Common Patterns of Morbidity and Multi-Morbidity and Their Impact on Health-Related Quality of Life (original) (raw)
Related papers
2014
Background There is limited evidence about the impact of specific patterns of multi-morbidity on health-related quality of life (HRQoL) from large samples of adult subjects. Methods We used data from the English General Practice Patient Survey 2011-2012. We defined multi-morbidity as the presence of two or more of 12 self-reported conditions or another (unspecified) long-term health problem. We investigated differences in HRQoL (EQ-5D scores) associated with combinations of these conditions after adjusting for age, gender, ethnicity, socioeconomic deprivation and the presence of a recent illness or injury. Analyses were based on 831,537 responses from patients aged 18 years or older in 8,254 primary care practices in England. Results Of respondents, 23 % reported two or more chronic conditions (ranging from 7 % of those under 45 years of age to 51 % of those 65 years or older). Multimorbidity was more common among women, White individuals and respondents from socioeconomically deprived areas. Neurological problems, mental health problems, arthritis and long-term back problem were associated with the greatest HRQoL deficits. The presence of three or more conditions was commonly associated with greater reduction in quality of life than that implied by the sum of the differences associated with the individual conditions. The decline in quality of life associated with an additional condition in people with two and three physical conditions was less for older people than for younger people. Multimorbidity was associated with a substantially worse HRQoL in diabetes than in other long-term conditions. With the exception of neurological conditions, the presence of a comorbid mental health problem had a more adverse effect on HRQoL than any single comorbid physical condition. Conclusion Patients with multi-morbid diabetes, arthritis, neurological, or long-term mental health problems have significantly lower quality of life than other people. People with long-term health conditions require integrated mental and physical healthcare services.
Multimorbidity and quality of life: a closer look
Health and Quality of Life Outcomes, 2007
The presence of multiple chronic conditions is associated with lower health related quality of life (HRQOL). Disease severity also influences HRQOL. To analyse the effects of all possible combinations of single diseases along with their severity on HRQOL seems cumbersome. Grouping diseases and their severity in specific organ domains may facilitate the study of the complex relationship between multiple chronic conditions and HRQOL. The goal of this study was to analyse impaired organ domains that affect the most HRQOL of patients with multiple chronic conditions in primary care and their possible interactions.
Health and Quality of Life Outcomes, 2012
Background: In the US, approximately 53% of adults have at least one chronic condition. Comorbid physical and mental health conditions often have an incremental negative impact on health-related quality of life (HRQL). Primary study objectives were to quantify the impact on HRQL of a) ≥ 1 physical condition , b) ≥ 1 comorbid mental health conditions added to a physical one, c) ≥ 1 mental health condition, and d) ≥ 1 comorbid physical conditions added to at least one related to mental health. Decrements were based on a "Healthy" reference group reporting no chronic conditions. Methods: Participants were sampled (n = 3877) from the US adult population as part of a 2009 normative survey. Demographics, number/ type of chronic conditions, and HRQL data were self-reported. HRQL was defined through SF-36v2 W Physical Component Summary (PCS) scores and Mental Component Summary (MCS) scores. Participant "morbidity" groupings included Healthy; Physical Health Condition only, Mental Health Condition only, and Physical and Mental Health (Comorbid). PCS and MCS scores were also analyzed by physical disease clusters (e.g., cardiovascular, gastrointestinal). Multivariate regression models were used for all analyses.
PLoS ONE, 2013
Background: Multimorbidity has a negative impact on health-related quality of life (HRQL). Previous studies included only a limited number of conditions. In this study, we analyse the impact of a large number of conditions on HRQL in multimorbid patients without preselecting particular diseases. We also explore the effects of these conditions on the specific dimensions of HRQL. Materials and Methods: This analysis is based on a multicenter, prospective cohort study of 3189 multimorbid primary care patients aged 65 to 85. The impact of 45 conditions on HRQL was analysed. The severity of the conditions was rated. The EQ-5D, consisting of 5 dimensions and a visual-analogue-scale (EQ VAS), was employed. Data were analysed using multiple ordinary least squares and multiple logistic regressions. Multimorbidity measured by a weighted count score was significantly associated with lower overall HRQL (EQ VAS), b = 21.02 (SE: 0.06). Parkinson's disease had the most pronounced negative effect on overall HRQL (EQ VAS), b = 212.29 (SE: 2.18), followed by rheumatism, depression, and obesity. With regard to the individual EQ-5D dimensions, depression (OR = 1.39 to 3.3) and obesity (OR = 1.44 to 1.95) affected all five dimensions of the EQ-5D negatively except for the dimension anxiety/depression. Obesity had a positive effect on this dimension, OR = 0.78 (SE: 0.07). The dimensions ''self-care'', OR = 4.52 (SE: 1.37) and ''usual activities'', OR = 3.59 (SE: 1.0), were most strongly affected by Parkinson's disease. As a limitation our sample may only represent patients with at most moderate disease severity. Conclusions: The overall HRQL of multimorbid patients decreases with an increasing count and severity of conditions. Parkinson's disease, depression and obesity have the strongest impact on HRQL. Further studies should address the impact of disease combinations which require very large sample sizes as well as advanced statistical methods.
The European Journal of Health Economics, 2014
Objectives Health-related quality of life (HRQoL) measures have been increasingly used in economic evaluations for policy guidance. We investigate the impact of 11 selfreported long-standing health conditions on HRQoL using the EQ-5D in a UK sample. Methods We used data from 13,955 patients in the South Yorkshire Cohort study collected between 2010 and 2012 containing the EQ-5D, a preference-based measure. Ordinary least squares (OLS), Tobit and two-part regression analyses were undertaken to estimate the impact of 11 long-standing health conditions on HRQoL at the individual level. Results The results varied significantly with the regression models employed. In the OLS and Tobit models, pain had the largest negative impact on HRQoL, followed by depression, osteoarthritis and anxiety/nerves, after controlling for all other conditions and sociodemographic characteristics. The magnitude of coefficients was higher in the Tobit model than in the OLS model. In the two-part model, these four long-standing health conditions were statistically significant, but the magnitude of coefficients decreased significantly compared to that in the OLS and Tobit models and was ranked from pain followed by depression, anxiety/nerves and osteoarthritis. Conclusions Pain, depression, osteoarthritis and anxiety/ nerves are associated with the greatest losses of HRQoL in the UK population. The estimates presented in this article should be used to inform economic evaluations when assessing health care interventions, though improvements can be made in terms of diagnostic information and obtaining longitudinal data.
BMC Public Health, 2014
Background: Health-related quality of life (HRQoL) is associated with adverse outcomes in disease-specific populations. This study examines whether it is also independent predictor of incident cancer, coronary heart disease (CHD) and mortality in the general population. Methods: The records of adult participants in the Scottish Health Survey 2003 were linked with hospital admissions, cancer registrations and death certificates. Cox proportional hazard models were used to explore the associations between quintiles of physical and mental component summary score (PCS and MCS respectively) of the SF-12 and adverse outcomes. Higher quintiles of both PCS and MCS indicate better health status. Results: Among the 5,272 study participants, the mean PCS score was 49 (standard deviation (SD) 10.3). Participants were followed-up for a mean of 7.6 years. On survival analysis the lowest quintile of PCS was a strong predictor of all-cause death (hazard ratio (HR) 2.81, 95% CI 1.76, 4.49), incident cancer (HR 1.63, 95% CI 1.10, 2.42), and CHD events (HR 1.99, 95% CI 1.00, 3.96), compared to the highest quintile. This association was independent of adiposity and other confounders. The mean MCS score 52 (SD 8.8). MCS quintile was not associated with incident cancer and CHD, and the association between MCS and all-cause death (HR 1.33, 95% CI 1.01, 1.75) became non-significant after adjustment for adiposity. Conclusion: Physical HRQoL is a significant predictor of a range of adverse outcomes, even after adjustment for adiposity and other confounders. This study highlights the importance of perceived health in the general population.
The Importance of Quality-of-Life Measures for people with Chronic Conditions
2019
Patient-reported health outcomes are vital to helping clinicians and public health officials understand the holistic burden of disease. Indeed, more federally funded studies now collect a range of patient-reported measures to assess the effects of chronic illness, disability, and treatment. One such measure is quality of life in the context of health and disease—known as health-related quality of life (HRQOL). Recent research recommends monitoring HRQOL to assess quality of care, particularly for patients with multimorbidity— two or more chronic conditions at the same time—given that the care of these patients may account for twothirds of health care expenditures (Markovski et al. 2019). HRQOL is also a focus of the Healthy People 2020 initiative (Office of Disease Prevention and Health Promotion 2010).
2014
Thank you for forwarding the comments of reviewers. We have revised the manuscript to take account of these comments and we believe our manuscript is now much improved. Below is our point by point response to these comments. Reviewer 1 Reviewer: Cyrille Delpierre Reviewer's report: This is an interesting paper aiming at studying the relationship between Health related quality of life (HRQoL) and subsequent health (mortality, incident cancer and CVD). Finding good predictors of health and mortality, easy to measure and use is an important topic for researchers involved in the study of health determinants. Because of the interest of the paper's objective, I would have some suggestions to improve the message and the key findings of this article Major comments In the introduction it seems important to me to distinguish the several subjective health indicators that can be used and studied. As an illustration, self-reported health (SRH) and HRQoL are not identical, do not measure the same aspect of health and do not vary in the same way according to socioeconomic status (Delpierre et al. BMC Public Health 2012): in the third paragraph, authors talk about SRH and to me this is different of HRQoL. Thus this introduction should clarify that several indicators exist and that this paper analyses specifically HRQoL. RESPONSE We have now added the following para to the introduction: "There is an ongoing debate that if a single question such as, self-reported health (SRH) is available and is consistently reported to be a reliable measure then why to use a lengthy and multiple item questionnaires such as SF-36 and SF-12. However, health status measured by SRH, GHQ-12 and different measures of HRQoL are not identical (Delpierre et al 2012, Forero et al 2013). The SRH has clear advantage of reducing burden on respondents,
Health and quality of life outcomes, 2017
Chronic diseases are highly prevalent and cluster in individuals (multimorbidity). This study investigated the association between multimorbidity and Health-Related Quality of Life (HRQoL), assessing the combination of chronic diseases highly correlated with this outcome. We conducted a household survey in 2015 in a random sample of 2912 South Australian adults (48.9 ± 18.1 years; 50.9% females), obtaining information on sociodemographics, lifestyle, and 17 chronic conditions clustered in four different groups (metabolic, cardiovascular, gastrointestinal, and musculoskeletal). Information on physical (PCS) and mental components scores (MCS) of HRQoL were assessed using the SF-12 questionnaire. Multivariable linear regression models considering individual diseases (mutually adjusted) and clusters within- and between-groups were used to test the associations. Only 41% of the sample was negative for all the investigated diseases. The most prevalent conditions were osteoarthritis, obesi...
Multimorbidity and quality of life in primary care: a systematic review
Health and Quality of …, 2004
Background: Many patients with several concurrent medical conditions (multimorbidity) are seen in the primary care setting. A thorough understanding of outcomes associated with multimorbidity would benefit primary care workers of all disciplines. The purpose of this systematic review was to clarify the relationship between the presence of multimorbidity and the quality of life (QOL) or health-related quality of life (HRQOL) of patients seen, or likely to be seen, in the primary care setting.