Study protocol for an epidemiological study ‘Multimorbidity − identifying the most burdensome patterns, risk factors and potentials to reduce future burden (MOLTO)’ based on the Finnish health examination surveys and the ongoing register-based follow-up

• Published in: BMJ open 2022-06, Vol.12 (6), p.e056073-e056073
• Main Authors: Jääskeläinen, Tuija, Koponen, Päivikki, Lundqvist, Annamari, Suvisaari, Jaana, Järvelin, Jutta, Koskinen, Seppo
• Format: Article
• Language: English
• Subjects: cardiology; Chronic illnesses; Comorbidity; Costs; Diabetes; diabetes & endocrinology; Disease prevention; Epidemiology; Health risk assessment; Health services; Lifestyles; mental health; Mortality; Public health; Quality of life; Risk factors; Sociodemographics; Well being
• ISSN: 2044-6055; 2044-6055
• DOI: 10.1136/bmjopen-2021-056073

IntroductionMultimorbidity, defined as the co-occurrence of two or more long-term medical conditions, is an increasing public health concern worldwide causing enormous burden to individuals, healthcare systems and societies. The most effective way of decreasing the burden caused by multimorbidity is to find tools for its successful prevention but gaps in research evidence limit capacities to develop prevention strategies. The aim of the MOLTO study (Multimorbidity - identifying the most burdensome patterns, risk factors and potentials to reduce future burden) is to provide novel evidence required for cost-effective prevention of multimorbidity by defining the multimorbidity patterns causing the greatest burden at the population level, by examining their risk and protective factors and by estimating the potentials to reduce the future burden.Methods and analysisThe MOLTO study is based on the data from the Finnish population-based cross-sectional (FINRISK 2002–2012, FinHealth 2017 the Migrant Health and Well-being Study 2010–2012) and longitudinal (Health 2000/2011) health examination surveys with individual-level link to administrative health registers, allowing register-based follow-up for the study participants. Both cross-sectional and longitudinal study designs will be used. Multimorbidity patterns will be defined using latent class analysis. The burden caused by multimorbidity as well as risk and protective factors for multimorbidity will be analysed by survival analysis methods such as Cox proportional hazards and Poisson regression models.Ethics and disseminationThe survey data have been collected following the legislation at the time of the survey. The ethics committee of the Hospital District of Helsinki and Uusimaa has approved the data collection and register linkages for each survey. The results will be published as peer-reviewed scientific publications.