Figure - available from: SN Social Sciences
This content is subject to copyright. Terms and conditions apply.
Source publication
COVID-19, within a short period of time, grew into a pandemic. The timely identification of places and populations at great risk of COVID-19 infection would aid disease control. In Nigeria, where a variety of recommended and adopted non-pharmaceutical interventions seem to have limited effectiveness, the number of cases is still increasing. To this...
Citations
... This approach was used by Decuir et al. [29] and Singh [30] to create area economic deprivation indices (analyzed in association with high-risk drug injection behaviors and mortality inequalities, respectively). It was also used by Frank et al. [31] to create a neighborhood walkability index, and by Lawal & Osayomi [32] in their creation of an index of place characteristics deemed to confer social vulnerability to COVID-19 in Nigeria. ...
The relationships between place (e.g., neighborhood) and HIV are commonly investigated. As measurements of place are multivariate, most studies apply some dimension reduction, resulting in one variable (or a small number of variables), which is then used to characterize place. Typical dimension reduction methods seek to capture the most variance of the raw items, resulting in a type of summary variable we call “disadvantage score”. We propose to add a different type of summary variable, the “vulnerability score,” to the toolbox of the researchers doing place and HIV research. The vulnerability score measures how place, as known through the raw measurements, is predictive of an outcome. It captures variation in place characteristics that matters most for the particular outcome. We demonstrate the estimation and utility of place-based vulnerability scores for HIV viral non-suppression, using data with complicated clustering from a cohort of people with histories of injecting drugs.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12874-023-02133-x.
... Three studies [9,23,28] used individual vulnerability indicator scores directly rather than constructing a composite vulnerability index. Of the 38 studies that constructed a composite vulnerability index, 16 used percentile-rank methods (eg, the CDC's SVI) [4][5][6][7]12,22,25,26,29,30,33,34,36,41,44,51] which assumed equal contribution of the chosen indicators and components to the overall vulnerability, 11 used principal component analysis (PCA) or factor analysis [11,32,35,37,38,43,[47][48][49]52] to explore main components of vulnerability from the chosen indicators and assign weights to each component regarding their contribution to the overall vulnerability, two [39,42] directly summed the indicator scores, and two [31,50] used the more sophisticated methods (ie, machine learning, generalized propensity modelling) to generate an overall vulnerability index. Thirteen studies provided insufficient justifications about the chosen statistical methods used to examine the associations between the vulnerability levels and health-related outcomes, and nine [6,11,14,24,31,37,39,42,48] only examined their univariate correlations. ...
... Of the 38 studies that constructed a composite vulnerability index, 16 used percentile-rank methods (eg, the CDC's SVI) [4][5][6][7]12,22,25,26,29,30,33,34,36,41,44,51] which assumed equal contribution of the chosen indicators and components to the overall vulnerability, 11 used principal component analysis (PCA) or factor analysis [11,32,35,37,38,43,[47][48][49]52] to explore main components of vulnerability from the chosen indicators and assign weights to each component regarding their contribution to the overall vulnerability, two [39,42] directly summed the indicator scores, and two [31,50] used the more sophisticated methods (ie, machine learning, generalized propensity modelling) to generate an overall vulnerability index. Thirteen studies provided insufficient justifications about the chosen statistical methods used to examine the associations between the vulnerability levels and health-related outcomes, and nine [6,11,14,24,31,37,39,42,48] only examined their univariate correlations. ...
... Twenty-six of the 29 studies reporting the associations of a composite vulnerability index with measures of cumulative COVID-19 morbidity reported a significant association between the two [4][5][6]10,11,15,[22][23][24][25][26][27][32][33][34][35][36][37]39,40,[42][43][44]48,49,52]. Three studies reported that COVID-19 cases or incidence rates initially rapidly increased in less vulnerable communities, but eventually became more widespread in more vulnerable communities [7,32,41]. ...
Background:
We aimed to conduct a narrative synthesis of components and indicators of community vulnerability to a pandemic and discuss their interrelationships from an ecological perspective.
Methods:
We searched from PubMed, Embase, Web of Science, PsycINFO, and Scopus (updated to November 2021) for studies focusing on community vulnerability to a pandemic caused by novel respiratory viruses on a geographic unit basis . Studies that reported the associations of community vulnerability levels with at least one disease morbidity or mortality outcome were included.
Results:
Forty-one studies were included. All were about the COVID-19 pandemic. Suitable temperature and humidity environments, advanced social and human development (including high population density and human mobility, connectivity, and occupations), and settings that intensified physical interactions are important indicators of vulnerability to viral exposure. However, the eventual pandemic health impacts are predominant in communities that faced environmental pollution, higher proportions of socioeconomically deprived people, health deprivation, higher proportions of poor-condition households, limited access to preventive health care and urban infrastructure, uneven social and human development, and racism. More stringent social distancing policies were associated with lower COVID-19 morbidity and mortality only in the early pandemic phases. Prolonged social distancing policies can disproportionately burden the socially disadvantaged and racially/ethnically marginalized groups.
Conclusions:
Community vulnerability to a pandemic is foremost the vulnerability of the ecological systems shaped by complex interactions between the human and environmental systems.
Registration:
PROSPERO (CRD42021266186).
The COVID-19 pandemic has demanded governments and diverse organizations to work on strategies to prepare and help communities. Increasing recognition of the importance of identifying vulnerable populations has raised a demand for better tools. One of these tools is the Social Vulnerability Index (SVI). The SVI was created in 2011 to identify and plan assistance for socially vulnerable populations during hazardous events, by providing disaster management personnel information to target specific areas. We aimed to evaluate and determine the social vulnerability in different provinces and districts of Peru in the context of the COVID-19 pandemic using an adapted version of the SVI index. Ecological, observational, and cross-sectional study was conducted. We adapted the SVI and collected indicators related to COVID-19. We organized and analyzed the population data of the 196 provinces of Peru, using data from government institutions. We found a distribution of high and very high SVI in the mountainous areas of Peru. High and very high social vulnerability indexes, due to the presence of some or all the variables were predominantly distributed in the provinces located in the southern and highlands of the country. The association between mortality rate and social SVI-COVID19 was inverse, the higher the vulnerability, the lower the mortality. Our results identify that the provinces with high and very high vulnerability indexes are mostly located in rural areas nearby the Andes Mountains, not having a direct correlation with COVID-19 mortality.
