Horsham District Council - Data Science Led Estate Renewal

6-monthly Health and Wellbeing Audit of existing temporary accommodation to inform district wide regeneration strategy

Horsham District Council - Data Science Led Estate Renewal

Executive summary


Access to good quality homes is essential for human health and wellbeing. However, little robust research has investigated the impact of collecting data on the relationship between (1) home design factors (e.g interior design, space) (2) community and home environment factors (e.g do you feel safe where you live?) and (3) management factors (e.g application for getting a home, application process) on resident’s wellbeing over time. 


This collaborative project between Horsham District Council (HDC) and LifeProven was formed with the ambitious aim to collect GDPR compliant data (Data) from residents living in HDC temporary accommodation over a 12-month period using Internationally validated information from World Health Organisation, to understand the impact of relationship factors 1-3 on HDC’s resident’s quality of life. 


This is the first ever longitudinal (forward looking) data informed research study to track and evaluate the impact of the home and its management on resident health. Data captured by LifeProven was analysed with statistics (data-science) that account for unexplained factors to provide robust and reliable results. 


This novel research study using robust research methods has not only quantified the robust relationship between the home environment and resident wellbeing over time but has provided HDC valuable information on the areas where change is possible within its housing portfolio and services. The object results from this research study demonstrate the positive impact HDC temporary accommodation is now having on its residents, with Data providing a platform and process for ongoing monitoring and improvement for future temporary developments. 



Background & aims


The majority of our time and financial resources are spent in the home environment. Despite this commitment, there is a dearth of high quality, context specific research investigating the relationship between the home environment and its accessibility with health, wellbeing and quality of life over time among home dwellers. Additionally, there is minimal academic or published information available on the influence of the relationship between Council management and the handling of residents on their quality of life and wellbeing over time. 


To date, most of the research investigating the impact of the home on the health and wellbeing of residents has been reactive and focused solely on adverse outcomes from poor housing and accessibility issues. Minimal prospective (forward looking) research has attempted to take a proactive and preventive approach to understand the impact of the home, Council services and management offered, combined with data informed decision making to improve the residents experience and outcomes. 



Gaps in the evidence prior to starting this Project 


There is an absence of robust prospective data that considers the influence of Council residents’ perspectives on the suitability, accessibility, adaptability of the home and relationship this has on quality of life, health and wellbeing. Specific gaps include: 


1. Absence of prospective data investigating the relationship between home design, Council management and services with quality of life, and wellbeing over time. 


2. Absence of robust data investigating the impact of lifestyle behaviors in the home, access to green space and accessibility with quality of life and wellbeing over time. 

 


Project Aims 


This ambitious program of work commenced with the following aims: 


1. Provide a benchmark of Horsham District Council (HDC) residents satisfaction with their home and the relationship with quality of life (QOL) by investigating the baseline relationship between HDC residents current housing, their perspective of suitability, management and council service process & lifestyle (including concerns about finances) with wellbeing and quality of life. 


2. Feedback findings from Aim 1 to HDC to inform future housing development process and specifically a new build temporary accommodation development at Rowan Drive, comprising 16 units. 


3. Investigate changes in the relationship between residents housing, their perspective of suitability, management and council service process & lifestyle (including concerns about finances) with wellbeing and quality of life over a 12-month period.



Methods


In order to understand the relationship between residents QOL, wellbeing and satisfaction with housing, Council management and experiences we conducted survey collecting data at baseline and each quarter over 12 months. The data reports from baseline and the results found informed the design and build/management process over this period of time.


In order to do this, we used validated reliable scales to capture quality of life, wellbeing, lifestyle (e.g physical activity). Data and the information obtained is only as good as the quality of measures used. That is why we spent a lot of time developing the optimal metrics to capture QOL, wellbeing and lifestyle and life satisfaction over time.


Secondly, in order to ensure we measured the relationship between the above outcomes with meaningful metrics on homes we undertook a control group survey of approximately 150 people. This process helped us identify the process through which we focused on key areas that matter to people in three key areas:


1. Home design factors (e.g interior design, space)


2. Community and home environment factors (e.g do you feel safe where you live?)


3. Management factors (e.g application for getting a home, application process).


In order to move beyond simply presenting a summary of average scores at each time point (which could be influenced by many factors), we investigated the relationship between wellbeing, QOL and lifestyle with the above housing metrics using the Pearson correlation co-efficient. This enables the direct comparison between the score of two items (i.e a correlation score) ranging from 0 (no relationship at all) to 1 (perfect correlation – rarely occurs in real world research outside of laboratories).


Moreover, this type of analysis enables the ‘controlling’ of other factors (or variables) such as age, income which can affect the relationship between two variables. Thus, adding in this additional analysis provides a substantially clearer picture than presenting mean values or raw percentages.

All variables have been used in a positive framework, so variables move from 0 (no relationship at all to 1 (perfect relationship). A correlation of 0.2 is small, 0.21-0.5 is medium and above 0.5 is large. However, in order to be meaningful, the P-Value also needs to be less than 0.05. For a Pearson’s coefficient if it is a high score (from 0-10) on a variable (e.g natural day light on average = average score across 100 people is 7.4 (with a range of 1-10)) and high score on QOL (e.g with average score of 6.5, range of 1-10) then this means there is a positive score of 0.586 (P-Value = 0.001). The correlation is reduced lower than the raw comparison of data because of two primary reasons.


1. This analysis compares 100 data points who have data for natural daylight and QOL and there is a large range from 1-10 on both scores.


2. The correlation adjusts for other factors including age, sex and education status which take into account for some of the ‘noise' (other factors which could affect the direct relationship between these variables). If there is a less positive relationship the Pearson’s value will be smaller (greater?) and non-significant (p value over 0.05).



Results HDC


HDC have been provided the data science results for their resident’s wellbeing and QOL with house the Home design factors, community and home environment factors and finally management factors (e.g application for getting a home, application process) at baseline and each quarter, including an end of year report. The focus of this results summary here is to provide a high-level overview of the key changes that have happened over time from the baseline to the end of 12 months with residents QOL and wellbeing across these key areas.


A final part of the results is to present a brief overview of the level of changes noted in Rowan drive residents over time.

The final surveys included a total of 73 residents across all sites. The response rate from the surveys substantially increased from baseline.



Home Design


The key changes noted over the study period in changes of Home Design factors were:


Maintained positive relationships (with a positive Pearson result and P-Value <0.05)


Interior design

Residents consistently rated that higher satisfaction to interior   design (higher on 0-10) was associated with better QOL.


Natural daylight

Residents consistently rated that higher satisfaction of access to   natural daylight (higher on 0-10) was associated with better QOL.


Condition of home

Residents consistently rated that higher quality of the home   conditions satisfaction (higher on 0-10) was associated with better QOL.


Noise level from neighbours

Residents consistently rated that higher satisfaction (less noise)   (higher on 0-10) was associated with better QOL.


Security of home

Residents consistently rated that higher satisfaction (more security)   (higher on 0-10) was associated with better QOL.


Improved from no positive relationship to significant positive relationship (with a positive Pearson result and p value <0.05)


Layout of home

Residents rated a positive change from baseline (where there was no   relationship) to a higher quality of satisfaction (higher on 0-10) being   associated with better QOL.


Home meets needs

Residents rated a positive change from baseline (where there was no   relationship) to a higher quality of satisfaction (higher on 0-10) being   associated with better QOL.


Access to green space

Residents rated a positive change from baseline (where there was no   relationship) to a higher quality of satisfaction (higher on 0-10) being   associated with better QOL.


Better neighbours

Residents   rated a positive change from baseline (where there was no relationship) to a   higher quality of satisfaction (higher on 0-10) being associated with better   QOL.


Access to local jobs

Residents rated a positive change from baseline (where there was no   relationship) to a higher quality of satisfaction (higher on 0-10) being   associated with better QOL.



Council management factors


The key changes noted over the study period in changes of community and home factors were:


Maintained positive relationships (with a positive Pearson result and p value <0.05)


Home makes resident feel good

Residents consistently rated that higher satisfaction that their home   makes them feel good (higher on 0-10) was associated with better QOL.


Residents feel home improves QOL

Residents consistently rated that higher satisfaction that their home   improves their QOL (higher on 0-10) was associated with better QOL.


Improved from no positive relationship to significant positive relationship (with a positive Pearson result and p value <0.05)


Residents feeling safe in their homes

Residents rated a positive change from baseline (where there was no   relationship) to a higher quality of satisfaction (higher on 0-10) being   associated with better QOL.



Conclusion


In this novel, prospective study we have shown that it is possible to use quantitative data feedback to improve the home environment, place and management and consequently the resident's quality of life.  Specifically, using robust metrics, we have shown that over a 12-month period, through data driven approaches meaningful improvements can be seen across multiple domains thus demonstrating objective value and improving residents experience and quality of life.

Furthermore, this study has shown that through the detailed understanding of how a building, its environment, management and operation impacts the lives of end-users health and wellbeing using robust data science, that accurately defined refurbishment and development work scopes can be developed, ensuring budgetary spend is targeted towards building elements that will have the greatest return economic and social return, providing a transparent, consistent and reliable portfolio management platform.

Horsham District Council - Data Science Led Estate Renewal
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