Our search for papers on multimorbidity that were published during the period May-August 2020 has been completed. As in previous searches, we have prepared a PDF file that can be accessed following this link. Probably, there are some publications that were not detected by our search strategy using the terms “multimorbidity”, “multi-morbidity” and the expression “multiple chronic diseases” in PubMed (https://www.ncbi.nlm.nih.gov/pubmed), but we are sure that most publications on the subject are included in the list. All references are also included in our library. Feel free to share with anyone interested in multimorbidity.
By Iris Ho and Bruce Guthrie Centre for Population and Health Sciences, Usher Institute, University of Edinburgh, United Kingdom
Many people, nowadays, are living with multiple chronic conditions. However, there remains no agreed method to measure this common phenomenon.
In research, there are varying conditions included in a multimorbidity measure. The number of conditions included in a measure can range from 2 to 285. As for selection of conditions, some included disease category (e.g. gastrointestinal disease), whereas others included relatively smaller disease category (e.g. chronic liver disease) or individual conditions (e.g. hepatitis, or liver cirrhosis).
In addition, methods used to count conditions differ across studies. There are two main types of counting methods. One is using a simple count of conditions to estimate how common multimorbidity is. The other type of measures is applying weights for each chronic condition based on disease severity and its impact on an outcome, and the total weighting score is used to predict the impact of multimorbidity on the outcome, such as mortality, physical disability, hospitalisation, or quality of life. Within each type of measures, researchers have used different reference definitions and weighting schemes while counting.
Due to the inconsistent definitions of multimorbidity and measurement methods, estimates of multimorbidity prevalence and burden cannot be directly compared across studies. Therefore, we want to gather your opinions and experience relevant to multimorbidity. Your opinions can help to shape future research, clinical and policy decisions on management of multimorbidity.
We are looking for:
1) Members of the public who are interested in multiple chronic conditions or have chronic illness experiences
2) Academics, clinicians, service providers, policy makers who are interested in multimorbidity or have undertaken multimorbidity-relevant work.
This study consists of two to four rounds of survey (most likely 2-3 rounds depending on when consensus is reached). The round-one survey is currently published online and the other rounds will be distributed subsequently later.
If you are interested in the topic and want to know more about this study, please get in touch with the researcher, Iris Ho (iris.s.ho@ed.ac.uk), or the principal investigator, Bruce Guthrie (bruce.guthrie@ed.ac.uk).
By Ana I. González (picture on the left), Andreas Meid (picture in the middle), Truc S Dinh, Jeanet W Blom, Marjan van den Akker, Petra JM Elder, Ulrich Thiem, Daniela Küllenberg de Gaudry, Kym Snell, Rafael Perera, Karin MA Swart, Henrik Rudolf, Hans-Joachim Trampish, Joerg J Meerpohl, Benno Flaig, Ghaisom Kom, Walter E Haefeli, Paul P Glasziou, Ferdinand M Gerlach and Christiane Muth (picture on the right*)
Multimorbidity and polypharmacy increase the risk for inappropriate prescriptions and underuse of appropriate medication which may lead to patient deterioration in health-related quality of life (dHRQoL) (1,2). In this heterogeneous general practice population, it would be helpful to identify those patients at high risk of dHRQoL since they may benefit most from complex interventions designed to improve their well-being (3–5).
We aimed to develop and validate a prognostic model to predict dHRQoL at six-month follow-up in older patients with at least one chronic condition and one chronic prescription in general practice (6).
We harmonized individual participant data from five cluster-randomized trials from the Netherlands and Germany. dHRQoL was defined as a decrease in the EQ-5D-3L index score of at least 5% from baseline to 6-month follow-up. Prognostic variables included baseline socio-demographics and lifestyle, morbidity, medication, functional status and well-being related variables. The model was developed using logistic regression with a stratified-intercept to account for between-study heterogeneity in baseline risk. Prognostic variables were selected in complete cases and then refitted in multiply imputed data to obtain the final model equation. Internal validation was performed using bootstrapping within studies to assess reproducibility and internal internal-external cross-validation (IECV) was used to evaluate generalisability.
The complete-case population consisted of 3,582 patients. In 1,046 (29%) patients, health-related quality of life (HRQoL) deteriorated at six-month follow-up. Selected baseline variables contributing significantly to the prediction related to single conditions (i.e. coronary heart disease), prescribed medication (i.e. drugs for acid-related disorders), inappropriate medication (i.e. systemic corticosteroids for maintenance in COPD), medication underuse (e.g. angiotensin converting enzyme inhibitors in heart failure), functional status, and well-being (i.e. HRQoL at baseline and depression), with most prognostic relevance attributable to baseline HRQoL and functional status. Bootstrap internal validation of the final model showed a C-statistic of 0.71 (0.69 to 0.72) and a calibration slope of 0.88 (0.78 to 0.98). With the trials as validation datasets in the IECV loop, the final model provided a pooled C-statistic of 0.68 (0.65 to 0.70) and calibration-in-the-large of 0 (-0.13 to 0.13).
This first IPD-based prognostic model for dHRQoL in older patients with multiple chronic conditions and medication in general practice performed well in discrimination, calibration, and generalisability and might thus help clinicians identify older patients at high risk of dHRQoL.
This work was supported by the German Innovation Funds according to § 92a (2) Volume V of the Social Insurance Code (§ 92a Abs. 2, SGB V – Fünftes Buch Sozialgesetzbuch), grant number: 01VSF16018.The funder had no role in developing the protocol for this review.
Barnett K, Mercer SW, Norbury M, Watt G, Wyke S, Guthrie B. Epidemiology of multimorbidity and implications for health care, research, and medical education: A cross-sectional study. Lancet. 2012;380(9836):37–43.
Saqlain M, Ali H, Kamran S, Munir MU, Jahan S, Mazhar F. Potentially inappropriate medications use and its association with health-related quality of life among elderly cardiac patients. Qual Life Res. 2020 May 20;
Romskaug R, Skovlund E, Straand J, Molden E, Kersten H, Pitkala KH, et al. Effect of Clinical Geriatric Assessments and Collaborative Medication Reviews by Geriatrician and Family Physician for Improving Health-Related Quality of Life in Home-Dwelling Older Patients Receiving Polypharmacy. JAMA Intern Med. 2020 Feb 1;180(2):181.
Rankin A, Cadogan CA, Patterson SM, Kerse N, Cardwell CR, Bradley MC, et al. Interventions to improve the appropriate use of polypharmacy for older people. Cochrane Database Syst Rev. 2018 Sep 3;
Smith SM, Wallace E, O’Dowd T, Fortin M. Interventions for improving outcomes in patients with multimorbidity in primary care and community settings. Cochrane Database Syst Rev [Internet]. 2016 Mar 15; Available from: http://doi.wiley.com/10.1002/14651858.CD006560.pub3
González-González AI, Meid AD, Dinh TS, Blom JW, van den Akker M, Elders PJM, et al. A prognostic model predicted deterioration in health-related quality of life in older patients with multimorbidity and polypharmacy. J Clin Epidemiol. 2021;130:1–12.
