Population-based Estimates for High Risk of Severe COVID-19 Disease due to Age and Underlying Health Conditions

Laires, P.A.; Nunes, Carla

doi:10.20344/amp.14222

Cited by 10 publications

(13 citation statements)

References 18 publications

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“…As a result, a number of environmental and anthropogenic factors were identified as potential drivers. Namely clinical-epidemiological conditions (Bai et al, 2020;Guan et al, 2020), climatic conditions (Chan et al, 2011;Paez et al, 2020;Quilodrán et al, 2020), socioeconomic and demographic structure (Laires and Nunes, 2020;Maroko et al, 2020;Marques, 2020;Murgante et al, 2020;Roy et al, 2020;Prazeres et al, 2021) and human mobility patterns (Chen et al, 2020;Melo et al, 2020;Orea and Álvarez, 2020;Tamagusko and Ferreira, 2020). These associations provide information on possible mechanisms by which the spread or impact of COVID-19 is hampered or magnified, in turn assisting the design of preventive or mitigation measures.…”

Section: Introductionmentioning

confidence: 99%

Spatial correlates of COVID-19 first wave across continental Portugal

Barbosa¹,

Silva

Capinha

et al. 2022

Geospat Health

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The first case of COVID-19 in continental Portugal was documented on the 2nd of March 2020 and about seven months later more than 75 thousand infections had been reported. Although several factors correlate significantly with the spatial incidence of COVID-19 worldwide, the drivers of spatial incidence of this virus remain poorly known and need further exploration. In this study, we analyse the spatiotemporal patterns of COVID-19 incidence in the at the municipality level and test for significant relationships between these patterns and environmental, socioeconomic, demographic and human mobility factors to identify the mains drivers of COVID-19 incidence across time and space. We used a generalized liner mixed model, which accounts for zero inflated cases and spatial autocorrelation to identify significant relationships between the spatiotemporal incidence and the considered set of driving factors. Some of these relationships were particularly consistent across time, including the ‘percentage of employment in services’; ‘average time of commuting using individual transportation’; ‘percentage of employment in the agricultural sector’; and ‘average family size’. Comparing the preventive measures in Portugal (e.g., restrictions on mobility and crowd around) with the model results clearly show that COVID-19 incidence fluctuates as those measures are imposed or relieved. This shows that our model can be a useful tool to help decision-makers in defining prevention and/or mitigation policies.

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Section: Introductionmentioning

confidence: 99%

Spatial correlates of COVID-19 first wave across continental Portugal

Barbosa¹,

Silva

Capinha

et al. 2022

Geospat Health

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“…[12][13][14] In addition, COVID-19 has already been shown to impact individuals with certain preexisting health conditions at a greater rate. 15 Both federal and state policymakers looked to Medicaid as a central tool in their response to the national emergency. 16 However, whether differences exist in COVID-19 outcomes between communities of Medicaid and non-Medicaid expansion states remains unknown.…”

Section: Introductionmentioning

confidence: 99%

“Association between Median Household Income, State Medicaid Expansion Status, and COVID-19 Outcomes Across US Counties”

Apenyo

Vera-Urbina

Ahmad

et al. 2021

Preprint

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Objective: The relationship between socioeconomic status and its interaction with State Medicaid-expansion policies on COVID-19 outcomes across United States (US) counties are uncertain. To determine the association between median-household-income and its interaction with State Medicaid-expansion status on COVID-19 incidence and mortality in US counties Methods: Longitudinal, retrospective analysis of 3142 US counties (including District of Columbia) to study the relationship between County-level median-household-income (defined by US Census Bureau Small-Area-Income-and-Poverty-Estimates) and COVID-19 incidence and mortality per 100000 of the population in US counties from January 20, 2020 through December 6, 2020. County median-household-income was log-transformed and stratified by quartiles. Medicaid-expansion status was defined by US State Medicaid-expansion adoption as of first reported US COVID-19 infection, January 20, 2020. Multilevel mixed-effects generalized-linear-model with negative binomial distribution and log link function compared quartiles of median-household-income and COVID-19 incidence and mortality, reported as incidence-risk-ratio (IRR) and mortality-risk-ratio (MRR), respectively. Models adjusted for county socio-demographic and comorbidity conditions, population density, and hospitals, with a random intercept for states. Multiplicative interaction tested for Medicaid-expansion*income quartiles on COVID-19 incidence and mortality. Results: There was no significant difference in COVID-19 incidence across counties by income quartiles or by Medicaid expansion status. Conversely, significant differences exist between COVID-19 mortality by income quartiles and by Medicaid expansion status. The association between income quartiles and COVID-19 mortality was significant only in counties from non-Medicaid-expansion states but not significant in counties from Medicaid-expansion states (P<0.01 for interaction). For non-Medicaid-expansion states, counties in the lowest income quartile had a 41% increase in COVID-19 mortality compared to counties in the highest income quartile (MRR 1.41, 95% CI: 1.25-1.59). Conclusions and Relevance: Median-household-income was not related to COVID-19 incidence but negatively related to COVID-19 mortality in US counties of states without Medicaid-expansion. It was unrelated to COVID-19 mortality in counties of states that adopted Medicaid-expansion. These findings suggest that expanded healthcare coverage should be investigated further to attenuate the excessive COVID-19 mortality risk associated with low-income communities.

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“…However, while some of these epidemiological findings have been previously recognized [8,24], other aspects relating to multimorbidity and COVID-19 addressed in the present study are new or less well described, and, at the same time, they answer to previously identified gaps in the research [18], such as considering not only underlying conditions but also sociodemographic factors [8] and clinical characteristics.…”

Section: Discussionmentioning

confidence: 52%

“…This observed high prevalence was not unexpected. It was previously suggested that 22% of individuals worldwide may be at increased risk of severe COVID-19 [8], and in Portugal, 15.5% of the population might be at high risk for complications from COVID-19 according to the 2014 Portuguese National Health Interview Survey [24]; therefore, it is likely that higher frequencies will be found in chronic adult individuals, as is the case of the population of the present study.…”

Section: Discussionmentioning

confidence: 62%

Sociodemographic and Clinical Determinants of Multimorbidity of Underlying Conditions That Increase the Risk of Severe Illness from COVID-19 in Chronic Adult Individuals

Prazeres

Castro

Teixeira

2022

BioMed

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Multimorbid patients represent a special population of vulnerable individuals who suffer from two or more long-term conditions. They are a very prevalent group with an increased risk of death from COVID-19. The present study aimed to identify the sociodemographic and clinical determinants of multimorbidity of underlying conditions that increase the risk of severe COVID-19 in chronic adult individuals by analyzing data from the Portuguese National Health Survey 2019. The inclusion sample consisted of 7859 adult residents in Portugal who had at least one chronic condition. The health conditions considered for multimorbidity were CKD, COPD, heart conditions, diabetes mellitus, obesity, and smoking. In Portugal, approximately 6 out of every 10 individuals with chronic diseases suffer from one or more conditions that are on the list of those at increased risk of severe COVID-19 disease, and approximately 2 out of every 10 individuals have multimorbidity. Obesity and diabetes are the most frequent risk factors. Timely interventions (e.g., regular medical follow-up for preventive health services and health information) targeting multimorbidity in males and individuals with low educational levels, a poor health status, and low functionality may help to reduce the risk of severe COVID-19 and post-COVID-19 sequelae, and to improve health in a large proportion of the population.

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Population-based Estimates for High Risk of Severe COVID-19 Disease due to Age and Underlying Health Conditions

Cited by 10 publications

References 18 publications

Spatial correlates of COVID-19 first wave across continental Portugal

Spatial correlates of COVID-19 first wave across continental Portugal

“Association between Median Household Income, State Medicaid Expansion Status, and COVID-19 Outcomes Across US Counties”

Sociodemographic and Clinical Determinants of Multimorbidity of Underlying Conditions That Increase the Risk of Severe Illness from COVID-19 in Chronic Adult Individuals

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