This dataset was developed to study trends in the adoption of state public health laws during 1980-2010. Specifically, the dataset covers annual trends in seatbelt laws, speed limits for passenger vehicles on rural interstates, minimum legal drinking ages, drunk driving laws, laws prohibiting the purchase of alcohol on Sundays, regulations for registering purchased kegs and/or prohibitions against selling kegs, beer taxes and total alcohol tax revenues, motorcycle and bicycle helmet laws, cigarette taxes, cigarette advertising bans, bans on workplace smoking, bans on smoking in restaurants and bars, and tobacco taxes (total revenue). The dataset contains information about these laws for each year between 1980 and 2010, inclusive. In addition, it contains variables that describe the social, economic, demographic, health care, political, and crime chacteristics of the states in each of these years.

Data set for M. Paul and M. Dredze, "Discovering health topics in social media using topic models". This includes the set of tweets used in the experiments, and the words associatedwith ailments discovered by the Ailment Topic Aspect Model (ATAM). Contact: Michael Paul (mpaul39@gmail.com)Released June 26, 2014 atam.topwords.csv- The most probable words for each ailment. The first column is the ailment ID.The second column indicates if it is a general (G), symptom (S), or treatment (T) word.The third column is the word. The fourth column is the probability. Words are shownin descending order of probability until 90% of the probability mass is accumulatedfor each ailment or until probabilities drop below 1.0e-4. atam.tweets.x.csv (for x=[0-9])- The tweets used in the study. The first column is the tweet ID. The second columnindicates the ailment ID for the ailment sampled for that tweet.(See the atam.topwords.csv file for the most probable words associated with each ailment ID.)Full tweets can be downloaded using the tweet ID through the Twitter API(https://dev.twitter.com/docs/api/1.1). keywords.txt - The set of 269 health-related keywords used in our keyword-filtered Twitter stream as part of our dataset. keywords_x.txt (for x={diseases,symptoms,treatments})- The set of approximately 20,000 keyphrases crawled from wrongdiagnosis.com describingthe names of diseases, symptoms, and treatments and medications. These keyword lists areused to create input for ATAM (which requires phrases to be labeled as symptoms or treatments),and also to initially filter our dataset when constructing our health classifiers.

Data description In 2014, we randomly selected 1200 of 4520 medical students enrolled in a private Mexican university. 776 Medical students agreed to participate (64.6% response). Variables- unique anonymous identifier: "id"- school year in course (1,2,3,4): "school_year"- semester in course (1,2,3,4,5,6,7,8): "semester"- age in years: "age" - binary gender: "gender"- Height in meters: "height"- Weight in kg: "weight"- PHQ-9: "phq1", "phq2", ... "phq9"- Whether if the student has seriously thought dropping out of school: "thoughts_of_dropping_out"- Previous depression diagnosis: "previous_depression_diagnosis"- Previous medical treatment for depression: "previous_depression_treatment" - GAD-7: "gad1", "gad2", ... "gad7"- Previous anxiety diagnosis: "previous_anxiety_diagnosis"- Previous medical treatment for anxiety: "previous_anxiety_treatment" - Epworth Sleepiness Scale: "epworth1", "epworth2", ..."epworth9".- Self reported usual bed time (going to sleep): "bed_time"- Self reported usual wake up time: "wake_up_time"- Self reported night sleep hours: "reported_sleep_hours"- Self reported number of times for taking naps in a week: "times_week_nap"- Self reported usual duration of such naps: "nap_duration"- Self reported number of extra-school weekly hours dedicated to studying: "weekly_study_hours"- Self reported grade point average (1.0-10.0) to date: "grades"References: PHQ-9: Adewuya AO, Ola BA, Afolabi OO. Validity of the patient health questionnaire (PHQ-9) as a screening tool for depression amongst Nigerian university students. J Affect Disord. 2006;96:89- 93. GAD-7: Garcia-Campayo J, Zamorano E, Ruiz MA et al. Cultural adaptation into Spanish of the generalized anxiety disorder-7 (GAD-7) scale as a screening tool. Health and Quality of Life Outcomes. 2010;8. The GAD-7 scale was accurate for diagnosing generalised anxiety disorder. Spitzer RL, Kroenke K, Williams JB, et al. A brief measure for assessing generalised anxiety disorder: the GAD-7. Arch Intern Med 2006;166:1092–7. Epworth Sleepiness Scale: Johns, Murray W. "A new method for measuring daytime sleepiness: the Epworth sleepiness scale." sleep 14.6 (1991): 540-545. Campos-Morales, Rosa M., et al. "Sleepiness, performance and mood state in a group of Mexican undergraduate students." Biological rhythm research 36.1-2 (2005): 9-13.

In 2008, a group of uninsured low-income adults in Oregon was selected by lottery to be given the chance to apply for Medicaid. This lottery provides an opportunity to gauge the effects of expanding access to public health insurance on the health care use, financial strain, and health of low-income adults using a randomized controlled design. This file contains Oregon Health Insurance Experiment public use data, as well as replication code for Finkelstein et al. (2012, QJE), Baicker et al. (2013, NEJM), Taubman et al. (2014, Science), and Finkelstein et al. (2016, NEJM).

The notes field contains the full MEDLINE (Ovid) search strategy for practice context in health professions. The file in this dataset contains the full MEDLINE (Ovid), EMBASE (Ovid), and CINAHL (EBSCO) search strategies for workplace or organizational context or competencies in health professions. Search date: December 10, 2019

Coral disease transmission experiments were completed for dark-spot syndrome on Sidereastrea siderea and yellow-band disease on Orbicella faveolata, as described in Randall et al. 2016. Following experimentation, microbial communities were extracted from tissue samples to determine whether any potential pathogen may have transmitted from healthy to exposed corals. Microbial communities on healthy corals were compared with diseased corals to identify any potential pathogens.

Experimental diseased and healthy corals were sampled and their microbial communities were analyzed using 454 Illumina pyrosequencing of the amplified 16S rRNA gene on the V1-V3 hypervariable region.

These datasets are concordance files that link the Geographic Classification for Health (GCH) to statistical geographies and geographic units commonly used in health research and analysis in Aotearoa New Zealand (NZ). More information about the develppment of the GCH is available in our Open Access publication. Our long-term aim is the comprehensive and accurate understanding of urban-rural variation in health outcomes and healthcare utilization at both national and regional levels. This is best achieved by the widespread uptake of the GCH by health researchers and health policy makers. The GCH is straightforward to use and most users will only need the relevant concordance file.
Statistical Area 1s (SA1s, small statistical areas which are the output geography for population data) were used as the building blocks for the Geographic Classification for Health (GCH) and are the preferred small areas when undertaking the analysis of health data using the GCH. It is however appreciated that a lot of health data is not available at the SA1 level and GCH concordance files are also available for Domicile (Census Area Units, CAU) and Statistical Area 2s (SA2) and Meshblock. The following concordance files are available in excel format:

SA12018_to_GCH2018.csv This concordance file applies a GCH category to each SA1 in NZ SA22018_to_GCH2018.csv This concordance file applies a GCH category to each SA2 in NZ MoH_HDOM_to_GCH2018.csv This concordance file applies a GCH category to each Domicile in NZ. Please read the additional information below if you plan to use this concordance file. MoH_MB_to_GCH2018.csv This concordance file applies a GCH category to each Meshblock in NZ. Please read the additional information below if you plan to use this concordance file.

Additional information relating to geographic units used by the Ministry of Health:

MoH_HDOM_to_GCH2018.csv This file has been designed specifically to add GCH to the Ministry of Health (MoH) datasets containing Domicile codes. Use this file if your dataset contains only Domicile codes. If your dataset also contains Meshblock codes, then use the MoH Meshblock to GCH concordance file. This file includes 2006 and 2013 domicile codes. The 2013 domiciles are still current as of 2022, and this file will still work well with data outside those years. Domicile boundaries do not align well with SA1 boundaries, and longitudinal health data usually contains some older Domiciles which have been phased out and replaced with multiple smaller Domiciles. These deprecated Domiciles may overlap multiple SA1s. Usually, all such SA1s belong to the same GCH category. Occasionally, a Domicile will overlap more than one GCH category. When this happens, we have assigned the GCH category to which the majority of people living in that Domicile belong. By necessity, this will allocate a minority of people in those Domiciles to a GCH category to which they do not belong.
MoH_MB_to_GCH2018.csv This file has been designed specifically to add GCH to Ministry of Health (MoH) datasets containing Meshblock codes. This file includes 2018, 2013, 2006, and 2001 Meshblock codes, but will still work well with data outside those years. Meshblock boundaries from census 2018 fit perfectly and completely within the Statistics New Zealand Statistical Area 1s (SA1) boundaries on which GCH is based. However, longitudinal health data usually contains some older Meshblocks which have been phased out and replaced by multiple smaller Meshblocks. These deprecated Meshblocks may overlap multiple SA1s. Usually, all such SA1s belong to the same GCH category. Occasionally, a Meshblock will overlap more than one GCH category. When this happens, we have assigned the GCH category to which the majority of people living in that Meshblock belong. By necessity, this will allocate a minority of people in those Meshblocks to a GCH category to which they do not belong.

This interactive visualization represents data extracted from 767 articles included in a scoping review on the topic of using biological feedback as a behavior change technique in adults. The height of each node represents the quantity of articles from which that variable was extracted. The links between nodes demonstrate the proportional relationship between variables. Viewers can filter the diagram by year and variable to best suit their interests. The filtered data, which include the DOIs and PubMed IDs, can then be downloaded so the viewer can use the relevant articles to support their own research.

For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu This item is part of the University of Arizona Libraries 2023 Data Visualization Challenge and was awarded second place in the graduate/professional student category.

Percentage of the population with self-reported mental health outcomes of anxiety, bipolar and depression for Statistical Area 2 (2018) units. Original data sourced from Census 2018 and New Zealand Health Survey 2017/18 and 2018/19. Data provided are synthetic data produced from spatial microsimulation modelling.

The National Longitudinal Study of Adolescent to Adult Health (Add Health) Parent Study Public Use collection includes data gathered as part of the Add Health longitudinal survey of adolescents. The original Add Health survey is a longitudinal study of a nationally representative sample of adolescents in grades 7-12 in the United States during the 1994-1995 school year. In Wave 1 of the Add Health Study (1994-1995), a parent of each Add Health Sample Member (AHSM) was interviewed. The Add Health Parent Study gathered social, behavioral, and health survey data in 2015-2017 from the parents of Add Health Sample members who were originally interviewed at Wave 1 (1994-1995). Wave 1 Parents were asked about their adolescent children, their relationships with them, and their own health.

The Add Health Parent Study interview is a comprehensive survey of Add Health parents' family relations, education, religious beliefs, physical and mental health, social support, and community involvement experiences. In addition, survey data contains cognitive assessments, a medications log linked to a medications database lookup table, and household financial information collection. The survey also includes permission for administrative data linkages and includes data from a Family Health History Leave-Behind questionnaire. Interviews were conducted with parents' spouse/partner when available.

Research domains targeted in the survey and research questions that may be addressed using the Add Health Parent Study data include:

• Health Behaviors and Risks Many health conditions and behaviors run in families; for example, cardiovascular disease, obesity and substance abuse. How are health risks and behaviors transmitted across generations or clustered within families? How can we use information on the parents' health and health behavior to better understand the determinants of their (adult) children's health trajectories?
• Cognitive Functioning and Non-Cognitive Personality Traits What role does the intergenerational transmission of personality and locus of control play in generating intergenerational persistence in education, family status, income and health? How do the personality traits of parents and children, and how they interact, influence the extent and quality of intergenerational relationships and the prevalence of assistance across generations?
• Decision-Making, Expectations, and Risk Preferences Do intergenerational correlations in risk preferences represent intergenerational transmission of preferences? If so, are the transmission mechanisms a factor in biological and environmental vulnerabilities? Does the extent of genetic liability vary in response to both family-specific and generation-specific environmental pressures?
• Family Support, Relationship Quality and Ties of Obligation How does family complexity affect intergenerational obligations and the strength of relationship ties? As parents near retirement: What roles do they play in their children's lives and their children in their lives? What assistance are they providing to their adult children and grandchildren? What do they receive in return? And how do these ties vary with divorce, remarriage and familial estrangement?
• Economic Status and Capacities What are the economic capacities of the parents' generation as they reach their retirement years? How have fared through the wealth and employment shocks of the Great Recession? Are parents able to provide for their own financial need? And, do they have the time and financial resources to help support their children and grandchildren and are they prepared to do so?

OBJECTIVES

Poor discharge communication is associated with negative health outcomes in high-income countries. However, quality of discharge communication has received little attention in India and many other low and middle-income countries. Primary Objective To investigate verbal and documented discharge communication for chronic non-communicable disease (NCD) patients. Secondary objective To explore the relationship between quality of discharge communication and health outcomes.

METHODS:

Design Prospective study.

Setting: Three public hospitals in Himachal Pradesh and Kerala states, India.

Participants: 546 chronic NCD (chronic respiratory disease, cardiovascular disease or diabetes) patients. Piloted questionnaires were completed at admission, discharge and Five and eighteen-week follow-up covering health status, health-seeking behaviour and healthcare information exchange practices. Logistic regression was used to explore the relationship between quality of discharge communication and health outcomes.

Outcome Measures:

Primary: Patient recall and experiences of verbal and documented discharge communication.

Secondary: Death, hospital readmission and self-reported deterioration of NCD/s.

RESULTS

All patients received discharge notes, which were predominantly on minimally structured sheets of paper (71%); 31% of notes contained all of the following information required for facilitating continuity of care: diagnosis, medication information, lifestyle advice, and follow-up instructions. Patient reports indicated notable variations in verbal information provided during discharge consultations; 50% received ongoing treatment/management information and 23% received lifestyle advice. Within 18 weeks of follow-up, 25 (5%) patients had died, 69 (13%) had been readmitted and 62 (11%) reported that their chronic NCD/s had deteriorated. Significant associations were found between low-quality documented discharge communication and death (AOR=3.00; 95% CI 1.27,7.06) and low-quality verbal discharge communication and self-reported deterioration of chronic NCD/s (AOR=0.46; 95% CI 0.25,0.83) within 18-weeks of follow-up.

CONCLUSIONS

Sub-optimal discharge practices may be compromising the continuity and safety of chronic NCD patient care. Structured protocols, documents and training are required to improve discharge communication, healthcare integration and overall NCD management.

There are several Microsoft Word documents here detailing data creation methods and with various dictionaries describing the included and derived variables.The Database Creation Description is meant to walk a user through some of the steps detailed in the SAS code with this project.The alphabetical list of variables is intended for users as sometimes this makes some coding steps easier to copy and paste from this list instead of retyping.The NIS Data Dictionary contains some general dataset description as well as each variable's responses.

We propose a methodology to classify individuals into few but meaningful health groups by estimating a panel Markov switching model that exploits rich information from panel household surveys. Using the HRS, we identify four persistent health groups, depending on individual's physical and mental disabilities. Our classification outperforms existing health measures at explaining entry in nursing homes, home health care, out-of-pocket medical expenses, and mortality for individuals in the HRS, ELSA, and SHARE. Through a workhorse model of savings, we recover an asset cost of bad health that is twice as big as when using self-reported health.

Home Health Agencies (HHA) provide at home skilled nursing, personal care and therapeutic services. Hospices provide palliative care and alleviate the physical, emotional, social and spiritual discomforts of an individual who is experiencing the last phases of life due to the existence of a terminal disease. In addition, hospices provide supportive care for the primary care giver and the family of the hospice patient. Home health agencies and hospices submit an annual utilization report to the Office at the end of each calendar year. The report includes information on services capacity, visits, utilization, patient characteristics, and capital/equipment expenditures, and gross revenues. The documentation, including report forms, is available for each reporting year.

269 health-related keywords that are used in our keyword-filtered Twitter stream.

This is the dataset for the resulting Reddit Health Taxonomy for the paper 1.Data Format: each line corresponds to one symptom community (Level-2) having the following format:depression:anxiety 1:1 519 6A73 Mixed depressive and anxiety disorder 314468192 anxieti,depress,panic attack,anxious,social anxieti,suicidal thought,...depression:anxiety -- is the community name, i.e., its corresponding condition,1:1 -- community id,519 -- number of symptoms in the community,6A73 Mixed depressive and anxiety disorder 314468192 -- ICD-11 code corresponding to the selected condition,anxieti,depress,panic attack,anxious,social anxieti,suicidal thought,... -- symptoms sorted by their importance for this community (InfoMap flow value; see the paper for more info).Empty lines separate the Level-1 communities that consist of several Level-1 communities.[1] Šćepanović, S., Aiello, L. M., Zhou, K., Joglekar, S., & Quercia, D. (2021, May). The Healthy States of America: Creating a Health Taxonomy with Social Media. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 15, pp. 621-632).

Healthcare expenditure statistics, produced to the international definitions of the System of Health Accounts 2011.

Subcategories may not sum to aggregates due to rounding.

The dataset and code that supports our analysis on " The cumulative effect of PM2.5 components is larger than the effect of PM2.5 mass on child health in India" are attached herewith

We are releasing under the CC-BY licence a new large-scale dataset for Automatic Symptom Detection (ASD) and Automatic Diagnosis (AD) systems in the medical domain. The dataset contains patients synthesized using a proprietary medical knowledge base and a commercial rule-based AD system. Patients in the dataset are characterized by their socio-demographic data, a pathology they are suffering from, a set of symptoms and antecedents related to this pathology, and a differential diagnosis. The symptoms and antecedents can be binary, categorical and multi-choice, with the potential of leading to more efficient and natural interactions between ASD/AD systems and patients. To the best of our knowledge, this is the first large-scale dataset that includes the differential diagnosis, and non-binary symptoms and antecedents. For more information, please check our paper.