Double trouble: the impact of multimorbidity and deprivation on preference-weighted health related quality of life a cross sectional analysis of the Scottish Health Survey
© Lawson et al.; licensee BioMed Central Ltd. 2013
Received: 16 January 2013
Accepted: 11 June 2013
Published: 20 August 2013
To investigate the association between multimorbidity and Preference_Weighted Health Related Quality of Life (PW_HRQoL), a score that combines physical and mental functioning, and how this varies by socioeconomic deprivation and age.
The Scottish Health Survey (SHeS) is a cross-sectional representative survey of the general population which included the SF-12, a survey of HRQoL, for individuals 20 years and over.
For 7,054 participants we generated PW_HRQoL scores by running SF-12 responses through the SF-6D algorithm. The resulting scores ranged from 0.29 (worst health) to 1 (perfect health). Using ordinary least squares, we first investigated associations between scores and increasing counts of longstanding conditions, and then repeated for multimorbidity (2+ conditions). Estimates were made for the general population and quintiles of socioeconomic deprivation. For multimorbidity, the analyses were repeated stratifying the population by age group (20–44, 45–64, 65+).
45% of participants reported a longstanding condition and 18% reported multimorbidity. The presence of 1, 2, or 3+ longstanding conditions were associated with average reductions in PW_HRQoL scores of 0.081, 0.151 and 0.212 respectively. Reduction in scores associated with multimorbidity was 33% greater in the most deprived quintile compared to the least deprived quintile, with the biggest difference (80%) in the 20–44 age groups. There were no significant gender differences.
PW_HRQoL decreases markedly with multimorbidity, and is exacerbated by higher deprivation and younger age. There is a need to prioritise interventions to improve the HRQoL for (especially younger) adults with multimorbidity in deprived areas.
What Is Known?
Prevalence and premature onset of multimorbidity increases as socioeconomic position worsens. Previous studies have investigated the effect of multimorbidity on Health Related Quality of Life (HRQoL) on separate physical and mental health states. There is limited data on how HRQoL falls as the number of conditions increase, and how estimates vary across the general population.
Leaving physical and mental health as separate categories can inhibit assessment of overall HRQoL. The use of a Preference_Weighted Health Related Quality of Life (PW_HRQoL) score provides a single summary measure of overall health, by weighting mental and physical states by their perceived importance as part of overall HRQoL. The use of a single score enables a simple and consistent assessment of the impact of conditions and how this varies across the population. Economists term PW_HRQoL scores health utilities.
What this study adds?
This is the first study to estimate how the impact of multimorbidity on PW_HRQoL scores varies by age group and socioeconomic deprivation. Multimorbidity has a substantial negative impact on HRQoL which is most severe in areas of deprivation, especially in younger adults.
Measuring the burden of multimorbidity using PW_HRQoL provides consistency with how economists measure HRQoL; changes in which can be used in economic evaluation to assess the cost effectiveness of interventions.
KeywordsMultimorbidity Preference_Weighted Health Related Quality of Life (PW_HRQoL) Deprivation Inequality
The prevalence of longstanding conditions and multimorbidity is increasing globally, primarily as a result of increased life expectancies . It is of major concern to policy makers and wider society because of the associated, rising, cost and complexity of health care and the changes to the organisation and delivery of care that will be necessary to manage it effectively. A recent Cochrane review, of interventions in patients with multimorbidity in primary care and community settings, reported a lack of evidence-based interventions to help people to better manage their conditions .
The association between multimorbidity on Health Related Quality of Life (HRQoL) remains under researched [3–5]. A previous review and subsequent study found a negative association between multimorbidity and HRQoL [6, 7]. However, it is largely unknown how the impact varies within the general population, with no studies assessing the impact of multimorbidity by socioeconomic deprivation . There is also limited understanding regarding the extent to which overall HRQoL falls as the number of conditions increase, and as case-mix changes.
A key issue is how HRQoL is measured. Commonly used instruments leave mental and physical health as separate dimensions. This can be important for exploring which aspects of health may suffer as a result of experiencing longstanding conditions; however, it inhibits direct comparisons of the overall severity of different conditions, and the extent to which overall HRQoL falls as the number of conditions increase. A single score summarising overall HRQoL is required. Preference Weighted Health Related Quality of Life (PW_HRQoL) combines mental and physical health into a single summary score, which can range from 0 (death) to 1 (perfect health), and certain scores also measure states worse than death. Instruments that can be used to create PW_HRQoL scores include the SF-6D , EuroQol-5D , and Health Utilities Index . PW_HRQoL scores are referred to as health utilities by economists. Measuring the burden of multimorbidity using PW_HRQoL provides consistency with how economists measure HRQoL; changes in which can be used in economic evaluation to assess the cost effectiveness of interventions.
We are only aware of two studies to date that have investigated the association between PW_HRQoL scores and increasing numbers of longstanding conditions: one in a US general population  and the other in two General Practices in Manchester, England . Consistent findings were that PW_HRQoL scores fell markedly as the number of conditions increased. However, both studies were limited in that they considered only cardiovascular and respiratory conditions. To strengthen generalisations regarding the associations of multimorbidity and HRQoL it is important to include a wider set of conditions and undertake measurement in the general population.
The studies also did not consider the associations between multimorbidity, HRQoL and socioeconomic deprivation. It is known that multimorbidity is more common and develops earlier in deprived areas, and that people living in deprived areas experience a range of problems that add to the considerable complexity of clinical and self-management support of multimorbidity . A previous study found that deprived patients may suffer from reduced levels of personal capacity or community support with which to manage or mitigate the impact of multimorbidity . Further, the existence of the ‘inverse care law’ , where the availability of services does not reflect the greater needs of deprived populations may result in insufficient health care resources to help people manage their conditions.
The main objective of this study was to use data from the 2003 Scottish Health Survey to examine whether and to what degree PW_HRQoL in those living with multimorbidity varies by socioeconomic deprivation and age. It was hypothesised that the impact of multimorbidity on PW_HRQoL would increase as socioeconomic deprivation worsens.
Longstanding conditions included in Scottish Health Survey 2003
Type of longstanding condition
Grouped (system of body)
Number/% within grouped conditions
1 Cancer (neoplasm) including lumps, masses, tumours & growths
Neoplasms & begin growths
2 Diabetes. Incl. Hyperglycemia
Endocrine and metabolic
3 Other endocrine/metabolic
4 Mental llness.anxiety/despression/nerves
5 Mental handicap
8 other problems of nervous system
9 Cataract/poor eye sight/blindness
10 Other eye complaints
11 Poor hearing/deafness
12 Tinnitus/noise in the ear
13 Menieres discease/ear complaints causing balance problem
14 Other ear complaints
15 Stroke/cerebral haemorrhage/cerebral thrombosis
Heart & circulatory system
16 Heart attack/angina
17 Hypertension/high blood pressure/blood pressure
18 Other heart problems
19 Piles/haemorrhoids incl Varicose Veins in anus
20 Varicose Veins/phlebitis in lower extremities
21 Other blood vessels/embolic
25 Other respiratory complaints
26 Stomach ulcer/ulcer (nes)/abdominal hemia/rupture
27Other digestive complaints (e.g. stomach, liver, pancreas)
28 Complaints of bowel/colon (e.ge large intestine, caecum, bowel)
29 complaints of teeth/mouth, tongue
30 kidney complaints
31 Urinary tract infection
32 Other bladder problems/incontinence
33 Reproductive system disorders
35 Black problems/slipped disc .spine/neck
36 Other Problems of bones/joints/muscles
37 Infection and parasitic diseases
38 Disorders of blood and blood forming organs
Blood & related organs
39 skin complaints
40 Other Complaints
For the purposes of the study, multimorbidity was defined as the presence of two or more longstanding conditions from different systems of the body.
Generating PW_HRQoL scores
In 2003 the SHeS included the SF-12 questionnaire, a self-reported measure of functional health status, which all adults aged 20 years and over were asked to complete. To generate a single PW_HRQoL score we used the SF-6D algorithm . This algorithm involves preference-weighting 6 of the SF-12 question responses (3 physical health and 3 mental health) by the desirability for different health states. Preference-weights were derived from a survey representative of the UK general population (not the SHeS respondents) intended to enhance the generalisability of the results . Summing across weighted question responses generates a single PW_HRQoL score for each respondent.
Variables used in the analysis
We constructed a dataset containing respondents aged 20 years and over. Variables included: PW_HRQoL scores, respondent age, sex, presence of longstanding conditions, presence of multimorbidity, and socioeconomic deprivation as defined by the Scottish Index of Multiple Deprivation (SIMD) (http://www.scotland.gov.uk/Topics/Statistics/SIMD). SIMD is an aggregated measure of material deprivation derived from 37 indicators in seven domains (income, employment, health, education, access to services, housing and crime) and is determined at the data zone level (geographical areas with a median population of 769). Individuals in the SHeS 2003 are assigned a SIMD score based on their postcode of residence. SIMD scores ranged from 0 (least deprived) to 87.6 (most deprived). The Scottish Government defined cut-off values in the SIMD score to divide the general population into equally sized quintiles, used in its stratified sampling methodology. SIMD1 was defined as the least deprived quintile, with SIMD 2, 3, 4 and 5 denoting increasing levels of deprivation.
We created a dependent variable representing “ill health”, by subtracting PW_HRQoL scores from 1 (perfect health). Generalized Linear Modelling (GLM) was then used to test the suitability of different regression models. Following guidance by Glick et al.  we used the Modified-Park test to select the most appropriate model. Further, we applied the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to assess model parsimony.
The most appropriate model specification was a Gaussian distribution with a link function, identified by the Modified-Park test. This model was also the most efficient, having the lowest AIC and BIC scores. This model is equivalent to ordinary least squares.
Three separate regression models were generated. First, we assessed the association between reductions in the PW_HRQoL score and increasing numbers of conditions (1, 2, 3+), measured as categorical variables. This was done for the sample as a whole and then stratified by quintiles of socioeconomic deprivation. The reference category was “no reported conditions”. The aim was to assess how PW_HRQoL declines as longstanding conditions accumulate. Following this analysis, we secondly assessed the association of multimorbidity, defined as 2+ conditions, with reductions in the PW_HRQoL score in the sample as a whole, and then stratified by quintiles of socioeconomic deprivation. Defining multimorbidity as 2+ conditions enabled statistical power to be largely maintained in the analysis. Finally, we assessed the associations between multimorbidity and reductions in HRQoL by age group (defined as 20–45, 45–64, 65+ years), for the sample as a whole and then stratified by quintiles of socioeconomic deprivation. For the second and third models, the reference category was “no multimorbidity” (i.e. 0 or 1 condition). All estimates were adjusted for sex and age; the latter modelled as a continuous variable.
Scottish Health Survey (SHeS) 2003
The SHeS 2003 had a response rate of 77% and overall 11,442 individuals were surveyed. There was 23% missing households which were evenly spread across the general population, defined by SIMD. No bias was expected.
Prevalence of longstanding conditions and multimorbidity in Scottish Health Survey 2003
No. of subjects
(mean, sd, max)
No of conditions
Rates of multimorbidity
Table 2 shows the breakdown of the sample used in the analysis, including the prevalence of longstanding conditions and multimorbidity. 3,117 (45%) of survey respondents reported a history of one or more longstanding conditions, with 1,242 (18%) identified as multimorbid (2 or more conditions). The prevalence of multimorbidity was similar for men and women, and increased markedly with age. Overall, the prevalence was 3 times higher (27% vs 9%) in the most deprived quintile (SIMD5) compared to the least deprived quintile (SIMD1).
Preference Weighted Health Related Quality of Life (PW_HRQoL) scores
Impact of increasing counts of longstanding conditions
Impact of multimorbidity
Impact of multimorbidity by age group
This study has shown that multimorbidity is associated with substantial and progressive reductions in Health Related Quality of Life (HRQoL), as measured by a Preference_Weighted Health Related Quality of Life score (PW_HRQoL). This score provided a single summary measure of overall health, by weighting mental and physical states by their perceived importance as part of overall HRQoL. The use of a single score enables a simple and consistent assessment of the impact of conditions and how this varies across the population.
The clinical significance of multimorbidity is considerable. The PW_HRQoL measure used (SF-6D) ranges from 0.29 (worst health) to 1 (perfect health). The mean PW_HRQoL score was 0.84, and the average reduction in the score of respondents suffering from multimorbidity was 0.141 (compared to respondents with no or one longstanding condition) - a reduction of 17%.
Multimorbidity was associated with larger reductions in PW_HRQoL scores amongst participants living in the most deprived areas, with this deprivation effect most marked in younger multimorbid adults. While it is known that those living in the most deprived areas have a higher prevalence of multimorbidity of longstanding conditions , this is the first study to show that the impact of multimorbidity on PW_HRQoL score is also significantly greater in more deprived areas.
Comparison with other studies
Previous studies also showed that PW_HRQoL falls markedly as the number of conditions increase. However, these studies were limited to six conditions (CVD and respiratory) [12, 13]. Further, no analysis of the impact of multimorbidity conditional upon age and socioeconomic position was undertaken.
Strengths and weaknesses
The SHeS 2003 included 40 longstanding conditions which is a greater amount than most previous studies [5, 6]. The prevalence of obesity as an independent longstanding condition was not considered in the analysis. Obesity was not included in the SHeS definition of longstanding conditions, and the construction of a variable may have introduced double counting given several conditions included in the analysis are likely to be symptoms of obesity.
The inclusion of the SF-12 within the SHeS 2003 enabled a PW_HRQoL score to be estimated (using the SF-6D) permitting investigation into the association of multimorbidity and overall HRQoL, rather than separately on physical and mental health which most previous studies have done. Further, the size of the dataset and the comprehensive definition of socioeconomic deprivation (SIMD) meant that associations could be estimated by age group and quintiles of socioeconomic deprivation.
By using PW_HRQoL scores to estimate the burden of multimorbidity provides consistency with how economists measure the (HRQoL) burden of illness, termed health utilities; changes in which can be used in economic evaluation to assess the cost effectiveness of interventions. The clinical significance of multimorbidity as measured by PW_HRQoL demonstrates the potential need to develop interventions to lessen the impact of multimorbidity, rather than the traditional focus on single conditions.
The use of cross-sectional survey data limits the analysis to measurements of association. In addition, the SHeS does not provide data on duration or severity of conditions. These weaknesses are common to previous studies. A further weakness in national cross-sectional studies is that there is usually a poorer response rate within communities of lower socioeconomic position. We analysed the SHeS by quintiles of socioeconomic deprivation, as measured by the Scottish Index of Multiple Deprivation (SIMD). The most deprived quintile was underrepresented (16%) and this may have comprised representativeness, if for instance missing respondents were skewed toward the most deprived affecting case mix. However, the direction of any potential bias in our estimates is unknown.
A further potential limitation is that the sample was for 2003. It is possible that the nature of the relationship between PW_HRQoL, multimorbidity and deprivation may have changed over time. It is difficult to assess the likelihood of this limitation. The SHeS 2003 was the only SHeS survey that has included the SF-12 to enable this analysis to be undertaken.
Further work could usefully focus on longitudinal studies to assess the causal impact of incurring longstanding conditions and developing multimorbidity throughout the life course [19, 20]. Such studies could encompass both longitudinal cohort methods, and qualitative studies to better understand why the impact of multimorbidity varies by deprivation and age.
The hypothesis that the impact of multimorbidity on PW_HRQoL would be greater in more deprived groups compared to less deprived groups was largely corroborated. However, the deprivation gradient was not uniform, with the fourth and fifth most deprived quintiles having experienced similar declines in PW_HRQoL.
The general deprivation gradient may be the result of the existence of the ‘inverse care law’ , where the availability of services does not reflect the greater needs of deprived populations may result in insufficient health care resources to help people manage their conditions. Further, this may be compounded by deprived groups having reduced levels of personal capacity (e.g. reduced health literacy, agency) or community support available with which to manage or mitigate the impact of multimorbidity . The impact of multimorbidity in younger (20–44 years) people in the most deprived quintile was 80% greater than those in the least deprived quintile. In addition, the deprivation gradient may (also) be due to case-mix where young deprived groups can suffer from greater mental health problems . It is important to further investigate the associations between deprivation and multimorbidity to assess both its nature and the reasons underlying observed differences, in order to inform how best to address the deprivation gradient.
The inverse relationship between increasing age and the impact of multimorbidity may reflect greater levels of impairment due to multimorbidity in younger groups, or it may simply reflect that people of different ages adapt their expectations in different ways in response to living with chronic conditions. The reversal of this age gradient for the least deprived quintile needs to be explored further.
The paper stratified the SHeS into three age groups, with the eldest group 65 years and above. Due to statistical power concerns it was not possible to stratify age further. However, given that the incidence of multimorbidity increases further in older age groups , it is important, where possible, to investigate the consequent impacts on HRQoL.
The analysis focussed on the association between counts of conditions and HRQoL, rather investigating the impact of particular case-mix. A study by Fortin  found that different combinations of conditions are associated with different impacts on physical and mental health, as measured separately using the SF-36. Future work could usefully undertake a similar exercise exploring how case-mix impacts on an overall PW_HRQoL score (combining physical and mental health). Using a single score would permit systematic comparisons of the overall severity of case-mix, and identify patient groups who experience the greatest reductions in quality of life.
It is important to assess to what extent the reduction in PW_HRQoL from multimorbidity is actually modifiable to inform the development of interventions. Research could usefully investigate individual and community capacity to support effective self-management and adjustment to conditions.
Finally, estimating the burden of multimorbidity on PW_HRQoL would also provide consistency with how economic evaluation measures the impacts of interventions. PW_HRQoL scores (termed health utilities by economists) are used to generate quality adjusted life years (QALYs) by weighting length of life with quality of life, measured by the scores. Given that there has been limited research on this topic to date, assessing the burden of multimorbidity using PW_HRQoL scores in the general population may help strengthen the economic case for funding the development and implementation of interventions.
Preference_Weighted Health Related Quality of Life (PW_HRQoL) decreases markedly with multimorbidity, and this is exacerbated by higher deprivation and younger age. There a need to prioritise interventions to improve quality of life for (especially younger) adults with multimorbidity in deprived areas.
- WHO: Global status report on noncommunicable diseases 2010: description of the global burden of NCDs, their risk factors and determinants. 2011, Geneva: World Health Organisation
- Smith SM: Interventions for improving outcomes in patients with multimorbidity in primary care and community settings. Cochrane Database Syst Rev. 2012, Issue 4
- Fortin M: A systematic review of prevalence studies on multimorbidity: toward a more uniform methodology. Ann Fam Med. 2012, 10 (2): 142-151. 10.1370/afm.1337.View ArticlePubMedPubMed Central
- Fortin M: Multimorbidity is common to family practice: is it commonly researched?. Can Fam Physician. 2005, 51: 244-245.PubMed
- Fortin M: Multimorbidity and quality of life: a closer look. Health Qual Life Outcomes. 2007, 5: 52-10.1186/1477-7525-5-52.View ArticlePubMedPubMed Central
- Fortin M: Multimorbidity and quality of life in primary care: a systematic review. Health Qual Life Outcomes. 2004, 2: 51-10.1186/1477-7525-2-51.View ArticlePubMedPubMed Central
- Fortin M: Relationship between multimorbidity and health-related quality of life of patients in primary care. Qual Life Res. 2006, 15 (1): 83-91. 10.1007/s11136-005-8661-z.View ArticlePubMed
- Mangin D: Beyond diagnosis: rising to the multimorbidity challenge. BMJ. 2012, 344: e3526-10.1136/bmj.e3526.View ArticlePubMed
- Brazier JE, Roberts J: The estimation of a preference-based measure of health from the SF-12. Medical Care. 2004, 42 (9): 851-859. 10.1097/01.mlr.0000135827.18610.0d.View ArticlePubMed
- EuroQol Group: EQ-5D-5L User Guide. 2011,http://www.euroqol.org/fileadmin/user_upload/Documenten/PDF/Folders_Flyers/UserGuide_EQ-5D-5L.pdf,
- Horsman J: The Health Utilities Index (HUI(R)): concepts, measurement properties and applications. Health Qual Life Outcomes. 2003, 1 (1): 54-10.1186/1477-7525-1-54.View ArticlePubMedPubMed Central
- Lubetkin EI: Relationship among sociodemographic factors, clinical conditions, and health-related quality of life: examining the EQ-5D in the U.S. general population. Qual Life Res. 2005, 14 (10): 2187-2196. 10.1007/s11136-005-8028-5.View ArticlePubMed
- Heyworth IT: How do common chronic conditions affect health-related quality of life?. Br J Gen Pract. 2009, 59 (568): e353-e358. 10.3399/bjgp09X453990.View ArticlePubMedPubMed Central
- O'Brien R: An ‘endless struggle’: a qualitative study of general practitioners’ and practice nurses’ experiences of managing multimorbidity in socio-economically deprived areas of Scotland. Chronic Illn. 2011, 7 (1): 45-59. 10.1177/1742395310382461.View ArticlePubMed
- Mercer SW, Watt GCM: The inverse care law: clinical primary care encounters in deprived and affluent areas of Scotland. Ann Fam Med. 2007, 5 (6): 503-510. 10.1370/afm.778.View ArticlePubMedPubMed Central
- Scottish Executive: The Scottish Health Survey 2003: Volume 4, Technical Report. 2005,http://www.scotland.gov.uk/Publications/2005/11/25145024/50278,
- Glick HA: Economic Evaluation of Clinical Trials. 2007, Oxford University Press
- Barnett K: Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. Lancet. 2012, 380 (9836): 32-43.View Article
- France EF: Multimorbidity in primary care: a systematic review of prospective cohort studies. Br J Gen Pract. 2012, 62 (597): e297-e307. 10.3399/bjgp12X636146.View ArticlePubMedPubMed Central
- Mercer S: Improving the health of people with multimorbidity: the need for prospective cohort studies. J Comorbidity. 2011, 1: 4-7.View Article
- Gordon DS: Dimensions of diversity: population differences and health improvement opportunities. 2010, Glasgow: NHS Health Scotland
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.