This dataset is about countries per year in Syrian Arab Republic. It has 64 rows. It features 4 columns: country, hospital beds, and urban population.

This dataset is about countries per year in Western Europe. It has 9 rows and is filtered where the date is 2021. It features 4 columns: country, hospital beds, and urban population.

This dataset is about countries per year in Netherlands. It has 64 rows. It features 4 columns: country, hospital beds, and urban population.

This dataset is about countries per year in Burundi. It has 64 rows. It features 4 columns: country, hospital beds, and urban population.

Patient satisfaction with most recent hospital care received in past 12 months, by sex, household population aged 15 and over, Canada, provinces and territories.

Contained within the 3rd Edition (1957) of the Atlas of Canada is a plate consisting of four condensed maps that show urban populations of the people living in Canada. The two maps at the top of this plate show the night-time distribution of population, circa 1956 for Metropolitan Toronto and part of Montreal Island. These two maps actually show the distribution of persons in their permanent homes, without adjustments for such persons that may be absent from their homes at night. Persons in short term, transient residence, such as those in hotels and hospitals are not represented. Another map shows the distribution of urban population across Canada, circa 1951. The definition of urban includes all persons residing in cities, towns and villages of 1000 population or more, whether incorporated or unincorporated, as well as the population of all parts of the census metropolitan areas. A smaller scale map of Canada shows urban areas. These are areas in which urban communities of 1000 or more population are 15 or fewer miles apart.

The Veterans Health Administration (VHA) pays for care provided to VA beneficiaries in non-VA hospitals through its contract hospitalization program as mandated by Congress in the late 1980s. The Non-VA Hospital System (NVH) software captures the patient's Demographics, Provider, Hospital Name and Location, Medicare Provider Number, Diagnoses and Procedures for which the patient received care during his/her inpatient stay. The data is received from either the patient or the medical center providing the care (normally on a UB-92 form). The billing office employee enters the information into Veterans Health Information Systems and Technology Architecture and sends information to the Austin Information Technology Center (AITC). The non-VA hospitals are reimbursed at Medicare rates based on the Prospective System (PPS). PPS uses the appropriate Diagnostic Related Groups (DRGs). Each DRG has a different rate-adjusted reimbursement based on the regional and urban/rural designation of the provider non-VA Hospitals. NVH is housed at the AITC and uses software developed by the AITC in conjunction with 3M and the Center for Medicare and Medicaid Services (CMS). It is a batch system written in Common Business Oriented Language, ALC, and Statistical Analysis Software. Processing occurs daily.

The DC Metropolitan Area Drug Study (DCMADS) was
conducted in 1991, and included special analyses of homeless and
transient populations and of women delivering live births in the DC
hospitals. DCMADS was undertaken to assess the full extent of the
drug problem in one metropolitan area. The study was comprised of 16
separate studies that focused on different sub-groups, many of which
are typically not included or are underrepresented in household
surveys. The Homeless and Transient Population
study examines the prevalence of illicit drug, alcohol, and tobacco
use among members of the homeless and transient population aged 12 and
older in the Washington, DC, Metropolitan Statistical Area (DC
MSA). The sample frame included respondents from shelters, soup
kitchens and food banks, major cluster encampments, and literally
homeless people. Data from the questionnaires include history of
homelessness, living arrangements and population movement, tobacco,
drug, and alcohol use, consequences of use, treatment history, illegal
behavior and arrest, emergency room treatment and hospital stays,
physical and mental health, pregnancy, insurance, employment and
finances, and demographics. Drug specific data include age at first
use, route of administration, needle use, withdrawal symptoms,
polysubstance use, and perceived risk.This study has 1 Data Set.

This dataset is about countries per year in Argentina. It has 1 row and is filtered where the date is 2021. It features 4 columns: country, hospital beds, and urban population.

Dataset no longer updated: Due to changes in the collection and availability of data on COVID-19, this dataset is no longer updated. Latest information about COVID-19 is available via the UKHSA data dashboard. The UK government publish daily data, updated weekly, on COVID-19 cases, vaccinations, hospital admissions and deaths. This note provides a summary of the key data for London from this release. Data are published through the UK Coronavirus Dashboard, last updated on 23 March 2023. This update contains: Data on the number of cases identified daily through Pillar 1 and Pillar 2 testing at the national, regional and local authority level Data on the number of people who have been vaccinated against COVID-19 Data on the number of COVID-19 patients in Hospital Data on the number of people who have died within 28 days of a COVID-19 diagnosis Data for London and London boroughs and data disaggregated by age group Data on weekly deaths related to COVID-19, published by the Office for National Statistics and NHS, is also available. Key Points On 23 March 2023 the daily number of people tested positive for COVID-19 in London was reported as 2,775 On 23 March 2023 it was newly reported that 94 people in London died within 28 days of a positive COVID-19 test The total number of COVID-19 cases identified in London to date is 3,146,752 comprising 15.2 percent of the England total of 20,714,868 cases In the most recent week of complete data (12 March 2023 - 18 March 2023) 2,951 new cases were identified in London, a rate of 33 cases per 100,000 population. This compares with 2,883 cases and a rate of 32 for the previous week In England as a whole, 29,426 new cases were identified in the most recent week of data, a rate of 52 cases per 100,000 population. This compares with 26,368 cases and a rate of 47 for the previous week Up to and including 22 March 2023 6,452,895 people in London had received the first dose of a COVID-19 vaccine and 6,068,578 had received two doses Up to and including 22 March 2023 4,435,586 people in London had received either a third vaccine dose or a booster dose On 22 March 2023 there were 1,370 COVID-19 patients in London hospitals. This compares with 1,426 patients on 15 March 2023. On 22 March 2023 there were 70 COVID-19 patients in mechanical ventilation beds in London hospitals. This compares with 72 patients on 15 March 2023. Update: From 1st July updates are weekly From Friday 1 July 2022, this page will be updated weekly rather than daily. This change results from a change to the UK government COVID-19 Dashboard which will move to weekly reporting. Weekly updates will be published every Thursday. Daily data up to the most recent available will continue to be added in each weekly update. Data summary Local authority data Demographics Notes on data sources Source: UK Coronavirus Dashboard. For more information see: Coronavirus (COVID-19) in the UK - About the Data. Cases Data UK Health Security Agency (UKHSA) reports new and cumulative cases identified by Pillar 1 and Pillar 2 testing. Pillar 1 testing relates to tests carried out in UKHSA laboratories or NHS Hospitals for those with clinical need, and health and care workers. Pillar 2 testing relates to tests carried out on the wider population in Lighthouse laboratories, public, private, and academic sector laboratories or using lateral flow devices. The cases data is published by day for Countries within the UK, and Regions, Upper Tier Local Authority (UTLA) and Lower Tier Local Authority (LTLA) within England. The data used here is taken from the regional and UTLA level cases data. Notice: Changes to COVID-19 case reporting As of 31 January 2022, UKHSA moved all COVID-19 case reporting in England to use an episode-based definition which includes possible reinfections. Those testing positive beyond 90 days of a previous infection are now counted as a separate infection episode (a possible reinfection episode). Previously people who tested positive for COVID-19 were only counted once in case numbers published on the daily dashboard, at the date of the first infection. Full details of the changes can be found here Changes to COVID-19 testing in England The availability of free COVID-19 tests in England changed on 1 April 2022. Information on who can access free tests has been published by UKHSA. Changes to patient testing in the NHS in England have also been published by NHS England. Deaths data Data on COVID-19 associated deaths in England are produced by UKHSA from multiple sources linked to confirmed case data. Deaths are only included if the deceased had a positive test for COVID-19 and died within 28 days of the first positive test. Postcode of residence for deaths is collected at the time of testing. This is supplemented, where available, with information from ONS mortality records, Health Protection Team reports and NHS Digital Patient Demographic Service records. Full details of the methodology are available in the technical summary of the PHE data series on deaths in people with COVID-19. Hospital admissions data UKHSA publish the daily total number of patients admitted to hospital, patients in hospital and patients in beds which can deliver mechanical ventilation with COVID-19. In England this includes COVID-19 patients being treated in NHS acute hospitals, mental health and learning disability trusts, and independent service providers commissioned by the NHS. Vaccination data UKHSA publish the number of people who have received a COVID-19 vaccination, by day on which the vaccine was administered. Data are reported daily and can be updated for historical dates as vaccinations given are recorded on the relevant system. Therefore, data for recent dates may be incomplete. Vaccinations that were carried out in England are reported in the National Immunisation Management Service which is the system of record for the vaccination programme in England. Only people aged 12 and over who have an NHS number and are currently alive are included. Age is defined as a person's age at 31 August 2021. The data includes counts of vaccinations by age band, dose, region, and local authority. Additional analysis of the vaccine roll out in London can be found here. ONS population estimates The counts of vaccines given has been converted to percentage of the population vaccinated using the ONS 2020 mid-year population estimates. This is a different population estimate to that used on the UK Coronavirus Dashboard for sub-national data. The UK Coronavirus Dashboard uses people aged 16 and over in the National Immunisation Management Service (NIMS), which is based on GP registrations. In more urban areas like London, NIMS is likely to give an overestimate of the population due to increased population mobility increasing the likelihood duplicate or out of date GP records. Due to the differences in population estimates the percentage of the population vaccinated given here will be higher than the figures included for London on the UK Coronavirus Dashboard. Data and Resources phe_deaths_age_london.csv Source: https://coronavirus.data.gov.uk/ phe_deaths_london_boroughs.csv Source: https://coronavirus.data.gov.uk/ phe_vaccines_age_london_boroughs.csv

Return visit admissions (RVA), which are instances where patients discharged from the emergency department (ED) rapidly return and require hospital admission, have been associated with quality issues and adverse outcomes. We developed and validated a machine learning model to predict 72-hour RVA using electronic health records (EHR) data. Study data were extracted from EHR data in 2019 from three urban EDs. The development and independent validation datasets included 62,154 patients from two EDs and 73,453 patients from one ED, respectively. Multiple machine learning algorithms were evaluated, including deep significance clustering (DICE), regularized logistic regression (LR), Gradient Boosting Decision Tree, and XGBoost. These machine learning models were also compared against an existing clinical risk score. To support clinical actionability, clinician investigators conducted manual chart reviews of the cases identified by the model. Chart reviews categorized predicted cases across index ED discharge diagnosis and RVA root cause classifications. The best-performing model achieved an AUC of 0.87 in the development site (test set) and 0.75 in the independent validation set. The model, which combined DICE and LR, boosted predictive performance while providing well-defined features. The model was relatively robust to sensitivity analyses regarding performance across age, race, and by varying predictor availability but less robust across diagnostic groups. Clinician examination demonstrated discrete model performance characteristics within clinical subtypes of RVA. This machine learning model demonstrated a strong predictive performance for 72- RVA. Despite the limited clinical actionability potentially due to model complexity, the rarity of the outcome, and variable relevance, the clinical examination offered guidance on further variable inclusion for enhanced predictive accuracy and actionability.

This dataset presents the footprint of statistics of potentially preventable hospitalisations (PPH). PPH does not mean that a patient admitted for that condition did not need to be hospitalised at the time of admission. Rather the hospitalisation could have potentially been prevented through the provision of appropriate preventative health interventions and early disease management in primary care and community-based care settings. PPH rates are indicators of the effectiveness of non-hospital care. The data spans the financial years of 2013-2017 and is aggregated to 2017 Department of Health Primary Health Network (PHN) areas, based on the 2016 Australian Statistical Geography Standard (ASGS).

The data is sourced from the Australian Institute of Health and Welfare (AIHW) - National Hospital Morbidity Database (NHMD), which is a compilation of episode-level records from admitted patient morbidity data collection systems in Australian public and private hospitals.

For further information about this dataset visit the data source:Australian Institute of Health and Welfare - Potentially Preventable Hospitalisations Data Tables.

Please note:

• AURIN has spatially enabled the original data using the Department of Health - PHN Areas.

• In tables presenting measures by PPH condition, some hospitalisations may account for multiple PPH conditions. As a result, conditions may not sum to categories, and categories may not sum to Total PPH.

• The counting unit for this publication were episodes of stay, measured by financial year of separation. This may be a complete hospital stay (to discharge, transfer, or death), or a part of the stay if there was a change of care type (for example from acute to rehabilitation). As a record is included for each hospitalisation, not for each patient, patients hospitalised more than once or transferred between hospitals in the financial year will have more than one record.

• Episodes for unqualified newborn care, posthumous organ procurement or hospital boarders were excluded.

• Population counts are based on estimated resident populations at 30 June for each year. Australian estimated resident population data are sourced from Australian demographic statistics (ABS cat. no. 3101.0).

• There have been changes to PHN boundaries over time. The 2016-17 data uses concordances from 2017 based on the 2016 ASGS and 2016 Census population while previous years use the concordance files from 2015.

• National totals include data where the place of usual residence was overseas, no fixed abode, offshore and migratory, and undefined but these data are excluded from PHN and SA3 area estimates.

• All data for an area were suppressed (marked NP) if the number of rounded PPH was less than 5. Values assigned NP in the original data have been set to null.

This dataset presents the footprint of the number of emergency department presentations in public hospitals by patient demographics and location. Mental health-related emergency department (ED) presentations are defined as presentations to public hospital EDs that have a principal diagnosis of mental and behavioural disorders. However, the definition does not fully capture all potential mental health-related presentations to EDs such as intentional self-harm, as intent can be difficult to identify in an ED environment and can also be difficult to code. The data spans the financial years of 2014-2018 and is aggregated to Statistical Area Level 3 (SA3) geographic areas from the 2016 Australian Statistical Geography Standard (ASGS).

State and territory health authorities collect a core set of nationally comparable information on most public hospital ED presentations in their jurisdiction, which is compiled annually into the National Non-Admitted Patient Emergency Department Care Database (NNAPEDCD). The data reported for 2014–15 to 2017–18 is sourced from the NNAPEDCD. Information about mental health-related services provided in EDs prior to 2014–15 was supplied directly to the Australian Institute of Health and Welfare (AIHW) by states and territories.

Mental health services in Australia (MHSA) provides a picture of the national response of the health and welfare service system to the mental health care needs of Australians. MHSA is updated progressively throughout each year as data becomes available. The data accompanies the Mental Health Services - In Brief 2018 Web Report.

For further information about this dataset, visit the data source:Australian Institute of Health and Welfare - Mental health services in Australia Data Tables.

Please note:

• AURIN has spatially enabled the original data.

• Caution is required when conducting time-series analyses. The data source changed in 2014–15 from data provided by state and territory health authorities (2004–05 to 2013–14) to the NNAPEDCD. Additionally, due to the methodology applied for mapping the data over time, years prior to 2017–18 may be an undercount or data may not be displayed where SA3s have changed over time.

• Mental health-related emergency department presentations included in this report are those that had a principal diagnosis that fell within the Mental and behavioural disorders chapter (Chapter 5) of ICD-10-AM (codes F00–F99) or the equivalent ICD-9-CM or SNOMED codes. It does not include codes for self-harm or poisoning.

• From 2014–15 onwards, diagnosis information was not reported using a uniform classification. The mapping of SNOMED codes (used by NSW) to ICD-10AM may lead to an under-estimation of mental health-related presentations.

• Changes in the volume of patients over time for NSW may be attributed, in part, to the increased number of hospitals included in the data for this jurisdiction.

BackgroundThe relationship between passive smoking exposure (PSE) and breast cancer risk is of major interest.ObjectiveTo evaluate the relationship between PSE from partners and breast cancer risk stratified by hormone-receptor (HR) status in Chinese urban women population.DesignHospital-based matched case control study.SettingChinese urban breast cancer patients without current or previous active smoking history in China Medical University 1st Hospital, Liaoning Province, China between Jan 2009 and Nov 2009.PatientsEach breast cancer patient was matched 1∶1 with healthy controls by gender and age (±2 years) from the same hospital.MeasurementsThe authors used unconditional logistic regression analyses to estimate odds ratio for women with PSE from partners and breast cancer risk.Results312 pairs were included in the study. Women who endured PSE had significantly increased risk of breast cancer (adjusted OR: 1.46; 95% CI: 1.05–2.03; P = 0.027), comparing with unexposed women. Women who exposed to >5 cigarettes/day also had significant increased risk (adjusted OR: 1.99; 95% CI: 1.28–3.10; P = 0.002), as were women exposed to passive smoke for 16–25 years (adjusted OR: 1.87 95% CI: 1.22–2.86; P = 0.004), and those exposed to > 4 pack-years (adjusted OR: 1.71 95% CI: 1.17–2.50; P = 0.004). Similar trends were significant for estrogen receptor (ER)/progesterone receptor (PR) double positive subgroup(adjusted OR: 1.71; 2.20; 1.99; 1.92, respectively), but not for ER+/PR−, ER−/PR+, or ER−/PR− subgroups.Limitationslimitations of the hospital-based retrospective study, lack of information on entire lifetime PSE and low statistical power.ConclusionsOur findings provide further evidence that PSE from partners contributes to increased risk of breast cancer, especially for ER/PR double positive breast cancer, in Chinese urban women.

Analysis of ‘COVID-19 State Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/nightranger77/covid19-state-data on 30 September 2021.

--- Dataset description provided by original source is as follows ---

This dataset is a per-state amalgamation of demographic, public health and other relevant predictors for COVID-19.

Deaths, Infections and Tests by State

The COVID Tracking Project: https://covidtracking.com/data/api

Used positive, death and totalTestResults from the API for, respectively, Infected, Deaths and Tested in this dataset. Please read the documentation of the API for more context on those columns

Predictor Data and Sources

Population (2020)

Density is people per meter squared https://worldpopulationreview.com/states/

ICU Beds and Age 60+

https://khn.org/news/as-coronavirus-spreads-widely-millions-of-older-americans-live-in-counties-with-no-icu-beds/

GDP

https://worldpopulationreview.com/states/gdp-by-state/

Income per capita (2018)

https://worldpopulationreview.com/states/per-capita-income-by-state/

Gini

https://en.wikipedia.org/wiki/List_of_U.S._states_by_Gini_coefficient

Unemployment (2020)

Rates from Feb 2020 and are percentage of labor force
https://www.bls.gov/web/laus/laumstrk.htm

Sex (2017)

Ratio is Male / Female
https://www.kff.org/other/state-indicator/distribution-by-gender/

Smoking Percentage (2020)

https://worldpopulationreview.com/states/smoking-rates-by-state/

Influenza and Pneumonia Death Rate (2018)

Death rate per 100,000 people
https://www.cdc.gov/nchs/pressroom/sosmap/flu_pneumonia_mortality/flu_pneumonia.htm

Chronic Lower Respiratory Disease Death Rate (2018)

Death rate per 100,000 people
https://www.cdc.gov/nchs/pressroom/sosmap/lung_disease_mortality/lung_disease.htm

Active Physicians (2019)

https://www.kff.org/other/state-indicator/total-active-physicians/

Hospitals (2018)

https://www.kff.org/other/state-indicator/total-hospitals

Health spending per capita

Includes spending for all health care services and products by state of residence. Hospital spending is included and reflects the total net revenue. Costs such as insurance, administration, research, and construction expenses are not included.
https://www.kff.org/other/state-indicator/avg-annual-growth-per-capita/

Pollution (2019)

Pollution: Average exposure of the general public to particulate matter of 2.5 microns or less (PM2.5) measured in micrograms per cubic meter (3-year estimate)
https://www.americashealthrankings.org/explore/annual/measure/air/state/ALL

Medium and Large Airports

For each state, number of medium and large airports https://en.wikipedia.org/wiki/List_of_the_busiest_airports_in_the_United_States

Temperature (2019)

Note that FL was incorrect in the table, but is corrected in the Hottest States paragraph
https://worldpopulationreview.com/states/average-temperatures-by-state/
District of Columbia temperature computed as the average of Maryland and Virginia

Urbanization (2010)

Urbanization as a percentage of the population https://www.icip.iastate.edu/tables/population/urban-pct-states

Age Groups (2018)

https://www.kff.org/other/state-indicator/distribution-by-age/

School Closure Dates

Schools that haven't closed are marked NaN https://www.edweek.org/ew/section/multimedia/map-coronavirus-and-school-closures.html

Note that some datasets above did not contain data for District of Columbia, this missing data was found via Google searches manually entered.

--- Original source retains full ownership of the source dataset ---

BackgroundTrauma is predicted to become the third leading cause of death in India by 2020, which indicate the need for urgent action. Trauma scores such as the international classification of diseases injury severity score (ICISS) have been used with great success in trauma research and in quality programmes to improve trauma care. To this date no valid trauma score has been developed for the Indian population.Study designThis retrospective cohort study used a dataset of 16047 trauma-patients from four public university hospitals in urban India, which was divided into derivation and validation subsets. All injuries in the dataset were assigned an international classification of disease (ICD) code. Survival Risk Ratios (SRRs), for mortality within 24 hours and 30 days were then calculated for each ICD-code and used to calculate the corresponding ICISS. Score performance was measured using discrimination by calculating the area under the receiver operating characteristics curve (AUROCC) and calibration by calculating the calibration slope and intercept to plot a calibration curve.ResultsPredictions of 30-day mortality showed an AUROCC of 0.618, calibration slope of 0.269 and calibration intercept of 0.071. Estimates of 24-hour mortality consistently showed low AUROCCs and negative calibration slopes.ConclusionsWe attempted to derive and validate a version of the ICISS using SRRs calculated from an Indian population. However, the developed ICISS-scores overestimate mortality and implementing these scores in clinical or policy contexts is not recommended. This study, as well as previous reports, suggest that other scoring systems might be better suited for India and other Low- and middle-income countries until more data are available.

The 1997 the Kyrgyz Republic Demographic and Health Survey (KRDHS) is a nationally representative survey of 3,848 women age 15-49. Fieldwork was conducted from August to November 1997. The KRDHS was sponsored by the Ministry of Health (MOH), and was funded by the United States Agency for International Development. The Research Institute of Obstetrics and Pediatrics implemented the survey with technical assistance from the Demographic and Health Surveys (DHS) program. The purpose of the KRDHS was to provide data to the MOH on factors which determine the health status of women and children such as fertility, contraception, induced abortion, maternal care, infant mortality, nutritional status, and anemia. Some statistics presented in this report are currently available to the MOH from other sources. For example, the MOH collects and regularly publishes information on fertility, contraception, induced abortion and infant mortality. However, the survey presents information on these indices in a manner which is not currently available, i.e., by population subgroups such as those defined by age, marital duration, education, and ethnicity. Additionally, the survey provides statistics on some issues not previously available in the Kyrgyz Republic: for example, breastfeeding practices and anemia status of women and children. When considered together, existing MOH data and the KRDHS data provide a more complete picture of the health conditions in the Kyrgyz Republic than was previously available. A secondary objective of the survey was to enhance the capabilities of institutions in the Kyrgyz Republic to collect, process, and analyze population and health data. MAIN FINDINGS FERTILITY Fertility Rates. Survey results indicate a total fertility rate (TFR) for all of the Kyrgyz Republic of 3.4 children per woman. Fertility levels differ for different population groups. The TFR for women living in urban areas (2.3 children per woman) is substantially lower than for women living in rural areas (3.9). The TFR for Kyrgyz women (3.6 children per woman) is higher than for women of Russian ethnicity (1.5) but lower than Uzbek women (4.2). Among the regions of the Kyrgyz Republic, the TFR is lowest in Bishkek City (1.7 children per woman), and the highest in the East Region (4.3), and intermediate in the North and South Regions (3.1 and3.9, respectively). Time Trends. The KRDHS data show that fertility has declined in the Kyrgyz Republic in recent years. The decline in fertility from 5-9 to 0-4 years prior to the survey increases with age, from an 8 percent decline among 20-24 year olds to a 38 percent decline among 35-39 year olds. The declining trend in fertility can be seen by comparing the completed family size of women near the end of their childbearing years with the current TFR. Completed family size among women 40-49 is 4.6 children which is more than one child greater than the current TFR (3.4). Birth Intervals. Overall, 30 percent of births in the Kyrgyz Republic take place within 24 months of the previous birth. The median birth interval is 31.9 months. Age at Onset of Childbearing. The median age at which women in the Kyrgyz Republic begin childbearing has been holding steady over the past two decades at approximately 21.6 years. Most women have their first birth while in their early twenties, although about 20 percent of women give birth before age 20. Nearly half of married women in the Kyrgyz Republic (45 percent) do not want to have more children. Additional one-quarter of women (26 percent) want to delay their next birth by at least two years. These are the women who are potentially in need of some method of family planning. FAMILY PLANNING Ever Use. Among currently married women, 83 percent report having used a method of contraception at some time. The women most likely to have ever used a method of contraception are those age 30-44 (among both currently married and all women). Current Use. Overall, among currently married women, 60 percent report that they are currently using a contraceptive method. About half (49 percent) are using a modern method of contraception and another 11 percent are using a traditional method. The IUD is by far the most commonly used method; 38 percent of currently married women are using the IUD. Other modern methods of contraception account for only a small amount of use among currently married women: pills (2 percent), condoms (6 percent), and injectables and female sterilization (1 and 2 percent, respectively). Thus, the practice of family planning in the Kyrgyz Republic places high reliance on a single method, the IUD. Source of Methods. The vast majority of women obtain their contraceptives through the public sector (97 percent): 35 percent from a government hospital, and 36 percent from a women counseling center. The source of supply of the method depends on the method being used. For example, most women using IUDs obtain them at women counseling centers (42 percent) or hospitals (39 percent). Government pharmacies supply 46 percent of pill users and 75 percent of condom users. Pill users also obtain supplies from women counseling centers or (33 percent). Fertility Preferences. A majority of women in the Kyrgyz Republic (45 percent) indicated that they desire no more children. By age 25-29, 20 percent want no more children, and by age 30-34, nearly half (46 percent) want no more children. Thus, many women come to the preference to stop childbearing at relatively young ages-when they have 20 or more potential years of childbearing ahead of them. For some of these women, the most appropriate method of contraception may be a long-acting method such as female sterilization. However, there is a deficiency of use of this method in the Kyrgyz Republic. In the interests of providing a broad range of safe and effective methods, information about and access to sterilization should be increased so that individual women can make informed decisions about using this method. INDUCED ABORTION Abortion Rates. From the KRDHS data, the total abortion rate (TAR)-the number of abortions a woman will have in her lifetime based on the currently prevailing abortion rates-was calculated. For the Kyrgyz Republic, the TAR for the period from mid-1994 to mid-1997 is 1.6 abortions per woman. The TAR for the Kyrgyz Republic is lower than recent estimates of the TAR for other areas of the former Soviet Union such as Kazakhstan (1.8), and Yekaterinburg and Perm in Russia (2.3 and 2.8, respectively), but higher than for Uzbekistan (0.7). The TAR is higher in urban areas (2.1 abortions per woman) than in rural areas (1.3). The TAR in Bishkek City is 2.0 which is two times higher than in other regions of the Kyrgyz Republic. Additionally the TAR is substantially lower among ethnic Kyrgyz women (1.3) than among women of Uzbek and Russian ethnicities (1.9 and 2.2 percent, respectively). INFANT MORTALITY In the KRDHS, infant mortality data were collected based on the international definition of a live birth which, irrespective of the duration of pregnancy, is a birth that breathes or shows any sign of life (United Nations, 1992). Mortality Rates. For the five-year period before the survey (i.e., approximately mid-1992 to mid1997), infant mortality in the Kyrgyz Republic is estimated at 61 infant deaths per 1,000 births. The estimates of neonatal and postneonatal mortality are 32 and 30 per 1,000. The MOH publishes infant mortality rates annually but the definition of a live birth used by the MOH differs from that used in the survey. As is the case in most of the republics of the former Soviet Union, a pregnancy that terminates at less than 28 weeks of gestation is considered premature and is classified as a late miscarriage even if signs of life are present at the time of delivery. Thus, some events classified as late miscarriages in the MOH system would be classified as live births and infant deaths according to the definitions used in the KRDHS. Infant mortality rates based on the MOH data for the years 1983 through 1996 show a persistent declining trend throughout the period, starting at about 40 per 1,000 in the early 1980s and declining to 26 per 1,000 in 1996. This time trend is similar to that displayed by the rates estimated from the KRDHS. Thus, the estimates from both the KRDHS and the Ministry document a substantial decline in infant mortality; 25 percent over the period from 1982-87 to 1992-97 according to the KRDHS and 28 percent over the period from 1983-87 to 1993-96 according to the MOH estimates. This is strong evidence of improvements in infant survivorship in recent years in the Kyrgyz Republic. It should be noted that the rates from the survey are much higher than the MOH rates. For example, the KRDHS estimate of 61 per 1,000 for the period 1992-97 is twice the MOH estimate of 29 per 1,000 for 1993-96. Certainly, one factor leading to this difference are the differences in the definitions of a live birth and infant death in the KRDHS survey and in the MOH protocols. A thorough assessment of the difference between the two estimates would need to take into consideration the sampling variability of the survey's estimate. However, given the magnitude of the difference, it is likely that it arises from a combination of definitional and methodological differences between the survey and MOH registration system. MATERNAL AND CHILD HEALTH The Kyrgyz Republic has a well-developed health system with an extensive infrastructure of facilities that provide maternal care services. This system includes special delivery hospitals, the obstetrics and gynecology departments of general hospitals, women counseling centers, and doctor's assistant/midwife posts (FAPs). There is an extensive network of FAPs throughout the rural areas. Delivery. Virtually all births in the Kyrgyz Republic (96 percent) are delivered at health facilities: 95 percent in delivery hospitals and another 1 percent in either general hospitals

This dataset presents the footprint of the percentage of adults who were admitted to any hospital in the preceding 12 months. The data spans the years of 2013-2017 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS).

The data is sourced from the Australian Bureau of Statistics (ABS) 2016-17 Patient Experience Survey, collected between 1 July 2016 and 30 June 2017. It also includes data from previous Patient Experience Surveys conducted in 2013-14, 2014-15 and 2015-16. The Patient Experience Survey is conducted annually by the ABS and collects information from a representative sample of the Australian population. The Patient Experience Survey is one of several components of the Multipurpose Household Survey, as a supplement to the monthly Labour Force Survey. The Patient Experience Survey collects data on persons aged 15 years and over, who are referred to as adults for this data collection.

For further information about this dataset, visit the data source:Australian Institute of Health and Welfare - Patient experiences in Australia Data Tables.

Please note:

• AURIN has spatially enabled the original data using the Department of Health - PHN Areas.

• Percentages are calculated based on counts that have been randomly adjusted by the ABS to avoid the release of confidential data.

• As an indication of the accuracy of estimates, 95% confidence intervals were produced. These were calculated by the ABS using standard error estimates of the proportion.

• Some of the patient experience measures for 2016-17 have age-standardised rates presented. Age-standardised rates are hypothetical rates that would have been observed if the populations studied had the same age distribution as the standard population.

• Crude rates are provided for all years. They should be used for understanding the patterns of actual service use or level of experience in a particular PHN.

• The Patient Experience Survey excludes persons aged less than 15 years, persons living in non-private dwellings and the Indigenous Community Strata (encompassing discrete Aboriginal and Torres Strait Islander communities).

• Data for Northern Territory should be interpreted with caution as the Patient Experience Survey excluded the Indigenous Community Strata, which comprises around 25% of the estimated resident population of the Northern Territory living in private dwellings.

• Rows that contain a "#" in "Interpret with Caution" indicates that the estimate has a relative standard error of 25% to 50%, which indicates a high level of sampling error relative to its value and must be taken into account when comparing this estimate with other values.

• NP - Not available for publication. The estimate is considered to be unreliable. Values assigned to NP in the original data have been set to null.

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

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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