Maximum healthcare needs for older people are complex due to diseases, comorbidities, or disabilities, including long-term or mental and physical health issues. Various residential and nursing care homes help care for older people, especially those with special medical needs. Among these special medical needs, dementia is one medical condition requiring exceptional care for the affected to prevent adverse effects of the situation they usually encounter. Dementia is a health condition that involves impairments to memory and thinking due to any injury or disease-causing damage to the brain. Older people suffer from different diseases, which cause cognitive disability and long-term ailments and directly affect patients' quality of life. Given the cognitive impairment dementia causes to older people, it is difficult for the care providers to accurately assess the impact on every individual to formulate a person-centered care plan. During the COVID-19 pandemic, due to administrative restrictions on social distancing to prevent transmission of this disease, caregivers and elderly persons feel tremendous mental stress, further aggravating their problems because of loneliness. Thus, there is a requirement to do the study and analyze the effects on older people to provide quality and person-centered care. Due to the above factors as significant challenges in the current context, there is an ardent need for the results of variegated studies besides a thorough analysis of available literature analyzed to provide proper evidence to the care providers. It will pave the way for understanding the actual impact of the condition in its natural context. In this regard, a literature review and the results of the studies are discussed. This research brings into the limelight all those factors in the context of previous studies and data analysis of the current situation.Categories: Internal Medicine, Psychiatry, Psychology Keywords: healthcare, dementia care homes, quality of life of dementia patients, impact of dementia on older people, dementia

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New researchers are usually very curious about the recipe that could accelerate the chances of their paper getting accepted in a reputed forum (journal/conference). In search of such a recipe, we investigate the profile and peer review text of authors whose papers almost always get accepted at a venue (Journal of High Energy Physics in our current work). We find authors with high acceptance rate are likely to have a high number of citations, high h-index, higher number of collaborators etc. We notice that they receive relatively lengthy and positive reviews for their papers. In addition, we also construct three networks -co-reviewer, cocitation and collaboration network and study the network-centric features and intra-and inter-category edge interactions. We find that the authors with high acceptance rate are more 'central' in these networks; the volume of intra-and inter-category interactions are also drastically different for the authors with high acceptance rate compared to the other authors. Finally, using the above set of features, we train standard machine learning models (random forest, XGBoost) and obtain very high class wise precision and recall. In a followup discussion we also narrate how apart from the author characteristics, the peer-review system might itself have a role in propelling the distinction among the different categories which could lead to potential discrimination and unfairness and calls for further investigation by the system admins.

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