Change in treatment burden among people with multimorbidity: a follow-up survey

Abstract

Background: Treatment burden is the workload of being a patient and its impact on wellbeing. Little is known about change in treatment burden over time for people with multimorbidity. Aim: To quantify change in treatment burden, determine factors associated with this change, and evaluate a revised single-item measure for high treatment burden in older adults with multimorbidity. Design and setting: A 2.5-year follow-up of a cross-sectional postal survey via six general practices in Dorset, England. Method: GP practices identified participants of the baseline survey. Data on treatment burden (measured using the Multimorbidity Treatment Burden Questionnaire; MTBQ), sociodemographics, clinical variables, health literacy and financial resource were collected. Change in treatment burden was described, and associations assessed using regression models. Diagnostic test performance metrics evaluated the single-item measure relative to the MTBQ. Results: 301 participants were recruited (77.6% response rate). Overall, there was a 2.6% increase in treatment burden. 98 (32.6%) and 53 (17.6%) participants experienced an increase and decrease, respectively, in treatment burden category. An increase in treatment burden was associated with having more than five long-term conditions (ß:8.26 (95% CI: 4.20 to 12.32) and living >10 minutes (vs.≤10 minutes) from the GP (aß:3.88 (95% CI: 1.32 to 6.43)), particularly for participants with limited health literacy (mean difference: aß:9.59 (95% CI: 2.17 to 17.00)). The single-item measure performed moderately- sensitivity: 56.5%; specificity: 92.5%. Conclusion: Treatment burden changes over time. Improving access to primary care, particularly for those living further away from services, and enhancing health literacy, may mitigate increases in burden.

Travel burden for patients with multimorbidity – Proof of concept study in a Dutch tertiary care center

“…Previous research about exploring variables being associated with a higher travel burden in patients with multimorbidity is very limited. Hounkpatin et al showed that patients with limited health literacy are especially at risk for having a high travel related treatment burden ( Hounkpatin et al, 2022 ). In addition, literature showed that the travel burden for patients with multimorbidity is especially straining to patients with deprived SES and to patients living in remote areas ( Rosbach & Andersen, 2017 ; Sav et al, 2013 ).…”

“…Literature has demonstrated the role of travel burden on health, as it is for example associated with a delay in cancer diagnosis, leading to more advanced disease at diagnosis, inappropriate treatment, worse prognosis and lower quality of life ( Ambroggi et al, 2015 ). Moreover, travel burden seems to be an important part of overall treatment burden for patients with multimorbidity ( Hounkpatin et al, 2022 ; Rosbach & Andersen, 2017 ). In a time of increasing treatment and travel burden of a growing patient population, it is worth noting that travel burden is something to be taken into account during treatment decision-making.…”

Experience-based Investigation and Co-design of Psychosis Centred Integrated Care Services for Ethnically Diverse People with Multimorbidity (CoPICS): study protocol

“…We will use the theoretical model established by Andersen and Newman to investigate the experience of the care system among patient participants 43. This model identifies three key types of factors that could influence the use of healthcare services: predisposing features (eg, age, sex, ethnicity, previous illnesses), enabling features (eg, income, access to care, employment) and needs (eg, symptoms, diagnosis, functioning) 17. Furthermore, participants will be encouraged to indicate any care they would have preferred to receive but did not, as well as suggest reasons or barriers for why this was the case.…”

“… 43 This model identifies three key types of factors that could influence the use of healthcare services: predisposing features (eg, age, sex, ethnicity, previous illnesses), enabling features (eg, income, access to care, employment) and needs (eg, symptoms, diagnosis, functioning). 17 Furthermore, participants will be encouraged to indicate any care they would have preferred to receive but did not, as well as suggest reasons or barriers for why this was the case. The removal of these barriers may have cost implications and could also impact outcomes, which we will explore using economic modelling methods.…”

“…Such complex bio-social pathways among racialised populations appear to drive the onset of SMIs such as psychosis and other long-term physical or mental health conditions. People living with psychosis and multimorbidity face a huge burden of medical and social problems, leading to poorer health outcomes, high rates of polypharmacy, treatment burden, lower quality of life, feelings of vulnerability, disability and even premature mortality 15–17. Due to health-related burdens, it can be challenging for those with multimorbidity to participate in traditional forms of research.…”