BackgroundOver recent years there has been a strong movement towards the improvement of vital statistics and other types of health data that inform evidence-based policies. Collecting such data is not cost free. To date there is no systematic framework to guide investment decisions on methods of data collection for vital statistics or health information in general. We developed a framework to systematically assess the comparative costs and outcomes/benefits of the various data methods for collecting vital statistics.MethodologyThe proposed framework is four-pronged and utilises two major economic approaches to systematically assess the available data collection methods: cost-effectiveness analysis and efficiency analysis. We built a stylised example of a hypothetical low-income country to perform a simulation exercise in order to illustrate an application of the framework.FindingsUsing simulated data, the results from the stylised example show that the rankings of the data collection methods are not affected by the use of either cost-effectiveness or efficiency analysis. However, the rankings are affected by how quantities are measured.ConclusionThere have been several calls for global improvements in collecting useable data, including vital statistics, from health information systems to inform public health policies. Ours is the first study that proposes a systematic framework to assist countries undertake an economic evaluation of DCMs. Despite numerous challenges, we demonstrate that a systematic assessment of outputs and costs of DCMs is not only necessary, but also feasible. The proposed framework is general enough to be easily extended to other areas of health information.

Introduction

Mental Health Statistics: Mental health is vital to well-being, influencing how people think, feel, and act. In recent years, there has been increasing recognition of its significance as societies become more aware of the far-reaching effects mental health disorders have on individuals, families, and communities.

Mental health statistics provide crucial insights into these conditions' prevalence, causes, and consequences, enabling policymakers, healthcare providers, and researchers to understand emerging trends better. This data supports effective resource allocation and the development of targeted interventions to tackle mental health issues.

We can pinpoint high-risk groups and regions that require additional support by examining these trends. Additionally, these insights help inform public health initiatives focused on reducing stigma and promoting mental health awareness. Accurate statistics are essential for shaping evidence-based policies emphasizing prevention, early intervention, and improving access to mental health services. As mental health continues to gain attention, continuous data collection and research will be key to addressing the global mental health crisis effectively.

Mental Health Services Children & Young People

Monthly Statistics on Referrals, Contacts and Care

By data.world's Admin [source]

About this dataset

This dataset provides essential information on the mental health services provided to children and young people in England. The data contained within the Mental Health Services Data Set (MHSDS) - Children & Young People covers a variety of different categories during a given reporting period, including primary level details, secondary level descriptions, number of open referrals for children's and young people's mental health services at the end of the reporting period, as well as number of first attended contacts for referrals open in the reporting period aged 0-18. It also provides insight into how many people are in contact with mental health services aged 0 to 18 at the time of reporting, how many referrals starting during this time were self-refreshers and more. This dataset includes valuable information that is necessary to better track and understand trends in order to provide more effective care

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How to use the dataset

This guide will provide you with an overview of the data contained in this dataset as well as information on how to effectively use it for your own research or personal purposes. Let's get started!

Overview of Data Fields

• REPORTING_PERIOD: The month and year of the reporting period (Date)
• BREAKDOWN: The type of breakdown of the data (String)
• PRIMARY_LEVEL: The primary level of the data (String)
• PRIMARY_LEVEL_DESCRIPTION: A description at the primary level of the data (String)
• SECONDARY_LEVEL: The secondary level of the data (String)

Research Ideas

• Evaluating the efficacy of existing mental health services for children and young people by examining changes in relationships between different aspects of service delivery (e.g. referral activity, hospital spell activity, etc).
• Analysing geographical trends in mental health services to inform investment decisions and policies across different regions.
• Identifying areas of high need among vulnerable or marginalised citizens, such as those aged 0-18 or those with particular genetic makeup, to better target resources and support those most in need of help

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: mhsds-monthly-cyp-data-file-feb-fin-2017-1.csv | Column name | Description | |:-------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------| | REPORTING_PERIOD | The period of time for which the data was collected. (String) | | BREAKDOWN | The breakdown of the data by age group. (String) | | PRIMARY_LEVEL | The primary level of the data. (String) | | PRIMARY_LEVEL_DESCRIPTION ...

Japan WHO Health Indicators

Substance Abuse, Mental Health, and Disease Statistics

By Humanitarian Data Exchange [source]

About this dataset

This dataset from the World Health Organization’s data portal contains a wide array of health indicators for Japan, covering topics such as mortality and global health estimates, sustainable development goals, millennium development goals, health systems, infectious diseases, health financing, public health and environment, substance use and mental health, tobacco use and violence prevention , HIV/AIDS and other sexually-transmitted infections (STIs), nutrition intake levels, urban healthcare practices,, noncommunicable disease management methods , neglected tropical diseases surveillance infrastructure statistics medical equipment technology demographic profiles , youth healthcare access policies international he Heath regulations monitoring framework insecticide resistance protocol oral health advancements Universal Health Coverage (UHC) strategies financial protection AMR GLASS ICD SEXUAL AND REPRODUCTIVE HEALTH resources. The dataset also provides links to individual indicator metadata. Please note that additional information regarding each indicator is available in those resource descriptions. Information was sourced from the WHO database and was last updated on 2020-09-16

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How to use the dataset

This dataset provides a wealth of information about the health and safety indicators in Japan. It contains data from World Health Organization's (WHO) data portal, covering multiple categories such as mortality and global health estimates, sustainable development goals, millennium development goals (MDGs), malaria and tuberculosis, child health, infectious diseases, world health statistics, demography and socioeconomic statistics etc. This guide will provide an overview of the content of this dataset as well as instructions on how to use it.

The dataset consists of several columns which describe various aspects of each indicator: GHO Code – The code for the Global Health Observatory indicator; GHO Display – The name of the Global Health Observatory indicator; GBDChildCauses (CODE) – The code for the Global Burden of Disease Child Causes Indicator; GBDChildCauses (DISPLAY) – The name of the Global Burden Of Disease Child Causes Indicator; PublishState (CODE) - The code for the publication state; PublishState(DISPLAY)-The name of the publication state; Year(CODE)-The code for year;; Year(DISPLA & YEAR)(URL); Region(CODE & REGION)(DISPL ®ION)(URL); Country (& COUNTRY)(DISPL & COUNTRY)(URL); AgeGroup (& AGEGROUP)(COD &AGEGROUP); Sex ((SEX CODE))  Sex DISPLAY ; GHECAUSES&GHECause(DisplayGHEconse URL&CHILDCause Code cCHILDCUSE DISP、 CHILDCUSE URL Display Value、Numericlow HIGH  STD ERR StdDev Comments。

In order to begin using this dataset you will have to download it from Kaggle. After downloading you can view its contents using any application like a spreadsheet. You can also rewrite all or part of it into other formats such as JSON if necessary. Once completed follow these steps to get analytics about your data:

• Preparing Your Data - Start by eliminating all irrelevant columns that don't contain useful information or could potentially confuse or mislead your analysis process like comments column which contain notes on certain entries in this set rather than numbers or statistical values related to them..

• Calculate/ Analyze relevant indicators - Use function formulas that come with your application suite like average median mode min max calculations etc so that you can know exactly what kindof , indicators is being used in

Research Ideas

• Analysis of healthcare improvements or shortfalls across Japan over time.
• Tracking the prevalence of various types of Noncommunicable Diseases (NCDs) in Japan, including mental health issues, to inform public policy and interventions.
• Examination of the infrastructure spending in Japanese healthcare to help inform other nations’ decisions on investment levels for health services delivery

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: all-health-indicators-for-japan-18.csv | Column name | Description | |:-----------------------------|:------------------------------------------------------------------...

The NCHS National Post-acute and Long-term Care Study (NPALS) collects data on post-acute and long-term care providers every two years. The goal is to monitor post-acute and long-term care settings with reliable, accurate, relevant, and timely statistical information to support and inform policy, research, and practice. These data tables provide an overview of the geographic, organizational, staffing, service provision, and user characteristics of paid, regulated long-term and post-acute care providers in the United States. The settings include adult day services centers, home health agencies, hospices, inpatient rehabilitation facilities, long-term care hospitals, and nursing homes.

Abstract

This study sought to develop a user-friendly decision-making tool to explore country-specific estimates for costs and economic consequences of different options for scaling screening and psychosocial interventions for women with common perinatal mental health problems in Malawi. We developed a simple simulation model using a structure and parameter estimates that were established iteratively with experts, based on published trials, international databases and resources, statistical data, best practice guidance and intervention manuals. The model projects annual costs and returns to investment from 2022 to 2026. The study perspective is societal, including health expenditure and productivity losses. Outcomes in the form of health-related quality of life are measured in Disability Adjusted Life Years, which were converted into monetary values. Economic consequences include those that occur in the year in which the intervention takes place. Results suggest that the net benefit is relatively small at the beginning but increases over time as learning effects lead to a higher number of women being identified and receiving (cost‑)effective treatment. For a scenario in which screening is first provided by health professionals (such as midwives) and a second screening and the intervention are provided by trained and supervised volunteers to equal proportions in group and individual sessions, as well as in clinic versus community setting, total costs in 2022 amount to US$ 0.66 million and health benefits to US$ 0.36 million. Costs increase to US$ 1.03 million and health benefits to US$ 0.93 million in 2026. Net benefits increase from US$ 35,000 in 2022 to US$ 0.52 million in 2026, and return-on-investment ratios from 1.05 to 1.45. Results from sensitivity analysis suggest that positive net benefit results are highly sensitive to an increase in staff salaries. This study demonstrates the feasibility of developing an economic decision-making tool that can be used by local policy makers and influencers to inform investments in maternal mental health

Description of data set

Iteratively, information was gathered from desk-based searches and from talking to and exchanging emails with experts in the maternal health field to establish a model structure and the parameter values. This included the development of an information request form that presents a list of parameters, parameter values and details about how the values were estimated and the data sources. We collected information on: Intervention’s effectiveness; prevalence rates; population and birth estimates; proportion of women attending services; salaries and reimbursement rates for staff and volunteers; details about training, supervision, intervention delivery (e.g., frequency, duration); unit costs, and data needed to derive economic consequences (e.g. women’s income, health weights). Data were searched from the following sources: published randomised controlled trials and meta-analyses; WHO published guidance and intervention manual; international databases and resources (WHO-CHOICE, Global Burden of Disease Database; International Monetary Fund; United Nations Treasury, World Bank, Global Investment Framework for Women’s and Children’s Health). We consulted two groups of experts: one group included individuals with clinical, research or managerial expertise in funding, managing, delivering, or evaluating screening of common mental health problems and PSIs; the second group included individuals from the Malawi Government, Ministry of Health Reproductive Health Unit and Non-Communicable Disease Committee and Mental Health Unit. The first group of experts provided information from research and administrative data systems concerned with implementing and evaluating screening for maternal mental health and the delivery of interventions. The second group of experts from the Malawi Government provided information on unit costs for hospital use and workforce data, as well as information on how training and supervision might be delivered at scale. Individuals were identified by colleagues of this team based or part-time based in Malawi, which included a psychiatrist specialising in perinatal mental health (co-author RS) and the coordinator of the African Maternal Mental Health Alliance (co-author DN), an organisation concerned with disseminating information and evidence on perinatal mental health to policy makers and influencers, and the wider public.

Mexico-WHO Health Indicators

Demographic, Disease, and Treatment Coverage Data

By Humanitarian Data Exchange [source]

About this dataset

This Kaggle dataset contains a wide array of health and socioeconomic indicators relating to Mexico. It covers topics ranging from mortality and global health estimates, to Sustainable Development Goals, Millennium Development Goals (MDGs), Health Systems, Malaria and Tuberculosis, Child Health, Infectious Diseases, World Health Statistics, Health Financing and Public Heath & Environment. Furthermore, it includes indicators for Substance Use & Mental Health; Tobacco use; Injuries & Violence; HIV/AIDS & Other STIs; Nutrition; Urban Health; Noncommunicable Diseases (NCDs); Neglected Tropical Diseases (NTDs); Infrastructure; Essential Technologies in healthcare systems; Demographic & Socioeconomic Statistics. Finally it features indicators surrounding International Regulations Monitoring Frameworks as well as Insecticides Resistance amongst other topics.

This dataset is bursting with information on how Mexico stands in a variety of different aspects across its development spectrum- enabling researchers to gain deeper insight into the country's ecosystem as well as providing them with the data required to pinpoint potential ‘hotspots’- Areas which may require heightened attention either from policy makers or individuals looking for smarter ways through which their efforts might benefit their target population most efficiently. Don’t miss your chance at unlocking the power of this comprehensive dataset so you can make sure that no stone is left unturned when it comes to realising tangible outcomes from your research!

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How to use the dataset

The dataset is organized into several key categories and each category contains a number of different indicators related to that particular area of healthcare. In order to better understand any given indicator in more detail, each one also has an associated metadata page with additional information about its definition and calculation method.

In order to make use of the data in this dataset there are several steps you will need to take: - Decide what aspect or area of healthcare you would like to explore further in more detail; - Review/understand any associated metadata provided regarding its definition or calculation method;
- Download any necessary files containing relevant numbers or figures;
- Analyze or explore this data further;
6 Use your findings to inform decisions about policy interventions for improving general public health outcomes in Mexico!

Research Ideas

• Analyzing Mexico's progress towards achieving the desired health indicators for the Sustainable Development Goals (SDGs).
• Examining how access to healthcare and mental health services vary by region, as well as disparities in treatment within regions.
• Developing machine learning models to predict outcome based on different factors such as environment and socioeconomic status

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: infrastructure-indicators-for-mexico-11.csv | Column name | Description | |:---------------------------|:---------------------------------------------------------------| | GHO (CODE) | The Global Health Observatory code for the indicator. (String) | | GHO (DISPLAY) | The name of the indicator. (String) | | GHO (URL) | The URL for the indicator. (URL) | | PUBLISHSTATE (CODE) | The code for the publication state of the indicator. (String) | | PUBLISHSTATE (DISPLAY) | The name of the publication state of the indicator. (String) | | PUBLISHSTATE (URL) | The URL for the publication state of the indicator. (URL) | | YEAR (CODE) | The code for the year of the indicator. (String) | | YEAR (DISPLAY) | The name of the year of the indicator. (String) | | YEAR (URL) | The URL for the year of the indicator. (URL) | | REGION (CODE) | The code for the region of the indicator. (String) | | REGION (DISPLAY) | The name of the region of the indicator. (String) | | REGION (URL) |...

California Infectious Disease Cases

Rates and Counts By County, Disease, Sex, and Year (2001-2014)

By Health [source]

About this dataset

This dataset provides comprehensive information on the number and rate of infectious diseases in California. Focusing on counties, sexes, and various diseases between 2001-2014, it offers powerful insights into the health status of its citizens. Its data also reveals trends in the spread of common illnesses in this state. Whether you are an epidemiologist looking to inform public health policy or a researcher seeking to investigate particular illnesses within certain populations, this dataset contains all the necessary information to answer your questions. Explore it today and discover hidden stories waiting to be uncovered!

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How to use the dataset

This dataset contains counts and rates of infectious diseases in California by county, disease, sex, and year. This dataset can be used to generate trends to understand the changes in incidence of different types of diseases over time and across counties or between sexes.

To use this dataset: - Select the columns you are interested in exploring - these could include Disease, County, Sex or Year. - Filter out the rows that do not relate to your question - for example filtering by a specific county or disease. - Examine the average rate per 100000 people for each group you selected as well as its lower and upper confidence intervals (CI). - Use Rate as your dependent variable for analysis; Population is likely also important determining factors. Make sure to check if any Rates have 'unstable' flags.
- Visualise or statistically analyse your data using suitable methods such as descriptive statistics (means/medians/mode etc.)for comparison between 2+ groups or correlation/regression based models when comparing one variable to another over time etc.

Research Ideas

• Analyzing the geographic spread of infectious diseases over time to identify areas in need of increased education, resources, and care.
• Comparing rates of disease by sex to identify and understand any gender-based differences in infectious disease cases.
• Using the Unstable column to determine whether a particular county or region needs further study of a certain type of infectious disease due to unusual spikes or drops in rate or count during a specific year

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Infectious_Disease_Cases_by_County_Year_and_Sex_2001-2014.csv | Column name | Description | |:---------------|:---------------------------------------------------------------------------------------------------------------| | Disease | The type of infectious disease reported. (String) | | County | The county in California where the cases were reported. (String) | | Year | The year in which the cases were reported. (Integer) | | Sex | The gender of the individuals who contracted the disease. (String) | | Population | The population size of the county in which the cases were reported. (Integer) | | Rate | The rate of infection per 100 thousand people living in the county. (Float) | | CI.lower | The lower confidence interval associated with the rate of infection. (Float) | | CI.upper | The upper confidence interval associated with the rate of infection. (Float) ...

The Productive Healthy Ageing Profile data update for January 2022 has been published by the Office for Health Improvement and Disparities (OHID).

This tool provides data and links to relevant guidance and further information on a wide range of topics relevant to healthy ageing. Indicators can be examined at local, regional or national level.

The aim of this tool is to support OHID productive healthy ageing policy and inform public health leads and the wider public health system about relevant key issues.

This release contains updated adult social care indicators relating to:

• independent living support
• permanent admissions to residential and nursing care homes

If you would like to contact us about the tool email: ProfileFeedback@phe.gov.uk.

BackgroundTwo World Health Organization comparative assessments rated the quality of South Africa’s 1996 mortality data as low. Since then, focussed initiatives were introduced to improve civil registration and vital statistics. Furthermore, South African cause-of-death data are widely used by research and international development agencies as the basis for making estimates of cause-specific mortality in many African countries. It is hence important to assess the quality of more recent South African data.MethodsWe employed nine criteria to evaluate the quality of civil registration mortality data. Four criteria were assessed by analysing 5.38 million deaths that occurred nationally from 1997–2007. For the remaining five criteria, we reviewed relevant legislation, data repositories, and reports to highlight developments which shaped the current status of these criteria.FindingsNational mortality statistics from civil registration were rated satisfactory for coverage and completeness of death registration, temporal consistency, age/sex classification, timeliness, and sub-national availability. Epidemiological consistency could not be assessed conclusively as the model lacks the discriminatory power to enable an assessment for South Africa. Selected studies and the extent of ill-defined/non-specific codes suggest substantial shortcomings with single-cause data. The latter criterion and content validity were rated unsatisfactory.ConclusionIn a region marred by mortality data absences and deficiencies, this analysis signifies optimism by revealing considerable progress from a dysfunctional mortality data system to one that offers all-cause mortality data that can be adjusted for demographic and health analysis. Additionally, timely and disaggregated single-cause data are available, certified and coded according to international standards. However, without skillfully estimating adjustments for biases, a considerable confidence gap remains for single-cause data to inform local health planning, or to fill gaps in sparse-data countries on the continent. Improving the accuracy of single-cause data will be a critical contribution to the epidemiologic and population health evidence base in Africa.

2013 KHHEUS explores the health-seeking behavior, use of healthcare services, out-of-pocket health spending, and health insurance coverage of Kenyan households. The first health survey to take place since Kenya decentralized its government; the 2013 KHHEUS collects data from the country’s 47 newly-created counties. By interviewing members of 33,675 households and comparing results with those of previous years (2003 and 2007), the 2013 survey provides important insights into how healthcare utilization, spending, and insurance coverage have changed in Kenya over the past decade.

The 2013 KHHEUS was conducted by the Kenya Ministry of Health with support from the USAID-funded Health Policy Project and in conjunction with the Kenya National Bureau of Statistics. The survey provides critical evidence to inform the development of Kenya’s latest health financing strategy and policy decisions related to the future universal health coverage and the National Hospital Insurance Fund, and will support the wider national health accounts estimation process.

DHHS develops the LGA Statistical Profiles on an annual basis, to support and inform health and human service planning and policy development. The profiles contain data for all 79 LGAs, as well as for DHHS Areas and former Regions.

The 2015 Profiles provide measures on a broad range of topics including:

Population Diversity Disadvantage and social engagement Housing, transport and education Health status and service utilisation Child and family characteristics and service utilisation Rankings are provided to enable comparison of LGAs, along with the Victorian averages.

This dataset presents the footprint of the crude percentage of adults who are overweight. Adults were classified as overweight if their Body Mass Index (BMI) was greater than or equal to 25 and less than 30. As an indication of the accuracy of estimates, 95% confidence intervals were produced. These were calculated by the Australian Bureau of Statistics (ABS) using standard error estimates of the proportion. The data spans the financial years of 2011-2012 and 2014-2015, and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS). Health risk factors are attributes, characteristics or exposures that increase the likelihood of a person developing a disease or health disorder. Examples of health risk factors include risky alcohol consumption, physical inactivity and high blood pressure. High-quality information on health risk factors is important in providing an evidence base to inform health policy, program and service delivery. For further information about this dataset, visit the data source: Australian Institute of Health and Welfare - Health Risk Factors in 2014-2015 Data Tables. Please note: AURIN has spatially enabled the original data using the Department of Health - PHN Areas.

This dataset presents the footprint of the crude percentage of adults who are daily smokers. A current daily smoker was defined as a person who smokes one or more cigarettes, roll-your-own cigarettes, cigars or pipes at least once a day. Chewing tobacco, electronic cigarettes (and similar) and the smoking of non-tobacco products were excluded. As an indication of the accuracy of estimates, 95% confidence intervals were produced. These were calculated by the Australian Bureau of Statistics (ABS) using standard error estimates of the proportion. The data spans the financial years of 2011-2012 and 2014-2015, and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS). Health risk factors are attributes, characteristics or exposures that increase the likelihood of a person developing a disease or health disorder. Examples of health risk factors include risky alcohol consumption, physical inactivity and high blood pressure. High-quality information on health risk factors is important in providing an evidence base to inform health policy, program and service delivery. For further information about this dataset, visit the data source: Australian Institute of Health and Welfare - Health Risk Factors in 2014-2015 Data Tables. Please note: AURIN has spatially enabled the original data using the Department of Health - PHN Areas.

This dataset presents the footprint of the crude percentage of adults who have high blood pressure. High blood pressure (or hypertension), is defined as including any of the following; systolic blood pressure greater than or equal to 140 mmHg, or; diastolic blood pressure greater than or equal to 90 mmHg, or; receiving medication for high blood pressure. As an indication of the accuracy of estimates, 95% confidence intervals were produced. These were calculated by the Australian Bureau of Statistics (ABS) using standard error estimates of the proportion. The data spans the financial year of 2014-2015 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS). Health risk factors are attributes, characteristics or exposures that increase the likelihood of a person developing a disease or health disorder. Examples of health risk factors include risky alcohol consumption, physical inactivity and high blood pressure. High-quality information on health risk factors is important in providing an evidence base to inform health policy, program and service delivery. For further information about this dataset, visit the data source: Australian Institute of Health and Welfare - Health Risk Factors in 2014-2015 Data Tables. Please note: AURIN has spatially enabled the original data using the Department of Health - PHN Areas.

This dataset presents the footprint of the crude percentage of adults who perform insufficient weekly physical activity. Insufficient physical activity is defined as those aged 18-64 years who did not complete over 150 minutes of physical activity, and at least 5 sessions over a week, and those aged 65+ years who did not complete 30 minutes of activity on at least 5 days in a week. As an indication of the accuracy of estimates, 95% confidence intervals were produced. These were calculated by the Australian Bureau of Statistics (ABS) using standard error estimates of the proportion. The data spans the financial year of 2014-2015 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS).

Health risk factors are attributes, characteristics or exposures that increase the likelihood of a person developing a disease or health disorder. Examples of health risk factors include risky alcohol consumption, physical inactivity and high blood pressure. High-quality information on health risk factors is important in providing an evidence base to inform health policy, program and service delivery.

For further information about this dataset, visit the data source: Australian Institute of Health and Welfare - Health Risk Factors in 2014-2015 Data Tables.

Please note:

• AURIN has spatially enabled the original data using the Department of Health - PHN Areas.

• The health risks factors reported are known to vary with age and the different PHN area populations are known to have a range of age structures. As such, comparisons of results between the PHN areas should be made with caution because the crude rates presented do not account for these age differences.

• Adults are defined as persons aged 18 years and over.

• Values assigned to "n.p." in the original data have been removed from the data.

• Data for PHN107 should be interpreted with caution, as the estimates have a relative standard error of 25% to 50%.

• Data for PHN701 (Northern Territory) should be interpreted with caution as the National Health Survey excluded discrete Aboriginal and Torres Strait Islander communities and very remote areas, which comprise around 28% of the estimated resident population of the Northern Territory living in private dwellings.

BackgroundDigital health is redefining nursing and midwifery practice, fuelled by national and international priorities for health improvement and workforce planning. Developing digitally enabled healthcare systems can help enable universal health coverage and improve safety outcomes while offering solutions to workforce shortages. However, research suggests that nursing/midwifery leaders are often absent from the strategic planning, design, and implementation of digitally enabled healthcare service models and the associated technological systems that directly impact practice.ObjectivesThis paper presents the protocol for a sequential, multi-method exploration of digital health policy implementation and its impact on practice. This investigation from the perspective of national nursing/midwifery leaders, will increase understanding of the impact these professions have on national decision-making, which will be used to inform digital healthcare policy implementation and development across Europe and beyond.MethodsA purposive sample of national nursing/midwifery leaders across the WHO European region will be recruited. In Phase 1, individuals will be invited via email to participate in an anonymous online survey, with findings used to inform the topic guide for online focus groups in Phase 2. Descriptive statistical analysis of the survey dataset will be used to understand the range of countries, roles, contexts, participant experiences, and perceptions on which the findings are based. Where possible, analysis will be undertaken, e.g., by country, and participant role to identify any patterns, gaps, and key areas for further exploration during Phase 2. Survey respondents will be offered the opportunity to participate in an online focus group. Free text questions from the survey and data from focus groups will be transcribed verbatim and analysed using a reflexive thematic approach.DiscussionThe study outlined within this paper will generate empirical data on to what extent and how national nursing/midwifery leaders influence the progress of digital healthcare, based on their experiences implementing key European policy. In gaining a better understanding of this policy implementation, and the role played by nursing and midwifery leaders, the factors that facilitate or hinder this process can be identified and better managed, to maximise the benefits of digital healthcare for population outcomes moving forward.Ethical approvalEthical approval for this study was granted on 10.12.25 by Anglia Ruskin University [ID ETH2425−0725]

This dataset contains raw quantitative survey data collected as part of an exploratory study into the mental health status and overall well-being of professional and non-professional construction workers in Lagos State, Nigeria. The data was gathered through a structured questionnaire designed to assess various aspects of psychological and emotional health. The dataset includes demographic information about the respondents, as well as their responses to a series of questions related to their feelings, behaviours, and coping mechanisms. This makes the dataset suitable for a wide range of statistical analyses, including descriptive statistics, correlation studies, and group comparisons.

This dataset is a valuable resource for researchers, public health professionals, and construction industry stakeholders interested in occupational health and safety, mental well-being, and the psychological impact of the construction work environment. It provides empirical data to inform the development of mental health support programs and policies for construction workers.

This dataset presents the footprint of the age-standardised percentage of adults who are overweight. Uncontrolled high blood pressure (measured high blood pressure) is defined as including any of the following; measured systolic blood pressure of 140 mmHg or more, or; diastolic blood pressure of 90 mmHg or more, and; irrespective of the use of blood pressure medication. As an indication of the accuracy of estimates, 95% confidence intervals were produced. These were calculated by the Australian Bureau of Statistics (ABS) using standard error estimates of the proportion. The data spans the financial year of 2014-2015 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS). Health risk factors are attributes, characteristics or exposures that increase the likelihood of a person developing a disease or health disorder. Examples of health risk factors include risky alcohol consumption, physical inactivity and high blood pressure. High-quality information on health risk factors is important in providing an evidence base to inform health policy, program and service delivery. For further information about this dataset, visit the data source: Australian Institute of Health and Welfare - Health Risk Factors in 2014-2015 Data Tables. Please note: AURIN has spatially enabled the original data using the Department of Health - PHN Areas. Age-standardisation is a method of removing the influence of age when comparing populations with different age structures - either different populations at the same time or the same population at different times. For this data the Australian estimated resident population of people aged 18 and over as at 30 June 2001 has been used as the standard population. Adults are defined as persons aged 18 years and over. Data for PHN701 (Northern Territory) should be interpreted with caution as the National Health Survey excluded discrete Aboriginal and Torres Strait Islander communities and very remote areas, which comprise around 28% of the estimated resident population of the Northern Territory living in private dwellings.

This dataset presents the footprint of the crude percentage of adults who have uncontrolled high blood pressure. Uncontrolled high blood pressure (measured high blood pressure) is defined as including any of the following; measured systolic blood pressure of 140 mmHg or more, or; diastolic blood pressure of 90 mmHg or more, and; irrespective of the use of blood pressure medication. As an indication of the accuracy of estimates, 95% confidence intervals were produced. These were calculated by the Australian Bureau of Statistics (ABS) using standard error estimates of the proportion. The data spans the financial year of 2014-2015 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS). Health risk factors are attributes, characteristics or exposures that increase the likelihood of a person developing a disease or health disorder. Examples of health risk factors include risky alcohol consumption, physical inactivity and high blood pressure. High-quality information on health risk factors is important in providing an evidence base to inform health policy, program and service delivery. For further information about this dataset, visit the data source: Australian Institute of Health and Welfare - Health Risk Factors in 2014-2015 Data Tables. Please note: AURIN has spatially enabled the original data using the Department of Health - PHN Areas. The health risks factors reported are known to vary with age and the different PHN area populations are known to have a range of age structures. As such, comparisons of results between the PHN areas should be made with caution because the crude rates presented do not account for these age differences. Adults are defined as persons aged 18 years and over. Values assigned to "n.p." in the original data have been removed from the data. Data for PHN701 (Northern Territory) should be interpreted with caution as the National Health Survey excluded discrete Aboriginal and Torres Strait Islander communities and very remote areas, which comprise around 28% of the estimated resident population of the Northern Territory living in private dwellings.