This dataset provides a realistic look at how important lifestyle and human characteristics are used to calculate health insurance rates. It presents a wide range of people, each characterized by their age, gender, BMI, number of dependents, smoking status, and geographic location, as well as the associated insurance bills they received.

We can find important patterns by examining this dataset, like: Why smokers pay noticeably higher premiums How age and BMI affect medical expenses Whether insurance costs are higher in some areas The connection between charges and family size

The number of people with private health insurance which is additional health cover to that provided under Medicare, to reimburse all or part of the cost of hospital or other medical services incurred by an individual, 2014-15 (all entries that were classified as not shown, not published or not applicable were assigned a null value; no data was provided for Maralinga Tjarutja LGA, in South Australia). The data is by LGA 2015 profile (based on the LGA 2011 geographic boundaries). For more information on statistics used please refer to the PHIDU website, available from: http://phidu.torrens.edu.au/ Source: Estimates for Population Health Areas (PHAs) are modelled estimates and were produced by the ABS; estimates at the LGA and PHN level were derived from the PHA estimates.

The USRDS is the largest and most comprehensive national ESRD surveillance system in the US (Collins et al., 2015). The USRDS contains data on all ESRD cases in the US through the Medical Evidence Report CMS-2728 which is mandated for all new patients diagnosed with ESRD (Foley and Collins, 2013). Detailed information about the USRDS can be found on their website (http://www.usrds.org). The EQI was constructed for 2000-2005 for all US counties and is composed of five domains (air, water, built, land, and sociodemographic), each composed of variables to represent the environmental quality of that domain. Domain-specific EQIs were developed using principal components analysis (PCA) to reduce these variables within each domain while the overall EQI was constructed from a second PCA from these individual domains (L. C. Messer et al., 2014). To account for differences in environment across rural and urban counties, the overall and domain-specific EQIs were stratified by rural urban continuum codes (RUCCs) (U.S. Department of Agriculture, 2015). This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Human health data are not available publicly. EQI data are available at: https://edg.epa.gov/data/Public/ORD/NHEERL/EQI. Format: Data stored as csv files. This dataset is associated with the following publication: Kosnik, M., D. Reif, D. Lobdell, T. Astell-Burt, X. Feng, J. Hader, and J. Hoppin. Associations between access to healthcare, environmental quality, and end-stage renal disease survival time: Proportional-hazards models of over 1,000,000 people over 14 years. PLoS ONE. Public Library of Science, San Francisco, CA, USA, 14(3): e0214094, (2019).

Context

This public dataset contains data concerning the public and private insurance companies provided by IRDAI(Insurance Regulatory and Development Authority of India) from 2013-2022. This is a multi-index data and can be a great practice to hone manipulation of pandas multi-index dataframes. Mainly, the business of the companies (total premiums and number of policies), subscription information(number of people subscribed), Claims incurred and the Network hospitals enrolled by Third Party Administrators are attributes focused by the dataset.

Content

The Excel file contains the following data | Table No.| Contents| | --- | --- | |**A**|**III.A: HEALTH INSURANCE BUSINESS OF GENERAL AND HEALTH INSURERS**| |62| Health Insurance - Number of Policies, Number of Persons Covered and Gross Premium| |63| Personal Accident Insurance - Number of Policies, Number of Persons Covered and Gross Premium| |64| Overseas Travel Insurance - Number of Policies, Number of Persons Covered and Gross Premium| |65| Domestic Travel Insurance - Number of Policies, Number of Persons Covered and Gross Premium| |66| Health Insurance - Net Premium Earned, Incurred Claims and Incurred Claims Ratio| |67| Personal Accident Insurance - Net Premium Earned, Incurred Claims and Incurred Claims Ratio| |68| Overseas Travel Insurance - Net Earned Premium, Incurred Claims and Incurred Claims Ratio| |69| Domestic Travel Insurance - Net Earned Premium, Incurred Claims and Incurred Claims Ratio| |70| Details of Claims Development and Aging - Health Insurance Business| |71| State-wise Health Insurance Business| |72| State-wise Individual Health Insurance Business| |73| State-wise Personal Accident Insurance Business| |74| State-wise Overseas Insurance Business| |75| State-wise Domestic Insurance Business| |76| State-wise Claims Settlement under Health Insurance Business| |**B**|**III.B: HEALTH INSURANCE BUSINESS OF LIFE INSURERS**| |77| Health Insurance Business in respect of Products offered by Life Insurers - New Busienss| |78| Health Insurance Business in respect of Products offered by Life insurers - Renewal Business| |79| Health Insurance Business in respect of Riders attached to Life Insurance Products - New Business| |80| Health Insurance Business in respect of Riders attached to Life Insurance Products - Renewal Business| |**C**|**III.C: OTHERS**| |81| Network Hospital Enrolled by TPAs| |82| State-wise Details on Number of Network Providers |

This table contains data on the perceived state of health and on contacts with providers of medical care of the Dutch population in private households. These data can be grouped by several personal characteristics.

Data available from: 2014

Status of the data: final.

Changes as of 5 March 2021: The figures for 2020 have been added. The table was expanded with figures about the SF-12. The 'Short Format 12' or the SF-12 questionnaire for short is a selection of 12 questions from the SF-36. Based on this questionnaire, the summary measure for physical health (norm score physical) and a summary measure for psychological health (norm score psychological) were calculated and added to the table. The results of the 12 separate items were also included. In addition, the value (and associated lower and upper limit) for oral health has omitted for the 12 to 16 year olds age group and the 12 to 18 year old age group in 2019. These figures were only related to the age 15 year or 15 to 18 year because the question was posed to people 15 years of age or older.

When will new data be published? Data on reporting year 2021 will be published in the second quarter of 2022.

From 2015, long-term care will be financed by several laws: the Long-Term Care Act (Wlz), the Social Support Act 2015 (Wmo 2015), the Healthcare Insurance Act and the Youth Act. Under these laws, care can be purchased as care in kind or paid for from a personal budget (pgb). Budget holders enter into agreements with care providers and expense claims are submitted for the use of care from a personal budget. This table shows the number and share of people using a personal budget (PGB) for long-term care in the year under review and on the reference date, and the number of care providers per person. This table only contains persons who have been granted a PGB and are registered in the Municipal Personal Records Database (BRP) at the reference time. These are personal budgets that are actually used to purchase care and care providers for whom a claim has been submitted. The figures are broken down by gender and age on 31 December of the budget holder's reporting year, the number of care providers per year per budget holder and healthcare act and sector, and the level of care according to indication. The self-employed of the indication is determined on the commencement date of the declaration. Claims can continue if the indication has been adjusted. As a result, people can end up in several self-employed categories at the same time, so that the sum of the detailed data can deviate from the total. The mental healthcare sector is not included in this table due to low numbers. Data available from: 2015 Status of the figures: The figures for the last year are provisional, the figures for previous years are final. Changes as of January 31, 2023: - The provisional figures for 2020 have been made final. - Added the provisional figures for 2021 without the data of the Health Insurance Act because these data are not yet available. Changes as of 1 June 2022: The 'regulations' are now called 'care laws' and have been expanded with the Youth Act and Zvw from 2015. The most recent population data have been used in this redesign. As a result, the figures for 2015-2018 have been adjusted by less than 1 percent. When will new numbers come out? The provisional figures will be published no later than 12 months after the end of the year under review. With a new publication, the figures for the previous years are given the final status.

Context

Machine Learning with R by Brett Lantz is a book that provides an introduction to machine learning using R. As far as I can tell, Packt Publishing does not make its datasets available online unless you buy the book and create a user account which can be a problem if you are checking the book out from the library or borrowing the book from a friend. All of these datasets are in the public domain but simply needed some cleaning up and recoding to match the format in the book.

Content

Columns - age: age of primary beneficiary

• sex: insurance contractor gender, female, male

• bmi: Body mass index, providing an understanding of body, weights that are relatively high or low relative to height, objective index of body weight (kg / m ^ 2) using the ratio of height to weight, ideally 18.5 to 24.9

• children: Number of children covered by health insurance / Number of dependents

• smoker: Smoking

• region: the beneficiary's residential area in the US, northeast, southeast, southwest, northwest.

• charges: Individual medical costs billed by health insurance

Acknowledgements

The dataset is available on GitHub here.

Inspiration

Can you accurately predict insurance costs?

Information on hospitalizations of COPD patients from electronic health records linked to air pollution concentrations for the study period. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Can be requested through NCTracts https://tracs.unc.edu/index.php/services/comparative-effectiveness-research/data-linkage. Format: Data used in this analysis include electronic health records from the UNC healthcare system. This dataset is associated with the following publication: Cowan, K., L. Wyatt, T. Luben, J. Sacks, C. Ward-Caviness, and K. Rappazzo. Effect measure modification of the association between short-term exposures to PM2.5 and hospitalizations by longs-term PM2.5 exposure among a cohort of people with Chronic Obstructive Pulmonary Disease (COPD) in North Carolina, 2002–2015. ENVIRONMENTAL HEALTH. Academic Press Incorporated, Orlando, FL, USA, 22: 49, (2023).

ObjectivesAs part of a larger mixed-methods study into harm reduction in the hospital setting and people with lived experience of methamphetamine use, stigma was found to be a prominent issue. The aim of this secondary analysis was to investigate the issue of stigma.DesignParticipants completed a one-time qualitative interview component to assess their experiences in the hospital setting.SettingThe study setting included secondary and tertiary care in Southwestern Ontario, Canada. Participants who had received care from these settings were also recruited from an overdose prevention site, a primary healthcare center, a national mental health organization, an affordable housing agency, and six homeless-serving agencies between October 2020 and April 2021.ParticipantsA total of 104 individuals completed the qualitative component of a mixed-methods interview. Sixty-seven participants identified as male, thirty-six identified as female, and one identified as non-binary. Inclusion criteria included past or current use of methamphetamine, having received services from a hospital, and being able to communicate in English.MethodsOpen-ended questions regarding experiences in the hospital setting were asked in relation to the lived experience of methamphetamine. A secondary analysis was conducted post-hoc using a thematic ethnographic approach due to prominent perceptions of stigma.ResultsThree themes were identified. The first theme identified that substance use was perceived as a moral and personal choice; the second theme pertained to social stigmas such as income, housing and substance use, and consequences such as being shunned or feeling less worthy than the general patient population; and the third theme highlighted health consequences such as inadequate treatment or pain management.ConclusionThis study revealed that stigma can have consequences that extend beyond the therapeutic relationship and into the healthcare of the individual. Additional training and education for healthcare providers represents a key intervention to ensure care is non-stigmatizing and patient-centered, as well as changing hospital culture.

This table contains data on the perceived state of health and on contacts with providers of medical care of the Dutch population from 0 years on in private households. These data can be grouped by several personal characteristics. For several topics a different age demarcation applies. The age boundaries are listed at the relevant topics. Data available from: 2014 Status of the data: final. Changes by April 3, 2025: Data about 2024 were added. The table has been expanded with a number of new variables. In addition to the percentage of people with (very) good perceived health, the percentage of people who answer fair and (very) bad to the question about perceived health is now also shown. The percentage of people who answer fair and (very) bad to the question about perceived oral health has also been added for perceived oral health. In addition, information about oral complaints (toothache and bleeding gums) has been added for people aged 12 years or older. Information about functional complaints has been added for people aged 65 years or older (chewing food and swallowing food). Information about brushing teeth/molars and information about flossing, using a toothpick, and brushing for people aged 1 year or older has been added. Finally, a variable "Contact dentist and/or dental hygienist" has been added and 'Contact dental hygienist' is now shown for the entire population and no longer for people aged 12 years or older. Figures on the average number of ‘OECD disabilities per person in the population’ and on the average number of ‘OECD disabilities per person with at least 1 disability’ for education level, income and wealth in 2014 were corrected. Changes by November 12, 2024: The subject folder 'Mental Health Inventory (MHI-5), 12 plus' was added. There are two topics within this folder. Firstly, the new topic 'feelings of anxiety or depression, 4 wks'. Secondly, the topic 'psychological distress, past 4 weeks'. The latter topic could previously be found in this table under the name 'psychological distress (MHI-5

Due to the coronavirus illness (COVID-19) disruption, it would seem that this is now starting to affect the quality and coverage of some of our statistics, such as an increase in non-submissions for some datasets. We are also starting to see some different patterns in the submitted data. For example, fewer patients are being admitted to and discharged from hospital. Therefore, data should be interpreted with care over the COVID-19 period. This is a monthly report on publicly funded community services for people of all ages using data from the Community Services Data Set (CSDS) reported in England for July 2020. It has been developed to help achieve better outcomes and provide data that will be used to commission services in a way that improves health, reduces inequalities, and supports service improvement and clinical quality. This is the first report from the new version of the dataset, CSDS v1.5. As an uplift from v1.0, the v1.5 dataset collects additional data on a person’s care plan details, employment status and social & personal circumstances. These statistics are classified as experimental and should be used with caution. Experimental statistics are new official statistics undergoing evaluation. More information about experimental statistics can be found on the UK Statistics Authority website.

Beurteilung des Gesundheitssystems im Land. Persönliche Gesundheit. Gesundheitsversicherung. Themen: Lebenszufriedenheit (Glücklichsein); Vertrauen in das Bildungssystem und das Gesundheitssystem des Landes; Forderung nach einer Änderung des Gesundheitssystems; Rechtfertigung besserer medizinischer Versorgung und Bildung für Personen mit höherem Einkommen; Beurteilung des Gesundheitssystems des Landes (Skala: Einschätzung der Verbesserung des Gesundheitssystems, Beanspruchung von Gesundheitsleistungen über den notwendigen Bedarf hinweg, Bereitstellung von Basisgesundheitsleistungen durch den Staat, ineffizientes Gesundheitssystem); Bereitschaft zur Zahlung höherer Steuern zur Erhöhung der Gesundheitsversorgung für alle im Land; Einstellung zur öffentlichen Finanzierung von vorbeugenden medizinischen Checks, Behandlung von HIV/AIDS, Programmen zur Verhinderung von Fettleibigkeit sowie Organtransplantationen; Einstellung zum Zugang zu staatlich geförderter Gesundheitsversorgung für Menschen mit fremder Staatsbürgerschaft bzw. selbstschädigendem Gesundheitsverhalten; geschätzter Anteil von Menschen ohne Zugang zum Gesundheitssystem; Ursachen schwerwiegender Gesundheitsprobleme (gesundheitsschädliches Verhalten, Umwelt, Gene, Armut); Einstellung zur Bereitstellung einer Herzoperation für Patienten, die rauchen, die schon alt sind sowie bei solchen mit jungen Kindern; Einstellung zu alternativer Medizin (bessere Lösungen für Gesundheitsprobleme als konventionelle Medizin, verspricht mehr als sie halten kann); allgemeine Beurteilung von Ärzten im Land (Skala: vertrauenswürdig, diskutieren sämtliche Behandlungsoptionen mit ihren Patienten, geringe medizinische Fähigkeiten, kümmern sich mehr um ihr Einkommen als um ihre Patienten, Offenheit im Umgang mit Behandlungsfehlern); Häufigkeit von Problemen in den letzten vier Wochen: in Bezug auf Arbeit oder Haushaltsaktivitäten aufgrund gesundheitlicher Probleme, körperlich starke Schmerzen, Unglücklichsein und Depressionen, Verlust des Selbstvertrauens und unüberwindliche Probleme; Häufigkeit von Arztbesuchen und von Besuchen bei alternativen Heilpraktikern im letzten Jahr; Krankenhausaufenthalt im letzten Jahr; Gründe für nicht erhaltene notwendige medizinische Behandlung (Zahlungsschwierigkeiten, zeitliche Schwierigkeiten oder andere Verpflichtungen, erforderliche Behandlung ist am Wohnort nicht verfügbar, zu lange Wartelisten); Wahrscheinlichkeit des Zugangs zur bestmöglichen Behandlung im Land bei einer schweren Krankheit und zu freier Arztwahl; Zufriedenheit mit dem Gesundheitssystem im Land; Zufriedenheit mit dem letzten Arztbesuch, bei alternativen Heilpraktikern und mit dem letzten Krankenhausaufenthalt; Anzahl täglich gerauchter Zigaretten; Häufigkeit des Konsums von vier oder mehr alkoholischen Getränken pro Tag; Häufigkeit anstrengender körperlicher Aktivitäten und des Konsums von Obst und Gemüse; Selbsteinschätzung der Gesundheit; chronische Krankheit oder Behinderung; Größe und Gewicht; Art der persönlichen Gesundheitsversicherung; Beurteilung des Schutzes der persönlichen Gesundheitsversicherung. Optionale Fragen: Gesundheitsversicherung deckt ab: verordnete Medikamente, zahnmedizinische Versorgung und Krankenhausaufenthalte; Notwendigkeit einer Überweisung des Hausarztes vor dem Besuch eines Facharztes; Einschränkung sozialer Aktivitäten wegen gesundheitlicher Probleme. Demographie: Geschlecht; Alter; Geburtsjahr; Jahre der Schulbildung; Schulbildung (länderspezifisch); höchster Bildungsgrad; Erwerbstätigkeit; Wochenarbeitszeit; Beschäftigungsverhältnis; Beschäftigtenzahl; Vorgesetztenfunktion; Anzahl der beaufsichtigten Beschäftigten; Art der Organisation; Beruf (ISCO-88); Haupterwerbsstatus; Zusammenleben mit einem Partner; Gewerkschaftsmitgliedschaft; Konfession (länderspezifisch); Konfessionsgruppen; Kirchgangshäufigkeit; Selbsteinschätzung auf einer Oben-unten-Skala; Wahlbeteiligung bei der letzten Wahl und gewählte Partei (länderspezifisch); Einschätzung der gewählten Partei links-rechts; Ethnizität (länderspezifisch); Kinderzahl; Haushaltsgröße; Einkommen des Befragten (länderspezifisch); Haushaltseinkommen (länderspezifisch); Familienstand; Urbanisierungsgrad; Region (länderspezifisch). Für den Ehepartner bzw. Partner wurde erfragt: Erwerbstätigkeit; Wochenarbeitszeit; Beschäftigungsverhältnis; Vorgesetztenfunktion; Beruf (ISCO-88); Haupterwerbsstatus. Zusätzlich verkodet wurde: Interviewdatum; Case substitution flag; Erhebungsmethode; Gewichtungsfaktor. Evaluation of health care system in the country. Personal health. Health insurance. Themes: satisfaction with life (happiness); confidence in the educational system and the health system of the country; changes of health care system is needed; justification of better medical supply and better education for people with higher incomes; assessment of the health care system of the country (scale: estimation of improvement of the health care system, usage of health care services more than necessary, government should provide only basic health care services, inefficient health care system); willingness to pay higher taxes to improve the level of health care for all people in the country; attitude towards public funding of: preventive medical checkups, treatment of HIV/AIDS, programs to prevent obesity and conduct organ transplants; attitude towards the access to publicly funded health care for people without citizenship of the country and even if they behave in ways that damage their health; estimated part of people without access to the health care system; causes of severe health problems (behavior that damages health, environment, genes, poverty); evaluation of patients for smoking habits, age and the presence of young children for a needed heart operation; attitude towards alternative (traditional or folk) medicine (provides better solutions for health problems than conventional medicine, promises more than it is able to deliver); assessment of doctors in general in the country (scale: doctors can be trusted, discuss all treatment options with their patients, poor medical skills, more care about their earnings than about their patients, openness in dealing with mistakes during treatment); frequency of difficulties with work or household activities because of health problems, bodily aches or pains, unhappiness and depression, loss of self-confidence and insuperable problems in the past four weeks; frequency of doctor visits and of visiting an alternative (traditional/folk) health care practitioner during the past twelve months; stay in hospital or a clinic as an in-patient overnight during the last year; reasons why the respondent did not receive needed medical treatment (could not pay for it, could not take the time off work or because of other commitments, needed treatment was not available at the place of residence, too long waiting list); likelihood of getting the best treatment available in the country in the case of seriously illness and of treatment from the doctor of own choice; satisfaction with the health care system in the country; satisfaction with treatment at the last visit to a doctor, when attending alternative health care practitioner and with the last hospital stay; number of smoked cigarettes per day; frequency of drinking four or more alcoholic drinks on the same day, strenuous physical activity and of eating fresh fruit or vegetables; assessment of personal health; respondent has a long-standing illness, a chronic condition or a disability; height and weight of respondent; kind of personal health insurance; only respondents with health insurance: assessment of personal health insurance coverage. Optional items: personal health insurance covers the prescribed drugs, dental health care and in-patient health care in hospital; need of a referral from the family doctor before visiting a medical specialist; limitation of social activities with family or friends because of health problems. Demography: Sex; age; year of birth; years in school; education (country specific); highest completed degree; work status; hours worked weekly; employment relationship; number of employees; supervision of employees; number of supervised employees; type of organization: for-profit vs. non profit and public vs. private; occupation (ISCO-88); main employment status; living in steady partnership; union membership; religious affiliation or denomination (country specific); groups of religious denominations; attendance of religious services; top-bottom self-placement; vote in last general election; country specific party voted in last general election; party voted (left-right); ethnicity (country specific); number of children; number of toddlers; size of household; earnings of respondent (country specific); family income (country specific); marital status; place of living: urban – rural; region (country specific). Information about spouse and about partner on: work status; hours worked weekly; employment relationship: supervises other employees, occupation (ISCO-88); main employment status. Additionally encoded: date of interview; case substitution flag; mode of data collection; weight.

This table contains information about the number of young people up to the age of 18 who received one or more forms of curative (medical) mental health care (GGZ) under the Healthcare Insurance Act (Zvw) during the year and the associated costs for using this care , broken down by age, gender and type of care. Young people can receive several forms of mental health care during the year. From 1 January 2008, all mental health care aimed at healing (recovery or stabilization of the disorder) will always be financed from the Zvw during the first year. After that too, unless you stay in a mental healthcare institution, addiction institution or psychiatric department of a hospital (PAAZ). If someone stays in an institution or PAAZ for more than a year, the AWBZ will take over the financing of the treatment and the stay. This table relates to the population of persons who meet each of the following three conditions: - they have been registered in the Municipal Personal Records Database (GBA) for at least 1 day during the relevant year; - they are obliged to take out basic insurance under the Zvw; - they have actually been insured for this basic insurance for at least 1 day during the relevant year. Data available from 2011 to 2014 Status of the figures: The figures in this table are final. Changes as of March 20, 2018: None. This table has been discontinued. Figures on youth care from 2015 onwards are published in separate tables (see section 3). When will new numbers come out? Not applicable anymore.

Das International Social Survey Programme (ISSP) ist ein länderübergreifendes, fortlaufendes Umfrageprogramm, das jährlich Erhebungen zu Themen durchführt, die für die Sozialwissenschaften wichtig sind. Das Programm begann 1984 mit vier Gründungsmitgliedern - Australien, Deutschland, Großbritannien und den Vereinigten Staaten - und ist inzwischen auf fast 50 Mitgliedsländer aus aller Welt angewachsen. Da die Umfragen auf Replikationen ausgelegt sind, können die Daten sowohl für länder- als auch für zeitübergreifende Vergleiche genutzt werden. Jedes ISSP-Modul konzentriert sich auf ein bestimmtes Thema, das in regelmäßigen Zeitabständen wiederholt wird. Details zur Durchführung der nationalen ISSP-Umfragen entnehmen Sie bitte der Dokumentation. Die vorliegende Studie konzentriert sich auf Fragen zu individueller Gesundheit und dem Gesundheitssystem. ISSP Health and Health Care I-II kumuliert die Daten der integrierten Datenfiles von- ISSP 2011 (ZA5800 Datendatei Version 3.0.0, https://doi.org/10.4232/1.12252) und- ISSP 2021 (ZA8000 Datendatei Version 2.0.0, https://doi.org/10.4232/5.ZA8000.2.0.0).Er umfasst Daten aus allen ISSP-Mitgliedsländern, die an mindestens zwei Modulen zum Thema Gesundheit und Gesundheitsversorgung teilnehmen. Der Datensatz enthält:- Kumulierte themenbezogene (substanzielle) Variablen, die in mindestens zwei Modulen des Gesundheitswesens und der Gesundheitsversorgung vorkommen und- Hintergrundvariablen, hauptsächlich zur Demografie, die in mindestens zwei Modulen des Bereichs Gesundheit und Gesundheitsversorgung vorkommen. Lebenszufriedenheit (Glück); Vertrauen in das nationale Gesundheitssystem; Rechtfertigung einer besseren Gesundheitsversorgung für Menschen mit höherem Einkommen; Zustimmung zu verschiedenen Aussagen über das Gesundheitssystem (Die Menschen nehmen Gesundheitsdienste mehr als nötig in Anspruch, die Regierung sollte nur begrenzte Gesundheitsdienste zur Verfügung stellen, im Allgemeinen ist das Gesundheitssystem im Land ineffizient); Bereitschaft, höhere Steuern zu zahlen, um das Niveau der Gesundheitsversorgung für alle Menschen im Land zu verbessern; Einstellung zum Zugang zur öffentlich finanzierten Gesundheitsversorgung für Menschen, die nicht die Staatsbürgerschaft des Landes besitzen, und auch dann, wenn sie sich gesundheitsschädigend verhalten; Meinung zu den Ursachen, warum Menschen unter schweren Gesundheitsproblemen leiden (gesundheitsschädigendes Verhalten, wegen der Umwelt, der sie bei der Arbeit oder am Wohnort ausgesetzt sind, wegen ihrer Gene, Armut); alternative/traditionelle oder volkstümliche Medizin bietet bessere Lösungen für Gesundheitsprobleme als die Schulmedizin/westliche traditionelle Medizin; allgemeine Beurteilung der Ärzte im Land (Ärzten kann man vertrauen, die medizinischen Fähigkeiten von Ärzten sind nicht so gut, wie sie sein sollten, Ärzte kümmern sich mehr um ihren Verdienst als um ihre Patienten); Häufigkeit von Schwierigkeiten bei der Arbeit oder im Haushalt aufgrund von Gesundheitsproblemen, körperlichen Beschwerden oder Schmerzen, Unzufriedenheit und Depressionen, Verlust des Selbstvertrauens und unüberwindbaren Problemen in den letzten vier Wochen; Häufigkeit von Arztbesuchen und Besuchen bei alternativen/traditionellen/volkstümlichen Heilpraktikern in den letzten 12 Monaten; Gründe, warum der Befragte die erforderliche medizinische Behandlung nicht in Anspruch genommen hat (konnte sie nicht bezahlen, konnte sich nicht von der Arbeit freinehmen oder hatte andere Verpflichtungen, die Warteliste war zu lang); Wahrscheinlichkeit, im Falle einer schweren Erkrankung die beste im Land verfügbare Behandlung zu erhalten; Zufriedenheit mit dem Gesundheitssystem im Land; Zufriedenheit mit der Behandlung beim letzten Arztbesuch und beim Besuch eines Heilpraktikers; Raucherstatus und Anzahl der gerauchten Zigaretten pro Tag; Häufigkeit des Konsums von vier oder mehr alkoholischen Getränken am selben Tag, von anstrengender körperlicher Betätigung von mindestens 20 Minuten und des Verzehrs von frischem Obst oder Gemüse; Einschätzung des persönlichen Gesundheitszustands; befragte Person leidet seit langem an einer Krankheit, einem chronischen Leiden oder einer Behinderung; Größe (in cm) und Gewicht (in kg); Art der persönlichen Krankenversicherung. Demographie: Geschlecht; Alter; Geburtsjahr; Status der rechtlichen Partnerschaft; fester Lebenspartner; Bildung: Jahre der Schulbildung; höchster Bildungsabschluss; derzeitiger Beschäftigungsstatus (Befragter und Partner); Beschäftigungsverhältnis (Befragter und Partner); wöchentliche Arbeitsstunden (Befragter und Partner); Beruf (ISCO 2008) (Befragter und Partner); Vorgesetztenfunktion (Befragter und Partner); Gewerkschaftsmitgliedschaft; Haushaltsgröße; Anzahl der Kinder über dem Schuleintrittsalter im Haushalt; Anzahl der Kinder unter dem Schulalter im Haushalt; Parteipräferenz (links-rechts); Teilnahme an der letzten Wahl; Besuch von Gottesdiensten; religiöse Hauptgruppen (abgeleitet); Selbsteinordnung auf einer Oben-Unten-Skala; subjektive soziale Schicht; Wohnort städtisch - ländlich; Haushaltseinkommensgruppen (abgeleitet). Zusätzlich verkodet: ID-Nummer des Befragten; eindeutige Kumulierungs-ID-Nummer des Befragten; ISSP-Moduljahr; Land; Länderstichprobe; Länderstichprobenjahr; Gewichtungsfaktor; administrative Art der Datenerhebung. The International Social Survey Programme (ISSP) is a continuous programme of cross-national collaboration running annual surveys on topics important for the social sciences. The programme started in 1984 with four founding members - Australia, Germany, Great Britain, and the United States – and has now grown to almost 50 member countries from all over the world. As the surveys are designed for replication, they can be used for both, cross-national and cross-time comparisons. Each ISSP module focuses on a specific topic, which is repeated in regular time intervals. Please, consult the documentation for details on how the national ISSP surveys are fielded. The present study focuses on questions about individual health and the health care system. ISSP Health and Health Care I-II cumulates the data of the integrated data files of • ISSP 2011 (ZA5800 Data file Version 3.0.0, https://doi.org/10.4232/1.12252) and • ISSP 2021 (ZA8000 Data file Version 2.0.0, https://doi.org/10.4232/5.ZA8000.2.0.0).It comprises data from all ISSP member countries participating in at least two Health and Health Care modules. The data set contains:• Cumulated topic-related (substantial) variables, which appear in at least two Health and Health Care and• background variables, mostly covering demographics, which appear in at least two Health and Health Care modules. Satisfaction with life (happiness); confidence in the national health care system; justification for better healthcare for people with higher incomes; agreement with various statements on the healthcare system (People use health care services more than necessary, the government should provide only limited health care services, in general, the health care system in the country is inefficient); willingness to pay higher taxes to improve the level of health care for all people in the country; attitude towards the access to publicly funded health care for people without citizenship of the country and even if they behave in ways that damage their health; opinion on causes why people suffer from severe health problems (because they behaved in ways that damaged their health, because of the environment they are exposed to at work or where they live, because of their genes, because they are poor); alternative/ traditional or folk medicine provides better solutions for health problems than mainstream/ Western traditional medicine; assessment of doctors in general in the country (doctors can be trusted, the medical skills of doctors are not as good as they should be, doctors care more about their earnings than about their patients); frequency of difficulties with work or household activities because of health problems, bodily aches or pains, unhappiness and depression, loss of self-confidence and insuperable problems in the past four weeks; frequency of visits to/ by a doctor and an alternative/ traditional/ folk health care practitioner during the past 12 months; reasons why the respondent did not receive needed medical treatment (could not pay for it, could not take the time off work or because of other commitments, the waiting list was too long); likelihood of getting the best treatment available in the country in the case of seriously illness; satisfaction with the health care system in the country; satisfaction with treatment at the last visit to a doctor and to an alternative health care practitioner; smoker status and number of smoked cigarettes per day; frequency of drinking four or more alcoholic drinks on the same day, of strenuous physical activity for at least 20 minutes, and of eating fresh fruit or vegetables; assessment of personal health status; respondent has a long-standing illness, a chronic condition, or a disability; respondent’s height (in cm) and weight (in kg); kind of personal health insurance. Demography: sex; age; years of birth; legal partnership status; steady life partner; education: years of schooling; highest education level; currently, formerly, or never in paid work (respondent and partner); employment relationship (respondent and partner); current employment status (respondent and partner); hours worked weekly (respondent and partner); occupation (ISCO 2008) (respondent and partner); supervising function at work (respondent and partner); number of other employees supervised; type of organization: for-profit vs. non-profit and public vs. private; trade union membership; household size; number of children above school entry age in household; number of children below school age in household; party affiliation (left-right);

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.

BackgroundMental health care (MHC) needs to shift towards person-centered care to better meet people’s individual needs. Open Dialogue (OD) is well-aligned with this perspective and brings it into practice. This study focuses on exploring the change process within a pilot project involving three MHC teams as they transition to a person-centered OD practice. Our aim is to identify and reflect on the challenges faced by MHC professionals in adopting person-centered care, and shedding light on the underlying complexity of these challenges. By gaining a better understanding of these obstacles, we hope to contribute to the adoption of the person-centered approach in MHC practice.MethodsOur research employed a qualitative design, involving a total of 14 semi-structured interviews with MHC professionals who were either trained in OD, OD trainees, or MHC professionals without OD training. To analyze the data, we utilized a hybrid approach that combined deductive – and inductive thematic analysis.ResultsWe identified four distinctive challenges: (1) understanding and knowledge transfer, (2) (inter)personal process, (3) emotional discomfort, and (4) the need for multi-stakeholder participation and support. In practice, these challenges intersect and the appearance of and relationships between these challenges are not linear or disentangleable.ConclusionUpon careful consideration of these interdependent challenges, it became evident that embedding a person-centered approach like OD brings about systemic change, leading to an unfamiliar situation X. The research findings indicated that understanding and conveying the concept of person-centered care in practical settings poses significant challenges. The field of knowledge management helps to capture the complexity of understanding and transferring this knowledge. The change process necessitates an (inter)personal process and elicits emotional discomfort, as person-centered OD practice confronts a deeply entrenched paradigm in MHC. Achieving a shared understanding of person-centered care requires dedicated time and attention, while introducing this approach prompts broader discussions on underlying values and human rights in MHC. Current implementation efforts may underestimate or overlook these underlying values, but initiating an open dialogue can serve as an initial step in addressing the complexities.

Bariatric surgery is an effective treatment for obesity. However, around one in five people experience significant weight regain. Acceptance and Commitment Therapy (ACT) teaches acceptance of and defusion from thoughts and feelings which influence behaviour, and commitment to act in line with personal values. To test the feasibility and acceptability of ACT following bariatric surgery a randomised controlled trial of 10 sessions of group ACT or Usual Care Support Group control (SGC) was delivered 15–18 months post bariatric surgery (ISRCTN registry ID: ISRCTN52074801). Participants were compared at baseline, 3, 6 and 12 months using validated questionnaires to assess weight, wellbeing, and healthcare use. A nested, semi-structured interview study was conducted to understand acceptability of the trial and group processes. 80 participants were consented and randomised. Attendance was low for both groups. Only 9 (29%) ACT participants completed > = half of the sessions, this was the case for 13 (35%) SGC participants. Forty-six (57.5%) did not attend the first session. At 12 months, outcome data were available from 19 of the 38 receiving SGC, and from 13 of the 42 receiving ACT. Full datasets were collected for those who remained in the trial. Nine participants from each arm were interviewed. The main barriers to group attendance were travel difficulties and scheduling. Poor initial attendance led to reduced motivation to return. Participants reported a motivation to help others as a reason to join the trial; lack of attendance by peers removed this opportunity and led to further drop out. Participants who attended the ACT groups reported a range of benefits including behaviour change. We conclude that the trial processes were feasible, but that the ACT intervention was not acceptable as delivered. Our data suggest changes to recruitment and intervention delivery that would address this.

For English, see below As of 1 January 2023, RIVM will no longer collect additional information. As a result, from January 1, 2023, we will no longer report data on infections among people over 70 living at home . File description: - This file contains the following numbers: (number of newly reported) positively tested individuals aged 70 and older living at home*, by safety region, per date of the positive test result. - (number of newly reported) deceased individuals aged 70 and older living at home who tested positive*, by safety region, by date on which the patient died. The numbers concern COVID-19 reports since the registration of the (residential) institution in OSIRIS with effect from questionnaire 5 (01-07-2020). * For reports from 01-07-2020, it is recorded whether the patient lives in an institution. Reports from 01-07-2020 are regarded as individuals aged 70 and older living at home if, according to the information known to the GGD, they: • Do not live in an institution AND • Are aged 70 or older AND • The person is not employed and is not a healthcare worker Persons whose residential facility/institution is not listed can still be excluded as individuals aged 70 and older living at home if they: • Can be linked to a known location of a disability care institution or nursing home on the basis of their 6-digit zip code OR • Have 'Disabled care institution' or 'Nursing home' as the location of the contamination mentioned. OR • Based on the content of free text fields, can be linked to a disability care institution or nursing home. The file is structured as follows: A set of records per date of with for each date: • A record for each security region (including 'Unknown') in the Netherlands, even if there are no reports for the relevant security region. The numbers are then 0 (zero). • Security region is unknown when a record cannot be assigned to one unique security region. A date 01-01-1900 is also included in this file for statistics whose associated date is unknown. The following describes how the variables are defined. Description of the variables: Version: Version number of the dataset. This version number is adjusted (+1) when the content of the dataset is structurally changed (so not the daily update or a correction at record level. The corresponding metadata in RIVMdata (https://data.rivm.nl) is also changed. Version 2 update (January 25, 2022): • An updated list of known nursing or care home locations and private residential care centers was received from the umbrella organization Patient Federation of the Netherlands on 03-12-2021. taken to determine whether individuals live in an institution Version 3 update (February 8, 2022) • From February 8, 2022, positive SARS-CoV-2 test results will be reported directly from CoronIT to RIVM. such as Testing for Access) and healthcare institutions (such as hospitals, nursing homes and general practitioners) that enter their positive SARS-CoV-2 test results via the Reporting Portal of GGD GHOR directly to RIVM. Reports that are part of the source and contact investigation sample and positive SARS-CoV-2 test results from healthcare institutions that are reported to the GGD via healthcare email are reported to RIVM via HPZone. From 8 February, the date of the positive test result is used and no longer the date of notification to the GGD. Version 4 update (March 24, 2022): • In version 4 of this dataset, records have been compiled according to the municipality reclassification of March 24, 2022. See description of the variable security_region_code for more information. Version 5 update (August 2, 2022): • The classification of persons aged 70 years and parents living independently has not been applied to reports that have only been received by RIVM since February 8, 2022 via an alternative reporting route. From 8 February to 1 August 2022, the number of reports from independently living persons aged 70 and parents was therefore underestimated by approximately 14%. As of August 2, 2022, this format will be retroactively updated. Version 6 update (September 1, 2022): - From September 1, 2022, the data will no longer be updated every working day, but on Tuesdays and Fridays. The data is retroactively updated on these days for the other days. - As of September 1, 2022, this dataset is split into two parts. The first part contains the dates from the start of the pandemic to October 3, 2021 (week 39) and contains "tm" in the file name. This data will no longer be updated. The second part contains the data from October 4, 2021 (week 40) and is updated every Tuesday and Friday. Date_of_report: Date and time on which the data file was created by RIVM. Date_of_statistic_reported: The date used for reporting the 70plus statistic living at home. This can be different for each reported statistic, namely: • For [Total_cases_reported] this is the date of the positive test result. • For [Total_deceased_reported] this is the date on which the patients died. Security_region_code: Security region code. The code of the security region based on the patient's place of residence. If the place of residence is not known, the safety region is based on the GGD that submitted the report, except for the Central and West Brabant and Brabant-Noord safety regions, since the GGD and safety region are not comparable for these regions. See also: https://www.cbs.nl/nl-nl/figures/detail/84721ENG?q=Veiliteiten From March 24, 2022, this file has been compiled according to the municipality classification of March 24, 2022. The municipality of Weesp has been merged into the municipality of Amsterdam . With this division, the Gooi- en Vechtstreek safety region has become smaller and the Amsterdam-Amstelland safety region larger; GGD Amsterdam has become larger and GGD Gooi- en Vechtstreek has become smaller (Municipal division on 1 January 2022 (cbs.nl). Security_region_name: Security region name. Security region name is based on the Security Region Code. See also: https://www.rijksoverheid.nl /topics/safety-regions-and-crisis-management/safety-regions Total_cases_reported: The number of new COVID-19 infected over-70s living at home reported to the GGD on [Date_of_statistic_reported].The actual number of COVID-19 infected over-70s living at home is higher than the number of reports in surveillance, because not everyone with a possible infection is tested. In addition, it is not known for every report whether this concerns a person over 70 living at home. Date_of_statistic_reported] The actual number of deceased people over 70 living at home who died of COVID-19 is higher than the number of reports in the surveillance, because not all deceased patients are tested and deaths are not legally reportable. Moreover, it is not known for every report whether this concerns a person over 70 living at home. Corrections made to reports in the OSIRIS source system can also lead to corrections in this database. In that case, numbers published by RIVM in the past may deviate from the numbers in this database. This file therefore always contains the numbers based on the most up-to-date data in the OSIRIS source system. The CSV file uses a semicolon as a separator. There are no empty lines in the file. Below are the column names and the types of values ​​in the CSV file: • Version: Consisting of a single whole number (integer). Is always filled for each row. Example: 2. • Date_of_report: Written in format YYYY-MM-DD HH:MM. Is always filled for each row. Example: 2020-10-16 10:00 AM. • Date_of_statistic_reported: Written in format YYYY-MM-DD. Is always filled for each row. Example: 2020-10-09. • Security_region_code: Consisting of 'VR' followed by two digits. Can also be empty if the region is unknown. Example: VR01. • Security_region_name: Consisting of a character string. Is always filled for each row. Example: Central and West Brabant. • Total_cases_reported: Consisting of only whole numbers (integer). Is always filled for each row. Example: 12. • Total_deceased_reported: Consisting of only whole numbers (integer). Is always filled for each row. Example: 8. ---------------------------------------------- ---------------------------------- Covid-19 statistics for persons aged 70 and older living outside an institution, by security region and date As of 1 January 2023, the RIVM will no longer collect additional information. As a result, from January 1, 2023, we will no longer report data on infections among people over 70 living at home. File description: This file contains the following numbers: - Number of newly reported persons aged 70 and older living at home who tested positive*, by security region, by date of the positive test result. - Number of newly reported deceased persons aged 70 and older living at home who tested positive*, by security region, by date on which the patient died. The numbers concern COVID-19 reports since the registration of the (residential) institution in OSIRIS with effect from questionnaire 5 (01-07-2020). * For reports from 01-07-2020, it is recorded whether the patient lives in an institution. For reports from 01-07-2020 persons aged 70 and older are considered to be living at home if, according to the information known to the PHS, they: • were not living in an institution AND • Are aged 70 years or older AND • The person is not employed and is not a healthcare worker Persons whose residential facility/institution is not listed can still be excluded as being an persons aged 70 and older living at home if they: • Based on their 6-digit zip code, can be linked to a known location of a care institution for the disabled or a nursing home OR • Have 'Disability care institution' or 'Nursing home' as the stated location of transmission. OR • Based on the content of free text fields, links can be made to a care institution for the disabled or a nursing home. The file is structured as follows: A set of records by date, with for

The corona crisis exacerbated social inequalities and hit precarious workers, who, as part of the ‘industrial reserve army’, often cover short-term or seasonal labor demands, particularly hard. While the corona crisis reduced many employment opportunities for precarious workers (e.g., in tourism), healthcare facilities, among others, needed additional labor power. However, the latter work activities were often associated with both increased infection risks and additional burdens due to corresponding protective requirements (e.g., working in protective clothing). Due to few highly demanded employment alternatives, support staff in healthcare facilities faced the dilemma of having to choose between (continuing) employment and protecting their health (Kößler et al., 2023). We expected a particularly strong manifestation of this employment-health dilemma (E-H dilemma) among non-medical workers in healthcare facilities (e.g., cleaners), as the corona crisis simultaneously confronted them with an economic threat (few alternative employment options) and a health threat (a heightened infection risk). Therefore, the project aimed to analyze under which circumstances the combination of an economic and a health threat led to an employment-health dilemma (Study 1). The aim was also to understand how employees coped with economic threats, health threats, and the employment-health dilemma (Study 2). To explore the E-H dilemma, we conducted 42 qualitative interviews with 45 non-medical workers in healthcare facilities. The interviews were based on a semi-structured interview guideline that we developed in a participatory manner with works councils and workers of comparable facilities. Interviews were then transcribed and anonymized. During data cleansing, we removed 6 interviews for methodological reasons (e.g., due to poor audio quality or sampling interviewees outside healthcare facilities) and 9 other interviews for content-related reasons (i.e., interviewees did not report an economic and/or health threat). Then we analyzed the remaining 27 interviews using qualitative content analysis by Mayring (2015). For this purpose, two independent coders inductively formed categories based on interview excerpts that dealt with economic threats, health threats, the EH dilemma (Study 1), and potential coping strategies (Study 2). The analysis of Study 1 showed that the antecedents of economic and health threats can be categorized at a societal, organizational, and personal level. For example, the loss of part-time jobs (societal level), internal organizational restructuring processes (organizational level), and formal training (individual level) contributed to the perception of an economic threat. The perception of a health threat was conditioned, among other things, by the availability of information (societal level), defective protective equipment (organizational level), and contact with people with pre-existing conditions (individual level). Some interviewees who felt the economic threat forced them to keep their employment despite a health threat reported an E-H dilemma. The analysis of Study 2 highlighted that workers used various coping strategies that can be mapped on two axes: On the one hand, these strategies may be either problem-oriented (e.g., naming problems) or emotion-oriented (e.g., cognitively avoiding problems). On the other hand, the mode of coping strategies was either cognitive (e.g., planning work) or behavioral (e.g., reducing stress during leisure activities). Kößler, F. J., Wesche, J. S., & Hoppe, A. (2023). In a no‐win situation: The employment–health dilemma. Applied Psychology, 72(1), 64–84. https://doi.org/10.1111/apps.12393

Here are a few use cases for this project:

1. Elderly Care Home Monitoring: The model can be used in elderly care homes or healthcare facilities to observe and monitor the movements and activities of the elderly residents. It can help in tracking whether an elderly person is standing, sitting, or involved in any other activities, and alert the staff if there are any abnormal patterns detected.

2. Home Automation System: The "Tracking Test 2" model can be integrated within smart homes designed particularly for elder populations. Based on whether the occupant is sitting, standing or laying, it can automate certain actions like switch on/off lights, control room temperature, etc., enhancing living convenience.

3. Personal Emergency Alert System: This application can help detect falls or accidents at home, by recognizing an elderly person's sudden change in position, and then alerting medical services or pre-set emergency contacts.

4. Rehabilitation and Physical Therapy: The model can provide assistance in physiotherapy sessions, tracking the movement patterns of elderly patients. It can help therapists in grading the progress and recommending exercises based on their physical state.

5. Assisted Living Technology Development: The model can be used to design and develop new technologies or aids for elderly people, helping them in maintaining their independence. It could be used to create systems that, for example, remind individuals to take medications when they are seated at a certain time, or automated household appliances when they are standing in a certain area.