Multimorbidity and quality of life: a closer look (original) (raw)
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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.
Common Patterns of Morbidity and Multi-Morbidity and Their Impact on Health-Related Quality of Life
2015
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.
Health and Quality of Life Outcomes, 2005
Background Measures of multimorbidity are often applied to source data, populations or outcomes outside the scope of their original developmental work. As the development of a multimorbidity measure is influenced by the population and outcome used, these influences should be taken into account when selecting a multimorbidity index. The aim of this study was to compare the strength of the association of health-related quality of life (HRQOL) with three multimorbidity indices: the Cumulative Illness Rating Scale (CIRS), the Charlson index (Charlson) and the Functional Comorbidity Index (FCI). The first two indices were not developed in light of HRQOL. Methods We used data on chronic diseases and on the SF-36 questionnaire assessing HRQOL of 238 adult primary care patients who participated in a previous study. We extracted all the diagnoses for every patient from chart review to score the CIRS, the FCI and the Charlson. Data for potential confounders (age, sex, self-perceived economic status and self-perceived social support) were also collected. We calculated the Pearson correlation coefficients (r) of the SF-36 scores with the three measures of multimorbidity, as well as the coefficient of determination, R2, while controlling for confounders. Results The r values for the CIRS (range: -0.55 to -0.18) were always higher than those for the FCI (-0.47 to -0.10) and Charlson (-0.31 to -0.04) indices. The CIRS explained the highest percent of variation in all scores of the SF-36, except for the Mental Component Summary Score where the variation was not significant. Variations explained by the FCI were significant in all scores of SF-36 measuring physical health and in two scales evaluating mental health. Variations explained by the Charlson were significant in only three scores measuring physical health. Conclusion The CIRS is a better choice as a measure of multimorbidity than the FCI and the Charlson when HRQOL is the outcome of interest. However, the FCI may provide a good option to evaluate the physical aspect of HRQOL for the ease in its administration and scoring. The Charlson index may not be recommended as a measure of multimorbidity in studies related to either physical or mental aspects of HRQOL.
International Journal of Statistics in Medical Research, 2016
Background: A crucial assumption underlying all disease-specific quality of life (QOL) measures, that patients can validly differentiate a specific disease in the presence of multiple chronic conditions, has not been tested using multiple methods. Our objective was to evaluate the convergent and discriminant validity of QOL attributions to specific diseases among adults with multiple chronic conditions (MCC). Methods: Adults age 18 and older (N=4,480) sampled from eight pre-identified condition groups (asthma, COPD, angina/MI with angina, congestive heart failure, diabetes, chronic kidney disease, osteoarthritis, rheumatoid arthritis) completed an Internet survey. Comorbid conditions were determined using a 35-condition checklist. Product-moment correlations were analyzed separately by pre-identified condition group using the multitrait-multimethod of construct validation, where traits were defined by 9-26 conditions and each condition was measured by two methods: disease severity rating and Disease-specific Quality of Life Impact Scale (QDIS) global rating. A third method (symptom or clinical marker) was available for the eight pre-identified conditions. Convergent validity was supported when correlations among different methods of measuring the same condition (trait) were substantial (r 0.40). Discriminant validity was supported when correlations between the same and different methods of measuring different conditions were significantly lower than corresponding convergent correlations. Results: In support of convergent validity, 22 of 24 convergent correlations were substantial (r=0.38-0.84, median=0.53). In support of discriminant validity, 833 of 924 tests (90.2%) yielded significantly higher convergent than discriminant correlations across the eight pre-identified conditions. Exceptions to this pattern of results were most often observed for comorbid conditions within the same clinical area. Conclusions: Collectively, convergent and discriminant test results support the construct validity of disease-specific QOL impact attributions across MCC within the eight pre-identified conditions. Noteworthy exceptions should be considered when interpreting some specific QOL impact attributions and warrant further study. Pursuit of a summary diseasespecific QOL impact score standardized across MCC is recommended.
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.
Health and Quality of Life Outcomes
Background: The aim of present study was to describe the effect of multimorbidity on Health-Related Quality of Life (HRQoL) in patients with coronary artery disease (CAD). Methods: A cross-sectional study with a simple sampling method of 296 patients undergoing coronary artery bypass surgery in a referral hospital of the northern part of Iran was conducted between April, 2015 and September, 2016. Multimorbidity was defined as the presence of at least two chronic diseases based on self-reporting and medical records. HRQoL was measured using the 36-item short form (SF-36) health status survey. We used analysis of variance (ANOVA) to assess the effect of multimorbidity on mental and physical component of HRQoL. Results: Approximately, 69% of CAD patients had at least one other disease like diabetes or hypertension. Patients without multimorbidity compared with patients with multimorbidity were significantly older (p = 0.012) and more educated (p = 0.002). Both physical and mental component score of HRQoL was better in patients without any morbidity (48.82 vs. 43.93 with 95%CI of mean difference: 3.37-6.42 and 54.85 vs. 50.44 with 95% CI of mean difference: 1.68-7.15, respectively). Both physical and mental component score was significantly lower in female and lower educated patients (physical mean score 43.07 vs. 46.54 with P = .001 and 42.53 vs. 46.82 with P < .001 and mental mean score 49.98 vs. 52.65 with P = .055 and 49.80 vs. 52.75 with P = .022 for sex and education, respectively). Also, two-way ANOVA showed that regards to morbidity, physical component score was greater in patients with lower education level than higher education level (P < .001). Conclusion: The findings of this study suggest that women, lower education level and overweight patients reported lower quality of life. HRQoL is affected by multimorbidity among CAD patients specially in less educated.
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.
Quality of Life Research, 2000
This study examined the contribution of the quality of life (QoL) domains physical, social and psychological functioning to the explanation of overall QoL. Various disorders may differentially affect QoL domains due to disease-specific factors and, consequently, the relationship between QoL domains and overall QoL may vary between diseases. We therefore studied this relationship for several diseases as well as the differential impact of these diseases on QoL. The present study had a cross-sectional design. We selected patients (aged 57 years and older) with one of the following eight chronic medical conditions: lung disorder, heart condition, hypertension, diabetes mellitus, back problems, rheumatoid arthritis, migraine, or dermatological disorders. The total group of respondents included 1457 patients and 1851 healthy subjects. Regression analyses showed that the domain of psychological functioning contributed to overall QoL for all disorders, whereas physical and social functioning contributed to overall QoL for some disorders. Differences were found between most patient groups and healthy subjects with respect to physical functioning; with respect to social and psychological functioning some groups differed from the healthy group. Explanations for the findings and implications for clinical practice are discussed.
Health and Quality of Life Outcomes
Background Interpretation of health-related quality of life (QOL) outcomes requires improved methods to control for the effects of multiple chronic conditions (MCC). This study systematically compared legacy and improved method effects of aggregating MCC on the accuracy of predictions of QOL outcomes. Methods Online surveys administered generic physical (PCS) and mental (MCS) QOL outcome measures, the Charlson Comorbidity Index (CCI), an expanded chronic condition checklist (CCC), and individualized QOL Disease-specific Impact Scale (QDIS) ratings in a developmental sample (N = 5490) of US adults. Controlling for sociodemographic variables, regression models compared 12- and 35-condition checklists, mortality vs. population QOL-weighting, and population vs. individualized QOL weighting methods. Analyses were cross-validated in an independent sample (N = 1220) representing the adult general population. Models compared estimates of variance explained (adjusted R2) and model fit (AIC) ...
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...