Treatment burden and associated factors: a population-based survey in Central Denmark Region 2017

Abstract Background Exploring treatment burden at a population level can provide evidence of the types of patients who need special attention and support. We aimed to determine factors associated with high perceived treatment burden in a population-based survey of adults living in the Central Denmark Region (23% of the Danish population). Methods The Danish Multimorbidity Treatment Burden Questionnaire (MTBQ) was included in the 2017 Danish population health survey. 28,627 individuals aged 25 years or over participated (64% response rate). Individuals who reported having one or more medical conditions or attending regular health check-ups were asked to complete the MTBQ. A global MTBQ score was calculated (range 0-100) and both the continuous scores and a four-category grouping of the scores into no, low, medium and high burden were used to statistically assess the association between treatment burden and sociodemographic and health-related factors. Results 13,407 individuals completed the Danish MTBQ (mean age 59 years). Treatment burden was negatively associated with self-related health (rs = -0.45, P < 0.0001), health-related quality of life (rs = -0.46/-0.51, P < 0.0001) and positively associated with the number of long-term conditions (rs = 0.26, P < 0.0001) and perceived stress (rs = 0.44, P < 0.0001). Higher treatment burden was associated with young age, male sex, high educational level, unemployment, not living with a spouse/cohabitant, living with child(ren) and specific long-term conditions, including heart disease, stroke, diabetes and mental illness. Conclusions This is the first known population-based study of treatment burden. The findings provide important evidence to policy makers and clinicians about sociodemographic groups at risk of higher treatment burden. We recommend that patient-perceived treatment burden is included when evaluating interventions targeting patients with long-term conditions and multimorbidity and health-care system reorganisations. Key messages • Treatment burden is associated with poor health and health-related quality of life and, among others, young age, male sex, unemployment, not living with a spouse, and specific long-term conditions. • We recommend that patient-perceived treatment burden is included when evaluating interventions targeting patients with long-term conditions and multimorbidity and health-care system reorganisations.


Background:
Smoking influences cellular and humoral immune responses and affects the immune system by increasing inflammation and decreasing activity against infections. The current study investigates the association between smoking and immunological response to SARS-CoV-2 in the Armenian population.

Methods:
We performed a nationwide cross-sectional seroepidemiological study among the adult population (!18 years old) in Armenia. We used a multi-stage cluster random sampling to recruit participants from the capital city and all regions of Armenia. We invited selected participants to primary healthcare facilities to provide blood samples for antibody testing followed by a phone survey on demographic characteristics, smoking status, and other variables. Logistic regression analysis was used to test the relationship between smoking and having SARS-CoV-2 antibodies adjusted for other covariates. Results: 3483 people participated in the study (71% women). The total sample included 16.8% current smokers (n = 571), 8.6% past smokers (n = 294) and 76.4% never smokers (n = 2538). The prevalence of SARS CoV-2 antibodies among current smokers was statistically significantly lower as compared with never smokers (46.9% vs 73.4%, p-value<0.001). In the multivariable logistic regression model, the odds of having SARS CoV-2 antibodies among the current smokers was 70% lower (OR 0.30, 95%CI: 0.22; 0.40) compared to never smokers, when adjusted for demographic factors and the time of PCR diagnosis of COVID-19. No statistically significant difference was found between past smokers and having SARS CoV-2 antibodies.

Conclusions:
In addition to being a risk factor for various chronic diseases, smoking weakens immune response to infectious diseases, including COVID-19, worsening the outcomes. The significantly lower level of antibody prevalence among smokers with previous PCR confirmed COVID 19 implies a poorer immune response to the infection and not a lower risk of getting the infection.

Key messages:
Smoking weakens immune response and contributes to a higher burden of infectious diseases, such as COVID-19. Lower level of antibody prevalence among smokers indicates a poorer immune response to the infection rather than a lower risk of getting the infection.

Background:
Exploring treatment burden at a population level can provide evidence of the types of patients who need special attention and support. We aimed to determine factors associated with high perceived treatment burden in a population-based survey of adults living in the Central Denmark Region (23% of the Danish population).

Methods:
The Danish Multimorbidity Treatment Burden Questionnaire (MTBQ) was included in the 2017 Danish population health survey. 28,627 individuals aged 25 years or over participated (64% response rate). Individuals who reported having one or more medical conditions or attending regular health check-ups were asked to complete the MTBQ. A global MTBQ score was calculated (range 0-100) and both the continuous scores and a four-category grouping of the scores into no, low, medium and high burden were used to statistically assess the association between treatment burden and sociodemographic and healthrelated factors. Results: 13,407 individuals completed the Danish MTBQ (mean age 59 years). Treatment burden was negatively associated with selfrelated health (rs = -0.45, P < 0.0001), health-related quality of life (rs = -0.46/-0.51, P < 0.0001) and positively associated with the number of long-term conditions (rs = 0.26, P < 0.0001) and perceived stress (rs = 0.44, P < 0.0001). Higher treatment burden was associated with young age, male sex, high educational level, unemployment, not living with a spouse/cohabitant, living with child(ren) and specific long-term conditions, including heart disease, stroke, diabetes and mental illness.

Conclusions:
This is the first known population-based study of treatment burden. The findings provide important evidence to policy makers and clinicians about sociodemographic groups at risk of higher treatment burden. We recommend that patientperceived treatment burden is included when evaluating

Background:
Population prevalence of chronic conditions can be estimated from national health surveys and from administrative data sources such as insurance records. This study evaluated the agreement between the Belgian Health Interview Survey (BHIS) and the Belgian compulsory health insurance data (BCHI) in ascertaining chronic hypertension, hypercholesterolemia and diabetes in Belgium.

Methods:
The most recent cycle of BHIS (2018) provided the selfreported prevalence of diabetes, hypertension, and hypercholesterolemia among a representative sample of Belgian adults. For BCHI, the chronic conditions were attributed for every individual in the BHIS reviewing the medication prescription records identified using the ATC/DDD system. These two data sources were linked through unique identifiers by STATBEL. Disease prevalence, measures of agreement, and measures of concordance were estimated. Logistic regression was performed to determine the factors affecting agreement between BHIS and BCHI's disease classifications.

Results:
Data linkage was done for 9,753 individuals aged 15 years and older. From the sample, BHIS and BCHI respectively identified 5.9% and 5.6% diabetes cases, 18% and 24% of hypertension cases, and 18% and 16% of hypercholesterolemia cases. The kappa coefficient between BCHI and self-reported diabetes, hypertension, and hypercholesterolemia was 0.79, 0.59, and 0.49, respectively. Gender, age, and subjective health significantly affected the agreement in chronic condition classification between BHIS and BCHI.

Conclusions:
Data on reimbursed drugs is a potential alternative method in the surveillance of chronic diabetes. This procedure could be used in estimating disease prevalence but further validation is needed to evaluate its applicability and bias in other chronic conditions.

Background:
Considering the growing prevalence of chronic disease and diabetes mellitus (DM) in Belgium, alongside population aging, insight into the economic burden of DM is essential for decision makers. To the best of our knowledge, there is no research on the subject in Belgium. Thus, our aim was to estimate the direct and indirect costs associated to DM in Belgium between 2013 and 2017.

Methods:
On a first phase, we performed a retrospective observational study, calculating the direct (i.e., ambulatory care, hospitalizations and medications) and indirect (work absenteeism, by multiplying mean daily wage and days absent from work) costs in the Belgian population with DM in 2013-2017. Data was retrieved from the Belgian Intermutualistic Agency (which manages compulsory health insurance) database and the Belgian Health Interview Survey database, namely DM prevalence, healthcare costs, days absent from work and sociodemographic and health factors. Subsequently, negative binomial regression models were used to assess the association of mean yearly costs to DM and adjustments for age, education level, physical activity, sugared drink consumption and bodymass index were included. Mean incremental costs were estimated through recycled predictions, considering the observed DM prevalence in Belgium in the study period and a counterfactual scenario with null prevalence.

Results:
We found a direct mean yearly incremental cost of E2 477 per DM patient, in Belgium, associated with age, low educational level and low physical activity. In the total Belgian population, the total yearly incremental healthcare cost of DM was E1.5 billion. Indirect yearly incremental cost of DM resulted to be not significantly different from the population without DM.
Conclusions: DM has a major economic burden in Belgium, one that is expected to continue to rise in the future, alongside population aging. These results are essential for health planning and resource allocation. Key messages: DM has a major economic burden in Belgium, especially when it comes to direct health expenditures with ambulatory care, hospitalizations and medications.
Considering the growing prevalence of DM and population aging, these results are essential for health planning and resource allocation.