Graph and download economic data for Medical Services Expenditures by Disease: Symptoms; Signs; and Ill-Defined Conditions Price Index, Blended Account Basis (SYSIILPIBLEND) from 2000 to 2021 about disease, healthcare, medical, health, expenditures, services, price index, indexes, price, and USA.

Disease and Operation Index

Graph and download economic data for Medical Services Expenditures by Disease Price Index, MEPS Account Basis (MDSBDSPIMEPS) from 2000 to 2021 about disease, healthcare, medical, health, expenditures, services, price index, indexes, price, and USA.

Each card is headed by the name of a disease or injury, followed by a list of patients treated for it, including: patient's name, age and sex, some clinical notes (usually including a short diagnosis and treatment) and a serial number. Patients who entries are in blue ink were discharged; those in red ink died.

This series was originally transferred to archives with minimal documentation which has not been evaluated. All information currently available to PROV has been incorporated into this series registration. This registration will be updated when further research is subsequently undertaken.

Summing up to the index of multiple disease occurrences, where the region with the lowest occurrences has a value of 100 and the region with the highest incidence is 0. The value is an aggregated index of the following dimensions: Incidence of acute myocardial infarction, number of 100000 inhabitants, prevalence of type 1 diabetes and type 2, number/100000 inhabitants, prevalence of lung cancer, women, number/100000 inhabitants, prevalence of lung cancer, men, number/100000 inhabitants, prevalence of prostate cancer, number/100000 inhabitants, incidence of breast cancer, number/100000 inhabitants, incidence of colon cancer, women, number/100000 inhabitants, incidence of colon cancer, men, number/100000 inhabitants, incidence of rectal cancer, women, number/100000 inhabitants, number/100000 inhabitants. value is given 100 and the lowest value may Some measures be reported separately for women and men. They are summed up as an average. All key figures are normalised, with the region with the lowest incidence of disease having 100 and no more than 0. This is then added to a total index.

Disease-income complexity index and the complexity of diseases.

Introduction: Current health care delivery relies on complex, computer-generated risk models constructed from insurance claims and medical record data. However, these models produce inaccurate predictions of risk levels for individual patients, do not explicitly guide care, and undermine health management investments in many patients at lesser risk. Therefore, this study prospectively validates a concise patient-reported risk assessment that addresses these inadequacies of computer-generated risk models. Methods: Five measures with well-documented impacts on the use of health services are summed to create a "What Matters Index." These measures are: 1) insufficient confidence to self-manage health problems, 2) pain, 3) bothersome emotions, 4) polypharmacy, and 5) adverse medication effects. We compare the sensitivity and predictive values of this index with two representative risk models in a population of 8619 Medicaid recipients. Results: The patient-reported "What Matters Index" and...

Polygons of Ebola virus disease index casesFields include:FID - unique shape file identifierShape - type of spatial reference (all Polygon)OUTBREAK - unique outbreak identifier that cross references the human index case ebola virus disease csv

NNDSS - In this Table, provisional cases* of notifiable diseases are displayed for United States, U.S. territories, and Non-U.S. residents. Notes: • These are weekly cases of selected infectious national notifiable diseases, from the National Notifiable Diseases Surveillance System (NNDSS). NNDSS data reported by the 50 states, New York City, the District of Columbia, and the U.S. territories are collated and published weekly as numbered tables available at https://www.cdc.gov/nndss/infectious-disease/index.html. Cases reported by state health departments to CDC for weekly publication are subject to ongoing revision of information and delayed reporting. Therefore, numbers listed in later weeks may reflect changes made to these counts as additional information becomes available. Case counts in the tables are presented as published each week. See also Guide to Interpreting Provisional and Finalized NNDSS Data. • Notices, errata, and other notes are available in the Notice To Data Users page https://www.cdc.gov/nndss/infectious-disease/notice-to-data-users.html. • The list of national notifiable infectious diseases and conditions and their national surveillance case definitions are available at https://ndc.services.cdc.gov/. This list incorporates the Council of State and Territorial Epidemiologists (CSTE) position statements approved by CSTE for national surveillance. Footnotes: *Case counts for reporting years 2024 and 2025 are provisional and subject to change. Cases are assigned to the reporting jurisdiction submitting the case to NNDSS, if the case's country of usual residence is the U.S., a U.S. territory, unknown, or null (i.e. country not reported); otherwise, the case is assigned to the 'Non-U.S. Residents' category. Country of usual residence is currently not reported by all jurisdictions or for all conditions. For further information on interpretation of these data, see https://www.cdc.gov/nndss/docs/Readers-Guide-WONDER-Tables-20210421-508.pdf. †Previous 52 week maximum and cumulative YTD are determined from periods of time when the condition was reportable in the jurisdiction (i.e., may be less than 52 weeks of data or incomplete YTD data). • Please refer to the Stacks publication for weekly updates to the footnote for influenza-associated pediatric mortality. U: Unavailable — The reporting jurisdiction was unable to send the data to CDC or CDC was unable to process the data. -: No reported cases — The reporting jurisdiction did not submit any cases to CDC. N: Not reportable — The disease or condition was not reportable by law, statute, or regulation in the reporting jurisdiction. NN: Not nationally notifiable — This condition was not designated as being nationally notifiable. NP: Nationally notifiable but not published. NC: Not calculated — There is insufficient data available to support the calculation of this statistic. Cum: Cumulative year-to-date counts. Max: Maximum — Maximum case count during the previous 52 weeks.

The records in this series were key components of the recordkeeping system used to manage Royal Children's Hospital (RCH) patient medical records during the period 1921 to 1937. From 1921 to 1937 RCH patient medical records were arranged in numerical order in volumes in two year blocks according to disease code. A disease code was a letter-number combination used to denote a disease. The disease code assigned to a patient (the patient's diagnosis) was the key for locating a patient's record.

The codes were defined in disease classification books. This series includes photocopies of a disease classification book and an alphabetical index to disease codes used by the RCH medical records section to locate patient records (the originals remain in the custody of the RCH). The series also contains a photocopy of the index that recorded the volume number in which patient medical records relating to each disease code were located.

The classification book outlines the coding schema used to code diseases. It is arranged in sections according to disease type. Letters of the alphabet are used to denote each type and a sequential number used to denote specific diseases. For example, L denotes diseases of the respiratory system. L424 denotes Sinusitis. The inscribed title of the book is Records Office Children's Hospital Classification of Diseases 1938 - 1952. The disease classification index is arranged alphabetically by disease name and records the alphanumeric code assigned to a disease. Its inscribed title is Records Office Index to Classification of Disease 1921 - 1952. Together, the book and the index cover the period 1921 to 1937.

The index to the volumes containing the patient medical records (inscribed Index to Volume Medical Records) is arranged in two-year blocks starting at 1921/22 and ending in 1936/37. Within each interval it is arranged in alpha-numeric order by disease code. Knowing the disease code assigned to a patient and their (approximate) year of admission, the index could be used to identify the volume in which the patient's medical record was located.

The original records are still used by RCH today in response to external enquiries.

Note that other key records used to manage RCH patient medical records for the period 1921 to 1937 (and outside this period too) remain in the custody of the RCH. The patient records are also in the custody of the RCH.

Prior to transfer to Public Record Office Victoria, the records in this series were maintained as part of the Royal Children's Hospital (RCH) Archives. They were part of RCH accession number 2009/011.

The series consists of disease and operation index cards for the Royal North Shore Hospital.

The cards largely feature the title of the disease, year and card number, followed by column headings for hospital number, age, sex, result, site, etiologic categories, operation and physician.

From 1963 a numeric code was used to identify the disease name for the card heading. Blue cards in the series provide an index to the number and the corresponding disease name.

The cards are bundled by year of creation and arranged in chronological order.

Surface and bottom temperature, salinity, and oxygen content at three locations in Lake Borgne and three locations in the Mississippi Sound were measured. In addition oyster reproductive, condition, and disease metrics were determined at these sites. Polycyclic aromatic hydrocarbon (PAH) content of the oyster tissue was measured.

Haiti and Brazil are the countries with the highest risk of vector-borne diseases in Latin America and the Caribbean. Based on an index score calculated in 2025, these countries had a respective risk of *** and *** for vector-borne diseases such as the Zika virus and the Dengue fever. Nicaragua and Venezuela tied third in the region, with an estimated risk score of ***.

Reference disease data set of neurological diseases along with their definitions, etiology, treatment, prognosis, ongoing research, clinical trials information and publications. The Disorder Index includes synonyms and research topics. Navigation is by letter of the alphabet.

aThe disease index (0–100) and total plant fresh weight were expressed on the basis of three replicates. Values with different superscripts in the same row differ significantly according to Tukey's HSD test (P

The slope index of inequality (SII) in circulatory disease mortality for persons under 75 years. The SII gives a single score describing the extent of inequality in each Local Authority, and is broadly comparable between areas. See below for further details on the SII. There are inequalities in health. For example, people living in more deprived areas tend to have shorter life expectancy, and higher prevalence and mortality rates of circulatory disease. Circulatory disease accounts for nearly 40% of all deaths among persons in England every year1. Reducing inequalities in premature mortality from all cancers is a national priority, as set out in the Department of Health’s Vital Signs Operating Framework 2008/09-2010/112 and the PSA Delivery Agreement 183. However, existing indicators for premature circulatory disease mortality do not take deprivation into account. This indicator has been produced in order to quantify inequalities in circulatory disease mortality by deprivation. This indicator has been discontinued and so there will be no further updates. Legacy unique identifier: P01370

No description is available. Visit https://dataone.org/datasets/10b39af61f663a18592860d58bc7f30b for complete metadata about this dataset.

Distribution of the household population by Children's body mass index (BMI) according to the Center for Disease Control (CDC) classification system, by sex and age group.

The interactive maps are visual representations of the Social Vulnerability Index (SVI). Data were extracted from the US Census and the American Community Survey.

2014 to 2016, 3-year average. Rates are age-standardized. County rates are spatially smoothed. The data can be viewed by sex and race/ethnicity. Data source: National Vital Statistics System. Additional data, maps, and methodology can be viewed on the Interactive Atlas of Heart Disease and Stroke https://www.cdc.gov/heart-disease-stroke-atlas/about/index.html