Sociodemographic predictors and transportation patterns of COVID-19 infection and mortality

Social Drivers of COVID-19 Disease Severity in Pregnant Patients

OBJECTIVE

While coronavirus disease 2019 (COVID-19), the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has had global impact in all populations, certain groups of patients have experienced disproportionate rates of morbidity and mortality. The purpose of this study was to assess the relationship between COVID-19 disease severity, demographic variables, race and ethnicity, and social determinants of health among pregnant patients in a diverse urban population.

STUDY DESIGN

A retrospective analysis was performed of all pregnant patients diagnosed with COVID-19 at two urban tertiary care centers in Houston, TX between March and August 2020. Maternal demographic, COVID-19 illness criteria, and delivery characteristics were collected. The Centers for Disease Control and Prevention Social Vulnerability Index (SVI) and COVID-19 Community Vulnerability Index (CCVI) were obtained based on a patients' census tract of residence. Analyses compared persons with asymptomatic, mild, or severe-critical disease at diagnosis.

RESULTS

A total of 317 persons tested positive for COVID-19 during this time period. Asymptomatic persons were more likely to be diagnosed at later gestational ages, but there were no other differences in baseline maternal characteristics. Persons with more severe disease had greater social vulnerability specifically for housing and transportation than those with mild disease (mean SVI [standard error]: 0.72 [0.06] vs. 0.58 [0.2], p = 0.03). Total SVI, total CCVI, and other themed SVI and CCVI indices were not significantly different between groups.

CONCLUSION

In this cohort of pregnant persons infected with SARS-CoV-2, an association was shown between disease severity and increased vulnerability in living conditions and transportation. Drivers of the pandemic and COVID-19 outcomes are complex and multifactorial, and likely change over time. However, continued efforts to accurately identify and measure social determinants of health in medicine will likely help identify geographic areas and patient populations that are at risk of higher disease burden. This could facilitate preventative and mitigation measures in these areas in future disaster or pandemic situations.

KEY POINTS

· SVI and CCVI estimate social determinants of health.. · COVID-19 is associated with housing and transportation vulnerability.. · Social determinants contribute to disease burden in pregnancy..

The effect of the urban exposome on COVID-19 health outcomes: A systematic review and meta-analysis

BACKGROUND

The global severity of SARS-CoV-2 illness has been associated with various urban characteristics, including exposure to ambient air pollutants. This systematic review and meta-analysis aims to synthesize findings from ecological and non-ecological studies to investigate the impact of multiple urban-related features on a variety of COVID-19 health outcomes.

METHODS

On December 5, 2022, PubMed was searched to identify all types of observational studies that examined one or more urban exposome characteristics in relation to various COVID-19 health outcomes such as infection severity, the need for hospitalization, ICU admission, COVID pneumonia, and mortality.

RESULTS

A total of 38 non-ecological and 241 ecological studies were included in this review. Non-ecological studies highlighted the significant effects of population density, urbanization, and exposure to ambient air pollutants, particularly PM2.5. The meta-analyses revealed that a 1 μg/m3 increase in PM2.5 was associated with a higher likelihood of COVID-19 hospitalization (pooled OR 1.08 (95% CI:1.02-1.14)) and death (pooled OR 1.06 (95% CI:1.03-1.09)). Ecological studies, in addition to confirming the findings of non-ecological studies, also indicated that higher exposure to nitrogen dioxide (NO2), ozone (O3), sulphur dioxide (SO2), and carbon monoxide (CO), as well as lower ambient temperature, humidity, ultraviolet (UV) radiation, and less green and blue space exposure, were associated with increased COVID-19 morbidity and mortality.

CONCLUSION

This systematic review has identified several key vulnerability features related to urban areas in the context of the recent COVID-19 pandemic. The findings underscore the importance of improving policies related to urban exposures and implementing measures to protect individuals from these harmful environmental stressors.

COVID-19 mortality and deprivation: pandemic, syndemic, and endemic health inequalities

COVID-19 has exacerbated endemic health inequalities resulting in a syndemic pandemic of higher mortality and morbidity rates among the most socially disadvantaged. We did a scoping review to identify and synthesise published evidence on geographical inequalities in COVID-19 mortality rates globally. We included peer-reviewed studies, from any country, written in English that showed any area-level (eg, neighbourhood, town, city, municipality, or region) inequalities in mortality by socioeconomic deprivation (ie, measured via indices of multiple deprivation: the percentage of people living in poverty or proxy factors including the Gini coefficient, employment rates, or housing tenure). 95 papers from five WHO global regions were included in the final synthesis. A large majority of the studies (n=86) found that COVID-19 mortality rates were higher in areas of socioeconomic disadvantage than in affluent areas. The subsequent discussion reflects on how the unequal nature of the pandemic has resulted from a syndemic of COVID-19 and endemic inequalities in chronic disease burden.

Prediction of COVID-19 Infections for Municipalities in the Netherlands: Algorithm Development and Interpretation

Background

COVID-19 was first identified in December 2019 in the city of Wuhan, China. The virus quickly spread and was declared a pandemic on March 11, 2020. After infection, symptoms such as fever, a (dry) cough, nasal congestion, and fatigue can develop. In some cases, the virus causes severe complications such as pneumonia and dyspnea and could result in death. The virus also spread rapidly in the Netherlands, a small and densely populated country with an aging population. Health care in the Netherlands is of a high standard, but there were nevertheless problems with hospital capacity, such as the number of available beds and staff. There were also regions and municipalities that were hit harder than others. In the Netherlands, there are important data sources available for daily COVID-19 numbers and information about municipalities.

Objective

We aimed to predict the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants per municipality in the Netherlands, using a data set with the properties of 355 municipalities in the Netherlands and advanced modeling techniques.

Methods

We collected relevant static data per municipality from data sources that were available in the Dutch public domain and merged these data with the dynamic daily number of infections from January 1, 2020, to May 9, 2021, resulting in a data set with 355 municipalities in the Netherlands and variables grouped into 20 topics. The modeling techniques random forest and multiple fractional polynomials were used to construct a prediction model for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants per municipality in the Netherlands.

Results

The final prediction model had an R² of 0.63. Important properties for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants in a municipality in the Netherlands were exposure to particulate matter with diameters <10 μm (PM10) in the air, the percentage of Labour party voters, and the number of children in a household.

Conclusions

Data about municipality properties in relation to the cumulative number of confirmed infections in a municipality in the Netherlands can give insight into the most important properties of a municipality for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants in a municipality. This insight can provide policy makers with tools to cope with COVID-19 and may also be of value in the event of a future pandemic, so that municipalities are better prepared.

Predicting coronavirus disease (COVID-19) outcomes in the United States early in the epidemic

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Our study uses 50 US states’ public health surveillance datasets to measure COVID-19 outcomes relationships with populational, social, air travel related and environmental factors.

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Found associations are used to predict expected numbers of cases, hospitalizations and deaths due to COVID-19 early in the epidemic.

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The COVID-19 associated factors enplanements, population density, race, humidity and sun exposure predicted COVID-19 outcomes with reasonable accuracy in approximately 50% of states.

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This study models can help public health identify communities at higher risk for rapid growth of cases, hospitalizations and deaths in a future respiratory-disease epidemic like COVID-19.

By 21 October 2020, the coronavirus disease (COVID-19) epidemic in the United States (US) had infected 8.3 million people, resulting in 61,364 laboratory-confirmed hospitalizations and 222,157 deaths. Currently, policymakers are trying to better understand this epidemic, especially the human-to-human transmissibility of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in relation to social, populational, air travel related and environmental exposure factors. Our study used 50 US states’ public health surveillance datasets (January 1-April 1, 2020) to measure associations of confirmed COVID-19 cases, hospitalizations and deaths with these variables. Using the resulting associations and multivariate regression (Negative Binomial and Poisson), predicted cases, hospitalizations and deaths were generated for each US state early in the epidemic. Factors associated with a significantly increased risk of COVID-19 disease, hospitalization and death included: population density, enplanement, Black race and increased sun exposure; in addition, COVID-19 disease and hospitalization were also associated with morning humidity. Although predictions of the number of cases, hospitalizations and deaths due to COVID-19 were not accurate for every state, those states with a combination of large number of enplanements, high population density, high proportion of Black residents, high humidity or low sun exposure may expect more rapid than expected growth in the number of COVID-19 events early in the epidemic.