Interactive Summary Health Statistics for Adults provide annual estimates of selected health topics for adults aged 18 years and over based on final data from the National Health Interview Survey.

The datasets included on this page contain walking data from twelve unimpaired adults walking on a treadmill while wearing bilateral passive ankle exoskeletons. Datasets are four minutes long, and contain kinematic and ground reaction force data, and electromyography from seven leg muscles bilaterally.

The associated Python code can be used to generate data-driven predictive models of response to the ankle exoskeletons. The associated MATLAB code can be used to perform statistical analyses of the data.

This project includes the following software/data packages:

• Modeling and analysis : These files contain CSV files of inverse kinematics results, combined with experimental electromyography data and estimated exoskeleton torque profiles. Code for data-driven modeling and analysis are included.
• Simulation datasets : Datasets from the manuscript: Rosenberg MC, et al., "Predicting walking response to ankle exoskeletons using data-driven models," Submitted to: Journal of the Royal Society Interface, 2020.
• Template Signatures code : This package contains MATLAB-based code package to identify hybrid Template Signatures of center-of-mass dynamics during walking with ankle exoskeletons. Modeling, analysis, and plotting code sets are included.

  Some functions are unmodified from: Mangan NM, Kutz JN, Brunton SL, Proctor JL. Model selection for dynamical systems via sparse regression and information criteria. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences. 2017 Aug 31;473(2204):20170009.

• Template Signatures datasets : This package contains CSV files of center-of-mass kinematics, and foot position estimates from OpenSim 3.3 for 12 unimpaired adults and one adult with post-stroke hemiparesis during walking with and without ankle exoskeletons. Participant demographics are also included. A sample synthetic dataset of a spring-loaded inverted pendulum walker is included for validation of the Hybrid-SINDy algorithm.

Analysis of ‘NHIS Adult Summary Health Statistics’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/83cbf755-612a-40f8-9225-f3461dc5df01 on 11 February 2022.

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

Interactive Summary Health Statistics for Adults — 2019-2020 provide annual estimates of selected health topics for adults aged 18 years and over based on final data from the National Health Interview Survey.

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

The purpose of the study is to compare three methods of achieving weight loss in primary care medical practice. The study will be conducted in six primary care practices. Weight management will be provided to a total of 390 obese patients (who have 2 or more components of the metabolic syndrome) by their own primary care providers, in conjunction with the practices' auxiliary health professionals, including medical assistants.

This data is linked to survey responses generated for understanding implementation of strengths-based practice in England.

This dataset contains different supplementary material of Genovart et al. 2024, "Individual choices of wintering areas drive adult survival heterogeneity in a long-lived seabird", Ecology and Evolution, to be able to replicate these and similar results: - R code for users: R scripts allow to estimate values of zenith, zenith0 and alpha (mean & sd) for every geolocator from the calibration periods. Also, they allow to model twlight and geolocation data to ultimately estimate coordinates. - Data on three examples of the study, including: (a) geolocation data (.lig & .trn files), (b) key date information on activation and calibration of those three geolocators as well as the specific phenology of the examples (.csv files), and (c) a landmask we used for the data filtering (spatial polygons).

The adult day care software market is experiencing robust growth, driven by an aging global population and increasing demand for efficient and technologically advanced care solutions. The market's expansion is fueled by several key factors: the rising adoption of electronic health records (EHR) to improve care coordination and reduce medical errors, the increasing need for streamlined administrative tasks within adult day care centers, the growing preference for data-driven decision-making to optimize care delivery and resource allocation, and the potential for improved communication and engagement with family members through digital platforms. The market is segmented by software type (e.g., scheduling, billing, client management), deployment mode (cloud-based vs. on-premise), and user type (staff, caregivers, family). While the initial investment in software can be a restraint for some smaller facilities, the long-term benefits in terms of operational efficiency, improved care quality, and increased revenue often outweigh the costs. Competition among established players like WellSky and emerging startups is driving innovation and offering diverse solutions tailored to various needs and budgets. We estimate a current market size of approximately $250 million in 2025, projecting a compound annual growth rate (CAGR) of 15% over the forecast period (2025-2033), reaching an estimated $800 million by 2033. This substantial growth reflects the increasing reliance on technology to manage the complexities of adult day care operations. The competitive landscape includes both large, established players with extensive product portfolios and smaller, specialized companies offering niche solutions. Companies like Ankota, RAVAD Software, and WellSky are significant players, while others like StoriiCare and MyAdultDayCare cater to specific market segments or offer unique features. Further market segmentation and geographic expansion will likely define future growth trajectories. North America and Europe currently dominate the market, driven by high adoption rates and advanced healthcare infrastructure. However, growth opportunities exist in rapidly developing economies in Asia-Pacific and Latin America, spurred by increased government initiatives focused on aging population support and technological advancements in the healthcare sector. The integration of telehealth capabilities and AI-powered analytics within adult day care software is expected to be a major growth driver in the coming years.

Sample identification and summary of data. (XLSX 15Â kb)

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

The adult education market is experiencing robust growth, driven by a rising demand for upskilling and reskilling to meet the evolving needs of the workforce and personal enrichment. The market's expansion is fueled by several key factors, including the increasing adoption of online learning platforms offering flexibility and accessibility, the growing awareness of the importance of lifelong learning for career advancement, and government initiatives promoting adult education programs. While the precise market size for 2025 is unavailable, a reasonable estimation based on industry trends and assuming a conservative CAGR of 7% (a common rate for education sectors) from a hypothetical 2019 market size of $150 billion, would place the 2025 market size at approximately $220 billion. This growth is not uniform across all segments. Online teaching is expected to continue its rapid expansion, surpassing offline teaching in market share within the next decade, driven by technological advancements and increased digital literacy. The formal structured learning segment will remain dominant, however, the non-formal learning segment is experiencing significant growth, fueled by the rising popularity of short courses, workshops, and professional development programs that cater to specific skill sets. Geographic variations also exist, with North America and Europe currently holding the largest market shares. However, Asia-Pacific is projected to experience the most significant growth over the forecast period, driven by expanding economies and rising disposable incomes. Despite the significant growth potential, the adult education market faces certain challenges. These include the affordability of education, particularly for those from low-income backgrounds, competition from alternative learning resources, and the need for continuous curriculum updates to keep pace with technological advancements and evolving industry demands. Sustained growth will hinge on addressing these challenges through innovative financing models, improved accessibility to technology and resources, and the creation of flexible and relevant learning pathways that cater to diverse learner needs and preferences. The increasing emphasis on data-driven personalization and adaptive learning technologies will likely play a pivotal role in shaping the future of the adult education landscape. Furthermore, the emergence of micro-credentials and specialized certifications further diversifies the market and adds complexity to the competitive landscape.

BackgroundSegmentation of heterogeneous patient populations into parsimonious and relatively homogenous groups with similar healthcare needs can facilitate healthcare resource planning and development of effective integrated healthcare interventions for each segment. We aimed to apply a data-driven, healthcare utilization-based clustering analysis to segment a regional health system patient population and validate its discriminative ability on 4-year longitudinal healthcare utilization and mortality data.MethodsWe extracted data from the Singapore Health Services Electronic Health Intelligence System, an electronic medical record database that included healthcare utilization (inpatient admissions, specialist outpatient clinic visits, emergency department visits, and primary care clinic visits), mortality, diseases, and demographics for all adult Singapore residents who resided in and had a healthcare encounter with our regional health system in 2012. Hierarchical clustering analysis (Ward’s linkage) and K-means cluster analysis using age and healthcare utilization data in 2012 were applied to segment the selected population. These segments were compared using their demographics (other than age) and morbidities in 2012, and longitudinal healthcare utilization and mortality from 2013–2016.ResultsAmong 146,999 subjects, five distinct patient segments “Young, healthy”; “Middle age, healthy”; “Stable, chronic disease”; “Complicated chronic disease” and “Frequent admitters” were identified. Healthcare utilization patterns in 2012, morbidity patterns and demographics differed significantly across all segments. The “Frequent admitters” segment had the smallest number of patients (1.79% of the population) but consumed 69% of inpatient admissions, 77% of specialist outpatient visits, 54% of emergency department visits, and 23% of primary care clinic visits in 2012. 11.5% and 31.2% of this segment has end stage renal failure and malignancy respectively. The validity of cluster-analysis derived segments is supported by discriminative ability for longitudinal healthcare utilization and mortality from 2013–2016. Incident rate ratios for healthcare utilization and Cox hazards ratio for mortality increased as patient segments increased in complexity. Patients in the “Frequent admitters” segment accounted for a disproportionate healthcare utilization and 8.16 times higher mortality rate.ConclusionOur data-driven clustering analysis on a general patient population in Singapore identified five patient segments with distinct longitudinal healthcare utilization patterns and mortality risk to provide an evidence-based segmentation of a regional health system’s healthcare needs.

Right now we see that depression is one of the most common problems in our society. Most of the time people are committed suicide only cause of depression. And till now there is no proper lab test way for detecting depression. Generally, doctors are detecting depression by asking some knowledge-base questions. On the other hand, there are a good number of people using social media platforms right now, where they are sharing their daily experiences, emotion, and other activity with their friends. Twitter is one of the common social platforms and also popular for data collection. I was collecting these datasets from twitter based on some depressive words. I hope that this twitter datasets will help researchers to detect depression more precisely.

A number of species are affected by sex ratio meiotic drive (SR), a selfish genetic element located on the X chromosome that causes dysfunction of Y-bearing sperm. SR is transmitted to up to 100% of offspring, causing extreme sex ratio bias. SR in several species is found in a stable polymorphism at a moderate frequency, suggesting there must be strong frequency-dependent selection resisting its spread. We investigate the effect of SR on female and male egg-to-adult viability in the Malaysian stalk-eyed fly, Teleopsis dalmanni. SR meiotic drive in this species is old, and appears to be broadly stable at a moderate (~20%) frequency. We use large-scale controlled crosses to estimate the strength of selection acting against SR in female and male carriers. We find that SR reduces the egg-to-adult viability of both sexes. In females, homozygous females experience greater reduction in viability (sf = 0.242) and the deleterious effects of SR are additive (h = 0.511). The male deficit in viabil...

These data represent the predicted (modeled) prevalence of Frequent Mental Distress among adults (Age 18+) for each census tract in Colorado. Frequent Mental Distress is defined as experiencing more than 14 mentally unhealthy days within the past 30 days in which mental health was "not good." Health conditions for measuring mental health include stress, depression, and problems with emotions.The estimate for each census tract represents an average that was derived from multiple years of Colorado Behavioral Risk Factor Surveillance System data (2014-2017).CDPHE used a model-based approach to measure the relationship between age, race, gender, poverty, education, location and health conditions or risk behavior indicators and applied this relationship to predict the number of persons' who have the health conditions or risk behavior for each census tract in Colorado. We then applied these probabilities, based on demographic stratification, to the 2013-2017 American Community Survey population estimates and determined the percentage of adults with the health conditions or risk behavior for each census tract in Colorado.The estimates are based on statistical models and are not direct survey estimates. Using the best available data, CDPHE was able to model census tract estimates based on demographic data and background knowledge about the distribution of specific health conditions and risk behaviors.The estimates are displayed in both the map and data table using point estimate values for each census tract and displayed using a Quintile range. The high and low value for each color on the map is calculated based on dividing the total number of census tracts in Colorado (1249) into five groups based on the total range of estimates for all Colorado census tracts. Each Quintile range represents roughly 20% of the census tracts in Colorado. No estimates are provided for census tracts with a known population of less than 50. These census tracts are displayed in the map as "No Est, Pop < 50."No estimates are provided for 7 census tracts with a known population of less than 50 or for the 2 census tracts that exclusively contain a federal correctional institution as 100% of their population. These 9 census tracts are displayed in the map as "No Estimate."

The purpose of this multi-center event-driven study in participants with anemia associated with chronic kidney disease (CKD) to evaluate the safety and efficacy of daprodustat.

The purpose of this multi-center event-driven study in non-dialysis (ND) participants with anemia associated with chronic kidney disease (CKD) is to evaluate the safety and efficacy of daprodustat compared to darbepoetin alfa.

U.S And Europe Smart Adult Diapers Market size was valued at USD 321.45 Million in 2023 and is projected to reach USD 734.69 Million by 2031, growing at a CAGR of 12.53% from 2024 to 2031.

U.S And Europe Smart Adult Diapers Market Overview

A notable trend in the smart adult diaper market is the integration of sensor technology into digital healthcare solutions. These advanced diapers collect data to analyze trends, detect anomalies, and predict changes in the wearer’s condition, leading to timely interventions and optimized care plans. This data-driven approach also informs product development, allowing companies to create innovations that better meet user needs. Additionally, the increasing shift toward home healthcare services is fueling demand for smart adult diapers, specifically designed to meet the needs of in-home caregivers. This trend reflects the growing preference for personalized and home-based care solutions.

Analysis of ‘NHIS Adult Summary Health Statistics’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/758b48de-df3d-4e90-8b85-37dc3e534abd on 11 February 2022.

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

Interactive Summary Health Statistics for Adults — 2019-2020 provide annual estimates of selected health topics for adults aged 18 years and over based on final data from the National Health Interview Survey.

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

Percentiles corresponding to the 3 archetypal foot shapes of women and men obtained using variables.

Provide residential care services for individuals with disabilities aged 18 and above (individuals with special needs can be extended to 15 years old), who have a disability certificate through the evaluation of disability needs by the competent authority or possess a disability handbook, and have moderate or higher needs for day care services, activities of daily living, and social adaptation training. Services are available from Monday to Friday, from 8:00 to 17:00.