Introduction: While falls among the elderly is a public health issue, because of the social, medical, and economic burden they represent, the tools to predict falls are limited. Posturography has been developed to distinguish fallers from non-fallers, however, there is too little data to show how predictions change as older adults' physical abilities improve. The Postadychute-AG clinical trial aims to evaluate the evolution of posturographic parameters in relation to the improvement of balance through adapted physical activity (APA) programs.Methods: In this prospective, multicentre clinical trial, institutionalized seniors over 65 years of age will be followed for a period of 6 months through computer-assisted posturography and automatic gait analysis. During the entire duration of the follow-up, they will benefit from a monthly measurement of their postural and locomotion capacities through a recording of their static balance and gait thanks to a software developed for this purpose. The data gathered will be correlated with the daily record of falls in the institution. Static and dynamic balance measurements aim to extract biomechanical markers and compare them with functional assessments of motor skills (Berg Balance Scale and Mini Motor Test), expecting their superiority in predicting the number of falls. Participants will be followed for 3 months without APA and 3 months with APA in homogeneous group exercises. An analysis of variance will evaluate the variability of monthly measures of balance in order to record the minimum clinically detectable change (MDC) as participants improve their physical condition through APA.Discussion: Previous studies have stated the MDC through repeated measurements of balance but, to our knowledge, none appear to have implemented monthly measurements of balance and gait. Combined with a reliable measure of the number of falls per person, motor capacities and other precipitating factors, this study aims to provide biomechanical markers predictive of fall risk with their sensitivity to improvement in clinical status over the medium term. This trial could provide the basis for posturographic and gait variable values for these elderly people and provide a solution to distinguish those most at risk to be implemented in current practice in nursing homes.Trial Registration: ID-RCB 2017-A02545-48.Protocol Version: Version 4.2 dated January 8, 2020.

OBJECTIVE: To determine the characteristics of elderly who died by falling in Rio Grande do Sul state, Brazil, from 2006 to 2011. METHODS: We analyzed 2,126 deaths from falls in the state from 2006 to 2011, registered in the Brazilian Mortality Information System. Statistical analyzes were performed using the SPSS 17.0 computer application. RESULTS: The chance of death from falls in the elderly is significantly higher for females, age group above 69 years and elderly people with white skin color, widowed or single. There was a 41.8% increase in specific mortality rates fall during the study period, the highest rate occurring in 2011 31.56 deaths fall among 100,000 elderly, higher for females and age 80 years or more. CONCLUSION: It was found that the mortality rate from falls increased from 2006 to 2011 in that state, being highest for those aged 80 and over, relevance of results for the development of public policies for the elderly.

This dataset reports the daily reported number of the 7-day moving average rates of Deaths involving COVID-19 by vaccination status and by age group.

Learn how the Government of Ontario is helping to keep Ontarians safe during the 2019 Novel Coronavirus outbreak.

Effective November 14, 2024 this page will no longer be updated. Information about COVID-19 and other respiratory viruses is available on Public Health Ontario’s interactive respiratory virus tool: https://www.publichealthontario.ca/en/Data-and-Analysis/Infectious-Disease/Respiratory-Virus-Tool

Data includes:

• Date on which the death occurred
• Age group
• 7-day moving average of the last seven days of the death rate per 100,000 for those not fully vaccinated
• 7-day moving average of the last seven days of the death rate per 100,000 for those fully vaccinated
• 7-day moving average of the last seven days of the death rate per 100,000 for those vaccinated with at least one booster

Additional notes

As of June 16, all COVID-19 datasets will be updated weekly on Thursdays by 2pm.

As of January 12, 2024, data from the date of January 1, 2024 onwards reflect updated population estimates. This update specifically impacts data for the 'not fully vaccinated' category.

On November 30, 2023 the count of COVID-19 deaths was updated to include missing historical deaths from January 15, 2020 to March 31, 2023.

CCM is a dynamic disease reporting system which allows ongoing update to data previously entered. As a result, data extracted from CCM represents a snapshot at the time of extraction and may differ from previous or subsequent results. Public Health Units continually clean up COVID-19 data, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes and current totals being different from previously reported cases and deaths. Observed trends over time should be interpreted with caution for the most recent period due to reporting and/or data entry lags.

The data does not include vaccination data for people who did not provide consent for vaccination records to be entered into the provincial COVaxON system. This includes individual records as well as records from some Indigenous communities where those communities have not consented to including vaccination information in COVaxON.

“Not fully vaccinated” category includes people with no vaccine and one dose of double-dose vaccine. “People with one dose of double-dose vaccine” category has a small and constantly changing number. The combination will stabilize the results.

Spikes, negative numbers and other data anomalies: Due to ongoing data entry and data quality assurance activities in Case and Contact Management system (CCM) file, Public Health Units continually clean up COVID-19, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes, negative numbers and current totals being different from previously reported case and death counts.

Public Health Units report cause of death in the CCM based on information available to them at the time of reporting and in accordance with definitions provided by Public Health Ontario. The medical certificate of death is the official record and the cause of death could be different.

Deaths are defined per the outcome field in CCM marked as “Fatal”. Deaths in COVID-19 cases identified as unrelated to COVID-19 are not included in the Deaths involving COVID-19 reported.

Rates for the most recent days are subject to reporting lags

All data reflects totals from 8 p.m. the previous day.

This dataset is subject to change.

Mortality from accidental falls (ICD-10 W00-W19 equivalent to ICD-9 E880-888 excluding E887). To reduce deaths from accidental falls. Legacy unique identifier: P00089

The Research and Development Survey (RANDS) is a platform designed for conducting survey question evaluation and statistical research. RANDS is an ongoing series of surveys from probability-sampled commercial survey panels used for methodological research at the National Center for Health Statistics (NCHS). RANDS estimates are generated using an experimental approach that differs from the survey design approaches generally used by NCHS, including possible biases from different response patterns and sampling frames as well as increased variability from lower sample sizes. Use of the RANDS platform allows NCHS to produce more timely data than would be possible using traditional data collection methods. RANDS is not designed to replace NCHS’ higher quality, core data collections. Below are experimental estimates of loss of work due to illness with coronavirus for three rounds of RANDS during COVID-19. Data collection for the three rounds of RANDS during COVID-19 occurred between June 9, 2020 and July 6, 2020, August 3, 2020 and August 20, 2020, and May 17, 2021 and June 30, 2021. Information needed to interpret these estimates can be found in the Technical Notes. RANDS during COVID-19 included a question about the inability to work due to being sick or having a family member sick with COVID-19. The National Health Interview Survey, conducted by NCHS, is the source for high-quality data to monitor work-loss days and work limitations in the United States. For example, in 2018, 42.7% of adults aged 18 and over missed at least 1 day of work in the previous year due to illness or injury and 9.3% of adults aged 18 to 69 were limited in their ability to work or unable to work due to physical, mental, or emotional problems. The experimental estimates on this page are derived from RANDS during COVID-19 and show the percentage of U.S. adults who did not work for pay at a job or business, at any point, in the previous week because either they or someone in their family was sick with COVID-19. Technical Notes: https://www.cdc.gov/nchs/covid19/rands/work.htm#limitations

This dataset contains all studies and code necessary to reproduce the findings reported in the manuscript and its supplementary materials.

Social vulnerability is defined as the disproportionate susceptibility of some social groups to the impacts of hazards, including death, injury, loss, or disruption of livelihood. In this dataset from Climate Ready Boston, groups identified as being more vulnerable are older adults, children, people of color, people with limited English proficiency, people with low or no incomes, people with disabilities, and people with medical illnesses. Source:The analysis and definitions used in Climate Ready Boston (2016) are based on "A framework to understand the relationship between social factors that reduce resilience in cities: Application to the City of Boston." Published 2015 in the International Journal of Disaster Risk Reduction by Atyia Martin, Northeastern University.Population Definitions:Older Adults:Older adults (those over age 65) have physical vulnerabilities in a climate event; they suffer from higher rates of medical illness than the rest of the population and can have some functional limitations in an evacuation scenario, as well as when preparing for and recovering from a disaster. Furthermore, older adults are physically more vulnerable to the impacts of extreme heat. Beyond the physical risk, older adults are more likely to be socially isolated. Without an appropriate support network, an initially small risk could be exacerbated if an older adult is not able to get help.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract for population over 65 years of age.Attribute label: OlderAdultChildren: Families with children require additional resources in a climate event. When school is cancelled, parents need alternative childcare options, which can mean missing work. Children are especially vulnerable to extreme heat and stress following a natural disaster.Data source: 2010 American Community Survey 5-year Estimates (ACS) data by census tract for population under 5 years of age.Attribute label: TotChildPeople of Color: People of color make up a majority (53 percent) of Boston’s population. People of color are more likely to fall into multiple vulnerable groups aswell. People of color statistically have lower levels of income and higher levels of poverty than the population at large. People of color, many of whom also have limited English proficiency, may not have ready access in their primary language to information about the dangers of extreme heat or about cooling center resources. This risk to extreme heat can be compounded by the fact that people of color often live in more densely populated urban areas that are at higher risk for heat exposure due to the urban heat island effect.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract: Black, Native American, Asian, Island, Other, Multi, Non-white Hispanics.Attribute label: POC2Limited English Proficiency: Without adequate English skills, residents can miss crucial information on how to preparefor hazards. Cultural practices for information sharing, for example, may focus on word-of-mouth communication. In a flood event, residents can also face challenges communicating with emergency response personnel. If residents are more sociallyisolated, they may be less likely to hear about upcoming events. Finally, immigrants, especially ones who are undocumented, may be reluctant to use government services out of fear of deportation or general distrust of the government or emergency personnel.Data Source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract, defined as speaks English only or speaks English “very well”.Attribute label: LEPLow to no Income: A lack of financial resources impacts a household’s ability to prepare for a disaster event and to support friends and neighborhoods. For example, residents without televisions, computers, or data-driven mobile phones may face challenges getting news about hazards or recovery resources. Renters may have trouble finding and paying deposits for replacement housing if their residence is impacted by flooding. Homeowners may be less able to afford insurance that will cover flood damage. Having low or no income can create difficulty evacuating in a disaster event because of a higher reliance on public transportation. If unable to evacuate, residents may be more at risk without supplies to stay in their homes for an extended period of time. Low- and no-income residents can also be more vulnerable to hot weather if running air conditioning or fans puts utility costs out of reach.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract for low-to- no income populations. The data represents a calculated field that combines people who were 100% below the poverty level and those who were 100–149% of the poverty level.Attribute label: Low_to_NoPeople with Disabilities: People with disabilities are among the most vulnerable in an emergency; they sustain disproportionate rates of illness, injury, and death in disaster events.46 People with disabilities can find it difficult to adequately prepare for a disaster event, including moving to a safer place. They are more likely to be left behind or abandoned during evacuations. Rescue and relief resources—like emergency transportation or shelters, for example— may not be universally accessible. Research has revealed a historic pattern of discrimination against people with disabilities in times of resource scarcity, like after a major storm and flood.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract for total civilian non-institutionalized population, including: hearing difficulty, vision difficulty, cognitive difficulty, ambulatory difficulty, self-care difficulty, and independent living difficulty. Attribute label: TotDisMedical Illness: Symptoms of existing medical illnesses are often exacerbated by hot temperatures. For example, heat can trigger asthma attacks or increase already high blood pressure due to the stress of high temperatures put on the body. Climate events can interrupt access to normal sources of healthcare and even life-sustaining medication. Special planning is required for people experiencing medical illness. For example, people dependent on dialysis will have different evacuation and care needs than other Boston residents in a climate event.Data source: Medical illness is a proxy measure which is based on EASI data accessed through Simply Map. Health data at the local level in Massachusetts is not available beyond zip codes. EASI modeled the health statistics for the U.S. population based upon age, sex, and race probabilities using U.S. Census Bureau data. The probabilities are modeled against the census and current year and five year forecasts. Medical illness is the sum of asthma in children, asthma in adults, heart disease, emphysema, bronchitis, cancer, diabetes, kidney disease, and liver disease. A limitation is that these numbers may be over-counted as the result of people potentially having more than one medical illness. Therefore, the analysis may have greater numbers of people with medical illness within census tracts than actually present. Overall, the analysis was based on the relationship between social factors.Attribute label: MedIllnesOther attribute definitions:GEOID10: Geographic identifier: State Code (25), Country Code (025), 2010 Census TractAREA_SQFT: Tract area (in square feet)AREA_ACRES: Tract area (in acres)POP100_RE: Tract population countHU100_RE: Tract housing unit countName: Boston Neighborhood

Estimates for the total death count of the Second World War generally range somewhere between 70 and 85 million people. The Soviet Union suffered the highest number of fatalities of any single nation, with estimates mostly falling between 22 and 27 million deaths. China then suffered the second greatest, at around 20 million, although these figures are less certain and often overlap with the Chinese Civil War. Over 80 percent of all deaths were of those from Allied countries, and the majority of these were civilians. In contrast, 15 to 20 percent were among the Axis powers, and the majority of these were military deaths, as shown in the death ratios of Germany and Japan. Civilian deaths and atrocities It is believed that 60 to 67 percent of all deaths were civilian fatalities, largely resulting from war-related famine or disease, and war crimes or atrocities. Systematic genocide, extermination campaigns, and forced labor, particularly by the Germans, Japanese, and Soviets, led to the deaths of millions. In this regard, Nazi activities alone resulted in 17 million deaths, including six million Jews in what is now known as The Holocaust. Not only was the scale of the conflict larger than any that had come before, but the nature of and reasoning behind this loss make the Second World War stand out as one of the most devastating and cruelest conflicts in history. Problems with these statistics Although the war is considered by many to be the defining event of the 20th century, exact figures for death tolls have proven impossible to determine, for a variety of reasons. Countries such as the U.S. have fairly consistent estimates due to preserved military records and comparatively few civilian casualties, although figures still vary by source. For most of Europe, records are less accurate. Border fluctuations and the upheaval of the interwar period mean that pre-war records were already poor or non-existent for many regions. The rapid and chaotic nature of the war then meant that deaths could not be accurately recorded at the time, and mass displacement or forced relocation resulted in the deaths of many civilians outside of their homeland, which makes country-specific figures more difficult to find. Early estimates of the war’s fatalities were also taken at face value and formed the basis of many historical works; these were often very inaccurate, but the validity of the source means that the figures continue to be cited today, despite contrary evidence.

In comparison to Europe, estimate ranges are often greater across Asia, where populations were larger but pre-war data was in short supply. Many of the Asian countries with high death tolls were European colonies, and the actions of authorities in the metropoles, such as the diversion of resources from Asia to Europe, led to millions of deaths through famine and disease. Additionally, over one million African soldiers were drafted into Europe’s armies during the war, yet individual statistics are unavailable for most of these colonies or successor states (notably Algeria and Libya). Thousands of Asian and African military deaths went unrecorded or are included with European or Japanese figures, and there are no reliable figures for deaths of millions from countries across North Africa or East Asia. Additionally, many concentration camp records were destroyed, and such records in Africa and Asia were even sparser than in Europe. While the Second World War is one of the most studied academic topics of the past century, it is unlikely that we will ever have a clear number for the lives lost in the conflict.

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

Effect of suicide rates on life expectancy dataset

Abstract
In 2015, approximately 55 million people died worldwide, of which 8 million committed suicide. In the USA, one of the main causes of death is the aforementioned suicide, therefore, this experiment is dealing with the question of how much suicide rates affects the statistics of average life expectancy.
The experiment takes two datasets, one with the number of suicides and life expectancy in the second one and combine data into one dataset. Subsequently, I try to find any patterns and correlations among the variables and perform statistical test using simple regression to confirm my assumptions.

Data

The experiment uses two datasets - WHO Suicide Statistics[1] and WHO Life Expectancy[2], which were firstly appropriately preprocessed. The final merged dataset to the experiment has 13 variables, where country and year are used as index: Country, Year, Suicides number, Life expectancy, Adult Mortality, which is probability of dying between 15 and 60 years per 1000 population, Infant deaths, which is number of Infant Deaths per 1000 population, Alcohol, which is alcohol, recorded per capita (15+) consumption, Under-five deaths, which is number of under-five deaths per 1000 population, HIV/AIDS, which is deaths per 1 000 live births HIV/AIDS, GDP, which is Gross Domestic Product per capita, Population, Income composition of resources, which is Human Development Index in terms of income composition of resources, and Schooling, which is number of years of schooling.

LICENSE

THE EXPERIMENT USES TWO DATASET - WHO SUICIDE STATISTICS AND WHO LIFE EXPECTANCY, WHICH WERE COLLEECTED FROM WHO AND UNITED NATIONS WEBSITE. THEREFORE, ALL DATASETS ARE UNDER THE LICENSE ATTRIBUTION-NONCOMMERCIAL-SHAREALIKE 3.0 IGO (https://creativecommons.org/licenses/by-nc-sa/3.0/igo/).

[1] https://www.kaggle.com/szamil/who-suicide-statistics

[2] https://www.kaggle.com/kumarajarshi/life-expectancy-who

U.S. Household Mental Health & Covid-19

Assessing the Impact of the Pandemic

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

About this dataset

This dataset offers a closer look into the mental health care received by U.S. households in the last four weeks during the Covid-19 pandemic. The sheer scale of this crisis is inspiring people of all ages, backgrounds, and geographies to come together to tackle the problem. The Household Pulse Survey from the U.S. Census Bureau was published with federal agency collaboration in order to draw up accurate and timely estimates about how Covid-19 is impacting employment status, consumer spending, food security, housing stability, education interruption, and physical and mental wellness amongst American households. In order to deliver meaningful results from this survey data about wellbeing at various levels of society during this trying period – which includes demographic characteristics such as age gender race/ethnicity training attainment – each consulted household was randomly selected according to certain weighted criteria to maintain accuracy throughout the findings This dataset will help you explore what's it like on the ground right now for everyone affected by Covid-19 - Will it inform your decisions or point you towards new opportunities?

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How to use the dataset

This dataset contains information about the mental health care that U.S. households have received in the last 4 weeks, during the Covid-19 pandemic. This data is valuable when wanting to track and measure mental health needs across the country and draw comparisons between regions based on support available.

To use this dataset, it is important to understand each of its columns or variables in order to draw meaningful insights from the data. The ‘Indicator’ column indicates which type of indicator (percentage or absolute number) is being measured by this survey, while ‘Group’ and 'Subgroup' provide more specific details about who was surveyed for each indicator included in this dataset.

The Columns ‘Phase’ and 'Time Period' provide information regarding when each of these indicators was measured - whether during a certain phase or over a particular timespan - while columns such as 'Value', 'LowCI' & 'HighCI' show us how many individuals fell into what quartile range for each measurement taken (e.g., how many people reported they rarely felt lonely). Similarly, the column Suppression Flag helps us identify cases where value has been suppressed if it falls below a certain benchmark; this allows us to calculate accurate estimates more quickly without needing to sort through all suppressed values manually each time we use this dataset for analysis purposes. Finally, columns such as ‘Time Period Start Date’ & ‘Time Period End Date’ indicate which exact dates were used for measurements taken over different periods throughout those dates specified – useful when conducting time-series related analyses over longer periods of time within our research scope)

Overall, when using this dataset it's important to keep in mind exactly what indicator type you're looking at - percentage points or absolute numbers - as well its associated group/subgroup characteristics so that you can accurately interpret trends based on key findings had by interpreting any correlations drawn from these results!

Research Ideas

• Analyzing the effects of the Covid-19 pandemic on mental health care among different subgroups such as racial and ethnic minorities, gender and age categories.
• Identifying geographical disparities in mental health services by comparing state level data for the same time period.
• Comparing changes in mental health care indicators over time to understand how the pandemic has impacted people's access to care within a quarter or over longer periods

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. ...

Participants completed an online survey about their preferences over ways of reducing their risks of dying over time such that they obtained gains in life expectancy. The dataset includes the options they faced and their choices. It also includes some demographic information and other related preference questions (e.g. time preferences, risk preferences, sequence preferences).A key role of the UK government is to address causes of premature fatality. In the UK, air pollution leads to the loss of 340,000 years of life each year and workplace cancers led to the loss of over 140,000 years of life in 2010. Government policies can address the many causes of premature fatality, but these policies need to be evaluated to ensure they make the best use of public money. The question then becomes: what is the value of increasing a person's life expectancy? To address this question, researchers have introduced the concept of the Value Of a Life Year (VOLY). This VOLY is used in government policy evaluations as a measure of the benefits of policies including air pollution mitigation and workplace safety regulation, and thus it is crucial it is measured accurately. The VOLY is estimated using surveys of members of the public, in which people state how much they would pay for a given reduction in their risk of dying, or for a given increase in their life expectancy. The benefits being valued occur in the future. Crucially then, a key component of the VOLY is the effect of timing. Put simply, the further in the future something is, the less we tend to care about it. So a reduction in our risk of dying this year might be more valuable than a reduction in our risk of dying in the future, even if the effect on our overall life expectancy is the same. Unless we understand the influence of this 'discounting' for changes in life expectancy, we cannot accurately disentangle it from the true VOLY. This is the problem we aim to solve with our research. To solve it, our team of experimental economists will use an innovative mixture of experiments and surveys. Participants will play experimental games designed to include simplified models of the air pollution policies, so our team can learn the best ways to describe and measure discounting as it relates to delayed changes in risk. The survey will use the insights from the experiment and elicit individuals' preferences for reductions in their risks at different points in the future. Taken together, the experiments and survey will provide the first major investigation into how people discount their future life expectancy in the context of the VOLY. Our results will be important for policymakers in two ways. First, unless we can account for the effects of discounting on the VOLY, then policy estimates of the VOLY taken from current surveys might be wrong. If these incorrect estimates are used in the evaluation of policies aimed at improving life expectancy, then the value of the policies will be over- or under-estimated, which means public money is likely to be spent on the wrong policies. Second, when the government is evaluating policies where improvements in life expectancy happen in the future, as is the case for air pollution policies, they have to apply discounting to the value of the benefits. Our research will provide evidence about how governments should discount future gains in life expectancy, to make sure that public preferences are reflected in policymaking. Our research is also academically cutting-edge. It combines models from economics with insights from psychology to generate new methodological and empirical evidence about how discounting influences preferences for changes in risk, both for money outcomes (in the experiments) and for fatality risks (in the surveys). It also forges a new methodological agenda, which is the incorporation of incentivised experiments into policy-driven research projects. Overall, our research aims to provide the basis for changing the VOLY used in government policy, challenge existing guidance for discounting fatality risk reductions, and ultimately change how government money is spent, so that the policies implemented are those that improve the wellbeing of society. Survey programmed by the research team in o-tree and conducted online using a sample of respondents recruited on prolific.ac. The sample sex and age band distribution was selected to match those of the UK population (although the respondents were not restricted to be UK residents).

The primary data source for this study is the Northern Ireland Longitudinal Study (NILS), which in 2001 defined a representative cohort of c.28% of the population. It is formed from the linkage of the universal Health Card registration system, 2001 Census returns, and vital statistics data. NILS contains a unique Health and Care Number that enables linkage to other health service databases. It is maintained by the Northern Ireland Statistics and Research Agency (NISRA). The 2001 Census records provided most of the attributes of the NILS cohort members, also contextual information relating to household composition and interpersonal relationships, and characteristics of the household and area of residence. The vital events linked to NILS were used to determine whether a cohort member had been bereaved between April 2001 (the time of the Census) and the end of December 2009. The 2001 Census asked questions about relationship to other people living in the household, these questions were used to determine who a cohort member lived with, and the vital events records identified co-resident family members’ deaths. Approximately 96% of death records are routinely linked to the NILS dataset using a mixture of exact and probabilistic matching. Data relating to medications that have been prescribed by a General Practitioner and dispensed from community pharmacies have been collated centrally in an Enhanced Prescribing Database (EPD) since 2009. Each prescription record contains the individual’s Health and Care Number, a General Practice (GP) identifier, the drug name and British National Formulary (BNF) category. Information was extracted for antidepressant and anxiolytic medications (BNF categories 4.1.2 and 4.3) for the period January 1st to February 28th 2010. Health and Care Number allowed exact matching between prescribing and NILS records. The linkage process was carried out by the EPD and NILS data custodians. The linked dataset was then anonymised before being supplied to the researchers, and was held in a secure setting (9). At no time were patient identifiable data available. The data used for the Grief study is not publicly available, but researchers can make a request to link data for themselves by contacting the Northern Ireland Longitudinal Study Research Support Unit Everybody will face bereavement at some stage; but for some people, this can be a more difficult process. There are many factors that can influence how people cope with the loss of a loved one, including level of family support, financial resources, stress, and the circumstances surrounding death.By studying use of prescription medications to help with mental health, we can get a better understanding of how factors such as age, gender, family support, employment and religion affect how people cope after bereavement. By looking at circumstances of bereavement this study will also discover if the factors that help people cope - such as family support - are more or less important depending on how they lost their loved ones.The Grief Study is based on data from the Northern Ireland Longitudinal Study, this holds information on around 500,000 people. By linking this data with the Northern Ireland Mortality Study and Health and Social care information on prescriptions, the Grief Study aims to learn more about bereavement, mental health, complicated grief, and longer term outcomes for people who have lost a loved one.

Background After 5 years, most reports show that less than 10% of people maintain a 5% loss from initial body weight. Weight maintenance after 10 years is rarely assessed, especially in commercial programs. The current article reports weight maintenance in individuals who had participated 2 to 11 years earlier in a popular commercial weight loss program based on Canada's Food Guide called Mincavi.

   Methods
   Randomly picked subjects answered a telephone questionnaire. Participants, 291 adult women from various regions of the province of Quebec, had followed the program 2 to 11 years earlier for at least a month. Body weight at the beginning and at the end of treatment was recorded as well as actual weight, age and height. Existing records allowed partial verification of the sample.


   Results
   Based on corrected weights, percentage of women who maintained at least 5% of their initial weight loss are as following; 2 years = 43.6% (n = 55), 3 years = 33.3% (n = 42), 4 years = 23.8% (n = 42), 5–6 years = 38.2% (n = 55), 7–8 years = 29.4% (n = 51), and 9–11 years; 19.6% (n = 46). Five to eleven years after they had participated in the program 29.1% of all women maintained a weight loss of at least 5%, while 14.3% maintained a loss of at least 10%.


   Conclusions
   Even though success rate is not as high as could be wished for, results show that participation in the Mincavi program can lead to effective weight maintenance long after individuals have left it. These findings suggest more thorough studies should be conducted on this weight loss program.

Virtual Patient Model Assessment

Tracking Physical, Psychological and Cognitive Performance in Older Adults

By [source]

About this dataset

This dataset provides a comprehensive overview of the physical, psychological and cognitive health of a cohort of older adults. It contains data collected from medical experts during clinical assessments such as physical activity, nutrition, activity limitations, balance, depression and cognition. Additionally it includes parameters extracted from used devices such as average heart rate per day and average gait speed. Carefully coupled with this is detailed information relating to falls, fractures and loss of orientation within the group studied which can add even further insight into the overall trends in health for those aged 55 and above.

The dataset includes various scores capturing different aspects alongside statistics to better represent participants' lifestyles; not only does it feature basic metrics like gender or age but also complex measures like exhaustion or grip strength for each individual in the cohort. Furthermore an analytical exploration into nutrition measures (e.g., Body Mass Index), social interaction (e.g., phone calls) or leisure activities (clubs) could help uncover powerful correlations among them resulting in innovative strategies for improving well-being amongst elderly population groups

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How to use the dataset

This dataset provides a comprehensive overview of physical, psychological and cognitive health of a cohort of older adults. It includes parameters related to physical activity, nutrition, activity limitations, balance, depression, cognition and more. Through this dataset you can gain insights into the various factors affecting the health of elderlies in your population which could be helpful for researchers or practitioners in developing interventions to promote elderly health.

Before using this dataset it is advised to get familiar with the variables and fields provided. There are two sections within each variable: descriptive information such as gender and age group; and scores related to various aspects such as heart rate per day or average gait speed per month. You may also find additional coupled events like falls or fractures that can impact the assessment scores over time.

Once you have gone through all variables available in the dataset you may use simple statistical methods like measuring mean values of several key indicators (such as balance score or bmi score) across different characteristics (such as age group). Comparing these values allows researchers to identify trends amongst different groups within a population that would show differences on an individual level.

Other techniques that could be used include clustering techniques to observe patterns in data relating different indicators at once on comparative models; logistic regression which would help identify which predictors explain certain outcomes among elderly people well; or propensity matching-based approaches which suggest what kind of intervention should be given depending on each person’s characteristics based on an accumulated data source from elderly population research using this dataset . The usefulness of this dataset is not limited by stats only but it might also benefit from theoretical forms such as narrative geometry used for subjective analysis by placing story-telling elements along with formative assessments onto conceptual frameworks between inside natural ecosystems already running smoothly(between concepts) before disruption/disequilibrium happens due external stressors ecomorphonologically speaking . This will eventually help clinicians addressing psychological conditions verifying objective status via outcomes from metrics established earlier preferably prior experiments where involuntary independent behavior was detected influencing vital organ systems at homeostasis levels either causing positive adaptations / fitness ,or increasing vulnerability that when added up together shift towards severe distress turn proximally considering also other segments elsewhere varying across multiple networks simultaneous injections cumulated/integrated effects starting sometimes after take off periods way before ill health seems obviously concrete therefore important details concerning risk factors sometimes overlooked got noticed while capturing evidence based prospective by cross validated means completed longitudinal surveys taking advantage into being able understanding potentially confounding conditions sparedly manifested either forgotten beca...

The graph illustrates the number of deaths per day in the United States from 1950 to 2025. The x-axis represents the years, abbreviated from '50 to '24, while the y-axis indicates the daily number of deaths. Over this 75-year period, the number of deaths per day ranges from a low of 4,054 in 1950 to a high of 9,570 in 2021. Notable figures include 6,855 deaths in 2010 and 8,333 in 2024. The data shows a general upward trend in daily deaths over the decades, with recent years experiencing some fluctuations. This information is presented in a line graph format, effectively highlighting the long-term trends and yearly variations in daily deaths across the United States.

Contemporary '24/7' society requires many people to lose sleep. Despite numerous studies assessing sleepiness, there are aspects relevant to work settings that remain largely overlooked. Laboratory studies typically measure sleepiness within dull, controlled environments isolating participants from distractions. The key sign of sleepiness is a 'lapse' and failure to react to a stimulus, usually assumed to be a 'microsleep' (drooping eye-lids etc). However, sleep loss has other subtle effects, particularly a vulnerability to distraction and tendency to dwell on the distraction ('perseverate'). Both are due to failings of the frontal cortex ('executive centres') as sleep is critical for normal function here. Thus 'distractability' is a component of sleep loss, separate from 'microsleeps', and masked by usual laboratory procedures. Whereas distractability may be of little concern to sleepy people in an office environment, with background talking, telephones and movements in the visual periphery, for those monitoring surveillance screens, control rooms, driving at night etc, distractions are problematic. Moreover, older people are more likely to be 'caught off guard' in this manner. This research undertakes a series of studies with moderately sleep deprived young and older people, using contrived distractions in realistic settings (viewing security screens, mock control room, and office settings).

The leading causes of death by sex and ethnicity in New York City in since 2007. Cause of death is derived from the NYC death certificate which is issued for every death that occurs in New York City.

THIS DATASET WAS LAST UPDATED AT 8:11 PM EASTERN ON AUG. 30

OVERVIEW

2019 had the most mass killings since at least the 1970s, according to the Associated Press/USA TODAY/Northeastern University Mass Killings Database.

In all, there were 45 mass killings, defined as when four or more people are killed excluding the perpetrator. Of those, 33 were mass shootings . This summer was especially violent, with three high-profile public mass shootings occurring in the span of just four weeks, leaving 38 killed and 66 injured.

A total of 229 people died in mass killings in 2019.

The AP's analysis found that more than 50% of the incidents were family annihilations, which is similar to prior years. Although they are far less common, the 9 public mass shootings during the year were the most deadly type of mass murder, resulting in 73 people's deaths, not including the assailants.

One-third of the offenders died at the scene of the killing or soon after, half from suicides.

About this Dataset

The Associated Press/USA TODAY/Northeastern University Mass Killings database tracks all U.S. homicides since 2006 involving four or more people killed (not including the offender) over a short period of time (24 hours) regardless of weapon, location, victim-offender relationship or motive. The database includes information on these and other characteristics concerning the incidents, offenders, and victims.

The AP/USA TODAY/Northeastern database represents the most complete tracking of mass murders by the above definition currently available. Other efforts, such as the Gun Violence Archive or Everytown for Gun Safety may include events that do not meet our criteria, but a review of these sites and others indicates that this database contains every event that matches the definition, including some not tracked by other organizations.

This data will be updated periodically and can be used as an ongoing resource to help cover these events.

Using this Dataset

To get basic counts of incidents of mass killings and mass shootings by year nationwide, use these queries:

Mass killings by year

Mass shootings by year

To get these counts just for your state:

Filter killings by state

Definition of "mass murder"

Mass murder is defined as the intentional killing of four or more victims by any means within a 24-hour period, excluding the deaths of unborn children and the offender(s). The standard of four or more dead was initially set by the FBI.

This definition does not exclude cases based on method (e.g., shootings only), type or motivation (e.g., public only), victim-offender relationship (e.g., strangers only), or number of locations (e.g., one). The time frame of 24 hours was chosen to eliminate conflation with spree killers, who kill multiple victims in quick succession in different locations or incidents, and to satisfy the traditional requirement of occurring in a “single incident.”

Offenders who commit mass murder during a spree (before or after committing additional homicides) are included in the database, and all victims within seven days of the mass murder are included in the victim count. Negligent homicides related to driving under the influence or accidental fires are excluded due to the lack of offender intent. Only incidents occurring within the 50 states and Washington D.C. are considered.

Methodology

Project researchers first identified potential incidents using the Federal Bureau of Investigation’s Supplementary Homicide Reports (SHR). Homicide incidents in the SHR were flagged as potential mass murder cases if four or more victims were reported on the same record, and the type of death was murder or non-negligent manslaughter.

Cases were subsequently verified utilizing media accounts, court documents, academic journal articles, books, and local law enforcement records obtained through Freedom of Information Act (FOIA) requests. Each data point was corroborated by multiple sources, which were compiled into a single document to assess the quality of information.

In case(s) of contradiction among sources, official law enforcement or court records were used, when available, followed by the most recent media or academic source.

Case information was subsequently compared with every other known mass murder database to ensure reliability and validity. Incidents listed in the SHR that could not be independently verified were excluded from the database.

Project researchers also conducted extensive searches for incidents not reported in the SHR during the time period, utilizing internet search engines, Lexis-Nexis, and Newspapers.com. Search terms include: [number] dead, [number] killed, [number] slain, [number] murdered, [number] homicide, mass murder, mass shooting, massacre, rampage, family killing, familicide, and arson murder. Offender, victim, and location names were also directly searched when available.

This project started at USA TODAY in 2012.

Contacts

Contact AP Data Editor Justin Myers with questions, suggestions or comments about this dataset at jmyers@ap.org. The Northeastern University researcher working with AP and USA TODAY is Professor James Alan Fox, who can be reached at j.fox@northeastern.edu or 617-416-4400.