ABSTRACT
The risk of severe illness or death from COVID-19 is associated with specific demographic characteristics or composition of the population within geographic areas, and the spatial relationship between these areas. The aim of this paper is to identify areas with a higher concentration of population vulnerable to COVID-19, relying on the concept of spatial dependence. Hence, we focus on the share of vulnerable populations using several salient proxy measures at municipality level data for Serbia. The degree of vulnerability at the municipality level was determined by hotspot analysis, specifically the Getis-Ord Gi* statistics. The results indicate heterogeneity in the spatial patterning and typologies of clusters across Serbia. This spatial heterogeneity reveals potentially differing degrees of risk across municipalities. The results can inform decision-makers in the fight against COVID-19 by helping to identify those areas with vulnerable populations that if exposed may stress the local health care system. © 2022 University of Craiova, Faculty of Social Sciences, Department of Geography. All rights reserved.
ABSTRACT
The COVID-19 pandemic escalated in almost all parts of the world over a very short period of time. The speed of the spread was determined by the degree of mobility of the population, while the risk of severe illness or death depended on the population’s demographic characteristics, population health status, and the capacity of the health system to treat patients. This paper aims to assess spatio-temporal patterns of patients with COVID-19 in Serbia at the early stage and whether these patterns are linked to valid public health measures that were enforced during this period. The study adopted the local Moran’s index to identify the spatial grouping of the number of infected at a municipality level and joinpoint regression analysis to identify whether and when statistically significant changes occurred to the number of infected by gender and age groups, and to the number of deaths in the entire population. The results show the polarisation of the spatial grouping of the number of infected. Considering the change in the trend in the number of infected between genders, no significant difference was noticeable. When the age-gender categories of infected were examined, the differences became more significant. In addition, changes in the trend were associated with the tightening or loosening of public health measures. © 2022 Demographic Research Centre. All rights reserved.
ABSTRACT
In response to the COVID-19 pandemic, governments around the world imposed confinement and physical distancing directives for all citizens. Although essential to reduce the spread of the SARS-CoV-2 virus, these measures may have collateral consequences for older adults, such as increased psychological distress. Research suggests, as a population, older adults have been experiencing less psychological distress than younger adults during COVID-19. However, most of these studies examine mean levels of psychological distress and do not capture the heterogeneity of outcomes, like subgroups who may experience increased psychological distress. The goal of this longitudinal study was to use group-based trajectory modelling (GBTM) to identify meaningful subgroups that follow different trajectories of psychological distress among 645 older adults with a mean age of 78.69 (SD = 5.67). Participants were recruited from two research cohorts and newspaper ads. Telephone-based assessments were conducted across four time periods: T1-Spring 2020-first confinement, T2-Summer 2020-first deconfinement, T3-Fall 2020-second confinement, and T4-Winter/Spring 2021-continued confinement. Participants completed the Kessler 6-item Psychological Distress Scale (K6) to assess psychological distress at each time point and provided information on socioeconomic, medical, and psychosocial factors. Results indicated that the average psychological distress level was stable across the first three time points but slightly increased at the fourth assessment (Fig. 1). Using GBTM, three groups emerged to best characterize the different trajectories of psychological distress: resilient (50.5%), fluctuating (34.9%), and elevated (14.6%) distress groups (Fig. 1). Those in the fluctuating and elevated groups were more likely to have chronic mental health problems, mobility issues, insomnia symptoms, loneliness, COVID-19 related acute stress and general health anxiety than those in the resilient group. Those who lived in poverty, who could not use technology, and who took psychotropic medication had uniquely increased odds of being in the elevated group. These findings identify subgroups of older adults at greater risk of psychological distress with potential intervention targets to alleviate distress during and after the pandemic.