Exposure, Susceptibility, and Recovery: A Framework for Examining the Intersection of the Social and Physical Environments and Infectious Disease Risk

Timing of Infection as a Key Driver of Racial/Ethnic Disparities in Coronavirus Disease 2019 Mortality Rates During the Prevaccine Period

Disparities in coronavirus disease 2019 mortality are driven by inequalities in group-specific incidence rates (IRs), case fatality rates (CFRs), and their interaction. For emerging infections, such as severe acute respiratory syndrome coronavirus 2, group-specific IRs and CFRs change on different time scales, and inequities in these measures may reflect different social and medical mechanisms. To be useful tools for public health surveillance and policy, analyses of changing mortality rate disparities must independently address changes in IRs and CFRs. However, this is rarely done. In this analysis, we examine the separate contributions of disparities in the timing of infection-reflecting differential infection risk factors such as residential segregation, housing, and participation in essential work-and declining CFRs over time on mortality disparities by race/ethnicity in the US state of Michigan. We used detailed case data to decompose race/ethnicity-specific mortality rates into their age-specific IR and CFR components during each of 3 periods from March to December 2020. We used these estimates in a counterfactual simulation model to estimate that that 35% (95% credible interval, 30%-40%) of deaths in black Michigan residents could have been prevented if these residents were infected along the timeline experienced by white residents, resulting in a 67% (61%-72%) reduction in the mortality rate gap between black and white Michigan residents during 2020. These results clearly illustrate why differential power to "wait out" infection during an infectious disease emergency-a function of structural racism-is a key, underappreciated, driver of inequality in disease and death from emerging infections.

The association of combinations of social factors and SARs-CoV-2 infection: A retrospective population-based cohort study in Ontario, 2020-2021

Objective

The COVID-19 pandemic highlighted and exacerbated health inequities worldwide. While several studies have examined the impact of individual social factors on COVID infection, our objective was to examine how interactions of social factors were associated with the risk of testing positive for SARS-CoV-2 during the first two years of the pandemic.

Study design and setting

We conducted an observational cohort study using linked health administrative data for Ontarians tested for SARS-CoV-2 between January 1st, 2020, and December 31st, 2021. We constructed multivariable models to examine the association between SARS-CoV-2 positivity and key variables including immigration status (immigrants vs. other Ontarians), and neighbourhood variables for household size, income, essential worker status, and visible minority status. We report main and interaction effects using odds ratios and predicted probabilities, with age and sex controlled in all models.

Results

Of 6,575,523 Ontarians in the cohort, 88.5 % tested negative, and 11.5 % tested positive for SARS-CoV-2. In all models, immigrants and those living in neighbourhoods with large average household sizes had greater odds of testing positive for SARS-CoV-2. The strength of these associations increased with increasing levels of neighbourhood marginalization for income, essential worker proportion and visible minority proportion. We observed little change in the probability of testing positive across neighbourhood income quintiles among other Ontarians who live in neighbourhoods with smaller households, but a large change in probability among other Ontarians who live in neighbourhoods with larger households.

Conclusion

Our study found that SARS-CoV-2 positivity was greater among people with certain combinations of social factors, but in all cases the probability of testing positive was consistently greater for immigrants than for other Ontarians. Examining interactions of social factors can provide a more nuanced and more comprehensive understanding of health inequity than examining factors separately.

The embodiment of the neighborhood socioeconomic environment in the architecture of the immune system

There is growing recognition of the importance of immune health for understanding the origins of ageing-related disease and decline. Numerous studies have demonstrated consistent associations between the social determinants of health and immunosenescence (i.e. ageing of the immune system). Yet few studies have interrogated the relationship between neighborhood socioeconomic status (nSES) and biologically specific measures of immunosenescence. We used data from the US Health and Retirement Study to measure immunosenescence linked with neighborhood socioeconomic data from the National Neighborhood Data Archive to examine associations between indicators of nSES and immunosenescence. We found associations between both the ratio of terminally differentiated effector memory to naïve (EMRA:Naïve) CD4+ T cells and cytomegalovirus (CMV) immunoglobulin G (IgG) levels and nSES. For the CD4+ EMRA:Naïve ratio, each 1% increase in the neighborhood disadvantage index was associated with a 0.005 standard deviation higher value of the EMRA:Naïve ratio (95% CI: 0.0003, 0.01) indicating that living in a neighborhood that is 10% higher in disadvantage is associated with a 0.05 higher standardized value of the CD4+ EMRA:Naïve ratio. The results were fully attenuated when adjusting for both individual-level SES and race/ethnicity. For CMV IgG antibodies, a 1% increase in neighborhood disadvantage was associated a 0.03 standard deviation higher value of CMV IgG antibodies (β = 0.03; 95% CI: 0.002, 0.03) indicating that living in a neighborhood that is 10% higher in disadvantage is associated with a 0.3 higher standardized value of CMV. This association was attenuated though still statistically significant when controlling for individual-level SES and race/ethnicity. The findings from this study provide compelling initial evidence that large, nonspecific social exposures, such as neighborhood socioeconomic conditions, can become embodied in cellular processes of immune ageing.

Capturing the implications of residential segregation for the dynamics of infectious disease transmission

Occupational and residential segregation and other manifestations of social and economic inequity drive of racial and socioeconomic inequities in infection, severe disease, and death from a wide variety of infections including SARS-CoV-2, influenza, HIV, tuberculosis, and many others. Despite a deep and long-standing quantitative and qualitative literature on infectious disease inequity, mathematical models that give equally serious attention to the social and biological dynamics underlying infection inequity remain rare. In this paper, we develop a simple transmission model that accounts for the mechanistic relationship between residential segregation on inequity in infection outcomes. We conceptualize segregation as a high-level, fundamental social cause of infection inequity that impacts both who-contacts-whom (separation or preferential mixing) as well as the risk of infection upon exposure (vulnerability). We show that the basic reproduction number, ℛ 0 , and epidemic dynamics are sensitive to the interaction between these factors. Specifically, our analytical and simulation results and that separation alone is insufficient to explain segregation-associated differences in infection risks, and that increasing separation only results in the concentration of risk in segregated populations when it is accompanied by increasing vulnerability. Overall, this work shows why it is important to carefully consider the causal linkages and correlations between high-level social determinants - like segregation - and more-proximal transmission mechanisms when either crafting or evaluating public health policies. While the framework applied in this analysis is deliberately simple, it lays the groundwork for future, data-driven explorations of the mechanistic impact of residential segregation on infection inequities.

State variation in neighborhood COVID-19 burden across the United States

BACKGROUND

A lack of fine, spatially-resolute case data for the U.S. has prevented the examination of how COVID-19 infection burden has been distributed across neighborhoods, a key determinant of both risk and resilience. Without more spatially resolute data, efforts to identify and mitigate the long-term fallout from COVID-19 in vulnerable communities will remain difficult to quantify and intervene on.

METHODS

We leveraged spatially-referenced data from 21 states collated through the COVID Neighborhood Project to examine the distribution of COVID-19 cases across neighborhoods and states in the U.S. We also linked the COVID-19 case data with data on the neighborhood social environment from the National Neighborhood Data Archive. We then estimated correlations between neighborhood COVID-19 burden and features of the neighborhood social environment.

RESULTS

We find that the distribution of COVID-19 at the neighborhood-level varies within and between states. The median case count per neighborhood (coefficient of variation (CV)) in Wisconsin is 3078.52 (0.17) per 10,000 population, indicating a more homogenous distribution of COVID-19 burden, whereas in Vermont the median case count per neighborhood (CV) is 810.98 (0.84) per 10,000 population. We also find that correlations between features of the neighborhood social environment and burden vary in magnitude and direction by state.

CONCLUSIONS

Our findings underscore the importance that local contexts may play when addressing the long-term social and economic fallout communities will face from COVID-19.

Bayesian belief network modelling approach for predicting and ranking risk factors for malaria infections among children under 5 years in refugee settlements in Uganda

BACKGROUND

Malaria risk factors at household level are known to be complex, uncertain, stochastic, nonlinear, and multidimensional. The interplay among these factors, makes targeted interventions, and resource allocation for malaria control challenging. However, few studies have demonstrated malaria's transmission complexity, control, and integrated modelling, with no available evidence on Uganda's refugee settlements. Using the 2018-2019 Uganda's Malaria Indicator Survey (UMIS) data, an alternative Bayesian belief network (BBN) modelling approach was used to analyse, predict, rank and illustrate the conceptual reasoning, and complex causal relationships among the risk factors for malaria infections among children under-five in refugee settlements of Uganda.

METHODS

In the UMIS, household level information was obtained using standardized questionnaires, and a total of 675 children under 5 years were tested for malaria. From the dataset, a casefile containing malaria test results, demographic, social-economic and environmental information was created. The casefile was divided into a training (80%, n = 540) and testing (20%, n = 135) datasets. The training dataset was used to develop the BBN model following well established guidelines. The testing dataset was used to evaluate model performance.

RESULTS

Model accuracy was 91.11% with an area under the receiver-operating characteristic curve of 0.95. The model's spherical payoff was 0.91, with the logarithmic, and quadratic losses of 0.36, and 0.16 respectively, indicating a strong predictive, and classification ability of the model. The probability of refugee children testing positive, and negative for malaria was 48.1% and 51.9% respectively. The top ranked malaria risk factors based on the sensitivity analysis included: (1) age of child; (2) roof materials (i.e., thatch roofs); (3) wall materials (i.e., poles with mud and thatch walls); (4) whether children sleep under insecticide-treated nets; 5) type of toilet facility used (i.e., no toilet facility, and pit latrines with slabs); (6) walk time distance to water sources (between 0 and 10 min); (7) drinking water sources (i.e., open water sources, and piped water on premises).

CONCLUSION

Ranking, rather than the statistical significance of the malaria risk factors, is crucial as an approach to applied research, as it helps stakeholders determine how to allocate resources for targeted malaria interventions within the constraints of limited funding in the refugee settlements.

Invited Commentary: To Make Long-Term Gains Against Infection Inequity, Infectious Disease Epidemiology Needs to Develop a More Sociological Imagination

In a recent article in the Journal, Noppert et al. (Am J Epidemiol. 2023;192(3):475-482) articulated in detail the mechanisms connecting high-level "fundamental social causes" of health inequity to inequitable infectious disease outcomes, including infection, severe disease, and death. In this commentary, we argue that while intensive focus on intervening mechanisms is welcome and necessary, it cannot occur in isolation from examination of the way that fundamental social causes-including racism, socioeconomic inequity, and social stigma-sustain infection inequities even when intervening mechanisms are addressed. We build on the taxonomy of intervening mechanisms laid out by Noppert et al. to create a road map for strengthening the connection between fundamental cause theory and infectious disease epidemiology and discuss its implications for future research and intervention.

State Variation in Neighborhood COVID-19 Burden: Findings from the COVID Neighborhood Project

A lack of fine, spatially-resolute case data for the U.S. has prevented the examination of how COVID-19 burden has been distributed across neighborhoods, a known geographic unit of both risk and resilience, and is hampering efforts to identify and mitigate the long-term fallout from COVID-19 in vulnerable communities. Using spatially-referenced data from 21 states at the ZIP code or census tract level, we documented how the distribution of COVID-19 at the neighborhood-level varies significantly within and between states. The median case count per neighborhood (IQR) in Oregon was 3,608 (2,487) per 100,000 population, indicating a more homogenous distribution of COVID-19 burden, whereas in Vermont the median case count per neighborhood (IQR) was 8,142 (11,031) per 100,000. We also found that the association between features of the neighborhood social environment and burden varied in magnitude and direction by state. Our findings underscore the importance of local contexts when addressing the long-term social and economic fallout communities will face from COVID-19.

Exposure, perceived risk, and psychological distress among general population during the COVID-19 lockdown in Wuhan, China

Introduction

The COVID-19 pandemic that has been going on since the end of 2019 impacts people on both the physical and psychological levels. However, the psychological status, especially its underlying psychosocial mechanisms among the general population in Wuhan, the earliest epicenter and hardest-hit city in China during the pandemic, has not been well investigated. This study aimed to examine the relationships between exposures, perceived risk, and psychological distress among the general population in Wuhan during the COVID-19 lockdown.

Methods

Data were from a cross-sectional online survey conducted from 20 February to 4 March 2020. Final analyses included 4,234 Wuhan respondents. A 5-item Hopkins Symptom Checklist was adopted to assess respondents' psychological distress.

Results

It was found that nervousness, fear, and worry were the most common symptoms among Wuhan residents during the lockdown. Exposure within a close physical distance, exposure within the social network, and perceived risk are significantly positively related to respondents' psychological distress. Moreover, perceived risk mediated the effects of exposures on respondents' psychological condition.

Discussion

These findings conduce to identify the populations at higher risk of suffering psychological disturbance during the pandemic and are expected to inform the policymakers and mental health professionals to monitor and improve the perception of risk among the target population by appropriate interventions.