RESUMO
Background/Objectives: Due to their high frequency, common risk factors, and similar pathogenic mechanisms, musculoskeletal disorders (MSDs) are more likely to occur with other chronic illnesses, making them a "component disorder" of multimorbidity. Our objective was to assess the prevalence of multimorbidity and to identify the most common clusters of diagnosis within multimorbidity states, with the primary hypothesis that the most common clusters of multimorbidity are MSDs. Methods: The current study employed data from a population-based 2019 European Health Interview Survey (EHIS). Multimorbidity was defined as a ≥2 diagnosis from the list of 17 chronic non-communicable diseases, and to define clusters, the statistical method of hierarchical cluster analysis (HCA) was performed. Results: Out of 13,178 respondents, multimorbidity was present among 4398 (33.4%). The HCA method yielded six multimorbidity clusters representing the most common diagnoses. The primary multimorbidity cluster, which was prevalent among both genders, age groups, incomes per capita, and statistical regions, consisted of three diagnoses: (1) lower spine deformity or other chronic back problem (back pain), (2) cervical deformity or other chronic problem with the cervical spine, and (3) osteoarthritis. Conclusions: Given the influence of musculoskeletal disorders on multimorbidity, it is imperative to implement appropriate measures to assist patients in relieving the physical discomfort and pain they endure. Public health information, programs, and campaigns should be utilized to promote a healthy lifestyle. Policymakers should prioritize the prevention of MSDs by encouraging increased physical activity and a healthy diet, as well as focusing on improving functional abilities.
RESUMO
Background: The COVID-19 pandemic has had a significant impact on the global economy and public health, disrupting various aspects of daily life. Apart from its direct effects on physical health, it has also significantly affected the overall quality of life and mental health. This study employed a path analysis to explore the complex association among multiple factors associated with quality of life, anxiety, and depression in the general population of the Republic of Srpska during the pandemic's second year. Method: A cross-sectional study was conducted on a nationally representative sample (n = 1382) of the general population (adults aged 20+) during the second year of the COVID-19 pandemic in the Republic of Srpska, Bosnia, and Herzegovina. Assessment tools included the DASS-21 scale for depression, anxiety, and stress, along with the Brief COPE scale, Quality of Life Scale (QOLS), and Oslo Social Support Scale (OSSS-3). Sociodemographic factors and comorbidities were also assessed. Structural equation modeling was used to identify the direct and indirect links of various characteristics to quality of life, anxiety, and depression. Results: This study revealed a considerable prevalence of anxiety and depression symptoms (27.5% and 20.9%, respectively), with quality of life playing a significant mediating role. The constructed path model accounted for 33.1% of moderate to severe depression and 79.5% of anxiety. Negative coping was directly linked to anxiety and indirectly to depression via anxiety, while the absence of positive coping had both direct and indirect paths (through quality of life) on depression. Among variables that directly affected depression, anxiety had the highest effect. However, the bidirectional paths between anxiety and depression were also suggested by the model. Conclusions: Pandemic response strategies should be modified to effectively reduce the adverse effects on public mental health. Further research is necessary to assess the long-term effects of the pandemic on mental health and to analyze the contributing factors of anxiety and depression in the post-COVID period.