured databases with specific open-source obligations.

✅ Final Suggestion for Your Datasets: Use CC BY 4.0 for all your uploads, unless you have confidential/private data. It's perfect for:

Healthcare datasets

Sales/retail analytics

Kaggle portfolio building

Global recognition & impact

"Facilitate marketing campaigns with the healthcare email list from Infotanks Media that includes doctors, healthcare professionals, NPI numbers, physician specialties, and more. Buy targeted email lists of healthcare professionals and connect with doctors, specialists, and other healthcare professionals to promote your products and services. Hyper personalize campaigns to increase engagement for better chances of conversion. Reach out to our data experts today! Access 1.2 million physician contact database with 150+ specialities including chiropractors, cardiologists, psychiatrists, and radiologists among others. Get ready to integrate healthcare email lists from Infotanks Media to start email marketing campaigns through any CRM and ESP. Contact us right now! Ensure guaranteed lead generation with segmented email marketing strategies for specialists, departments, and more. Make the best use of target marketing to progress and move closer to your business goals with email listing services for healthcare professionals. Infotanks Media provides 100% verified healthcare email lists with the highest email deliverability guarantee of 95%. Get a custom quote today as per your requirements. Enhance your marketing campaigns with healthcare email lists from 170+ countries to build your global outreach. Request your free sample today! Personalize your business communication and interactions to maximize conversion rates with high quality contact data. Grow your business network in your target markets from anywhere in the world with a guaranteed 95% contact accuracy of the healthcare email lists from Infotanks Media. Contact data experts at Infotanks Media from the healthcare industry to get a quick sample for free. Write to us or call today!

Hyper target within and outside your desired markets with GDPR and CAN-SPAM compliant healthcare email lists that get integrated into your CRM and ESPs. Balance out the sales and marketing efforts by aligning goals using email lists from the healthcare industry. Build strong business relationships with potential clients through personalized campaigns. Call Infotanks Media for a free consultation. Explore new geographies and target markets with a focused approach using healthcare email lists. Align your sales teams and marketing teams through personalized email marketing campaigns to ensure they accomplish business goals together. Add value and grow revenue to take your business to the next level of success. Double up your business and revenue growth with email lists of healthcare professionals. Send segmented campaigns to monitor behaviors and understand the purchasing habits of your potential clients. Send follow up nurturing email marketing campaigns to attract your potential clients to become converted customers. Close deals sooner with detailed information of your prospects using the healthcare email list from Infotanks Media. Reach healthcare professionals on their preferred platform of communication with the email list of healthcare professionals. Identify, capture, explore, and grow in your target markets anywhere in the world with a fully verified, validated, and compliant email database of healthcare professionals. Move beyond the traditional approach and automate sales cycles with buying triggers sent through email marketing campaigns. Use the healthcare email list from Infotanks Media to engage with your targeted potential clients and get them to respond. Increase email marketing campaign response rate to convert better! Reach out to Infotanks Media to customize your healthcare email lists. Call today!"

Global Data: GDP, Life Expectancy & More

This dataset comprises 204 entries and 38 attributes, providing a comprehensive analysis of key economic and social indicators across various countries. It includes a diverse range of metrics, allowing for in-depth exploration of global trends related to GDP, education, health, and environmental factors.

Key Features:

• GDP: Gross Domestic Product (in current US dollars), representing the total economic output of a country.
• Sex Ratio: The ratio of males to females in the population, highlighting demographic trends.
• Life Expectancy: Average lifespan for males and females, an essential indicator of healthcare quality.
• Education Enrollment Rates: Data on primary, secondary, and post-secondary education enrollment for males and females, reflecting educational attainment.
• Unemployment Rate: Percentage of the labor force that is unemployed, indicating economic health.
• Homicide Rate: Number of homicides per 100,000 population, providing insight into safety and crime levels.
• Urban Population Growth: Rate of growth in urban populations, illustrating migration trends.
• CO2 Emissions: Carbon dioxide emissions per capita, an important measure of environmental impact.
• Forested Area: Percentage of land covered by forests, indicating biodiversity and environmental health.
• Tourist Numbers: Total number of international visitors, which can reflect a country's tourism potential.

Applications and Uses:

1. Research and Analysis: Ideal for researchers studying the correlation between economic performance and social indicators. This dataset can help identify trends and patterns relevant to global development.

2. Policy Development: Policymakers can utilize this data to inform decisions on education, healthcare, and environmental policies, aiming to improve national outcomes.

3. Machine Learning and Data Science: Data scientists can apply machine learning techniques to predict economic trends, analyze social impacts, or classify countries based on various indicators.

4. Educational Purposes: Suitable for students and educators in fields like economics, sociology, and environmental science for practical data analysis exercises.

5. Visualization Projects: Perfect for creating compelling visualizations that illustrate relationships between different metrics, aiding in public understanding and engagement.

By leveraging this dataset, users can uncover insights into how different factors influence a country's development, making it a valuable resource for diverse applications across various fields.

Electronic Health Legal Data

Exploring Laws and Regulations

By US Open Data Portal, data.gov [source]

About this dataset

This Electronic Health Information Legal Epidemiology dataset offers an extensive collection of legal and epidemiological data that can be used to understand the complexities of electronic health information. It contains a detailed balance of variables, including legal requirements, enforcement mechanisms, proprietary tools, access restrictions, privacy and security implications, data rights and responsibilities, user accounts and authentication systems. This powerful set provides researchers with real-world insights into the functioning of EHI law in order to assess its impact on patient safety and public health outcomes. With such data it is possible to gain a better understanding of current policies regarding the regulation of electronic health information as well as their potential for improvement in safeguarding patient confidentiality. Use this dataset to explore how these laws impact our healthcare system by exploring patterns across different groups over time or analyze changes leading up to new versions or updates. Make exciting discoveries with this comprehensive dataset!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

• Start by familiarizing yourself with the different columns of the dataset. Examine each column closely and look up any unfamiliar terminology to get a better understanding of what the columns are referencing.

• Once you understand the data and what it is intended to represent, think about how you might want to use it in your analysis. You may want to create a research question, or narrower focus for your project surrounding legal epidemiology of electronic health information that can be answered with this data set.

• After creating your research plan, begin manipulating and cleaning up the data as needed in order to prepare it for analysis or visualization as specified in your project plan or research question/model design steps you have outlined .

4 .Next, perform exploratory data analysis (EDA) on relevant subsets of data from specific countries if needed on specific subsets based on targets of interests (e.g gender). Filter out irrelevant information necessary for drawing meaningful insights; analyze patterns and trends observed in your filtered datasets ; compare areas which have differing rates e-health related rules and regulations tying decisions made by elected officials strongly driven by demographics , socioeconomics factors ,ideology etc.. . Look out for correlations using statistical information as needed throughout all stages in process from filtering out dis-informative subgroups from full population set til generating visualizations(graphs/ diagrams) depicting valid insight leveraging descriptive / predictive models properly validate against reference datasets when available always keep openness principal during gathering info especially when needs requires contact external sources such validating multiple sources work best provide strong seals establishing validity accuracy facts statement representing humans case scenarios digital support suitably localized supporting local languages culture respectively while keeping secure datasets private visible limited particular users duly authorized access 5 Finally create concrete summaries reporting discoveries create share findings preferably infographics showcasing evidence observances providing overall assessment main conclusions protocols developed so far broader community indirectly related interested professionals able benefit those results ideas complete transparently freely adapted locally ported increase overall global society level enhancing potentiality range impact derive conditions allowing wider adoption increased usage diffusion capture wide spread change movement affect global e-health legal domain clear manner

Research Ideas

• Studying how technology affects public health policies and practice - Using the data, researchers can look at the various types of legal regulations related to electronic health information to examine any relations between technology and public health decisions in certain areas or regions.
• Evaluating trends in legal epidemiology – With this data, policymakers can identify patterns that help measure the evolution of electronic health information regulations over time and investigate why such rules are changing within different states or countries.
• Analysing possible impacts on healthcare costs – Looking at changes in laws, regulations, and standards relate...

This dataset identifies whether a country has one or more midwife-led health units—facilities where midwives are the primary providers responsible for maternity care. Midwife-led care is linked to improved maternal and newborn outcomes, reduced unnecessary interventions, and high levels of service satisfaction. The presence of such units reflects the health system's trust in midwives’ autonomy and supports accessible, respectful, and cost-effective care. These models are particularly impactful in low-resource settings, where midwife-led continuity of care improves equity and outcomes.Data Source:This dataset was collected by the International Confederation of Midwives (ICM) and Novametrics.Data Dictionary: The data is collated with the following columns:Column headingContent of this columnPossible valuesRefNumerical counter for each row of data, for ease of identification1+CountryShort name for the country195 countries in total – all 194 WHO member states plus PalestineISO3Three-digit alphabetical codes International Standard ISO 3166-1 assigned by the International Organization for Standardization (ISO). e.g. AFG (Afghanistan)ISO22 letter identifier code for the countrye.g. AF (Afghanistan)ICM_regionICM Region for countryAFR (Africa), AMR (Americas), EMR (Eastern Mediterranean), EUR (Europe), SEAR (South east Asia) or WPR (Western Pacific)CodeUnique project code for each indicator:GGTXXnnnGG=data group e.g. OU for outcomeT = N for novice or E for ExpertXX = identifier number 00 to 30nnn = identifier name eg mmre.g. OUN01sbafor Outcome Novice Indicator 01 skilled birth attendance Short_nameIndicator namee.g. maternal mortality ratioDescriptionText description of the indicator to be used on websitee.g. Maternal mortality ratio (maternal deaths per 100,000 live births)Value_typeDescribes the indicator typeNumeric: decimal numberPercentage: value between 0 & 100Text: value from list of text optionsY/N: yes or noValue_categoryExpect this to be ‘total’ for all indicators for Phase 1, but this could allow future disaggregation, e.g. male/female; urban/ruraltotalYearThe year that the indicator value was reported. For most indicators, we will only report if 2014 or more recente.g. 2020Latest_Value‘LATEST’ if this is the most recent reported value for the indicator since 2014, otherwise ‘No’. Useful for indicators with time trend data.LATEST or NOValueIndicator valuee.g. 99.8. NB Some indicators are calculated to several decimal places. We present the value to the number of decimal places that should be displayed on the Hub.SourceFor Caesarean birth rate [OUN13cbr] ONLY, this column indicates the source of the data, either OECD when reported, or UNICEF otherwise.OECD or UNICEFTargetHow does the latest value compare with Global guidelines / targets?meets targetdoes not meet targetmeets global standarddoes not meet global standardRankGlobal rank for indicator, i.e. the country with the best global score for this indicator will have rank = 1, next = 2, etc. This ranking is only appropriate for a few indicators, others will show ‘na’1-195Rank out ofThe total number of countries who have reported a value for this indicator. Ranking scores will only go as high as this number.Up to 195TrendIf historic data is available, an indication of the change over time. If there is a global target, then the trend is either getting better, static or getting worse. For mmr [OUN04mmr] and nmr [OUN05nmr] the average annual rate of reduction (arr) between 2016 and latest value is used to determine the trend:arr <-1.0 = getting worsearr >=-1.0 AND <=1.0 = staticarr >1.0 = getting betterFor other indicators, the trend is estimated by comparing the average of the last three years with the average ten years ago:decreasing if now < 95% 10 yrs agoincreasing if now > 105% 10 yrs agostatic otherwiseincreasingdecreasing Or, if there is a global target: getting better,static,getting worseNotesClarification comments, when necessary LongitudeFor use with mapping LatitudeFor use with mapping DateDate data uploaded to the Hubthe following codes are also possible values:not reported does not apply don’t knowThis is one of many datasets featured on the Midwives’ Data Hub, a digital platform designed to strengthen midwifery and advocate for better maternal and newborn health services.

Multimorbidity, commonly defined as the co-occurrence of two-or-more long-term conditions in one individual, has been argued to be among the greatest global health challenges of our time. Health systems remain largely organised around specialist rather than generalist knowledge, which in many African nations translates into ‘siloed’ organisation of care, fuelled by ‘vertical’ single-disease programming. Multimorbidity has recently emerged on the health agendas of many lower-income countries, including in Africa. Yet with its conceptual origins in higher-income settings the global North, its meaning and utility in lower-resource settings remains abstract.

KnowM (2021-2024) was an interdisciplinary research collaboration to characterize the meaning, significance, and transformative potential of the concept of multimorbidity within a global health context, centred on a case study of Zimbabwe. In Zimbabwe, KnowM brought together stakeholders from across the country’s health system to critically interrogate the concept of multimorbidity and co-produce a formative agenda for responding to it in this setting. The specific objectives were: to understand how multimorbidity is being defined and framed as a global health challenge; to describe concepts, experiences, and responses to multimorbidity across different spaces within Zimbabwe’s health system; and to co-produce a conceptual framework and formative agenda for responding to multimorbidity in Zimbabwe.

The study was conducted in four provinces of Zimbabwe, including Harare, Bulawayo, Mashonaland East, and Matabeleland South, to represent both urban and rural settings. Within a participatory ethnographic study design, specific research methods included a health facility survey, participant-observation, in-depth interviews, audio-visual diaries, and participatory workshops. Through this holistic, bottom-up approach, KnowM sought to push thinking beyond the single disease paradigm and to open new conceptual pathways towards more integrated systems of research, training, and care in Zimbabwe, Africa, and wider field of global health.

The data deposited include the health facility health facility survey (n=30 surveys), in-depth interviews (n=45 transcripts), and fieldnote summaries from participant-observation and other stakeholder engagements during the study (n=23 fieldnote summaries). Data collection commenced with a survey of 30 health facilities at different levels of care, and included questions about services, staffing, and resources; about specific services and capacity related to multimorbidity, and more specific questions about care for particular non-communicable diseases (NCDs). Following the survey, participant-observation and in-depth interviews were conducted with a range of healthcare professionals in 10 facilities purposively sampled from the surveyed facilities. In parallel, we conducted interviews and audio-visual diaries with PLWMM (the latter not deposited for ethical reasons) to capture understandings, experiences, and challenges of (self-)managing multimorbidity and accessing care. To gain a perspective on multimorbidity beyond the patient and service delivery level, participant-observation and in-depth interviews were conducted with policymakers and public health practitioners, clinical academics and medical educators, health informaticians and data experts, and non-governmental organisation (NGO) representatives. Finally, participatory workshops (not deposited for ethical reasons) were held to collaboratively interpret and reflect on preliminary findings and draw out their significance and implications.

Findings suggest that multimorbidity, while a relatively new and emerging concept, revealed and amplified key tensions within the health system and wider field of global health. Participants described multimorbidity as complex, multifaceted, and rising, particularly among people living with HIV and among the elderly. However, it is currently challenging to respond to – or fully understand – due to various interconnected factors. These include disease-specific programme guidelines and monitoring and evaluation (M&E) systems; the considerably greater funding and visibility of HIV, TB and malaria compared to NCDs and mental health; and a fragmented, disenabling policy environment. While participants considered multimorbidity a meaningful and useful concept, with capacity and momentum to address multimorbidity currently concentrated within the HIV programme, there was concern that multimorbidity could itself become verticalized, undercutting its transformative potential. Participants agreed that responding to multimorbidity requires a decisive shift from vertical, disease-centred programming towards more integrated, person-centred approaches across the health system. Specific priorities included reinvigorating comprehensive chronic care at primary level; building multimorbidity into routine health information and M&E systems; fostering engagement and learning across disease programme areas; and strengthening ties between academia, policymakers, and ground-level experience to foster continuous, contextually-attuned learning.

The Global Roads Open Access Data Set, Version 1 (gROADSv1) was developed under the auspices of the CODATA Global Roads Data Development Task Group. The data set combines the best available roads data by country into a global roads coverage, using the UN Spatial Data Infrastructure Transport (UNSDI-T) version 2 as a common data model. All country road networks have been joined topologically at the borders, and many countries have been edited for internal topology. Source data for each country are provided in the documentation, and users are encouraged to refer to the readme file for use constraints that apply to a small number of countries. Because the data are compiled from multiple sources, the date range for road network representations ranges from the 1980s to 2010 depending on the country (most countries have no confirmed date), and spatial accuracy varies. The baseline global data set was compiled by the Information Technology Outreach Services (ITOS) of the University of Georgia. Updated data for 27 countries and 6 smaller geographic entities were assembled by Columbia University's Center for International Earth Science Information Network (CIESIN), with a focus largely on developing countries with the poorest data coverage.

Updates

• Notice of data discontinuation: Since the start of the pandemic, AP has reported case and death counts from data provided by Johns Hopkins University. Johns Hopkins University has announced that they will stop their daily data collection efforts after March 10. As Johns Hopkins stops providing data, the AP will also stop collecting daily numbers for COVID cases and deaths. The HHS and CDC now collect and visualize key metrics for the pandemic. AP advises using those resources when reporting on the pandemic going forward.

  • CDC Weekly case and death counts (national and state level)
  • CDC County level cases and deaths
  • HHS New hospital admissions
  • CDC NowCast COVID variant proportions (national and regional level)

• April 9, 2020

  • The population estimate data for New York County, NY has been updated to include all five New York City counties (Kings County, Queens County, Bronx County, Richmond County and New York County). This has been done to match the Johns Hopkins COVID-19 data, which aggregates counts for the five New York City counties to New York County.

• April 20, 2020

  • Johns Hopkins death totals in the US now include confirmed and probable deaths in accordance with CDC guidelines as of April 14. One significant result of this change was an increase of more than 3,700 deaths in the New York City count. This change will likely result in increases for death counts elsewhere as well. The AP does not alter the Johns Hopkins source data, so probable deaths are included in this dataset as well.

• April 29, 2020

  • The AP is now providing timeseries data for counts of COVID-19 cases and deaths. The raw counts are provided here unaltered, along with a population column with Census ACS-5 estimates and calculated daily case and death rates per 100,000 people. Please read the updated caveats section for more information.

• September 1st, 2020

  • Johns Hopkins is now providing counts for the five New York City counties individually.

• February 12, 2021

  • The Ohio Department of Health recently announced that as many as 4,000 COVID-19 deaths may have been underreported through the state’s reporting system, and that the "daily reported death counts will be high for a two to three-day period."
  • Because deaths data will be anomalous for consecutive days, we have chosen to freeze Ohio's rolling average for daily deaths at the last valid measure until Johns Hopkins is able to back-distribute the data. The raw daily death counts, as reported by Johns Hopkins and including the backlogged death data, will still be present in the new_deaths column.

• February 16, 2021

  - Johns Hopkins has reconciled Ohio's historical deaths data with the state.

  Overview

The AP is using data collected by the Johns Hopkins University Center for Systems Science and Engineering as our source for outbreak caseloads and death counts for the United States and globally.

The Hopkins data is available at the county level in the United States. The AP has paired this data with population figures and county rural/urban designations, and has calculated caseload and death rates per 100,000 people. Be aware that caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.

This data is from the Hopkins dashboard that is updated regularly throughout the day. Like all organizations dealing with data, Hopkins is constantly refining and cleaning up their feed, so there may be brief moments where data does not appear correctly. At this link, you’ll find the Hopkins daily data reports, and a clean version of their feed.

The AP is updating this dataset hourly at 45 minutes past the hour.

To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

Queries

Use AP's queries to filter the data or to join to other datasets we've made available to help cover the coronavirus pandemic

• Filter cases by state here

• Rank states by their status as current hotspots. Calculates the 7-day rolling average of new cases per capita in each state: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=481e82a4-1b2f-41c2-9ea1-d91aa4b3b1ac

• Find recent hotspots within your state by running a query to calculate the 7-day rolling average of new cases by capita in each county: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=b566f1db-3231-40fe-8099-311909b7b687&showTemplatePreview=true

• Join county-level case data to an earlier dataset released by AP on local hospital capacity here. To find out more about the hospital capacity dataset, see the full details.

• Pull the 100 counties with the highest per-capita confirmed cases here

• Rank all the counties by the highest per-capita rate of new cases in the past 7 days here. Be aware that because this ranks per-capita caseloads, very small counties may rise to the very top, so take into account raw caseload figures as well.

Interactive

The AP has designed an interactive map to track COVID-19 cases reported by Johns Hopkins.

@(https://datawrapper.dwcdn.net/nRyaf/15/)

Interactive Embed Code

<iframe title="USA counties (2018) choropleth map Mapping COVID-19 cases by county" aria-describedby="" id="datawrapper-chart-nRyaf" src="https://datawrapper.dwcdn.net/nRyaf/10/" scrolling="no" frameborder="0" style="width: 0; min-width: 100% !important;" height="400"></iframe><script type="text/javascript">(function() {'use strict';window.addEventListener('message', function(event) {if (typeof event.data['datawrapper-height'] !== 'undefined') {for (var chartId in event.data['datawrapper-height']) {var iframe = document.getElementById('datawrapper-chart-' + chartId) || document.querySelector("iframe[src*='" + chartId + "']");if (!iframe) {continue;}iframe.style.height = event.data['datawrapper-height'][chartId] + 'px';}}});})();</script>

Caveats

• This data represents the number of cases and deaths reported by each state and has been collected by Johns Hopkins from a number of sources cited on their website.
• In some cases, deaths or cases of people who've crossed state lines -- either to receive treatment or because they became sick and couldn't return home while traveling -- are reported in a state they aren't currently in, because of state reporting rules.
• In some states, there are a number of cases not assigned to a specific county -- for those cases, the county name is "unassigned to a single county"
• This data should be credited to Johns Hopkins University's COVID-19 tracking project. The AP is simply making it available here for ease of use for reporters and members.
• Caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.
• Population estimates at the county level are drawn from 2014-18 5-year estimates from the American Community Survey.
• The Urban/Rural classification scheme is from the Center for Disease Control and Preventions's National Center for Health Statistics. It puts each county into one of six categories -- from Large Central Metro to Non-Core -- according to population and other characteristics. More details about the classifications can be found here.

Johns Hopkins timeseries data - Johns Hopkins pulls data regularly to update their dashboard. Once a day, around 8pm EDT, Johns Hopkins adds the counts for all areas they cover to the timeseries file. These counts are snapshots of the latest cumulative counts provided by the source on that day. This can lead to inconsistencies if a source updates their historical data for accuracy, either increasing or decreasing the latest cumulative count. - Johns Hopkins periodically edits their historical timeseries data for accuracy. They provide a file documenting all errors in their timeseries files that they have identified and fixed here

Attribution

This data should be credited to Johns Hopkins University COVID-19 tracking project

Abstract In 2019, unprecedentedly among the official statistical institutes worldwide, the IBGE included a particular module on evaluating primary health care in its central population-based population survey, the National Health Survey (PNS-2019). The survey considered the reduced version of the Primary Care Assessment Tool (PCAT), developed and disseminated by Starfield and Shi, to assess the existence and extent of the structure and process characteristics of PHC services. It is the most significant probabilistic sample using this instrument ever conducted in a single country in the world that interviewed users aged 18 or over (n=9,677). The results of the Brazilian overall PCAT scores (5.9 [5.8; 5.9]) point to significant regional and intraregional contrasts, with the South of the country standing out with the best evaluations of primary care services (overall score = 6.3 [6.2; 6.5]) and the North with the worse (overall score = 5,5 [5,3; 5,7]). There were also statistically significant and more favorable differences between residents of households registered by family health teams, among older adults, and those using health services the most (adults with reported morbidities).

This dataset shows the percentage of newborns who received postnatal care from a trained health provider within two days of birth, based on UNICEF’s 'Delivery Care' dataset. Early postnatal care is essential for detecting complications such as infection, low birth weight, or feeding difficulties. This indicator supports efforts to strengthen newborn survival and improve health service delivery during the most vulnerable period of life.Data Dictionary: The data is collated with the following columns:Column headingContent of this columnPossible valuesRefNumerical counter for each row of data, for ease of identification1+CountryShort name for the country195 countries in total – all 194 WHO member states plus PalestineISO3Three-digit alphabetical codes International Standard ISO 3166-1 assigned by the International Organization for Standardization (ISO). e.g. AFG (Afghanistan)ISO22 letter identifier code for the countrye.g. AF (Afghanistan)ICM_regionICM Region for countryAFR (Africa), AMR (Americas), EMR (Eastern Mediterranean), EUR (Europe), SEAR (South east Asia) or WPR (Western Pacific)CodeUnique project code for each indicator:GGTXXnnnGG=data group e.g. OU for outcomeT = N for novice or E for ExpertXX = identifier number 00 to 30nnn = identifier name eg mmre.g. OUN01sbafor Outcome Novice Indicator 01 skilled birth attendance Short_nameIndicator namee.g. maternal mortality ratioDescriptionText description of the indicator to be used on websitee.g. Maternal mortality ratio (maternal deaths per 100,000 live births)Value_typeDescribes the indicator typeNumeric: decimal numberPercentage: value between 0 & 100Text: value from list of text optionsY/N: yes or noValue_categoryExpect this to be ‘total’ for all indicators for Phase 1, but this could allow future disaggregation, e.g. male/female; urban/ruraltotalYearThe year that the indicator value was reported. For most indicators, we will only report if 2014 or more recente.g. 2020Latest_Value‘LATEST’ if this is the most recent reported value for the indicator since 2014, otherwise ‘No’. Useful for indicators with time trend data.LATEST or NOValueIndicator valuee.g. 99.8. NB Some indicators are calculated to several decimal places. We present the value to the number of decimal places that should be displayed on the Hub.SourceFor Caesarean birth rate [OUN13cbr] ONLY, this column indicates the source of the data, either OECD when reported, or UNICEF otherwise.OECD or UNICEFTargetHow does the latest value compare with Global guidelines / targets?meets targetdoes not meet targetmeets global standarddoes not meet global standardRankGlobal rank for indicator, i.e. the country with the best global score for this indicator will have rank = 1, next = 2, etc. This ranking is only appropriate for a few indicators, others will show ‘na’1-195Rank out ofThe total number of countries who have reported a value for this indicator. Ranking scores will only go as high as this number.Up to 195TrendIf historic data is available, an indication of the change over time. If there is a global target, then the trend is either getting better, static or getting worse. For mmr [OUN04mmr] and nmr [OUN05nmr] the average annual rate of reduction (arr) between 2016 and latest value is used to determine the trend:arr <-1.0 = getting worsearr >=-1.0 AND <=1.0 = staticarr >1.0 = getting betterFor other indicators, the trend is estimated by comparing the average of the last three years with the average ten years ago:decreasing if now < 95% 10 yrs agoincreasing if now > 105% 10 yrs agostatic otherwiseincreasingdecreasing Or, if there is a global target: getting better,static,getting worseNotesClarification comments, when necessary LongitudeFor use with mapping LatitudeFor use with mapping DateDate data uploaded to the Hubthe following codes are also possible values:not reported does not apply don’t knowThis is one of many datasets featured on the Midwives’ Data Hub, a digital platform designed to strengthen midwifery and advocate for better maternal and newborn health services.

AI-Generated Synthetic Tabular Dataset Market Outlook

According to our latest research, the AI-Generated Synthetic Tabular Dataset market size reached USD 1.42 billion in 2024 globally, reflecting the rapid adoption of artificial intelligence-driven data generation solutions across numerous industries. The market is expected to expand at a robust CAGR of 34.7% from 2025 to 2033, reaching a forecasted value of USD 19.17 billion by 2033. This exceptional growth is primarily driven by the increasing need for high-quality, privacy-preserving datasets for analytics, model training, and regulatory compliance, particularly in sectors with stringent data privacy requirements.

One of the principal growth factors propelling the AI-Generated Synthetic Tabular Dataset market is the escalating demand for data-driven innovation amidst tightening data privacy regulations. Organizations across healthcare, finance, and government sectors are facing mounting challenges in accessing and sharing real-world data due to GDPR, HIPAA, and other global privacy laws. Synthetic data, generated by advanced AI algorithms, offers a solution by mimicking the statistical properties of real datasets without exposing sensitive information. This enables organizations to accelerate AI and machine learning development, conduct robust analytics, and facilitate collaborative research without risking data breaches or non-compliance. The growing sophistication of generative models, such as GANs and VAEs, has further increased confidence in the utility and realism of synthetic tabular data, fueling adoption across both large enterprises and research institutions.

Another significant driver is the surge in digital transformation initiatives and the proliferation of AI and machine learning applications across industries. As businesses strive to leverage predictive analytics, automation, and intelligent decision-making, the need for large, diverse, and high-quality datasets has become paramount. However, real-world data is often siloed, incomplete, or inaccessible due to privacy concerns. AI-generated synthetic tabular datasets bridge this gap by providing scalable, customizable, and bias-mitigated data for model training and validation. This not only accelerates AI deployment but also enhances model robustness and generalizability. The flexibility of synthetic data generation platforms, which can simulate rare events and edge cases, is particularly valuable in sectors like finance and healthcare, where such scenarios are underrepresented in real datasets but critical for risk assessment and decision support.

The rapid evolution of the AI-Generated Synthetic Tabular Dataset market is also underpinned by technological advancements and growing investments in AI infrastructure. The availability of cloud-based synthetic data generation platforms, coupled with advancements in natural language processing and tabular data modeling, has democratized access to synthetic datasets for organizations of all sizes. Strategic partnerships between technology providers, research institutions, and regulatory bodies are fostering innovation and establishing best practices for synthetic data quality, utility, and governance. Furthermore, the integration of synthetic data solutions with existing data management and analytics ecosystems is streamlining workflows and reducing barriers to adoption, thereby accelerating market growth.

Regionally, North America dominates the AI-Generated Synthetic Tabular Dataset market, accounting for the largest share in 2024 due to the presence of leading AI technology firms, strong regulatory frameworks, and early adoption across industries. Europe follows closely, driven by stringent data protection laws and a vibrant research ecosystem. The Asia Pacific region is emerging as a high-growth market, fueled by rapid digitalization, government initiatives, and increasing investments in AI research and development. Latin America and the Middle East & Africa are also witnessing growing interest, particularly in sectors like finance and government, though market maturity varies across countries. The regional landscape is expected to evolve dynamically as regulatory harmonization, cross-border data collaboration, and technological advancements continue to shape market trajectories globally.

Success.ai’s Healthcare Industry Leads Data empowers businesses and organizations to connect with key decision-makers and stakeholders in the global healthcare and pharmaceutical sectors. Leveraging over 170 million verified professional profiles and 30 million company profiles, this dataset includes detailed contact information, firmographic insights, and leadership data for hospitals, clinics, biotech firms, medical device manufacturers, pharmaceuticals, and other healthcare-related enterprises. Whether your goal is to pitch a new medical technology, partner with healthcare providers, or conduct market research, Success.ai ensures that your outreach and strategic planning are guided by reliable, continuously updated, and AI-validated data.

Why Choose Success.ai’s Healthcare Industry Leads Data?

1. Comprehensive Contact Information

   • Access verified work emails, phone numbers, and LinkedIn profiles of healthcare administrators, pharmaceutical executives, R&D directors, procurement officers, and medical staff.
   • AI-driven validation ensures 99% accuracy, reducing wasted efforts and fostering efficient communication.

2. Global Reach Across Healthcare Segments

   • Includes profiles from hospitals, private clinics, pharmaceutical companies, biotech startups, research institutions, and medical supply chain partners.
   • Covers North America, Europe, Asia-Pacific, South America, and the Middle East, enabling a global perspective on healthcare systems and opportunities.

3. Continuously Updated Datasets

   • Real-time updates reflect leadership changes, organizational shifts, and emerging trends in patient care, medical innovation, and regulatory compliance.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other global data privacy regulations, ensuring your data usage respects legal standards and patient confidentiality norms.

Data Highlights:

• 170M+ Verified Professional Profiles: Connect with healthcare and pharmaceutical professionals, decision-makers, and influencers worldwide.
• 50M Work Emails: AI-validated for direct, accurate communication and reduced bounce rates.
• 30M Company Profiles: Gain insights into organizational structures, operational scales, and specialization areas.
• 700M Global Professional Profiles: Enriched datasets to support market analysis, product development, and strategic planning.

Key Features of the Dataset:

1. Healthcare Decision-Maker Profiles

   • Identify and engage with CEOs, CIOs, CFOs, chief medical officers, hospital administrators, clinical directors, and procurement specialists.
   • Target professionals who influence equipment purchases, medical supply chain decisions, drug trial approvals, and healthcare delivery models.

2. Detailed Business Profiles

   • Access firmographic data, including company sizes, revenue ranges, key markets, and service lines for a holistic understanding of target organizations.
   • Leverage comprehensive insights to position your products, services, or solutions as tailored fits for specific operational needs.

3. Advanced Filters for Precision Targeting

   • Filter by region, specialty (oncology, cardiology, diagnostics, etc.), hospital size, pharmaceutical focus, or research areas.
   • Align campaigns with unique healthcare demands, reimbursement models, and regulatory environments.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data enable personalized messaging, highlight value propositions, and enhance engagement outcomes with healthcare stakeholders.

Strategic Use Cases:

1. Sales and Business Development

   • Present medical devices, pharma products, or healthcare IT solutions to hospital administrators, chief medical officers, and procurement managers.
   • Build relationships with decision-makers who oversee budgeting, supplier selection, and patient care initiatives.

2. Market Research and Product Innovation

   • Analyze trends in patient treatments, drug pipelines, and healthcare infrastructure to inform R&D and product roadmaps.
   • Identify emerging specialties, new treatment modalities, and growth markets to focus marketing, sales, and investment efforts.

3. Strategic Partnerships and Alliances

   • Connect with R&D directors, biotech executives, or hospital groups to explore collaborations, clinical trials, and joint ventures.
   • Foster partnerships that accelerate product development, enhance patient outcomes, and drive long-term competitiveness.

4. Recruitment and Talent Acquisition

   • Target HR professionals and department heads seeking qualified medical staff, researchers, pharmaceutical reps, and administrative personnel.
   • Offer staffing, training, or professional development services to healthcare institutions aiming to improve service delivery and compliance.

Why Choose Success.ai?

1. Best Price Guarantee
   • Access high-quality, verified data at...

The Personalised Healthcare Recommendations Dataset is a synthetic yet realistic health dataset created to support the development and evaluation of AI-driven personalised medicine tools. It simulates data that represents a diverse population with varied health behaviours, risk factors, and lifestyle attributes, providing a strong foundation for building preventive health models, recommender systems, and clinical decision-support tools.

This dataset combines the power of demographic, behavioural, and clinical indicators to generate actionable health insights. Each record is a mini health story, guiding models to understand how personal choices and background can impact chronic disease risk and overall wellness.

Why Should You Use This Dataset?

Because generic healthcare is out. Personalised care is the now and the future. Here’s why this dataset is worth your time:

- Perfect for training machine learning models for: Risk stratification (predict who's likely to develop conditions), Health recommender systems (tailored lifestyle or treatment advice) and Patient triaging or follow-up scheduling - Encourages Preventive Health Approaches: In a world flooded with treatment-based systems, this dataset shifts the focus to early intervention and personalised prevention, just like precision public health aims for. - Interdisciplinary Goldmine: Data scientists, bioinformaticians, clinicians, and health-tech engineers can all use this. It sits beautifully at the crossroads of: Healthcare analytics, Public health, Personalised medicine and Explainable AI

Why Is It Important?

Because healthcare isn’t one-size-fits-all, and it never should’ve been. This dataset is your playground for solving real-world health equity problems like:

Reducing hospital readmission by spotting at-risk individuals early Empowering patients to take action on their health through smart apps Assisting healthcare providers with AI-driven second opinions Creating more equitable care pathways by simulating a diverse population

This dataset comprises interviews conducted between 2016 and 2018 with health service users, health professionals and health system managers in the Municipality of São Paulo, Brazil. The interviews focused in particular on the primary health care services covering two of the poorest sub-municipal districts, Cidade Tiradentes and Sapopemba. The Unequal Voices project – Vozes Desiguais in Portuguese – aimed to strengthen the evidence base on the politics of accountability for health equity via multi-level case studies of health systems in Brazil and Mozambique. The project examined the trajectories of change in the political context and in patterns of health inequalities in Brazil and Mozambique, and carried out four case studies to compare the operation of different accountability regimes across the two countries and between different areas within each country. The case studies tracked shifts in accountability relationships among managers, providers and citizens and changes in health system performance, in order to arrive at a better understanding of what works for different poor and marginalised groups in different contexts. In each country the research team studied one urban location with competitive politics and a high level of economic inequality and one rural location where the population as a whole has been politically marginalised and under-provided with services. Health inequities - that is, inequalities in health which result from social, economic or political factors and unfairly disadvantage the poor and marginalised - are trapping millions of people in poverty. Unless they are tackled, the effort to fulfill the promise of universal health coverage as part of the fairer world envisaged in the post-2015 Sustainable Development Goals may lead to more waste and unfairness, because new health services and resources will fail to reach the people who need them most. In Mozambique, for example, the gap in infant mortality between the best-performing and worst-performing areas actually increased between 1997 and 2008, despite improvements in health indicators for the country as a whole. However, while many low- and middle-income countries are failing to translate economic growth into better health services for the poorest, some - including Brazil - stand out as having taken determined and effective action. One key factor that differentiates a strong performer like Brazil from a relatively weak performer like Mozambique is accountability politics: the formal and informal relationships of oversight and control that ensure that health system managers and service providers deliver for the poorest rather than excluding them. Since the mid-1990s, Brazil has transformed health policy to try to ensure that the poorest people and places are covered by basic services. This shift was driven by many factors: by a strong social movement calling for the right to health; by political competition as politicians realised that improving health care for the poor won them votes; by changes to health service contracting that changed the incentives for local governments and other providers to ensure that services reached the poor; and by mass participation that ensured citizen voice in decisions on health priority-setting and citizen oversight of services. However, these factors did not work equally well for all groups of citizens, and some - notably the country's indigenous peoples - continue to lag behind the population as a whole in terms of improved health outcomes. This project is designed to address the ESRC-DFID call's key cross-cutting issue of structural inequalities, and its core research question "what political and institutional conditions are associated with effective poverty reduction and development, and what can domestic and external actors do to promote these conditions?", by comparing the dimensions of accountability politics across Brazil and Mozambique and between different areas within each country. As Mozambique and Brazil seek to implement similar policies to improve service delivery, in each country the research team will examine one urban location with competitive politics and a high level of economic inequality and one rural location where the population as a whole has been politically marginalised and under-provided with services, looking at changes in power relationships among managers, providers and citizens and at changes in health system performance, in order to arrive at a better understanding of what works for different poor and marginalised groups in different contexts. As two Portuguese-speaking countries that have increasingly close economic, political and policy links, Brazil and Mozambique are also well-placed to benefit from exchanges of experience and mutual learning of the kind that Brazil is seeking to promote through its South-South Cooperation programmes. The project will support this mutual learning process by working closely with Brazilian and Mozambican organisations that are engaged in efforts to promote social accountability through the use of community scorecards and through strengthening health oversight committees, and link these efforts with wider networks working on participation and health equity across Southern Africa and beyond. This dataset comprises interviews conducted between 2016 and 2018 with health service users, health professionals and health system managers in the Municipality of São Paulo, Brazil. Interviewee sampling was purposive and made use of snowballing. The interviews focused in particular on the primary health care services covering two of the poorest suprefeituras (sub-municipal districts), Cidade Tiradentes and Sapopemba. The dataset includes a mix of transcripts and summary notes from individual and group interviews. All material is in Portuguese.

The Colleges and Universities feature class/shapefile is composed of all Post Secondary Education facilities as defined by the Integrated Post Secondary Education System (IPEDS, http://nces.ed.gov/ipeds/), National Center for Education Statistics (NCES, https://nces.ed.gov/), US Department of Education for the 2018-2019 school year. Included are Doctoral/Research Universities, Masters Colleges and Universities, Baccalaureate Colleges, Associates Colleges, Theological seminaries, Medical Schools and other health care professions, Schools of engineering and technology, business and management, art, music, design, Law schools, Teachers colleges, Tribal colleges, and other specialized institutions. Overall, this data layer covers all 50 states, as well as Puerto Rico and other assorted U.S. territories. This feature class contains all MEDS/MEDS+ as approved by the National Geospatial-Intelligence Agency (NGA) Homeland Security Infrastructure Program (HSIP) Team. Complete field and attribute information is available in the ”Entities and Attributes” metadata section. Geographical coverage is depicted in the thumbnail above and detailed in the "Place Keyword" section of the metadata. This feature class does not have a relationship class but is related to Supplemental Colleges. Colleges and Universities that are not included in the NCES IPEDS data are added to the Supplemental Colleges feature class when found. This release includes the addition of 175 new records, the removal of 468 no longer reported by NCES, and modifications to the spatial location and/or attribution of 6682 records.

All 174 trauma units in England, Wales and Northern Ireland regularly uploaded data describing the process, quality and outcome of the care they provided to the 67,302 people who presented with hip fracture in 2019. This report uses six NHFD key performance indicators (KPIs) to describe how the quality of care varies between hospitals and changes over time. The impact of COVID-19 on patient care and the organisation of trauma services will be examined in detail in next year’s NHFD report, but this year’s report helps units measure their readiness and prepare for the challenging time that we continue to face. Hip fracture is a serious and costly injury affecting mainly older people, and is more common in women. It usually results from the combination of weak bone structure (osteoporosis) and a fall. Around 76,000 hip fractures occur each year in the UK as a whole. Although there is good evidence on best practice in surgical, medical and rehabilitation care following hip fracture, such care and its outcomes – in terms of return home and also of mortality – continues to vary. The National Hip Fracture Database (NHFD), which was launched in 2007, aims to deliver improvements in the care of hip fracture patients. It documents case-mix, care and outcomes of hip fracture patients in England, Wales and Northern Ireland and is now, with more than 500,000 cases on record – by far the largest hip fracture audit in the world. It has demonstrated broad improvements at local and national level in patient care, and in England has supported the Department of Health’s highly successful Best Practice Tariff for hip fracture care. The work of the NHFD is now being replicated in Ireland, with the recent launch of Irish Hip Fracture Database, and similar developments are in hand in Australia and New Zealand, Canada and Hong Kong. The National Hip Fracture Database was founded as a collaboration between the British Orthopaedic Association and the British Geriatrics Society. It was developed between 2004 and 2007, and since 2009 it has received central funding as a national clinical audit via the Healthcare Quality Improvement Partnership (HQIP). Since April 2012 the NHFD has continued as part of the Falls and Fragility Fracture Audit Programme, managed on behalf of HQIP by the Royal College of Physicians (London). The audit covers England, Wales, Northern Ireland and the Isle of Man, however data files only refer to data for England and Wales.