Records documenting public health campaigns, epidemics, hospitals, and the development of American medicine.

This chart shows the 2-Year Impact of International Journal of Medical Science and Public Health over time and its percentile among journals.

Read reports on priority public health topics including opioid, stimulant, & other substance use, maternal & child health, and racial & health inequities

This dataset is sourced from the Illinois Department of Public Health and curated by the Cook County Department of Public Health. It covers a select set of causes of death across the County (excluding Chicago). To maintain confidentiality of individual medical records, counts below 5 and rates below 20 are masked with an asterisk (*). Please review metadata and values carefully and exercise caution when aggregating. Some columns only contain data across certain age groups. Some rows overlap across variables and geographic levels and aggregating without filtering may overestimate the counts. For example, aggregating counts across Place without excluding pre-aggregated counts for "Aggregated Region: Suburban Cook County" and "Aggregated Region: CCDPH Jurisdiction" will overstate the counts. Data prior to 2015 can be found here: Suburban Cook County Deaths (2000-2014).

The Centers for Medicare & Medicaid Services (CMS) EHR Incentive Program provides incentive payments for eligible hospitals to adopt and meaningfully use certified health IT. Among the requirements to receive an incentive payment, participating hospitals must report on public health measures. These measures include the electronic reporting of data regarding: immunizations, emergency department visits (syndromic surveillance), reportable infectious disease laboratory results, and electronic patient data to specialized registries, like cancert. As of 2015, stage 2 of the EHR Incentive Program requires hospitals to report on three public health measures, when applicable, and modified stage 2 of the program requires hospitals to report on two of the three measures. This dataset includes the percentage of hospitals who reported on these measures in program years, 2013, 2014 and 2015.

Notes. d  =  Hedges g  =  (M1 – M2)/SDpooled, small: d≤.20, medium: d≤.50, large: d≤.80; ns: p>.050, *: p≤.050, **: p≤.010, ***: p≤.001.

This dataset comprises public health records, surveillance systems, and environmental monitoring data collected from multiple regions over several years. It contains 43,689 entries that provide a comprehensive view of public health dynamics, essential for understanding disease dissemination and guiding effective control strategies. The data has been meticulously gathered from regional health departments, hospitals, laboratories, and public health organizations, ensuring a high level of quality, consistency, and completeness. Each record has been anonymized and aggregated to protect sensitive information.

This dataset serves as a vital resource for researchers, policymakers, and health professionals aiming to analyze and predict public health trends, assess the impact of environmental factors, and improve epidemic response strategies.

Features The dataset includes the following features:

Age: Age of the individual in years. Gender: Gender of the individual (Male, Female, Other). Location: Geographic location (Urban, Rural, Suburban). Ethnicity: Ethnicity of the individual. Socioeconomic Status (SES): Socioeconomic status categorized as Low, Medium, or High. Chronic Conditions: Presence of chronic health conditions. Vaccination Status: Whether the individual is vaccinated (Yes, No). Medical History: Previous medical history (None, Past Illness, Chronic). Immunity Level: Estimated level of immunity (Low, Medium, High). Reported Symptoms: Type and severity of symptoms reported. Transmission Rate: Rate of disease transmission within the population. Daily New Cases: Number of new cases reported daily. Healthcare Personnel Availability: Availability of healthcare workers in the region. Hospital Capacity: Number of beds and resources available in healthcare facilities. Environmental Factors: Data related to air quality, temperature, and other environmental variables influencing health outcomes. Hospitalization Requirement: Predicted level of hospitalization needed based on reported symptoms and medical history. This dataset is ideal for various analytical tasks, including predictive modeling, classification, and feature exploration, making it a valuable asset for advancing public health research.

This dataset is part of a series of seven datasets about Cook County births sourced from the Illinois Department of Public Health and curated by the Cook County Department of Public Health. This dataset includes counts and rates across method of delivery for the County (excluding Chicago).

The full data series is as follows:

1) Birth and Fertility 2) Birth Outcomes 3) Characteristics of Delivery 4) Infant Mortality 5) Initiated Breastfeeding 6) Pregnancy Health and Risk Factors 7) Sociodemographic Characteristics of Mother

To maintain confidentiality of individual medical records, counts below 5 and rates below 20 are masked with an asterisk (*).

Please review metadata and values carefully and exercise caution when aggregating. Some columns only contain data across certain age groups. Some rows overlap across variables and geographic levels and aggregating without filtering may overestimate the counts. For example, aggregating counts across Place without excluding pre-aggregated counts for "Aggregated Region: Suburban Cook County" and "Aggregated Region: CCDPH Jurisdiction" will overstate the counts.

Data prior to 2015 can be found here: Suburban Cook County Births (2000-2014)

MedlinePlus is the National Institutes of Health's Web site for patients and their families and friends. Produced by the National Library of Medicine, the world’s largest medical library, it brings you information about diseases, conditions, and wellness issues in language you can understand. MedlinePlus offers reliable, up-to-date health information, anytime, anywhere, for free.

Public alerts for the Chicago Department of Public Health's (CDPH) Health Alert Network (HAN).

The HAN provides CDPH with the capacity for quick, efficient, reliable, and secure web-based communication with CDPH staff, providers of medical care, laboratories, first responders and other local public health agencies and partners. The HAN facilitates CDPH’s day-to-day activities, including outbreak detection, investigation, and emergency response.

This dataset is published as a convenience to complement the HAN site, itself. While alerts generally are published promptly, for uses involving risk to health and/or life, please contact the HAN team directly to discuss other methods of receiving alerts.

The contents of this dataset, the HAN site, and CDPH's Web site are not intended to be substitutes for professional medical advice, diagnosis, or treatment. Always seek the advice of your physician or other qualified health provider with any questions you may have regarding a medical condition. Professional medical advice should not be ignored because of something you have read on this data portal or any CDPH site.

Output, citations, h-index, reach, spread and citation timing for Public Health, Capital Medical University and Public Health, University of Cagliari, each measure with its unit.

Context:

This synthetic healthcare dataset has been created to serve as a valuable resource for data science, machine learning, and data analysis enthusiasts. It is designed to mimic real-world healthcare data, enabling users to practice, develop, and showcase their data manipulation and analysis skills in the context of the healthcare industry.

Inspiration:

The inspiration behind this dataset is rooted in the need for practical and diverse healthcare data for educational and research purposes. Healthcare data is often sensitive and subject to privacy regulations, making it challenging to access for learning and experimentation. To address this gap, I have leveraged Python's Faker library to generate a dataset that mirrors the structure and attributes commonly found in healthcare records. By providing this synthetic data, I hope to foster innovation, learning, and knowledge sharing in the healthcare analytics domain.

Dataset Information:

Each column provides specific information about the patient, their admission, and the healthcare services provided, making this dataset suitable for various data analysis and modeling tasks in the healthcare domain. Here's a brief explanation of each column in the dataset - - Name: This column represents the name of the patient associated with the healthcare record. - Age: The age of the patient at the time of admission, expressed in years. - Gender: Indicates the gender of the patient, either "Male" or "Female." - Blood Type: The patient's blood type, which can be one of the common blood types (e.g., "A+", "O-", etc.). - Medical Condition: This column specifies the primary medical condition or diagnosis associated with the patient, such as "Diabetes," "Hypertension," "Asthma," and more. - Date of Admission: The date on which the patient was admitted to the healthcare facility. - Doctor: The name of the doctor responsible for the patient's care during their admission. - Hospital: Identifies the healthcare facility or hospital where the patient was admitted. - Insurance Provider: This column indicates the patient's insurance provider, which can be one of several options, including "Aetna," "Blue Cross," "Cigna," "UnitedHealthcare," and "Medicare." - Billing Amount: The amount of money billed for the patient's healthcare services during their admission. This is expressed as a floating-point number. - Room Number: The room number where the patient was accommodated during their admission. - Admission Type: Specifies the type of admission, which can be "Emergency," "Elective," or "Urgent," reflecting the circumstances of the admission. - Discharge Date: The date on which the patient was discharged from the healthcare facility, based on the admission date and a random number of days within a realistic range. - Medication: Identifies a medication prescribed or administered to the patient during their admission. Examples include "Aspirin," "Ibuprofen," "Penicillin," "Paracetamol," and "Lipitor." - Test Results: Describes the results of a medical test conducted during the patient's admission. Possible values include "Normal," "Abnormal," or "Inconclusive," indicating the outcome of the test.

Usage Scenarios:

This dataset can be utilized for a wide range of purposes, including: - Developing and testing healthcare predictive models. - Practicing data cleaning, transformation, and analysis techniques. - Creating data visualizations to gain insights into healthcare trends. - Learning and teaching data science and machine learning concepts in a healthcare context. - You can treat it as a Multi-Class Classification Problem and solve it for Test Results which contains 3 categories(Normal, Abnormal, and Inconclusive).

Acknowledgments:

• I acknowledge the importance of healthcare data privacy and security and emphasize that this dataset is entirely synthetic. It does not contain any real patient information or violate any privacy regulations.
• I hope that this dataset contributes to the advancement of data science and healthcare analytics and inspires new ideas. Feel free to explore, analyze, and share your findings with the Kaggle community.

Image Credit:

Image by BC Y from Pixabay

Public health (PH) skills are core to building responsive and appropriate health systems, and PH personnel including medical specialists are embedded in many countries' health systems. In South Africa, the medical specialty in PH, Public Health Medicine (PHM), has existed for over 40 years. Four years of accredited training plus success in a single national exit exam allows specialist registration with the Health Professions Council of South Africa (HPCSA). However, there are few posts designated specifically for PHM specialists in SA's health system. In view of uncertain roles, this research was designed to determine specialists' career paths, their work, job satisfaction, and perspectives on the future of the specialty. We combined three databases to generate the study population and invited all specialists to participate in an online or hard-copy survey. We found that in 2010, PHM was a small specialty of less 200 physicians. Of the 151 contactable, eligible physicians, 55.6% completed the questionnaire. Participants represented an aging group (median age = 49) of specialists and recent graduates were increasingly women. They largely worked in academic institutions (as managers, teachers, and researchers) and in the public sector health system; were motivated by a sense of social justice and their training was formative, exposing them to work settings which they later entered; were largely highly satisfied at work, but many worked in non-specialist positions. Indeed, one fifth had not registered with the HPCSA as specialists. They were concerned about the specialty's poor visibility and identity, but did not see other PH professionals as a threat. They believed that the specialty should refine its competencies, demonstrate its value and advocate for service positions at all levels of the public sector health service. PHM has a contribution to make—reorienting services to protect communities, preventing ill health, analyzing disease burdens locally, identifying innovations in a resource-constrained health service, largely preoccupied with curative care services.

1.7B+ CDC surveillance records on chronic disease, vaccination, and behavioral risk. Unified and queryable on Snowflake and Databricks Marketplace.

Financial overview and grant giving statistics of Academy of Medical and Public Health Services

Skills and competency are vital for the successful performance of any business function or activity in any organisation. The medical and health records management functions and activities in the public hospitals are no exception. If medical and health records are managed without appropriate skills and competency, they become inaccessible/non-locatable, are damaged or stolen, go missing or are misfiled, altered and even falsified. This eventually results in a chaotic healthcare service or even an inability by healthcare providers to render healthcare services. This study sought to develop aframework that may be applied to map-out standard requirements for officials responsible for medical and health records management to ensure that they are sufficiently capacitated with skills and competencies to effectively support public healthcare service in the digital age in Limpopo public hospitals. Data was collected using a questionnaire, as well as observations and interviews. The study discovered that the training and capacity building of records management staff in the public hospitals ofLimpopo, South Africa, is lacking to such an extent that healthcare services are compromised. The study recommends a framework for medical and health records management skills and competency development to support public healthcare service delivery and ensure the provision of the necessary resources.

Note: This dataset is historical only and there are not corresponding datasets for more recent time periods. For that more-recent information, please visit the Chicago Health Atlas at https://chicagohealthatlas.org.

This dataset contains a selection of 27 indicators of public health significance by Chicago community area, with the most updated information available. The indicators are rates, percents, or other measures related to natality, mortality, infectious disease, lead poisoning, and economic status. See the full description at https://data.cityofchicago.org/api/assets/2107948F-357D-4ED7-ACC2-2E9266BBFFA2.

Abstract: Introduction: Integration between educational institutions and health services in the training of medical residents requires changes on both sides, especially in the care setting, making it necessary to remodel the culture of the social actors that make up the SUS. Objective: The objective of this study was to performa situational diagnosis of the teaching-service integration in a Residency Program in Family and Community Medicine (PRMFC) by mapping the reported strengths, weaknesses, opportunities and threats. Method: Intervention research with preceptors and health managers in a municipality in the state of Ceará, Brazil. Data were collected through semi-structured interviews, submitted to the Descending Hierarchical Classification (DHC) with the aid of the IRaMuTeQ software, analyzed according to the axes of the SWOT matrix (strengths, weaknesses, opportunities, threats) and discussed with the support of literature. Results: Each DHC class generated the factors of each axis of the SWOT matrix, outlining as strong points: partnership between the agents of integration and inclusion of the resident in the daily routines of the teams; weaknesses: distance between preceptor and educational institution and absence of strategies for valuing preceptorship; opportunities: perspective of the Program to serve as a health management tool; and threats: problems in the management of health equipment, reflecting on the structure of services, Program management and quality of training. Conclusions: The analysis of the SWOT matrix enabled the identification of strengths and weaknesses in the PRMFC, supporting the development of strategies to strengthen teaching-service integration, with a view to improving health care management, training and execution.

Worry about safety (4); satisfaction with quality of health care in United States (3); most important factor in determining quality of health care patients receive (1); various health related actions (8); information comparing different doctors, hospitals, or health plans (16); likelihood of making various efforts to find information about quality of health care (8); comparing quality of doctors (8); choosing between two surgeons (1); comparing quality of hospitals (9); comparing quality of health plans (12); trust in employers as source of information about health plan quality (1); relying on friends/family as source of information about health plans (1); choosing between plans based on friends/experts (1); convenience of doctor/hospital location as influence on choice (1); coordination between health professionals (1); creating own set of medical records (1); experiences with health care professionals not having medical information (4); medical errors (6); causes of medical errors (9); preventing medical errors (13); how medical errors should be handled (3); reducing medical errors (4); experiences with medical errors (16); health insurance coverage (4); health status (2); use of health care in past 12 months (3); receiving regular medical treatment (1); problems communicating with doctor (1).

BackgroundMultimorbidity is a major challenge for healthcare systems. However, currently, its magnitude and impact in healthcare expenditures is still mostly unknown.ObjectiveTo present an overview of the prevalence and costs of multimorbidity by socioeconomic levels in the whole Basque population.MethodsWe develop a cross-sectional analysis that includes all the inhabitants of the Basque Country (N = 2,262,698). We utilize data from primary health care electronic medical records, hospital admissions, and outpatient care databases, corresponding to a 4 year period. Multimorbidity was defined as the presence of two or more chronic diseases out of a list of 52 of the most important and common chronic conditions given in the literature. We also use socioeconomic and demographic variables such as age, sex, individual healthcare cost, and deprivation level. Predicted adjusted costs were obtained by log-gamma regression models.ResultsMultimorbidity of chronic diseases was found among 23.61% of the total Basque population and among 66.13% of those older than 65 years. Multimorbid patients account for 63.55% of total healthcare expenditures. Prevalence of multimorbidity is higher in the most deprived areas for all age and sex groups. The annual cost of healthcare per patient generated for any chronic disease depends on the number of coexisting comorbidities, and varies from 637 € for the first pathology in average to 1,657 € for the ninth one.ConclusionMultimorbidity is very common for the Basque population and its prevalence rises in age, and unfavourable socioeconomic environment. The costs of care for chronic patients with several conditions cannot be described as the sum of their individual pathologies in average. They usually increase dramatically according to the number of comorbidities. Given the ageing population, multimorbidity and its consequences should be taken into account in healthcare policy, the organization of care and medical research.