The impact of multimorbidity patterns on health-related quality of life in the general population: results of the Belgian Health Interview Survey

Wilder, Lisa Van; Devleesschauwer, Brecht; Clays, Els; Buyser, Stefanie De; Heyden, Johan Van der; Charafeddine, Rana; Boeckxstaens, Pauline; Bacquer, Dirk De; Vandepitte, Sophie; Smedt, Delphine De

doi:10.1007/s11136-021-02951-w

Cited by 21 publications

(20 citation statements)

References 61 publications

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“…In our study we had n=178 (49%) males and n=184(51%) females. This was similar to earlier studies related to multimorbidity and quality of life conducted in Japan (15) and Belgium (5 ). Our sample mean for education was 12.07+/-4.2.…”

Section: Discussionsupporting

confidence: 91%

“…(3) Pakistan is facing a similar demographic transition as in other Asian counterparts residing in the region. (4) With this increase in the ageing populations there is an increase in ailments (5) It is evident in the current demographics data from developed as well as the underdeveloped and developing nations, that the ageing population is gripped under an array of illnesses that may hinder in the way of their daily activities, may completely leave them immobile or cause more inconvenience through these multiple co morbidities. (6) Multimorbidity and quality of life have been shown by studies to have inverse relationship.…”

Section: Introductionmentioning

confidence: 99%

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Multimorbidity and Quality of Life among Geriatric Population of Karachi Pakistan

Danish¹,

Ahmad²,

Fahad³

et al. 2023

PJMHS

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Objective: To determine association of quality of life and multimorbidity among geriatric population of Karachi Pakistan Methods: It was a cross sectional study conducted in district of Karachi district using cluster sampling technique. Sample had greater than 60 years of age restricting those with mental disorders, speech disorders or language barriers. Sample size was n=362. Data was collected using WOHQOL-BREF with four domains, social relationships, psychological health, physical health and environment domain. Data was collected by face to face interviews after informed consent keeping information confidential. p-value less than 0.05 was considered significant. For numerical data descriptive analysis was used and categorical data was expressed by frequency and percentages using SPSS version 24. Odds ratio was used to find association of multimorbidty and quality of life Results: Of total n=90 (25%) participants had none or single disease, with multimorbidity in n=272 (75%) participants. Most n=165 (46%) had hypertension, Diabetes was present in n=56 (16%). Bones and joints in n=133 (38%), Obesity in n=80(22%). Most n=144 (40%) rated their life quality as good, n=30 (8.3%) as very good, n=57 (15.5%) rated their quality of life as poor, n=3 (0.8%%) rated it very poor and remaining n=128(35.4%) were in between. Mean scores of Environmental domain had highest score then physical health, psychological domain and least for social domain. When association was seen in multimorbidity and life quality Environment domain displayed significant association with OR: 1.9 ( p value 0.009).In Environment and Social domain majority were found to be satisfied. Conclusion: Significant association was seen between Multimorbidity and Enviornmental domain in Quality of life. Keywords: Multimorbidity, Quality of life, Geriatrics

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Section: Discussionsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Multimorbidity and Quality of Life among Geriatric Population of Karachi Pakistan

Danish¹,

Ahmad²,

Fahad³

et al. 2023

PJMHS

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“…In addition, individuals who reported habitually taking medication obtained lower scores in the perception of HRQoL. Chronic disease conditions are a risk factor for a negative perception of QoL, 9,10,22,23 and this was the dependent variable with the highest predictive value. The literature has shown that this situation occurs mainly regarding the variables related to physical health and when we are in the presence of some specific conditions, including hypertension, diabetes, lower back pain, knee osteoarthritis, stroke and mental disorders or when there is multimorbidity.…”

Section: Quality Of Life and Health Variablesmentioning

confidence: 90%

“…The literature has shown that this situation occurs mainly regarding the variables related to physical health and when we are in the presence of some specific conditions, including hypertension, diabetes, lower back pain, knee osteoarthritis, stroke and mental disorders or when there is multimorbidity. 9,[23][24][25] It seems important to emphasize the fact that the presence of chronic disease does not seem to inhibit individuals from practicing PA.…”

Section: Quality Of Life and Health Variablesmentioning

confidence: 99%

Evaluation of health-related quality of life in a physically active senior population

Fontes¹,

Joaquim

Pereira

2022

Int J Res Med Sci

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Background: Physical activity (PA) is a strong determinant of health and one of the most important predictors of healthy aging and health-related quality of life (HRQoL). The aim of the study was to understand the HRQoL of a senior population practicing PA and its association with some socio-demographic characteristics, the levels of PA, and functional independence.Methods: This is a correlational study with a sample of 376 individuals of both sexes. The data collection instruments were a socio-demographic and health questionnaire, the international PA questionnaire (IPAQ)-short version, the Katz index, the Lawton and Brody index, and the WHOQOL-OLD and WHOQOL-BREF questionnaires.Results: The sample revealed a good perception of HRQoL, especially in the “psychological” and “social relations” domains. Sex showed a greater association with quality of life (QoL) than age. Multiple linear regression revealed that the variables with the most significant influence on the individuals’ QoL were related to health (“Presence of chronic disease” and “habitual use of medication”). The QoL related to "social relationships" is the domain with more predictors, with a markedly socio-demographic focus.Conclusions: Practicing PA proved to be a HRQoL predictor, where “sensory function” and HRQoL related to “social relations” are included. Despite PA practice and socio-demographic characteristics being able to predict some HRQoL domains, health-related variables showed a more robust presence in this prediction.

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“…The management of multimorbidity is challenging given that most guidelines and research are still targeted at single diseases. As a result, the negative impact of multimorbidity is often greater than the additive effects of individual diseases [6].…”

Section: Introductionmentioning

confidence: 99%

Topic modelling with ICD10-informed priors identifies novel genetic loci associated with multimorbidities in UK Biobank

Zhang

Jiang²,

Mentzer

et al. 2022

Preprint

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Studies of disease incidence have identified thousands of genetic loci associated with complex traits. However, many diseases occur in combinations that can point to systemic dysregulation of underlying processes that affect multiple traits. We have developed a data-driven method for identifying such multimorbidities from routine healthcare data that combines topic modelling through Bayesian non-negative matrix factorization with an informative prior derived from the hierarchical ICD10 coding system. Through simulation we show that the method, treeLFA, typically outperforms both latent Dirichlet allocation (LDA) and topic modelling with uninformative priors in terms of inference accuracy and generalisation to test data, and is robust to moderate deviation between the prior and reality. By applying treeLFA to data from UK Biobank we identify a range of multimorbidity clusters in the form of disease topics ranging from well-established combinations relating to metabolic syndrome, arthropathies and cancers, to other less well-known ones, and a disease-free topic. Through genetic association analysis of inferred topic loadings (topic-GWAS) and single diseases we find that topic-GWAS typically finds a much smaller, but only partially-overlapping, set of variants compared to GWAS of constituent disease codes. We validate the genetic loci (only) associated with topics through a range of approaches. Particularly, with the construction of PRS for topics, we find that compared to LDA, treeLFA achieves better prediction performance on independent test data. Overall, our findings indicate that topic models are well suited to characterising multimorbidity patterns, and different topic models have their own unique strengths. Moreover, genetic analysis of multimorbidity patterns can provide insight into the aetiology of complex traits that cannot be determined from the analysis of constituent traits alone.

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The impact of multimorbidity patterns on health-related quality of life in the general population: results of the Belgian Health Interview Survey

Cited by 21 publications

References 61 publications

Multimorbidity and Quality of Life among Geriatric Population of Karachi Pakistan

Multimorbidity and Quality of Life among Geriatric Population of Karachi Pakistan

Evaluation of health-related quality of life in a physically active senior population

Topic modelling with ICD10-informed priors identifies novel genetic loci associated with multimorbidities in UK Biobank

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