The primary data source for this study is the Northern Ireland Longitudinal Study (NILS), which in 2001 defined a representative cohort of c.28% of the population. It is formed from the linkage of the universal Health Card registration system, 2001 Census returns, and vital statistics data. NILS contains a unique Health and Care Number that enables linkage to other health service databases. It is maintained by the Northern Ireland Statistics and Research Agency (NISRA). The 2001 Census records provided most of the attributes of the NILS cohort members, also contextual information relating to household composition and interpersonal relationships, and characteristics of the household and area of residence.

The vital events linked to NILS were used to determine whether a cohort member had been bereaved between April 2001 (the time of the Census) and the end of December 2009. The 2001 Census asked questions about relationship to other people living in the household, these questions were used to determine who a cohort member lived with, and the vital events records identified co-resident family members’ deaths. Approximately 96% of death records are routinely linked to the NILS dataset using a mixture of exact and probabilistic matching.

Data relating to medications that have been prescribed by a General Practitioner and dispensed from community pharmacies have been collated centrally in an Enhanced Prescribing Database (EPD) since 2009. Each prescription record contains the individual’s Health and Care Number, a General Practice (GP) identifier, the drug name and British National Formulary (BNF) category. Information was extracted for antidepressant and anxiolytic medications (BNF categories 4.1.2 and 4.3) for the period January 1st to February 28th 2010. Health and Care Number allowed exact matching between prescribing and NILS records. The linkage process was carried out by the EPD and NILS data custodians. The linked dataset was then anonymised before being supplied to the researchers, and was held in a secure setting (9). At no time were patient identifiable data available.

The data used for the Grief study is not publicly available, but researchers can make a request to link data for themselves by contacting the Northern Ireland Longitudinal Study Research Support Unit

Everybody will face bereavement at some stage; but for some people, this can be a more difficult process. There are many factors that can influence how people cope with the loss of a loved one, including level of family support, financial resources, stress, and the circumstances surrounding death.By studying use of prescription medications to help with mental health, we can get a better understanding of how factors such as age, gender, family support, employment and religion affect how people cope after bereavement. By looking at circumstances of bereavement this study will also discover if the factors that help people cope - such as family support - are more or less important depending on how they lost their loved ones.The Grief Study is based on data from the Northern Ireland Longitudinal Study, this holds information on around 500,000 people. By linking this data with the Northern Ireland Mortality Study and Health and Social care information on prescriptions, the Grief Study aims to learn more about bereavement, mental health, complicated grief, and longer term outcomes for people who have lost a loved one.

Analysis of ‘COVID-19's Impact on Educational Stress’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/bsoyka3/educational-stress-due-to-the-coronavirus-pandemic on 28 January 2022.

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

The survey collecting this information is still open for responses here.

Context

I just made this public survey because I want someone to be able to do something fun or insightful with the data that's been gathered. You can fill it out too!

Content

Each row represents a response to the survey. A few things have been done to sanitize the raw responses: - Column names and options have been renamed to make them easier to work with without much loss of meaning. - Responses from non-students have been removed. - Responses with ages greater than or equal to 22 have been removed.

Take a look at the column description for each column to see what exactly it represents.

Acknowledgements

This dataset wouldn't exist without the help of others. I'd like to thank the following people for their contributions: - Every student who responded to the survey with valid responses - @radcliff on GitHub for providing the list of countries and abbreviations used in the survey and dataset - Giovanna de Vincenzo for providing the list of US states used in the survey and dataset - Simon Migaj for providing the image used for the survey and this dataset

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

This data was collected to investigate how intolerance to distress and anxiety affects some of the behaviors that can be changed in response to the Covid-19 pandemic. This dataset comes from a survey of 2,868 people in Turkey about the effects of the Covid -19 pandemic. The dataset is ideal for studying how the Covid -19 pandemic shaped people's intolerance to distress and anxiety. The survey looked at personal cleaning habits, bank/credit card usage, online shopping, personal security, and stockpiling. The data also included whether an individual or a household member had been officially diagnosed with Covid-19 and socio-demographic data.

• For detailed data descriptor, please visit: https://www.nature.com/articles/s41597-022-01563-4

• For citation of its data descriptor: Sari, E., Kağan, G., Karakuş, B.Ş. et al. Dataset on social and psychological effects of COVID-19 pandemic in Turkey. Sci Data 9, 441 (2022). https://doi.org/10.1038/s41597-022-01563-4

LifeSnaps Dataset Documentation

Ubiquitous self-tracking technologies have penetrated various aspects of our lives, from physical and mental health monitoring to fitness and entertainment. Yet, limited data exist on the association between in the wild large-scale physical activity patterns, sleep, stress, and overall health, and behavioral patterns and psychological measurements due to challenges in collecting and releasing such datasets, such as waning user engagement, privacy considerations, and diversity in data modalities. In this paper, we present the LifeSnaps dataset, a multi-modal, longitudinal, and geographically-distributed dataset, containing a plethora of anthropological data, collected unobtrusively for the total course of more than 4 months by n=71 participants, under the European H2020 RAIS project. LifeSnaps contains more than 35 different data types from second to daily granularity, totaling more than 71M rows of data. The participants contributed their data through numerous validated surveys, real-time ecological momentary assessments, and a Fitbit Sense smartwatch, and consented to make these data available openly to empower future research. We envision that releasing this large-scale dataset of multi-modal real-world data, will open novel research opportunities and potential applications in the fields of medical digital innovations, data privacy and valorization, mental and physical well-being, psychology and behavioral sciences, machine learning, and human-computer interaction.

The following instructions will get you started with the LifeSnaps dataset and are complementary to the original publication.

Data Import: Reading CSV

For ease of use, we provide CSV files containing Fitbit, SEMA, and survey data at daily and/or hourly granularity. You can read the files via any programming language. For example, in Python, you can read the files into a Pandas DataFrame with the pandas.read_csv() command.

Data Import: Setting up a MongoDB (Recommended)

To take full advantage of the LifeSnaps dataset, we recommend that you use the raw, complete data via importing the LifeSnaps MongoDB database.

To do so, open the terminal/command prompt and run the following command for each collection in the DB. Ensure you have MongoDB Database Tools installed from here.

For the Fitbit data, run the following:

mongorestore --host localhost:27017 -d rais_anonymized -c fitbit

For the SEMA data, run the following:

mongorestore --host localhost:27017 -d rais_anonymized -c sema

For surveys data, run the following:

mongorestore --host localhost:27017 -d rais_anonymized -c surveys

If you have access control enabled, then you will need to add the --username and --password parameters to the above commands.

Data Availability

The MongoDB database contains three collections, fitbit, sema, and surveys, containing the Fitbit, SEMA3, and survey data, respectively. Similarly, the CSV files contain related information to these collections. Each document in any collection follows the format shown below:

{ _id:

This data was collected to investigate how intolerance to distress and anxiety affects some of the behaviors that can be changed in response to the Covid-19 pandemic. This dataset comes from a survey of 2,868 people in Turkey about the effects of the Covid -19 pandemic. The dataset is ideal for studying how the Covid -19 pandemic shaped people's intolerance to distress and anxiety. The survey looked at personal cleaning habits, bank/credit card usage, online shopping, personal security, and stockpiling. The data also included whether an individual or a household member had been officially diagnosed with Covid-19 and socio-demographic data.

Abstract

According to the World Health Organization, pandemic fatigue poses a serious threat for managing COVID-19. Pandemic fatigue is characterized by progressive decline in adherence to social distancing (SDIS) guidelines, and is thought to be associated with pandemic-related emotional burnout. Little is known about the nature of pandemic fatigue; for example, it is unclear who is most likely to develop pandemic fatigue. We sought to evaluate this issue based on data from 5,812 American and Canadian adults recruited during the second year of the COVID-19 pandemic. Past-year decline in adherence to SDIS had a categorical latent structure according to Latent Class Analysis, consisting of a group adherent to SDIS (Class 1: 92% of the sample) and a group reporting a progressive decline in adherence to SDIS (i.e., pandemic fatigue; Class 2: 8% of the sample). Class 2, compared to Class 1, was associated with greater pandemic-related burnout, pessimism, and apathy about the COVID-19 pandemic. They also tended to be younger, perceived themselves to be more affluent, tended to have greater levels of narcissism, entitlement, and gregariousness, and were more likely to report having been previously infected with SARSCOV2, which they regarded as an exaggerated threat. People in Class 2 also self-reported higher levels of pandemic-related stress, anxiety, and depression, and described making active efforts at coping with SDIS restrictions, which they perceived as unnecessary and stressful. People in Class 1 generally reported that they engaged in SDIS for the benefit of themselves and their community, although 35% of this class also feared they would be publicly shamed if they did not comply with SDIS guidelines. The findings suggest that pandemic fatigue affects a substantial minority of people and even many SDIS-adherent people experience emotionally adverse effects (i.e., fear of being shamed). Implications for the future of SDIS are discussed.

This table contains estimates of Incomes (Median Equivalised, Median Disposable), Poverty (using the proportion of people below a half median equivalised disposable household income poverty line), Inequality (using the Gini coefficient) and financial stress (Had no access to emergency money, Can't afford a night out once a fortnight and Leaving low income from benefit). Leaving low income from benefit is the gross earning (expressed as a percentage of average full time earnings) required for a family to reach a 60% of median household income threshold from benefits of last resort (State welfare payments or income support). All estimates were derived using a spatial microsimulation model which used the Survey of Income and Housing and the 2011 Census data as base datasets, so they are synthetic estimates. This table forms part of the AURIN Social Indicators project.

Introduction: The immediate impact of coronavirus 2019 (COVID-19) on morbidity and mortality has raised the need for accurate and real-time data monitoring and communication. The aim of this study is to document the initial observations from multiple digital services providers during the COVID-19 crisis, especially those related to mental health and well-being.Methods: We used email and social media to announce an urgent call for support. Digital mental health services providers (N = 46), financial services providers (N = 4), and other relevant digital data source providers (N = 3) responded with quantitative and/or qualitative data insights. People with lived experience of distress, as service users/consumers, and carers are included as co-authors.Results: This study provides proof-of-concept of the viability for researchers and private companies to work collaboratively toward a common good. Digital services providers reported a diverse range of mental health concerns. A recurring observation is that demand for digital mental health support has risen, and that the nature of this demand has also changed since COVID-19, with an apparent increased presentation of anxiety and loneliness.Conclusion: Following this study, we will continue to work with providers in more in-depth ways to capture follow-up insights at regular time points. We will also onboard new providers to address data representativeness. Looking ahead, we anticipate the need for a rigorous process to interpret insights from an even wider variety of sources in order to monitor and respond to mental health needs.

Note: After May 3, 2024, this dataset will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, hospital capacity, or occupancy data to HHS through CDC’s National Healthcare Safety Network (NHSN). The related CDC COVID Data Tracker site was revised or retired on May 10, 2023.

This dataset represents weekly COVID-19 hospitalization data and metrics aggregated to national, state/territory, and regional levels. COVID-19 hospitalization data are reported to CDC’s National Healthcare Safety Network, which monitors national and local trends in healthcare system stress, capacity, and community disease levels for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN and included in this dataset represent aggregated counts and include metrics capturing information specific to COVID-19 hospital admissions, and inpatient and ICU bed capacity occupancy.

Reporting information:

• As of December 15, 2022, COVID-19 hospital data are required to be reported to NHSN, which monitors national and local trends in healthcare system stress, capacity, and community disease levels for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN represent aggregated counts and include metrics capturing information specific to hospital capacity, occupancy, hospitalizations, and admissions. Prior to December 15, 2022, hospitals reported data directly to the U.S. Department of Health and Human Services (HHS) or via a state submission for collection in the HHS Unified Hospital Data Surveillance System (UHDSS).
• While CDC reviews these data for errors and corrects those found, some reporting errors might still exist within the data. To minimize errors and inconsistencies in data reported, CDC removes outliers before calculating the metrics. CDC and partners work with reporters to correct these errors and update the data in subsequent weeks.
• Many hospital subtypes, including acute care and critical access hospitals, as well as Veterans Administration, Defense Health Agency, and Indian Health Service hospitals, are included in the metric calculations provided in this report. Psychiatric, rehabilitation, and religious non-medical hospital types are excluded from calculations.
• Data are aggregated and displayed for hospitals with the same Centers for Medicare and Medicaid Services (CMS) Certification Number (CCN), which are assigned by CMS to counties based on the CMS Provider of Services files.
• Full details on COVID-19 hospital data reporting guidance can be found here: https://www.hhs.gov/sites/default/files/covid-19-faqs-hospitals-hospital-laboratory-acute-care-facility-data-reporting.pdf

Metric details:

• Time Period: timeseries data will update weekly on Mondays as soon as they are reviewed and verified, usually before 8 pm ET. Updates will occur the following day when reporting coincides with a federal holiday. Note: Weekly updates might be delayed due to delays in reporting. All data are provisional. Because these provisional counts are subject to change, including updates to data reported previously, adjustments can occur. Data may be updated since original publication due to delays in reporting (to account for data received after a given Thursday publication) or data quality corrections.
• New COVID-19 Hospital Admissions (count): Number of new admissions of patients with laboratory-confirmed COVID-19 in the previous week (including both adult and pediatric admissions) in the entire jurisdiction.
• New COVID-19 Hospital Admissions (7-Day Average): 7-day average of new admissions of patients with laboratory-confirmed COVID-19 in the previous week (including both adult and pediatric admissions) in the entire jurisdiction.
• Cumulative COVID-19 Hospital Admissions: Cumulative total number of admissions of patients with laboratory-confirmed COVID-19 (including both adult and pediatric admissions) in the entire jurisdiction since August 1, 2020.
• Cumulative COVID-19 Hospital Admissions Rate: Cumulative total number of admissions of patients with laboratory-confirmed COVID-19 (including both adult and pediatric admissions) in the entire jurisdiction since August 1, 2020 divided by 2019 intercensal population estimate for that jurisdiction multiplied by 100,000.
• New COVID-19 Hospital Admissions Rate (7-day average) percent change from prior week: Percent change in the 7-day average new admissions of patients with laboratory-confirmed COVID-19 per 100,000 population compared with the prior week.
• New COVID-19 Hospital Admissions (7-Day Total): 7-day total number of new admissions of patients with laboratory-confirmed COVID-19 (including both adult and pediatric admissions) in the entire jurisdiction.
• New COVID-19 Hospital Admissions Rate (7-Day Total): 7-day total number of new admissions of patients with laboratory-confirmed COVID-19 (including both adult and pediatric admissions) for the entire jurisdiction divided by 2019 intercensal population estimate for that jurisdiction multiplied by 100,000.
• Total Hospitalized COVID-19 Patients: 7-day total number of patients currently hospitalized with laboratory-confirmed COVID-19 (including both adult and pediatric patients) for the entire jurisdiction.
• Total Hospitalized COVID-19 Patients (7-Day Average): 7-day average of the number of patients currently hospitalized with laboratory-confirmed COVID-19 (including both adult and pediatric patients) for the entire jurisdiction.
• COVID-19 Inpatient Bed Occupancy (7-Day Average): Percentage of all staffed inpatient beds occupied by patients with laboratory-confirmed COVID-19 (including both adult and pediatric patients) within the entire jurisdiction is calculated as an average of valid daily values within the past 7 days (e.g., if only three valid values, the average of those three is taken). Averages are separately calculated for the daily numerators (patients hospitalized with confirmed COVID-19) and denominators (staffed inpatient beds). The average percentage can then be taken as the ratio of these two values for the entire jurisdiction.
• COVID-19 Inpatient Bed Occupancy absolute change from prior week: The absolute change in the percent of staffed inpatient beds occupied by patients with laboratory-confirmed COVID-19 represents the week-over-week absolute difference between the 7-day average occupancy of patients with confirmed COVID-19 in staffed inpatient beds in the past 7 days, compared with the prior week, in the entire jurisdiction.
• COVID-19 ICU Bed Occupancy (7-Day Average): Percentage of all staffed inpatient beds occupied by adult patients with confirmed COVID-19 within the entire jurisdiction is calculated as a 7-day average of valid daily values within the past 7 days (e.g., if only three valid values, the average of those three is taken). Averages are separately calculated for the daily numerators (adult patients hospitalized with confirmed COVID-19) and denominators (staffed adult ICU beds). The average percentage can then be taken as the ratio of these two values for the entire jurisdiction.
• COVID-19 ICU Bed Occupancy absolute change from prior week: The absolute change in the percent of staffed ICU beds occupied by patients with laboratory-confirmed COVID-19 represents the week-over-week absolute difference between the average occupancy of patients with confirmed COVID-19 in staffed adult ICU beds for the past 7 days, compared with the prior week, in the in the entire jurisdiction.

Note: October 27, 2023: Due to a data processing error, reported values for avg_percent_inpatient_beds_occupied_covid_confirmed will appear lower than previously reported values by an average difference of less than 1%. Therefore, previously reported values for avg_percent_inpatient_beds_occupied_covid_confirmed may have been overestimated and should be interpreted with caution.

October 27, 2023: Due to a data processing error, reported values for abs_chg_avg_percent_inpatient_beds_occupied_covid_confirmed will differ from previously reported values by an average absolute difference of less than 1%. Therefore, previously reported values for abs_chg_avg_percent_inpatient_beds_occupied_covid_confirmed should be interpreted with caution.

December 29, 2023: Hospitalization data reported to CDC’s National Healthcare Safety Network (NHSN) through December 23, 2023, should be interpreted with caution due to potential reporting delays that are impacted by Christmas and New Years holidays. As a result, metrics including new hospital admissions for COVID-19 and influenza and hospital occupancy may be underestimated for the week ending December 23, 2023.

January 5, 2024: Hospitalization data reported to CDC’s National Healthcare Safety Network (NHSN) through December 30, 2023 should be interpreted with caution due to potential reporting delays that are impacted by Christmas and New Years holidays. As a result, metrics including new hospital admissions for COVID-19 and influenza and hospital occupancy may be underestimated for the week ending December 30, 2023.

Attitudes to the political, social and economic Transformation . Topics: Economic situation; economic transformations; development of private business, privatization of land and of large enterprises; buying and selling land; willingness to work for a private company; direction of foreign policy; freedom of expression of political views; return to socialism vs. develop capitalism; role of social groups; trust in family and relatives, oneself, neighbors, fellow citizens, god, colleagues, church, astrologers, mass media; police, communist party, political parties, "Rukh", nationalists, Verkhovna Rada (parliament), armed forces, government, president, private entrepreneurs, mangers of large state enterprises, trade unions (traditional and new); membership in organizations; leisure activities; newspapers read last week; interests in politics; capable political leaders; strong leader vs. democracy; multiparty system; political parties and movements, that deserve power; important political movements; participation and voting in the Verkhovna Rada (parliament) Elections (March 1998); trust in deputy elected in one´s district; opinion about the President Kuchma; preferred role of the president; preferred priority in the policies of the president; general political situation in Ukraine and Russia; joining the union of Russia and Belarus; Russian language as a official language; satisfaction with one´s own present position in society, one´s own contribution to society and with that what one gets from society; predominant influence on one´s own life; satisfied with outlook on life; mood last days; social position in society; ability to live under changing social conditions as regards to health, working, clothing, housing, economic knowledge, confidence in one´s own abilities, medical assistance, fashionable clothing, basic furniture, contemporary political knowledge, resolve in pursuing one´s goals, legal protection for defending one´s rights and interests, ability to have an adequate vacation, having a second, unofficial job, buying the most necessary products, initiative and independence in solving daily problems, adequate leisure time, opportunity to work to full potential, opportunity to eat according to one´s own tastes; general health condition; suffering from any chronic illnesses; frequency of catching a cold/flu last year; frequency of being sick; stressful situations during last year; consequences of the Chornobyl catastrophe for one´s own health; satisfaction with quality of life in one´s resident; close relatives living outside Ukraine; leaving current residence (influential factors); preferred place to live; satisfaction with living conditions; current living conditions; number of rooms; size of family; number of people living together in one room; equipment in the household; possession of goods; second resident; domestic animals/pets; material level of the family´s life (scale); second income; income group; salary last month and anything left for next months; responsibility for delayed payments of wages; average income of the last month; monthly income (per person) providing average life of one´s own family; monthly average income (per person) counted as poor/rich; changes of material conditions for medical services, vacation, leisure time, reliable information about events in Ukraine and in the world, raising children, freedom to express views, participation in cultural events, environmental situation, personal security, protection from the whims of bureaucrats and bodies of power, security of employment; frequency of hooliganism and robberies in one´s own district; decision which encroached on people´s interests and actions against it; probability of mass protest actions and participation in them; political protests; death penalty; attitudes towards ethnic groups; violation of ethnic groups; maintain of peace and order; frequency of changing place of employment; work in public or private sector; job satisfied; religious confession; nationality; native language; spoken languages; language of the interview. Einstellung zur politischen, sozialen und ökonomischen Transformation. Themen: Ökonomische Situation; ökonomische Transformation; Entwicklung der Privatwirtschaft; Privatisierung von Grund und Boden sowie großer Unternehmen; Kauf und Verkauf von Land; Bereitschaft in einem privaten Betrieb zu arbeiten; Ausrichtung der Außenpolitik; Meinungsfreiheit; Rückkehr zum Sozialismus oder Entwicklung des Kapitalismus; Rolle sozialer Gruppen; Vertrauen in Institutionen; Freizeitaktivitäten; Zeitungslesen letzte Woche; Interesse an Politik; fähige politische Führer; starker Führer vs. Demokratie; Mehrparteiensystem; politische Parteien und Bewegungen, die die Macht verdienen; wichtige politische Bewegungen; Wahlbeteiligung und Wahlverhalten bei der Parlamentswahl 1998; Vertrauen in gewählten Abgeordneten des Distrikts; Meinung über Prof. Kuchma; bevorzugte Rolle des Präsidenten; bevorzugte Prioritäten der Politik des Präsidenten; allgemeine politische Situation in der Ukraine und in Russland; Beitritt der Vereinigung von Weißrussland und Russland; Russisch als offizielle Sprache; Zufriedenheit mit der eigenen Position in der Gesellschaft, dem eigenen Beitrag zur Gesellschaft und mit dem, was man von der Gesellschaft bekommt; vorherrschender Einfluss auf das eigene Leben; Zufriedenheit mit den Lebensaussichten; Stimmung in den letzten Tagen; soziale Position in der Gesellschaft; Fähigkeit, unter sich verändernden Bedingungen zu leben; Häufigkeit von Erkältungskrankheiten im letzten Jahr; Krankheitshäufigkeit; Stress im letzten Jahr; Folgen der Tschernobyl-Katastrophe für die eigene Gesundheit; Zufriedenheit mit der Lebensqualität in der eigenen Wohnumgebung; enge Verwandte im Ausland; Bereitschaft den Wohnort zu wechseln; bevorzugter Wohnort; Zufriedenheit mit Lebensbedingungen; Anzahl der Wohnräume; Familiengröße; Haushaltsgröße; Haushaltsausstattung; Besitz von Gütern; zweiter Wohnsitz; Haustiere; materielles Lebensniveau der Familie (Skala); Zweiteinkommen; Einkommensgruppe; Einkommen des letzten Monats und was davon übrig ist; Verantwortlichkeiten für verspätete Zahlung; Durchschnittseinkommen des letzten Monats; monatliches Einkommen (pro Person); Durchschnittsleben für Familie gewährleisten; arm/reich; veränderte Bedingungen für die medizinische Versorgung; Urlaub; Freizeit; zuverlässige Informationen über die Ereignisse in der Ukraine und der Welt; Kindererziehung; Meinungsfreiheit; Teilnahme an kulturellen Veranstaltungen; Umweltsituation; persönliche Sicherheit; Schutz vor Behördenwillkür; Arbeitsplatzsicherheit; Häufigkeit von Überfällen in der Wohnumgebung; Übergriffe auf die Interessen der Menschen und Aktionen dagegen; Wahrscheinlichkeit von Massenprotesten und Teilnahme daran; politischer Protest; Todesstrafe; Haltung gegenüber ethnischen Gruppen; Menschenrechtsverletzungen ethnischer Gruppen; Aufrecherhaltung von Ruhe und Ordnung; Häufigkeit des Wechsels der Arbeitsstelle; Arbeit im öffentlichen oder privaten Sektor; Arbeitszufriedenheit; Religion; Nationalität; Muttersprache; weitere Sprachen; Interviewsprache. Quota sample (combined with route selection). Average bias from current social statistics does not exceed 2.0 percent. Quotenstichprobe (kombiniert mit Random Route). Durchschnittliche Abweichung von amtlicher Statistik nicht mehr als 2%.

This database is comprised of 603 participants who provided self-report data online in their school classrooms. The data was collected in 2016 and 2017. The dataset is comprised of 208 males (34%) and 395 females (66%). Their ages ranged from 12 to 15 years. Their age in years at baseline is provided. The majority were born in Australia. Data were drawn from students at two Australian independent secondary schools. The data contains total responses for the following scales: The Intolerance of Uncertainty Scale (IUS-12; Short form; Carleton et al, 2007) is a 12-item scale measuring two dimensions of Prospective and Inhibitory intolerance of uncertainty. Two subscales of the Children’s Automatic Thoughts Scale (CATS; Schniering & Rapee, 2002) were administered. The Peronalising and Social Threat were each composed of 10 items. UPPS Impulsive Behaviour Scale (Whiteside & Lynam, 2001) which is comprised of 12 items. Dispositional Envy Scale (DES; Smith et al, 1999) which is comprised of 8 items. Spence Children’s Anxiety Scale (SCAS; Spence, 1998) which is comprised of 44 items. Three subscales totals included were the GAD subscale (labelled SCAS_GAD), the OCD subscale (labelled SCAS_OCD) and the Social Anxiety subscale (labelled SCAS_SA). Each subscale was comprised of 6 items. Avoidance and Fusion Questionnaire for Youth (AFQ-Y; Greco et al., 2008) which is comprised of 17 items. Distress Disclosure Index (DDI; Kahn & Hessling, 2001) which is comprised of 12 items. Repetitive Thinking Questionnaire-10 (RTQ-10; McEvoy et al., 2014) which is comprised of 10 items. The Brief Fear of Negative Evaluation Scale, Straightforward Items (BFNE-S; Rodebaugh et al., 2004) which is comprised of 8 items. Short Mood and Feelings Questionnaire (SMFQ; Angold et al., 1995) which is comprised by 13 items. The Self-Compassion Scale Short Form (SCS-SF; Raes et al., 2011) which is comprised by 12 items. The subscales include Self Kindness, Self Judgment, Social Media subscales - These subscale scores were based on social media questions composed for this project and also drawn from three separate scales as indicated in the table below. The original scales assessed whether participants experience discomfort and a fear of missing out when disconnected from social media (taken from the Australian Psychological Society Stress and Wellbeing Survey; Australian Psychological Society, 2015a), style of social media use (Tandoc et al., 2015b) and Fear of Missing Out (Przybylski et al., 2013c). The items in each subscale are listed below. Pub_Share Public Sharing When I have a good time it is important for me to share the details onlinec On social media how often do you write a status updateb On social media how often do you post photosb Surveillance_SM On social media how often do you read the newsfeed On social media how often do you read a friend’s status updateb On social media how often do you view a friend’s photob On social media how often do you browse a friend’s timelineb Upset Share On social media how often do you go online to share things that have upset you? Text private On social media how often do you Text friends privately to share things that have upset you? Insight_SM Social Media Reduction I use social media less now because it often made me feel inadequate FOMO I am afraid that I will miss out on something if I don’t stay connected to my online social networksa. I feel worried and uncomfortable when I can’t access my social media accountsa. Neg Eff of SM I find it difficult to relax or sleep after spending time on social networking sitesa. I feel my brain ‘burnout’ with the constant connectivity of social mediaa. I notice I feel envy when I use social media.
I can easily detach from the envy that appears following the use of social media (reverse scored) DES_SM Envy Mean acts online Feeling envious about another person has led me to post a comment online about another person to make them laugh Feeling envious has led me to post a photo online without someone’s permission to make them angry or to make fun of them Feeling envious has prompted me to keep another student out of things on purpose, excluding her from my group of friends or ignoring them. Substance Use: Two items measuring peer influence on alcohol consumption were adapted from the SHAHRP “Patterns of Alcohol Use” measure (McBride, Farringdon & Midford, 2000). These items were “When I am with friends I am quite likely to drink too much alcohol” and “Substances (alcohol, drugs, medication) are the immediate way I respond to my thoughts about a situation when I feel distressed or upset. Angold, A., Costello, E. J., Messer, S. C., & Pickles, A. (1995). Development of a short questionnaire for use in epidemiological studies of depression in children and adolescents. International Journal of Methods in Psychiatric Research, 5(4), 237–249. Australian Psychological Society. (2015). Stress and wellbeing in Australia survey. https://www.headsup.org.au/docs/default-source/default-document-library/stress-and-wellbeing-in-australia-report.pdf?sfvrsn=7f08274d_4 Greco, L.A., Lambert, W. & Baer., R.A. (2008) Psychological inflexibility in childhood and adolescence: Development and evaluation of the Avoidance and Fusion Questionnaire for Youth. Psychological Assessment, 20, 93-102. https://doi.org/10.1037/1040-3590.20.2.9 Kahn, J. H., & Hessling, R. M. (2001). Measuring the tendency to conceal versus disclose psychological distress. Journal of Social and Clinical Psychology, 20(1), 41–65. https://doi.org/10.1521/jscp.20.1.41.22254 McBride, N., Farringdon, F. & Midford, R. (2000) What harms do young Australians experience in alcohol use situations. Australian and New Zealand Journal of Public Health, 24, 54–60 https://doi.org/10.1111/j.1467-842x.2000.tb00723.x McEvoy, P.M., Thibodeau, M.A., Asmundson, G.J.G. (2014) Trait Repetitive Negative Thinking: A brief transdiagnostic assessment. Journal of Experimental Psychopathology, 5, 1-17. Doi. 10.5127/jep.037813 Przybylski, A. K., Murayama, K., DeHaan, C. R., & Gladwell, V. (2013). Motivational, emotional, and behavioral correlates of fear of missing out. Computers in human behavior, 29(4), 1841-1848. https://doi.org/10.1016/j.chb.2013.02.014 Raes, F., Pommier, E., Neff, K. D., & Van Gucht, D. (2011). Construction and factorial validation of a short form of the self-compassion scale. Clinical Psychology and Psychotherapy, 18(3), 250-255. https://doi.org/10.1002/cpp.702 Rodebaugh, T. L., Woods, C. M., Thissen, D. M., Heimberg, R. G., Chambless, D. L., & Rapee, R. M. (2004). More information from fewer questions: the factor structure and item properties of the original and brief fear of negative evaluation scale. Psychological assessment, 16(2), 169. https://doi.org/10.1037/10403590.16.2.169 Schniering, C. A., & Rapee, R. M. (2002). Development and validation of a measure of children’s automatic thoughts: the children’s automatic thoughts scale. Behaviour Research and Therapy, 40(9), 1091-1109. . https://doi.org/10.1016/S0005-7967(02)00022-0 Smith, R. H., Parrott, W. G., Diener, E. F., Hoyle, R. H., & Kim, S. H. (1999). Dispositional envy. Personality and Social Psychology Bulletin, 25(8), 1007-1020. https://doi.org/10.1177/01461672992511008 Spence, S. H. (1998). A measure of anxiety symptoms among children. Behaviour Research and Therapy, 36(5), 545-566. https://doi.org/10.1016/S0005-7967(98)00034-5 Tandoc, E. C., Ferrucci, P., & Duffy, M. (2015). Facebook use, envy, and depression among college students: Is facebooking depressing? Computers in Human Behavior, 43, 139–146. https://doi.org/10.1016/j.chb.2014.10.053 Whiteside, S.P. & Lynam, D.R. (2001) The five factor model and impulsivity: using a structural model of personality to understand impulsivity. Personality and Individual Differences 30,669-689. https://doi.org/10.1016/S0191-8869(00)00064-7 The data was collected by Dr Danielle A Einstein, Dr Madeleine Fraser, Dr Anne McMaugh, Prof Peter McEvoy, Prof Ron Rapee, Assoc/Prof Maree Abbott, Prof Warren Mansell and Dr Eyal Karin as part of the Insights Project. The data set has the option of downloading an excel file (composed of two worksheet tabs) or CSV files 1) Data and 2) Variable labels.

This dataset, released April 2017, contains an Estimated number of people aged 18 years and over whose household could raise $2,000 within a week (modelled estimates), 2014; Estimated number of people aged 18 years and over who had government support as their main source of income in the last 2 years (modelled estimates), 2014; Estimated number of people aged 18 years and over who had government support as their main source of income, for 13 months or more, within the past 24 months (modelled estimates), 2014. The data is by Population Health Area (PHA) 2016 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS).

Population Health Areas, developed by PHIDU, are comprised of a combination of whole SA2s and multiple (aggregates of) SA2s, where the SA2 is an area in the ABS structure.

For more information please see the data source notes on the data.

Source: Modelled by PHIDU based on the ABS General Social Survey, 2014.

AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.

[Updated 28/01/25 to fix an issue in the ‘Lower’ values, which were not fully representing the range of uncertainty. ‘Median’ and ‘Higher’ values remain unchanged. The size of the change varies by grid cell and fixed period/global warming levels but the average difference between the 'lower' values before and after this update is 0.0.]What does the data show? The Annual Count of Tropical Nights is the number of days per year where the minimum daily temperature is above 20°C. It measures how many times the threshold is exceeded (not by how much). It measures how many times the threshold is exceeded (not by how much) in a year. The results should be interpreted as an approximation of the projected number of days when the threshold is exceeded as there will be many factors such as natural variability and local scale processes that the climate model is unable to represent.The Annual Count of Tropical Nights is calculated for two baseline (historical) periods 1981-2000 (corresponding to 0.51°C warming) and 2001-2020 (corresponding to 0.87°C warming) and for global warming levels of 1.5°C, 2.0°C, 2.5°C, 3.0°C, 4.0°C above the pre-industrial (1850-1900) period. This enables users to compare the future number of tropical nights to previous values. What are the possible societal impacts?The Annual Count of Tropical Nights indicates increased health risks and heat stress due to high night-time temperatures. It is based on exceeding a minimum daily temperature of 20°C, i.e. the temperature does not fall below 20°C for the entire day. Impacts include:Increased heat related illnesses, hospital admissions or death for vulnerable people.Increased heat stress, it is important the body has time to recover from high daytime temperatures during the lower temperatures at night.Other metrics such as the Annual Count of Summer Days (days above 25°C), Annual Count of Hot Summer Days (days above 30°C) and the Annual Count of Extreme Summer Days (days above 35°C) also indicate impacts from high temperatures, however they use different temperature thresholds.What is a global warming level?The Annual Count of Tropical Nights is calculated from the UKCP18 regional climate projections using the high emissions scenario (RCP 8.5) where greenhouse gas emissions continue to grow. Instead of considering future climate change during specific time periods (e.g. decades) for this scenario, the dataset is calculated at various levels of global warming relative to the pre-industrial (1850-1900) period. The world has already warmed by around 1.1°C (between 1850–1900 and 2011–2020), whilst this dataset allows for the exploration of greater levels of warming.The global warming levels available in this dataset are 1.5°C, 2°C, 2.5°C, 3°C and 4°C. The data at each warming level was calculated using a 21 year period. These 21 year periods are calculated by taking 10 years either side of the first year at which the global warming level is reached. This time will be different for different model ensemble members. To calculate the value for the Annual Count of Tropical Nights, an average is taken across the 21 year period. Therefore, the Annual Count of Tropical Nights show the number of tropical nights that could occur each year, for each given level of warming. We cannot provide a precise likelihood for particular emission scenarios being followed in the real world future. However, we do note that RCP8.5 corresponds to emissions considerably above those expected with current international policy agreements. The results are also expressed for several global warming levels because we do not yet know which level will be reached in the real climate as it will depend on future greenhouse emission choices and the sensitivity of the climate system, which is uncertain. Estimates based on the assumption of current international agreements on greenhouse gas emissions suggest a median warming level in the region of 2.4-2.8°C, but it could either be higher or lower than this level.What are the naming conventions and how do I explore the data?This data contains a field for each global warming level and two baselines. They are named ‘Tropical Nights’, the warming level or baseline, and ‘upper’ ‘median’ or ‘lower’ as per the description below. E.g. ‘Tropical Nights 2.5 median’ is the median value for the 2.5°C warming level. Decimal points are included in field aliases but not field names e.g. ‘Tropical Nights 2.5 median’ is ‘TropicalNights_25_median’. To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘Tropical Nights 2.0°C median’ values.What do the ‘median’, ‘upper’, and ‘lower’ values mean?Climate models are numerical representations of the climate system. To capture uncertainty in projections for the future, an ensemble, or group, of climate models are run. Each ensemble member has slightly different starting conditions or model set-ups. Considering all of the model outcomes gives users a range of plausible conditions which could occur in the future. For this dataset, the model projections consist of 12 separate ensemble members. To select which ensemble members to use, the Annual Count of Tropical Nights was calculated for each ensemble member and they were then ranked in order from lowest to highest for each location. The ‘lower’ fields are the second lowest ranked ensemble member. The ‘upper’ fields are the second highest ranked ensemble member. The ‘median’ field is the central value of the ensemble.This gives a median value, and a spread of the ensemble members indicating the range of possible outcomes in the projections. This spread of outputs can be used to infer the uncertainty in the projections. The larger the difference between the lower and upper fields, the greater the uncertainty.‘Lower’, ‘median’ and ‘upper’ are also given for the baseline periods as these values also come from the model that was used to produce the projections. This allows a fair comparison between the model projections and recent past. Useful linksThis dataset was calculated following the methodology in the ‘Future Changes to high impact weather in the UK’ report and uses the same temperature thresholds as the 'State of the UK Climate' report.Further information on the UK Climate Projections (UKCP).Further information on understanding climate data within the Met Office Climate Data Portal.

Anxiety affects human capabilities and behavior as much as it affects productivity and quality of life. It can be considered as the main cause of depression and suicide. Anxious states are easily detectable by humans due to their acquired cognition, humans interpret the interlocutor’s tone of speech, gesture, facial expressions and recognize their mental state. There is a need for non-invasive reliable techniques that perform the complex task of anxiety detection. DASPS database contains recorded Electroencephalogram (EEG) signals of 23 participants during anxiety elicitation by means of face-to-face psychological stimuli.DASPS mean to create a starting point for new researches in the field of anxiety detection.Neuro headset setup:The award winning EMOTIV EPOC+ is designed for scalable and contextual human brain research and provides access to professional grade brain data with a quick and easy to use design.Emotiv Epoc have 14 (+2 references) sensors placed at this locations: AF3, AF4, F3, F4, FC5, FC6, F7, F8, T7, T8, P7, P8, O1, O2 with a sampling rate of 128 SPS (2048 Hz internal) and a resolution of 14 bits 1 LSB = 0.51μV. Emotiv Epoc is a wireless device with 2.4GHz band.Stimulation Conditions:The recording is done during the annual workshop of Research Groups in Intelligent Machines Lab. In an isolated place, participants were asked to conduct the experiment individually in the presence of the psychotherapist.Participants were asked to minimize eyes blinks and body gestures in order to record signals with minimum artifacts as possible.Experimental Protocol:The participant is prepared to start the experiment, with closed eyes and minimizing gesture and speech. The psychotherapist starts by reciting the first situation and helps the subject imagining it. This phase is divided into two stages: recitation by the psychotherapist for the first 15 seconds and Recall by the subject for the last 15 seconds.When time is over, the subject is asked to rate how he felt during stimulation using the Self Assessment Manikin (SAM). It has two rows for rating: Valence and Arousal. This trial is repeated until the sixth situation. At the end of the experiment, some items from HAM-A are re-evaluated by the psychotherapist to adjust the participant’s anxiety level.Database contents:The database contains .edf files of the raw EEG data collected from the 23 participants. Raw data and preprocessed data stored on .mat format are also provided in this database.We provided a matlab script for the segmentation of each EEG signal into 6 segments corresponding to the 6 situations.Citations:All documents and papers that uses the Database for Anxious States based on a Psychological Stimulation (DASPS database) will acknowledge the use of the database by including an appropriate citation to the following:[1]: Asma Baghdadi, Yassine Aribi, Rahma Fourati, Najla Halouani, Patrick Siarry, and Adel M. Alimi. "DASPS: A Database for Anxious States based on a Psychological Stimulation." arXiv preprint arXiv:1901.02942 (2019).[2]: Asma Baghdadi, Yassine Aribi, Rahma Fourati, Najla Halouani, Patrick Siarry, and Adel Alimi. "Psychological stimulation for anxious states detection based on EEG-related features." Journal of Ambient Intelligence and Humanized Computing (2020): 1-15.

The dataset consists of air temperatures recorded longitudinally and reported at hourly intervals using Hobo MX1101, Hobo MX1102A and Hobo MX2301 devices. The monitoring period covered 1st May 2022 to 30th September 2022 inclusive – the full non-heating season in England.

The devices were deployed in 30 care homes across England: eleven in Greater London, nine in the north of England as far north as Newcastle-upon-Tyne, six in the Midlands, and four in the south of England including on the Isle of Wight. The locations monitored consisted of 22 offices (staff-only areas such as manager’s offices, administrator offices, nurse stations), 30 lounges (communal areas such as lounges, dining rooms and lounge/diners), and 30 bedrooms (single rooms, with a range of occupancy – some vacant, some occupied only at night, others occupied 24/7 depending on resident needs). In addition, outdoor temperatures were monitored at each of the 30 care homes.

As a result of global climate change, the UK is expected to experience hotter and drier summers, and heatwaves are expected to occur with greater frequency, intensity and duration. In 2003 and 2018, 2,091 and 863 heat-related deaths, respectively, were reported in England alone as a result of heatwaves, meaning future temperature increases could lead to a parallel rise in heat-related mortality. The UK also currently has a rapidly ageing population, with people aged 75 or over expected to account for 13% of the total population by 2035. Older populations are more vulnerable to climate-induced effects as they are more likely to have underlying, chronic health complications, making them more vulnerable to heat stress. The indoor environment is a principle moderator of heat exposure in older populations, who tend to spend the majority of their time indoors. Poor building design, the lack of effective heat management and diverging needs and preferences between staff and residents in care settings may contribute to increased indoor heat exposure with detrimental health impacts falling on the most vulnerable residents. Maladaptation to a warming climate, such as the uptake of air conditioning, could increase fuel bills in care homes, increase operational costs for businesses in the already financially stretched care sector, and increase building carbon emissions, thus undermining government efforts to reduce greenhouse gas emissions.

The one-year pilot project 'Climate Resilience of Care Settings' and previous small-scale studies led by our research team have shown that UK care homes are already overheating even under non-extreme summers. A key target for climate adaptation in care settings is to limit such risks by introducing passive cooling strategies via building design. However, preliminary modelling as part of the pilot project also demonstrated that common passive cooling strategies may not adequately mitigate overheating risk in the 2050s and 2080s. Further research into advanced passive cooling strategies, combined with human behaviour and organisational change is required to identify optimum climate adaptation pathways for UK's care provision.

The main aim of the project is to quantify climate related heat risks in care settings nationwide and enhance understanding of human behaviour, organisational capacity and governance to enable the UK's care provision to develop equitable adaptation pathways to rising heat stress under climate change. Building on the foundations of the pilot project, this novel, interdisciplinary project will collect, for the first time in the UK, longitudinal temperature and humidity data in a panel of 50 care settings in order to quantify the recurring risk of summertime overheating. We will also identify and assess social, institutional and cultural barriers and opportunities underpinning the governance of adaptation to a warmer climate in care and extra-care homes through surveys with residents, frontline care staff, managers and policy stakeholders. Within sub-samples of this panel, we will use innovative measurement techniques to collect residents' physiological data and study their relation with heat exposure and health impacts. Also for the first time in the UK, we will create a building stock model of the UK's care provision able to predict future overheating risks in care settings under a range of future climate change scenarios. This will help evaluate the effectiveness of near, medium and long term future overheating mitigation strategies and policies on thermal comfort and health outcomes. Throughout the project, we will continue to develop and expand the stakeholder community that was created during the pilot project. Through ongoing dialogue with our diverse network of stakeholders, we will explore organisational capacity and structures, and how these influence action and policy, in order to generate best practice guidance for practitioners, businesses and policymakers.

DESCRIPTION This table contains data on the percent of residents aged 16 years and older mode of transportation to work for ...

SUMMARY This table contains data on the percent of residents aged 16 years and older mode of transportation to work for California, its regions, counties, cities/towns, and census tracts. Data is from the U.S. Census Bureau, Decennial Census and American Community Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Commute trips to work represent 19% of travel miles in the United States. The predominant mode – the automobile - offers extraordinary personal mobility and independence, but it is also associated with health hazards, such as air pollution, motor vehicle crashes, pedestrian injuries and fatalities, and sedentary lifestyles. Automobile commuting has been linked to stress-related health problems. Active modes of transport – bicycling and walking alone and in combination with public transit – offer opportunities for physical activity, which is associated with lowering rates of heart disease and stroke, diabetes, colon and breast cancer, dementia and depression. Risk of injury and death in collisions are higher in urban areas with more concentrated vehicle and pedestrian activity. Bus and rail passengers have a lower risk of injury in collisions than motorcyclists, pedestrians, and bicyclists. Minority communities bear a disproportionate share of pedestrian-car fatalities; Native American male pedestrians experience four times the death rate Whites or Asian pedestrians, and African-Americans and Latinos experience twice the rate as Whites or Asians. More information about the data table and a data dictionary can be found in the About/Attachments section.

ind_id - Indicator ID ind_definition - Definition of indicator in plain language reportyear - Year that the indicator was reported race_eth_code - numeric code for a race/ethnicity group race_eth_name - Name of race/ethnic group geotype - Type of geographic unit geotypevalue - Value of geographic unit geoname - Name of a geographic unit county_name - Name of county that geotype is in county_fips - FIPS code of the county that geotype is in region_name - MPO-based region name; see MPO_County list tab region_code - MPO-based region code; see MPO_County list tab mode - Mode of transportation short name mode_name - Mode of transportation long name pop_total - denominator pop_mode - numerator percent - Percent of Residents Mode of Transportation to Work,
Population Aged 16 Years and Older LL_95CI_percent - The lower limit of 95% confidence interval UL_95CI_percent - The lower limit of 95% confidence interval percent_se - Standard error of the percent mode of transportation percent_rse - Relative standard error (se/value) expressed as a percent CA_decile - California decile CA_RR - Rate ratio to California rate version - Date/time stamp of a version of data

There is an increasing concern about a range of wider impacts of the pandemic response. Among these is the impact on problematic substance use. Feelings of isolation, stress and anxiety, lack of a regular schedule, boredom and limited available or accessible services for people who use substances may impact harms. Deaths, hospitalizations and visits to the emergency department are examples of harms.

NATSEM estimates of housing stress (2006 and 2010) and estimates of poverty variables (2006) of SLAs, excluding SLAs in Brisbane and Canberra, in Australia. These data were derived from spatial microsimulation using 2006 Census benchmarks (SPATIALMSM08b) applied to Australian Bureau of Statistics (ABS) Confidentialised Unit Record File data. For housing stress, the indicator is based on a commonly used measure of housing stress known as the 30/40 rule. Using this definition, a household is said to be in housing stress if it spends more than 30 per cent of its gross income on housing costs and if it also falls into the bottom 40 per cent of the equivalised disposable household income distribution. The poverty indicator represents the percentage of people in households where income is below the poverty line. The poverty line has been set at half the median OECD equivalised household disposable income.

The Adult Psychiatric Morbidity Survey, 2007 (APMS 2007) is the third survey of psychiatric morbidity in adults living in private households. The main aim of the survey was to collect data on poor mental health among adults aged 16 and over living in private households in England.

The specific objectives of the survey were:

• to estimate the prevalence of psychiatric morbidity according to diagnostic category in the adult household population of England. The survey included assessment of common mental disorders; psychosis; borderline and antisocial personality disorder; Asperger syndrome, substance misuse and dependency; and suicidal thoughts, attempts and self-harm
• to screen for characteristics of eating disorder, attention deficit hyperactivity disorder, posttraumatic stress disorder, and problem gambling
• to examine trends in the psychiatric disorders that have been included in previous survey years (1993 and 2000)
• to identify the nature and extent of social disadvantage associated with mental illness
• to gauge the level and nature of service use in relation to mental health problems, with an emphasis on primary care
• to collect data on key current and lifetime factors that might be associated with mental health problems, such as experience of stressful life events, abusive relationships, and work stress
• to collect data on factors that might be protective against poor mental health, such as social support networks and neighbourhood cohesion