Precautionary behaviours of individuals with multimorbidity during the COVID-19 pandemic

Multimorbidity in middle-aged women and COVID-19: binary data clustering for unsupervised binning of rare multimorbidity features and predictive modeling

BACKGROUND

Multimorbidity is typically associated with deficient health-related quality of life in mid-life, and the likelihood of developing multimorbidity in women is elevated. We address the issue of data sparsity in non-prevalent features by clustering the binary data of various rare medical conditions in a cohort of middle-aged women. This study aims to enhance understanding of how multimorbidity affects COVID-19 severity by clustering rare medical conditions and combining them with prevalent features for predictive modeling. The insights gained can guide the development of targeted interventions and improved management strategies for individuals with multiple health conditions.

METHODS

The study focuses on a cohort of 4477 female patients, (aged 45-60) in Piedmont, Italy, and utilizes their multimorbidity data prior to the COVID-19 pandemic from their medical history from 2015 to 2019. The COVID-19 severity is determined by the hospitalization status of the patients from February to May 2020. Each patient profile in the dataset is depicted as a binary vector, where each feature denotes the presence or absence of a specific multimorbidity condition. By clustering the sparse medical data, newly engineered features are generated as a bin of features, and they are combined with the prevalent features for COVID-19 severity predictive modeling.

RESULTS

From sparse data consisting of 174 input features, we have created a low-dimensional feature matrix of 17 features. Machine Learning algorithms are applied to the reduced sparsity-free data to predict the Covid-19 hospital admission outcome. The performance obtained for the corresponding models are as follows: Logistic Regression (accuracy 0.72, AUC 0.77, F1-score 0.69), Linear Discriminant Analysis (accuracy 0.7, AUC 0.77, F1-score 0.67), and Ada Boost (accuracy 0.7, AUC 0.77, F1-score 0.68).

CONCLUSION

Mapping higher-dimensional data to a low-dimensional space can result in information loss, but reducing sparsity can be beneficial for Machine Learning modeling due to improved predictive ability. In this study, we addressed the issue of data sparsity in electronic health records and created a model that incorporates both prevalent and rare medical conditions, leading to more accurate and effective predictive modeling. The identification of complex associations between multimorbidity and the severity of COVID-19 highlights potential areas of focus for future research, including long COVID and intervention efforts.

Comorbidities, multimorbidity and COVID-19

The influence of comorbidities on COVID-19 outcomes has been recognized since the earliest days of the pandemic. But establishing causality and determining underlying mechanisms and clinical implications has been challenging-owing to the multitude of confounding factors and patient variability. Several distinct pathological mechanisms, not active in every patient, determine health outcomes in the three different phases of COVID-19-from the initial viral replication phase to inflammatory lung injury and post-acute sequelae. Specific comorbidities (and overall multimorbidity) can either exacerbate these pathological mechanisms or reduce the patient's tolerance to organ injury. In this Review, we consider the impact of specific comorbidities, and overall multimorbidity, on the three mechanistically distinct phases of COVID-19, and we discuss the utility of host genetics as a route to causal inference by eliminating many sources of confounding. Continued research into the mechanisms of disease-state interactions will be crucial to inform stratification of therapeutic approaches and improve outcomes for patients.

Poverty in old age in times of COVID-19-Empirical results from Austria

Early in the pandemic, researchers were cautioning that COVID-19 and the associated health policy countermeasures would have an increased negative impact on groups that were already vulnerable before the pandemic. One of these groups are older people affected by poverty, who according to official figures make up 13.9% of older population in Austria. Even before the pandemic, their living situation was considered precarious. Not without reason, this group has been identified as a high-risk group of the pandemic, due to their increased likelihood of severe COVID-19 related illness and their limited monetary resources and thus lower chances of coping with the pandemic. Nevertheless, research on this group has remained sparse to date. Therefore, the aim of the study is to focus on older people (60+ years) below the poverty line and to compare them with non-poor individuals. Data from the SHARE (Survey of Health Aging and Retirement in Europe) project is used, combining data from the two SHARE Corona Surveys (summer 2020 and summer 2021) and the SHARE Corona Special Austria Survey (December 2020) to gain the most complete picture of life situation during the pandemic. Results demonstrate that older people in poverty were more likely to report poor subjective health before as well as during the pandemic yet were significantly more likely to refuse vaccination against COVID-19, despite adhering to other measures against the pandemic to the same extent as non-poor people. Restrictions in the health care system affected both groups equally and no significant differences in the frequency of social contacts could be found. However, older people below the poverty line were significantly more likely to rely on social support to obtain necessities during the pandemic and were less likely to use the internet. Together, these results point out that disadvantage exist for the older poor in some but not all areas of life during the pandemic. This paper is aimed at providing first insights into the lives of poor older persons during a taxing time and may perhaps inspire more in-depth study of this particularly understudied population.

Close kin influence COVID-19 precautionary behaviors and vaccine acceptance of older individuals

The family plays a central role in shaping health behaviors of its members through social control and support mechanisms. We investigate whether and to what extent close kin (i.e., partner and children) have mattered for older people in taking on precautionary behaviors (e.g., physical distancing) and vaccination during the COVID-19 pandemic in Europe. Drawing on data from the Survey of Health, Ageing and Retirement in Europe (SHARE), we combine its Corona Surveys (June-August 2020 and June-August 2021) with pre-COVID information (October 2019-March2020). We find that having close kin (especially a partner) is associated with a higher probability of both adopting precautionary behaviors and accepting a COVID-19 vaccine. Results are robust to controlling for other potential drivers of precautionary behaviors and vaccine acceptance, as well as to accounting for co-residence with kin. Our findings suggest that policy makers and practitioners may differently address kinless individuals when promoting public policy measures.