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.

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

VHA hospitals Timely Care Data

Performance on Clinical Measures and Processes of Care

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

About this dataset

This dataset provides an inside look at the performance of the Veterans Health Administration (VHA) hospitals on timely and effective care measures. It contains detailed information such as hospital names, addresses, census-designated cities and locations, states, ZIP codes county names, phone numbers and associated conditions. Additionally, each entry includes a score, sample size and any notes or footnotes to give further context. This data is collected through either Quality Improvement Organizations for external peer review programs as well as direct electronic medical records. By understanding these performance scores of VHA hospitals on timely care measures we can gain valuable insights into how VA healthcare services are delivering values throughout the country!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset contains information about the performance of Veterans Health Administration hospitals on timely and effective care measures. In this dataset, you can find the hospital name, address, city, state, ZIP code, county name, phone number associated with each hospital as well as data related to the timely and effective care measure such as conditions being measured and their associated scores.

To use this dataset effectively, we recommend first focusing on identifying an area of interest for analysis. For example: what condition is most impacting wait times for patients? Once that has been identified you can narrow down which fields would best fit your needs - for example if you are studying wait times then “Score” may be more valuable to filter than Footnote. Additionally consider using aggregation functions over certain fields (like average score over time) in order to get a better understanding of overall performance by factor--for instance Location.

Ultimately this dataset provides a snapshot into how Veteran's Health Administration hospitals are performing on timely and effective care measures so any research should focus around that aspect of healthcare delivery

Research Ideas

• Analyzing and predicting hospital performance on a regional level to improve the quality of healthcare for veterans across the country.
• Using this dataset to identify trends and develop strategies for hospitals that consistently score low on timely and effective care measures, with the goal of improving patient outcomes.
• Comparison analysis between different VHA hospitals to discover patterns and best practices in providing effective care so they can be shared with other hospitals in the system

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: csv-1.csv | Column name | Description | |:-----------------------|:-------------------------------------------------------------| | Hospital Name | Name of the VHA hospital. (String) | | Address | Street address of the VHA hospital. (String) | | City | City where the VHA hospital is located. (String) | | State | State where the VHA hospital is located. (String) | | ZIP Code | ZIP code of the VHA hospital. (Integer) | | County Name | County where the VHA hospital is located. (String) | | Phone Number | Phone number of the VHA hospital. (String) | | Condition | Condition being measured. (String) | | Measure Name | Measure used to measure the condition. (String) | | Score | Score achieved by the VHA h...

About the Dataset

This dataset contains Quality of Life indices for various countries around the globe, extracted from the Numbeo website. The data provides valuable metrics for comparing countries based on several aspects of living standards, which can assist in decisions such as choosing a place to live or analyzing global trends in quality of life.

OBS: The code to generate this dataset is presented on: https://www.kaggle.com/code/marcelobatalhah/web-scrapping-quality-of-life-index

Columns in the Dataset

1. Rank:
   The global rank of the country based on its Quality of Life Index according to Year (1 = highest quality of life).

2. Country:
   The name of the country.

3. Quality of Life Index:
   A composite index that evaluates the overall quality of life in a country by combining other indices, such as Safety, Purchasing Power, and Health Care.

4. Purchasing Power Index:
   Measures the relative purchasing power of the average consumer in a country compared to New York City (baseline = 100).

5. Safety Index:
   Indicates the safety level of a country. A higher score suggests a safer environment.

6. Health Care Index:
   Evaluates the quality and accessibility of healthcare in the country.

7. Cost of Living Index:
   Measures the relative cost of living in a country compared to New York City (baseline = 100).

8. Property Price to Income Ratio:
   Compares the affordability of real estate by dividing the average property price by the average income.

9. Traffic Commute Time Index:
   Reflects the average time spent commuting due to traffic.

10. Pollution Index:
    Rates the level of pollution in the country (air, water, etc.).

11. Climate Index:
    Rates the favorability of the climate in the country (higher = more favorable).

12. Year:
    Year when the metrics were extracted.

Key Insights from the Dataset

• The Quality of Life Index aggregates multiple indicators, making it a useful single metric to compare countries.
• Specific indices such as Safety Index or Health Care Index allow for focused analysis on areas like security or healthcare quality.
• Cost of Living Index and Purchasing Power Index can help determine the affordability of living in each country.

How the Data Was Collected

• The dataset was built using web scraping techniques in Python.
• The data was extracted from the "Quality of Life Rankings by Country" page on Numbeo.
• Libraries used:
  • requests for retrieving webpage content.
  • BeautifulSoup for parsing the HTML and extracting relevant information.
  • pandas for organizing and storing the data in a structured format.

Possible Applications

1. Relocation Decision Making:
   Use the dataset to compare countries and identify destinations with high quality of life, safety, and healthcare.

2. Global Analysis:
   Perform exploratory data analysis (EDA) to identify trends and correlations across quality of life metrics.

3. Visualization:
   Plot global maps, bar charts, or other visualizations to better understand the data.

4. Predictive Modeling:
   Use this dataset as a base for machine learning tasks, like predicting Quality of Life Index based on other metrics.

CARE PH hospitals with the number of registrants in database.

Context

In order to begin correlating global data based around infection rates, from the WHO data in the UNCOVER: Covid-19 challenge, found here, to quality of healthcare in a region, data relaying the availability of health care in nations around the globe is necessary as a first step to this analysis. Out of a general desire to provide this data to the data science community, and out of a desire to find ways to learn about, prepare for in whatever way possible, and beat, the COVID-19 pandemic of 2020, I'm making this data-set public for others to use, share, and study with.

Content

The data presented in the file below cover the following information... 1 set of Strings --> The country names 1 set of Integers --> The years in which the data were recorded (2010-2014). 6 sets of floats --> 6 columns of floats record the total density of health centers and hospitals (including provincial and specialized) to every 100,000 people within the country... thus generalizing the country's access to health care, and maintenance/creation of the health infrastructure needed to support the population.

Acknowledgements

Complete thanks for this data-set goes to the World Health Organization and the Global Health Observatory. This data can be found on the GHO's site, specifically here. In terms of the licensing, in order to underscore that this data is not mine, as well as ensure all steps are taken to make one's proper rights clear (and grant thanks for the data once again), the general data usage license agreement for the data-set used can be found here.

Inspiration

It is sadly true that this data on its own is unlikely to present any major answers. When combined with other datasets however, this may yield answers as to what factors of a countries existence may indicate its ability to maintain a large health infrastructure. In fact, determining how a country's finances, natural resource list (as just ideas), etc. relate to a country's ability to sustain a decent health infrastructure would be an extremely interesting question to answer. I hope you may find the data helpful in your endeavors!

Disclaimer: This is my first ever published data-set on Kaggle. While I've done my best to ensure it's fairly descriptive for any potential visitors, please do feel free to leave any comments you may have in the discussions section! I'm always open to finding ways to improve.

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

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

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 ...

Beurteilung des Gesundheitssystems im Land. Persönliche Gesundheit. Gesundheitsversicherung. Themen: Lebenszufriedenheit (Glücklichsein); Vertrauen in das Bildungssystem und das Gesundheitssystem des Landes; Forderung nach einer Änderung des Gesundheitssystems; Rechtfertigung besserer medizinischer Versorgung und Bildung für Personen mit höherem Einkommen; Beurteilung des Gesundheitssystems des Landes (Skala: Einschätzung der Verbesserung des Gesundheitssystems, Beanspruchung von Gesundheitsleistungen über den notwendigen Bedarf hinweg, Bereitstellung von Basisgesundheitsleistungen durch den Staat, ineffizientes Gesundheitssystem); Bereitschaft zur Zahlung höherer Steuern zur Erhöhung der Gesundheitsversorgung für alle im Land; Einstellung zur öffentlichen Finanzierung von vorbeugenden medizinischen Checks, Behandlung von HIV/AIDS, Programmen zur Verhinderung von Fettleibigkeit sowie Organtransplantationen; Einstellung zum Zugang zu staatlich geförderter Gesundheitsversorgung für Menschen mit fremder Staatsbürgerschaft bzw. selbstschädigendem Gesundheitsverhalten; geschätzter Anteil von Menschen ohne Zugang zum Gesundheitssystem; Ursachen schwerwiegender Gesundheitsprobleme (gesundheitsschädliches Verhalten, Umwelt, Gene, Armut); Einstellung zur Bereitstellung einer Herzoperation für Patienten, die rauchen, die schon alt sind sowie bei solchen mit jungen Kindern; Einstellung zu alternativer Medizin (bessere Lösungen für Gesundheitsprobleme als konventionelle Medizin, verspricht mehr als sie halten kann); allgemeine Beurteilung von Ärzten im Land (Skala: vertrauenswürdig, diskutieren sämtliche Behandlungsoptionen mit ihren Patienten, geringe medizinische Fähigkeiten, kümmern sich mehr um ihr Einkommen als um ihre Patienten, Offenheit im Umgang mit Behandlungsfehlern); Häufigkeit von Problemen in den letzten vier Wochen: in Bezug auf Arbeit oder Haushaltsaktivitäten aufgrund gesundheitlicher Probleme, körperlich starke Schmerzen, Unglücklichsein und Depressionen, Verlust des Selbstvertrauens und unüberwindliche Probleme; Häufigkeit von Arztbesuchen und von Besuchen bei alternativen Heilpraktikern im letzten Jahr; Krankenhausaufenthalt im letzten Jahr; Gründe für nicht erhaltene notwendige medizinische Behandlung (Zahlungsschwierigkeiten, zeitliche Schwierigkeiten oder andere Verpflichtungen, erforderliche Behandlung ist am Wohnort nicht verfügbar, zu lange Wartelisten); Wahrscheinlichkeit des Zugangs zur bestmöglichen Behandlung im Land bei einer schweren Krankheit und zu freier Arztwahl; Zufriedenheit mit dem Gesundheitssystem im Land; Zufriedenheit mit dem letzten Arztbesuch, bei alternativen Heilpraktikern und mit dem letzten Krankenhausaufenthalt; Anzahl täglich gerauchter Zigaretten; Häufigkeit des Konsums von vier oder mehr alkoholischen Getränken pro Tag; Häufigkeit anstrengender körperlicher Aktivitäten und des Konsums von Obst und Gemüse; Selbsteinschätzung der Gesundheit; chronische Krankheit oder Behinderung; Größe und Gewicht; Art der persönlichen Gesundheitsversicherung; Beurteilung des Schutzes der persönlichen Gesundheitsversicherung. Optionale Fragen: Gesundheitsversicherung deckt ab: verordnete Medikamente, zahnmedizinische Versorgung und Krankenhausaufenthalte; Notwendigkeit einer Überweisung des Hausarztes vor dem Besuch eines Facharztes; Einschränkung sozialer Aktivitäten wegen gesundheitlicher Probleme. Demographie: Geschlecht; Alter; Geburtsjahr; Jahre der Schulbildung; Schulbildung (länderspezifisch); höchster Bildungsgrad; Erwerbstätigkeit; Wochenarbeitszeit; Beschäftigungsverhältnis; Beschäftigtenzahl; Vorgesetztenfunktion; Anzahl der beaufsichtigten Beschäftigten; Art der Organisation; Beruf (ISCO-88); Haupterwerbsstatus; Zusammenleben mit einem Partner; Gewerkschaftsmitgliedschaft; Konfession (länderspezifisch); Konfessionsgruppen; Kirchgangshäufigkeit; Selbsteinschätzung auf einer Oben-unten-Skala; Wahlbeteiligung bei der letzten Wahl und gewählte Partei (länderspezifisch); Einschätzung der gewählten Partei links-rechts; Ethnizität (länderspezifisch); Kinderzahl; Haushaltsgröße; Einkommen des Befragten (länderspezifisch); Haushaltseinkommen (länderspezifisch); Familienstand; Urbanisierungsgrad; Region (länderspezifisch). Für den Ehepartner bzw. Partner wurde erfragt: Erwerbstätigkeit; Wochenarbeitszeit; Beschäftigungsverhältnis; Vorgesetztenfunktion; Beruf (ISCO-88); Haupterwerbsstatus. Zusätzlich verkodet wurde: Interviewdatum; Case substitution flag; Erhebungsmethode; Gewichtungsfaktor. Evaluation of health care system in the country. Personal health. Health insurance. Themes: satisfaction with life (happiness); confidence in the educational system and the health system of the country; changes of health care system is needed; justification of better medical supply and better education for people with higher incomes; assessment of the health care system of the country (scale: estimation of improvement of the health care system, usage of health care services more than necessary, government should provide only basic health care services, inefficient health care system); willingness to pay higher taxes to improve the level of health care for all people in the country; attitude towards public funding of: preventive medical checkups, treatment of HIV/AIDS, programs to prevent obesity and conduct organ transplants; attitude towards the access to publicly funded health care for people without citizenship of the country and even if they behave in ways that damage their health; estimated part of people without access to the health care system; causes of severe health problems (behavior that damages health, environment, genes, poverty); evaluation of patients for smoking habits, age and the presence of young children for a needed heart operation; attitude towards alternative (traditional or folk) medicine (provides better solutions for health problems than conventional medicine, promises more than it is able to deliver); assessment of doctors in general in the country (scale: doctors can be trusted, discuss all treatment options with their patients, poor medical skills, more care about their earnings than about their patients, openness in dealing with mistakes during treatment); frequency of difficulties with work or household activities because of health problems, bodily aches or pains, unhappiness and depression, loss of self-confidence and insuperable problems in the past four weeks; frequency of doctor visits and of visiting an alternative (traditional/folk) health care practitioner during the past twelve months; stay in hospital or a clinic as an in-patient overnight during the last year; reasons why the respondent did not receive needed medical treatment (could not pay for it, could not take the time off work or because of other commitments, needed treatment was not available at the place of residence, too long waiting list); likelihood of getting the best treatment available in the country in the case of seriously illness and of treatment from the doctor of own choice; satisfaction with the health care system in the country; satisfaction with treatment at the last visit to a doctor, when attending alternative health care practitioner and with the last hospital stay; number of smoked cigarettes per day; frequency of drinking four or more alcoholic drinks on the same day, strenuous physical activity and of eating fresh fruit or vegetables; assessment of personal health; respondent has a long-standing illness, a chronic condition or a disability; height and weight of respondent; kind of personal health insurance; only respondents with health insurance: assessment of personal health insurance coverage. Optional items: personal health insurance covers the prescribed drugs, dental health care and in-patient health care in hospital; need of a referral from the family doctor before visiting a medical specialist; limitation of social activities with family or friends because of health problems. Demography: Sex; age; year of birth; years in school; education (country specific); highest completed degree; work status; hours worked weekly; employment relationship; number of employees; supervision of employees; number of supervised employees; type of organization: for-profit vs. non profit and public vs. private; occupation (ISCO-88); main employment status; living in steady partnership; union membership; religious affiliation or denomination (country specific); groups of religious denominations; attendance of religious services; top-bottom self-placement; vote in last general election; country specific party voted in last general election; party voted (left-right); ethnicity (country specific); number of children; number of toddlers; size of household; earnings of respondent (country specific); family income (country specific); marital status; place of living: urban – rural; region (country specific). Information about spouse and about partner on: work status; hours worked weekly; employment relationship: supervises other employees, occupation (ISCO-88); main employment status. Additionally encoded: date of interview; case substitution flag; mode of data collection; weight.

BackgroundIn the absence of an active schistosomiasis control programme, the affected community is vulnerable to complications such as female genital schistosomiasis. Research has shown that female genital schistosomiasis is a challenge faced by many African women including those from South Africa. Since 2008, the South African National Department of Health has been trying to resuscitate the schistosomiasis control programme; the programme has not been fully established or implemented. However, there are some surveillance best practices that the country can institutionalise to improve control.Materials and methodsA descriptive analysis of urogenital schistosomiasis data from the National Health Laboratory Services, Notifiable Medical Conditions Surveillance System, and District Health Information System was conducted in 2023. A document review was also carried out in 2023 to determine surveillance best practices to guide the establishment of sentinel sites for improving schistosomiasis and female genital schistosomiasis control.ResultsThe Health Laboratory Services, Notifiable Medical Conditions Surveillance System, and District Health Information System are the existing surveillance and reporting systems. According to the Notifiable Medical Conditions Surveillance System (the overall and central notification system for the notifiable medical conditions), a total of 56529 urogenital schistosomiasis cases were reported nationwide between 2017 and 2021 (ranging from annual cases of 4140–15032). Most cases (>90%) were reported from public health facilities. The country’s Regulations on the surveillance and control of notifiable medical conditions stipulate that schistosomiasis is one of the priority conditions that should be notified (within 7 days of clinical or laboratory diagnosis) by all public and private health care providers, as well as public and private health laboratories. The Regulations did not specify female genital schistosomiasis as one of the notifiable medical conditions. As a result, there was no reported data on female genital schistosomiasis and true burden was not known.ConclusionThe data collected through the National Health Laboratory Services, Notifiable Medical Conditions Surveillance System, and District Health Information System demonstrate that there are formalised schistosomiasis reporting systems, but no female genital schistosomiasis reporting. The existence and use of these surveillance systems demonstrate the country’s potential to integrate the systems to enhance the prevention, surveillance, reporting, and management of schistosomiasis and introduction of surveillance for female genital schistosomiasis surveillance. Prioritisation of urogenital schistosomiasis and female genital schistosomiasis surveillance is paramount and will generate valuable information that will guide the review and implementation of the current and old policies that were developed by the National Department of Health and stakeholders.

"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!"

Abstract: Nearly 20 years after the implementation of Brazil’s psychiatric reform, the country has a hybrid model of mental health care, in which asylum services are part of the network that should replace them. This study aimed to critically analyze the supply of mental health services in Brazil in order to verify whether there is actually a replacement of asylum-based services by community-based services. A retrospective longitudinal study was performed on the supply of mental health services in Brazil from 2008 to 2017, building a single database on 5,570 municipalities (counties). As an ancillary tool for critical analysis of the psychiatric reform, we developed the Health Coverage Index for the Psychosocial Care Network (iRAPS, in Portuguese), considering the services legal parametrization. The results showed an increase in the supply of community-based services and a reduction in asylum services. However, the iRAPS tool found that 77% of the Brazilian population lives in areas with low or nonexistent coverage of community services. Small towns, with 16% of the Brazilian population, were those with the greatest increase in iRAPS. Only 439 cities, 7.9% of the total, showed total coverage by the Psychosocial Care Network (RAPS, in Portuguese), expressing only 6.69% of the national population. The metropolises, with 46% of the Brazilian population, did not show an increase in community services. The analysis of the supply of mental health services based on the iRAPS tool showed that the increase in community services did not occur homogeneously across the country.

Abstract Background There are substantial opportunities to improve the quality of cardiovascular care in developing countries through the implementation of a quality program. Objective To evaluate the effect of a Best Practice in Cardiology (BPC) program on performance measures and patient outcomes related to heart failure, atrial fibrillation and acute coronary syndromes in a subset of Brazilian public hospitals. Methods The Boas Práticas em Cardiologia (BPC) program was adapted from the American Heart Association’s (AHA) Get With The Guidelines (GWTG) Program for use in Brazil. The program is being started simultaneously in three care domains (acute coronary syndrome, atrial fibrillation and heart failure), which is an approach that has never been tested within the GWTG. There are six axes of interventions borrowed from knowledge translation literature that will address local barriers identified through structured interviews and regular audit and feedback meetings. The intervention is planned to include at least 10 hospitals and 1,500 patients per heart condition. The primary endpoint includes the rates of overall adherence to care measures recommended by the guidelines. Secondary endpoints include the effect of the program on length of stay, overall and specific mortality, readmission rates, quality of life, patients’ health perception and patients’ adherence to prescribed interventions. Results It is expected that participating hospitals will improve and sustain their overall adherence rates to evidence-based recommendations and patient outcomes. This is the first such cardiovascular quality improvement (QI) program in South America and will provide important information on how successful programs from developed countries like the United States can be adapted to meet the needs of countries with developing economies like Brazil. Also, a successful program will give valuable information for the development of QI programs in other developing countries. Conclusions This real-world study provides information for assessing and increasing adherence to cardiology guidelines in Brazil, as well as improvements in care processes. (Arq Bras Cardiol. 2019; xx(x):xxx-xxx)

Description: The data set consists of 190 cases and 285 variables. Abstract: The project aimed to assess healthcare professionals’ behaviour and related determinants; including knowledge, attitudes, social norms, and self-efficacy towards providing adequate and sufficient maternal and child healthcare services. A cross-sectional quantitative self-administered questionnaire was completed by the healthcare professional in the maternal and child healthcare services in the selected districts of the 9 provinces in South Africa. Information collected included basic demographics, education and continuous professional development (CPD) training related to maternal and child healthcare information; provision, knowledge, attitudes, norms, self-efficacy and intention to provide family planning services; provision of maternal care services, care during labour, neonatal care; and norms, self-efficacy and intention to provide maternal and child healthcare services. The primary research question for this project therefore was what the effects of healthcare professional's behaviour and related determinants in providing adequate and sufficient healthcare services to mothers and their children in South Africa are. The secondary question was what the factors associated with professional healthcare workers' best practices in maternal and child healthcare settings are. Face-to-face interview All health care workers who work in maternal and child care services at the sampled health care facilities were eligible to participate. Participation was voluntary. The sampling frame consisted of all public health facilities that provide maternity and infant services in South Africa. These facilities were stratified into 2 categories: Public Health Clinics and Community Health Centers (CHCs). A representative sample from each stratum was selected using the probability proportional to size approach. The sample consisted of 80 clinics and 26 CHCs, of which 90 of the clinics were based in rural settings while the other 90 were based in urban settings. Four community day centres, and 2 secondary level hospitals were also included in the study. The size of the facility was based on the estimated number of eligible women attending the facility. All nine provinces of South Africa were included in the selection process in order to reflect the diverse demographic and socio-economic profile of the country. Nurses and midwives healthcare workers were recruited at the facility level with the assistance of facility managers. Depending on the size of the facility either all the nurses and midwives or a sample of them were studied. The inclusion criteria for nurses and midwives were: all the nurses whose work includes delivering babies; and midwives who only deliver babies. Pregnant women were recruited from the clinic and community level. At the clinic the assistance of registered nurses that provide antenatal services was sought in recruiting the pregnant women. These women were invited to participate in the focus group discussions.

This statistic shows a ranking of the estimated number of hospital beds in 2024 in Europe, differentiated by country.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than *** countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

Age distribution of top 10 primary cancer sites from CARE PH registry.

Context

This are the official datasets used on the Medicare.gov Hospital Compare Website provided by the Centers for Medicare & Medicaid Services. These data allow you to compare the quality of care at over 4,000 Medicare-certified hospitals across the country.

Content

Dataset fields:

• Provider ID
• Hospital Name
• Address
• City
• State
• ZIP Code
• County Name
• Phone Number
• Hospital Type
• Hospital Ownership
• Emergency Services
• Meets criteria for meaningful use of EHRs
• Hospital overall rating
• Hospital overall rating footnote
• Mortality national comparison
• Mortality national comparison footnote
• Safety of care national comparison
• Safety of care national comparison footnote
• Readmission national comparison
• Readmission national comparison footnote
• Patient experience national comparison
• Patient experience national comparison footnote
• Effectiveness of care national comparison
• Effectiveness of care national comparison footnote
• Timeliness of care national comparison
• Timeliness of care national comparison footnote
• Efficient use of medical imaging national comparison
• Efficient use of medical imaging national comparison

Acknowledgements

Dataset was downloaded from [https://data.medicare.gov/data/hospital-compare]

Inspiration

If you just broke your leg, you might need to use this dataset to find the best Hospital to get that fixed!

Global Health Spending

Country Global Health Spending Over Time

By Eva Murray [source]

About this dataset

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

To get started with this data, begin by exploring the location and time columns as these will provide a breakdown of which countries are represented in the dataset as well as when each observation was collected. To drill down further into the analysis, use indicators, subjects and measures fields for comparison between healthcare spending for different topics like drug access or acute care across countries over time. The values field contains actual values related to healthcare spending while flag codes tell you if there are any discrepancies in data quality so it is important look into those too if necessary.

This dataset is useful for research relatedto how global health expenditures have varied across different countries over time and difference sources of funding among a few other applications. Understanding what's included in this dataset will help you determine how best to use it when doing comparative country-level analyses or international studies on healthcare funding sources over time

Research Ideas

• Identify countries with high public health spending as a percentage of GDP and determine if their population has better health outcomes than those with lower spending.
• Compare public health investments across various countries during the same period to ascertain areas that need more attention, such as medical research, vaccinations, medication and healthcare staffing.
• Determine the trends in health expenditures over time for key indicators such as life expectancy to gain insights into how well a country is managing its healthcare sector

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: DP_LIVE_18102020154144776.csv | Column name | Description | |:---------------|:-----------------------------------------| | LOCATION | Country or region of the data. (String) | | INDICATOR | Health spending indicator. (String) | | SUBJECT | Health spending subject. (String) | | MEASURE | Measurement of health spending. (String) | | FREQUENCY | Frequency of data collection. (String) | | TIME | Year of data collection. (Integer) | | Value | Value of health spending. (Float) | | Flag Codes | Codes related to data quality. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Eva Murray.

The aim was to investigate the relationship between guidelines and the medical practitioners’ perception of optimal care for patients attending with an apparently uncomplicated acute sore throat in five countries (Australia, Germany, Sweden, UK and USA).

Raw data are uploaded as an SPSS file as well as .csv. Descriptive labels and descriptions of values are found within the data file and in the PDF documentation. A scanned survey sheet is also provided along with notes on how it was coded at data entry.

Nigeria aims to enhance its healthcare quality index score of 84th out of 110 countries and its Sustainable Development Goals Index ranking of 146th out of 166. Due to increased population, disease burden, and patient awareness, healthcare demand is rising, putting pressure on funding and quality assurance. The Nigerian healthcare financing and its impacts are complex; this study gives insights into the trends. This questionnaire-based cross-sectional survey (conducted from June to August 2023) and 2010–2023 health budget analysis examined healthcare finance patterns and user attitudes (utilisation, preference and quality perceptions) in Nigeria. Data from government health budgets and a stratified random sample of 2,212 from nine states, obtained from the socioculturally diverse 237 million population, were analysed with a focus on trends, proportions, frequency distributions, and tests of association. Results show that the average rating of healthcare experiences did not vary significantly over the last decade. Healthcare system quality was rated mainly poor or very poor; structure (74.09%), services (61.66%), and cost (60.89%). While 87.36% used government healthcare facilities, 85.00% paid out-of-pocket, and 72.60% of them were dissatisfied with the value for money. Despite a preference for government facilities (71.43%), respondents cited high costs (62.75%), poor funding (85.65%), inadequate staffing (90.73%), and lack of essential medicines (88.47%) as major challenges. The budget analysis reveals an average government healthcare fund allocation of $7.12 compared with an estimated expenditure of $82.75 per person annually. Nigeria allocates only an average of 0.37% of GDP and 4.61% of the national budget to healthcare, comprising a maximum of 13.56% of total health expenditure. This study emphasises the urgent need for policy reforms and implementations to improve Nigeria’s healthcare financing and service quality. Targeted interventions are essential to address systemic challenges and meet population needs while aligning with international health services and best standards.

Das International Social Survey Programme (ISSP) ist ein länderübergreifendes, fortlaufendes Umfrageprogramm, das jährlich Erhebungen zu Themen durchführt, die für die Sozialwissenschaften wichtig sind. Das Programm begann 1984 mit vier Gründungsmitgliedern - Australien, Deutschland, Großbritannien und den Vereinigten Staaten - und ist inzwischen auf fast 50 Mitgliedsländer aus aller Welt angewachsen. Da die Umfragen auf Replikationen ausgelegt sind, können die Daten sowohl für länder- als auch für zeitübergreifende Vergleiche genutzt werden. Jedes ISSP-Modul konzentriert sich auf ein bestimmtes Thema, das in regelmäßigen Zeitabständen wiederholt wird. Details zur Durchführung der nationalen ISSP-Umfragen entnehmen Sie bitte der Dokumentation. Die vorliegende Studie konzentriert sich auf Fragen zu individueller Gesundheit und dem Gesundheitssystem. ISSP Health and Health Care I-II kumuliert die Daten der integrierten Datenfiles von- ISSP 2011 (ZA5800 Datendatei Version 3.0.0, https://doi.org/10.4232/1.12252) und- ISSP 2021 (ZA8000 Datendatei Version 2.0.0, https://doi.org/10.4232/5.ZA8000.2.0.0).Er umfasst Daten aus allen ISSP-Mitgliedsländern, die an mindestens zwei Modulen zum Thema Gesundheit und Gesundheitsversorgung teilnehmen. Der Datensatz enthält:- Kumulierte themenbezogene (substanzielle) Variablen, die in mindestens zwei Modulen des Gesundheitswesens und der Gesundheitsversorgung vorkommen und- Hintergrundvariablen, hauptsächlich zur Demografie, die in mindestens zwei Modulen des Bereichs Gesundheit und Gesundheitsversorgung vorkommen. Lebenszufriedenheit (Glück); Vertrauen in das nationale Gesundheitssystem; Rechtfertigung einer besseren Gesundheitsversorgung für Menschen mit höherem Einkommen; Zustimmung zu verschiedenen Aussagen über das Gesundheitssystem (Die Menschen nehmen Gesundheitsdienste mehr als nötig in Anspruch, die Regierung sollte nur begrenzte Gesundheitsdienste zur Verfügung stellen, im Allgemeinen ist das Gesundheitssystem im Land ineffizient); Bereitschaft, höhere Steuern zu zahlen, um das Niveau der Gesundheitsversorgung für alle Menschen im Land zu verbessern; Einstellung zum Zugang zur öffentlich finanzierten Gesundheitsversorgung für Menschen, die nicht die Staatsbürgerschaft des Landes besitzen, und auch dann, wenn sie sich gesundheitsschädigend verhalten; Meinung zu den Ursachen, warum Menschen unter schweren Gesundheitsproblemen leiden (gesundheitsschädigendes Verhalten, wegen der Umwelt, der sie bei der Arbeit oder am Wohnort ausgesetzt sind, wegen ihrer Gene, Armut); alternative/traditionelle oder volkstümliche Medizin bietet bessere Lösungen für Gesundheitsprobleme als die Schulmedizin/westliche traditionelle Medizin; allgemeine Beurteilung der Ärzte im Land (Ärzten kann man vertrauen, die medizinischen Fähigkeiten von Ärzten sind nicht so gut, wie sie sein sollten, Ärzte kümmern sich mehr um ihren Verdienst als um ihre Patienten); Häufigkeit von Schwierigkeiten bei der Arbeit oder im Haushalt aufgrund von Gesundheitsproblemen, körperlichen Beschwerden oder Schmerzen, Unzufriedenheit und Depressionen, Verlust des Selbstvertrauens und unüberwindbaren Problemen in den letzten vier Wochen; Häufigkeit von Arztbesuchen und Besuchen bei alternativen/traditionellen/volkstümlichen Heilpraktikern in den letzten 12 Monaten; Gründe, warum der Befragte die erforderliche medizinische Behandlung nicht in Anspruch genommen hat (konnte sie nicht bezahlen, konnte sich nicht von der Arbeit freinehmen oder hatte andere Verpflichtungen, die Warteliste war zu lang); Wahrscheinlichkeit, im Falle einer schweren Erkrankung die beste im Land verfügbare Behandlung zu erhalten; Zufriedenheit mit dem Gesundheitssystem im Land; Zufriedenheit mit der Behandlung beim letzten Arztbesuch und beim Besuch eines Heilpraktikers; Raucherstatus und Anzahl der gerauchten Zigaretten pro Tag; Häufigkeit des Konsums von vier oder mehr alkoholischen Getränken am selben Tag, von anstrengender körperlicher Betätigung von mindestens 20 Minuten und des Verzehrs von frischem Obst oder Gemüse; Einschätzung des persönlichen Gesundheitszustands; befragte Person leidet seit langem an einer Krankheit, einem chronischen Leiden oder einer Behinderung; Größe (in cm) und Gewicht (in kg); Art der persönlichen Krankenversicherung. Demographie: Geschlecht; Alter; Geburtsjahr; Status der rechtlichen Partnerschaft; fester Lebenspartner; Bildung: Jahre der Schulbildung; höchster Bildungsabschluss; derzeitiger Beschäftigungsstatus (Befragter und Partner); Beschäftigungsverhältnis (Befragter und Partner); wöchentliche Arbeitsstunden (Befragter und Partner); Beruf (ISCO 2008) (Befragter und Partner); Vorgesetztenfunktion (Befragter und Partner); Gewerkschaftsmitgliedschaft; Haushaltsgröße; Anzahl der Kinder über dem Schuleintrittsalter im Haushalt; Anzahl der Kinder unter dem Schulalter im Haushalt; Parteipräferenz (links-rechts); Teilnahme an der letzten Wahl; Besuch von Gottesdiensten; religiöse Hauptgruppen (abgeleitet); Selbsteinordnung auf einer Oben-Unten-Skala; subjektive soziale Schicht; Wohnort städtisch - ländlich; Haushaltseinkommensgruppen (abgeleitet). Zusätzlich verkodet: ID-Nummer des Befragten; eindeutige Kumulierungs-ID-Nummer des Befragten; ISSP-Moduljahr; Land; Länderstichprobe; Länderstichprobenjahr; Gewichtungsfaktor; administrative Art der Datenerhebung. The International Social Survey Programme (ISSP) is a continuous programme of cross-national collaboration running annual surveys on topics important for the social sciences. The programme started in 1984 with four founding members - Australia, Germany, Great Britain, and the United States – and has now grown to almost 50 member countries from all over the world. As the surveys are designed for replication, they can be used for both, cross-national and cross-time comparisons. Each ISSP module focuses on a specific topic, which is repeated in regular time intervals. Please, consult the documentation for details on how the national ISSP surveys are fielded. The present study focuses on questions about individual health and the health care system. ISSP Health and Health Care I-II cumulates the data of the integrated data files of • ISSP 2011 (ZA5800 Data file Version 3.0.0, https://doi.org/10.4232/1.12252) and • ISSP 2021 (ZA8000 Data file Version 2.0.0, https://doi.org/10.4232/5.ZA8000.2.0.0).It comprises data from all ISSP member countries participating in at least two Health and Health Care modules. The data set contains:• Cumulated topic-related (substantial) variables, which appear in at least two Health and Health Care and• background variables, mostly covering demographics, which appear in at least two Health and Health Care modules. Satisfaction with life (happiness); confidence in the national health care system; justification for better healthcare for people with higher incomes; agreement with various statements on the healthcare system (People use health care services more than necessary, the government should provide only limited health care services, in general, the health care system in the country is inefficient); willingness to pay higher taxes to improve the level of health care for all people in the country; attitude towards the access to publicly funded health care for people without citizenship of the country and even if they behave in ways that damage their health; opinion on causes why people suffer from severe health problems (because they behaved in ways that damaged their health, because of the environment they are exposed to at work or where they live, because of their genes, because they are poor); alternative/ traditional or folk medicine provides better solutions for health problems than mainstream/ Western traditional medicine; assessment of doctors in general in the country (doctors can be trusted, the medical skills of doctors are not as good as they should be, doctors care more about their earnings than about their patients); frequency of difficulties with work or household activities because of health problems, bodily aches or pains, unhappiness and depression, loss of self-confidence and insuperable problems in the past four weeks; frequency of visits to/ by a doctor and an alternative/ traditional/ folk health care practitioner during the past 12 months; reasons why the respondent did not receive needed medical treatment (could not pay for it, could not take the time off work or because of other commitments, the waiting list was too long); likelihood of getting the best treatment available in the country in the case of seriously illness; satisfaction with the health care system in the country; satisfaction with treatment at the last visit to a doctor and to an alternative health care practitioner; smoker status and number of smoked cigarettes per day; frequency of drinking four or more alcoholic drinks on the same day, of strenuous physical activity for at least 20 minutes, and of eating fresh fruit or vegetables; assessment of personal health status; respondent has a long-standing illness, a chronic condition, or a disability; respondent’s height (in cm) and weight (in kg); kind of personal health insurance. Demography: sex; age; years of birth; legal partnership status; steady life partner; education: years of schooling; highest education level; currently, formerly, or never in paid work (respondent and partner); employment relationship (respondent and partner); current employment status (respondent and partner); hours worked weekly (respondent and partner); occupation (ISCO 2008) (respondent and partner); supervising function at work (respondent and partner); number of other employees supervised; type of organization: for-profit vs. non-profit and public vs. private; trade union membership; household size; number of children above school entry age in household; number of children below school age in household; party affiliation (left-right);