Direct and indirect costs attributable to musculoskeletal disorders in Belgium

Abstract Background Within the European Union, musculoskeletal (MSK) disorders represent the most prevalent and costly work-related health problems affecting about 45 million workers. Since middle-aged people during their formative and peak income-earning years are predominantly affected, MSK disorders are the major contributors to the loss of productive life years in the workforce compared with other non-communicable diseases. This study aimed to summarize the average yearly economic impact of low back pain (LBP), neck pain (NKP), osteoarthritis (OST) and rheumatoid arthritis (RHE) in Belgium from 2013 to 2017. Methods Direct costs, measured by reimbursed expenditures for medical services and medications, were derived by the national health insurer. Indirect costs were computed by multiplying the mean number of days absent from work (derived by the Belgian health interview survey, as prevalence data) with the average gross daily wage. Multivariate regression models were used to explore the extent to which average yearly costs were associated with MSK disorders. The method of recycled predictions allowed to estimate the marginal effect of each MSK disorder on costs. Results 25% of Belgian adults were affected by at least one MSK disorder that incurred on average to 1,524€ per capita. LBP was the most costly disorder (2,405€ per capita) followed by NKP (2,260€ per capita). In the working population, 15% had at least one MSK disorder with an average indirect cost of 3,083€ per capita. People with LBP were the only showing a significantly higher indirect cost compared to a population without LBP, with an adjusted cost per capita of 5,875€. Conclusions The adult Belgian population is largely affected by MSK disorders. Every year the total adjusted healthcare cost amounted to more than 3 billion Euros. Additionally, on average every year Belgium spends around 2 billion Euros for work absenteeism related to one of the MSK disorders. Key messages MSK disorders have a great societal cost in Belgium. Intervening on the working population that is largely affected can help reducing absenteeism costs.


Background:
Governments and healthcare systems are facing multimorbidity (MM) as a major challenge due to the difficulties related to its proper identification and clinical management. Despite growing research on MM, its epidemiology is poorly understood due to the great complexity of underlying patterns of chronicity. The present review aims to identify the most frequent MM profiles and their social determinants.

Methods:
A systematic review following the PRISMA statement was conducted. The search strategy was performed by combining three sets of keywords (MM, inequalities and patterns) that were searched in Pubmed, Scopus, Web of Science, OVID, CINAHL Complete, and PsycINFO. Primary studies analysing MM patterns and their relationship with social determinants were included. The quality of the studies was assessed using the Axis tool quality assessment.

Results:
After the review process, 96 studies were selected from the 46,726 identified. The main methods used to identify MM patterns fell into five categories: latent class analysis (38.54%), cluster techniques (23.96%), factor analysis (19.79%), and machine learning (10.42%), and expert knowledge (7.29%). Latent class analysis was widely used, although in recent years the use of techniques based on machine learning has increased. The main patterns were cardiometabolic, cardiovascular, mental, musculoskeletal, complex MM, and respiratory diseases. Some MM profiles were more prevalent among lower-SES groups. In particular, patterns of mental multimorbidity were more prevalent among women and complex patterns were associated with low income.

Conclusions:
Results show different disease combinations among disparate social determinants such as gender, age, education, and socioeconomic status. Our results suggest that more and better designed studies are needed to improve clinical practice and health policies with the aim of enhancing the quality of patients with MM and their relationship to health system use and care. Key messages: Patterns of mental multimorbidity and complex multimorbidity were more prevalent among women and men of low socioeconomic status, respectively. An increasing number of studies are using a network-based approach to classify multimorbidity.

Background:
While diseases in contemporary and past populations are thoroughly studied, the knowledge about disability and the risks of getting it is poor. Like diseases, disabilities increase with growing age affecting primarily elderly groups. Whether this notion holds historically and for other groups at risk for disability and differences over time is not known. This study estimates the disability risks in Swedish populations c. 1800-1959 by age, sex and disability type (sensory, physical, mental).

Methods:
We use data on two historical populations in the 1800s (N = 36,500; 550 with disability) and 1900-1959 (N = 194,500; 4,700 with disability) drawn from digitized parish registers reporting socio-economic and demographic characteristics over lifetime and on disabilities. Cox proportional regressions estimate disability risks across time by group (age, sex, disability type).

Results:
Our preliminary results based on unadjusted estimates from 1900-1959 suggest that the disability risks doubled or more. In the 1950s, women had 2.6 times higher risk than 50 years before, while it was 2.0 for men. The major rise started in the 1930s (Men 1.51; Women: 1.67), and grew in the 1940s (Men 1.80; Women: 2.14). Next, we will assess these risks by group and in the 1800s.

Conclusions:
From 1900-1959, Swedish populations experienced consistently higher disability risks, which doubled for men and almost tripled for women. These risks increased while improvements in public health and economic growth would subsequently make Sweden internationally known as a modern welfare state. That health improvements did not reduce the disability risks but the reverse, was possibly due to higher recognition or labeling of disabilities.

Key messages:
Our study is unique in providing long-term results on populations at risk for disability while public health improved. Public concerns to confine disabled people for treatment in the early welfare era increased the disability risks beside longer life expectancy.

Background:
Multimorbidity is highly prevalent among older adults and associated with a shorter life expectancy. Many guidelines recommend tailoring preventive care of multimorbid people according to life expectancy. Indeed, patients with a relatively short life expectancy might not have the time to benefit from a preventive care intervention. Our objective was therefore to develop and internally validate a life expectancy estimator for older multimorbid adults.

Methods:
We analysed data of the OPERAM (OPtimising thERapy to prevent Avoidable hospital admissions in Multimorbid older people) cohort study in Bern, Switzerland. 822 hospitalized participants aged 70 years old or more, with multimorbidity (3 or more chronic medical conditions), and polypharmacy (use of 5 drugs or more for >30 days) were included. Our main outcome was time to all-cause mortality assessed during 3 years of follow-up. Candidate predictors included demographic variables (age, sex), clinical characteristics (Charlson-Comorbidity-Index, number of drugs, body mass index, weight loss), smoking, functional status variables (Barthel-Index, falls, nursing home residence), and hospitalization. We internally validated and optimism corrected the model using bootstrapping techniques. We transformed the 3-year mortality prognostic index into a life expectancy estimator using the Gompertz survival function.

Results:
At baseline, the participants (58% men) had a median age of 79 years (min: 70; max: 99). They took daily a median of 10 chronic medications (min: 5; max 38). During 3 years of follow-up, 292 participants (36%) died. The analysis is ongoing and results will be presented at the congress.

Conclusions:
A life expectancy estimator eventually helps personalising care to prevent under-and overuse of preventive care in the growing older population. Key messages: We provide the first life expectancy estimator for older multimorbid adults.