Health and Wellbeing Multi-Instrument Comparison Dataset

The SIPHER-HWMIC (Health and Wellbeing Multi-Instrument Comparison) dataset provides a unique resource for researchers and policymakers seeking to understand and compare different measures of health and wellbeing.

Traditionally, wellbeing research has focused on income as a key indicator.

SIPHER-HWMIC supports moving beyond income by capturing a broader spectrum of health and wellbeing outcomes through a large-scale, cross-sectional online survey of over 12,000 UK adults.

Wellbeing surveys often use different outcome indicators, meaning data may be available for one measure when another is required for a comparison. This dataset supports the creation of statistical mapping algorithms between different measures allowing more consistent and comparable wellbeing analysis across studies.

The data was collected in November 2022 via three commercial internet panels, with oversampling from Scotland. Respondents self-report on a comprehensive set of established wellbeing instruments, including EQ-5D-5L, HUI3, ICECAP-A, EQ-HWB-S, and SWEMWBS, alongside demographic, socioeconomic, work, and housing information.

The SIPHER - HWMIC dataset is available as part of the UK Data Service Data Collection

SIPHER-HWMIC Dataset: A Large-Scale Population Survey of Health and Wellbeing to Allow Comparisons between Outcome Measures, 2022. (SN9458) http://doi.org/10.5255/UKDA-SN-9458-1 .

Access SIPHER - HWMIC Dataset

We thank all respondents for their participation and the copyright holders for permission to reproduce instrument items.

Related Resources

Citation: Tsuchiya, A., Wickramasekera, N., University of Sheffield. (2025). SIPHER-HWMIC Dataset: A Large-Scale Population Survey of Health and Wellbeing to Allow Comparisons between Outcome Measures, 2022. [data collection]. UK Data Service. SN: 9458, http://doi.org/10.5255/UKDA-SN-9458-1

  • The indicator sets and questions included in the survey: SIPHER-7; ICECAP-A; EQ-5D-5L; SF-12 v2; HUI; WEMWBS; EQ-HWB; ONS-4; Understanding Society items on crime and housing; items from the Labour Force Survey, the Living Wage Foundation questionnaire; education, income, ethnicity, children, informal caregiving; gender, age; etc. Includes sampling weights to correct for age and sex with respect to the mid-year UK population estimate.

 

Techincal Information

Provides technical details of the characteristics including strengths and limitations for this dataset. 

CharacteristicDetails
Purpose A dataset with a battery of self-reporting health and wellbeing indicators from a large UK sample, oversampling from Scotland.
Context Different surveys use different health outcome indicators. Therefore, data might be available for one indicator set when another is required. For example, answers to SF-12 survey items are available but a WEMWBS value is required. This is a large-cross section online survey of the general public (n=12,401) where respondents are asked to self-report their health and wellbeing across a battery of questions. This dataset allows the estimation of a statistical mapping algorithm between the different indicator sets.
Strengths Different surveys have different health and wellbeing indicators, and this dataset allows the estimation of a statistical mapping algorithm between them. This would allow predicting SIPHER-7 information where the relevant variables are not available.
Limitations Currently not available.
Geography The survey collected data from participants resident in the UK with sampling quotas for age and for sex. Oversamples Scotland.
Variables / Indicators The indicator sets and questions included in the survey: SIPHER-7; ICECAP-A; EQ-5D-5L; SF-12 v2; HUI; WEMWBS; EQ-HWB; ONS-4; Understanding Society items on crime and housing; items from the Labour Force Survey, the Living Wage Foundation questionnaire; education, income, ethnicity, children, informal caregiving; gender, age; etc. Includes sampling weights to correct for age and sex with respect to the mid-year UK population estimate.
Time Period Data collected: late 2022.