Role of Geographic Risk Factors in COVID-19 Epidemiology: Longitudinal Geospatial Analysis

Disparities in Influenza, Pneumococcal, COVID-19 Vaccine Coverage in High-Risk Adults Aged 19 to 64 Years in Southeastern Minnesota, 2010-2021

BACKGROUND

Despite effective vaccines against influenza, pneumococcus, and COVID-19, uptake has been suboptimal.

RESEARCH QUESTION

Although disparities in vaccination by race and ethnicity have been observed, what is the role of other sociodemographic factors in US vaccine uptake?

STUDY DESIGN AND METHODS

We conducted a population-based study using the Rochester Epidemiology Project (REP), a comprehensive medical records linkage system, to assess effects of sociodemographic factors including race, ethnicity, individual-level socioeconomic status (SES) via the housing-based socioeconomic status index, education, population density (urban or nonurban), and marital status with uptake of influenza, pneumococcal, and COVID-19 vaccination in high-risk adults. Adults at high risk of invasive pneumococcal disease residing in four counties in southeastern Minnesota who were aged 19 to 64 years were identified. Vaccination data were obtained from the Minnesota Immunization Information Connection and REP from January 1, 2010, through December 31, 2021.

RESULTS

We identified 45,755 residents. Most were White (82%), non-Hispanic (94%), married (56%), and living in an urban setting (81%), with three-quarters obtaining at least some college education (74%). Although 45.1% were up to date on pneumococcal vaccines, 60.1% had completed the primary COVID-19 series. For influenza and COVID-19, higher SES, living in an urban setting, older age, and higher education positively correlated with vaccination. Magnitude of differences in race, education, and SES widened with booster vaccines.

INTERPRETATION

This high-risk population is undervaccinated against preventable respiratory diseases, especially influenza and pneumococcus. Although national data reported improvement of disparities in COVID-19 vaccination uptake observed early in the pandemic, our data demonstrated gaps related to race, education level, SES, and age that widened with booster vaccines. Communities with high social vulnerabilities often show increased risk of severe disease outcomes, yet demonstrate lower uptake of preventive services. This highlights the need to understand better vaccine compliance and access in rural, lower SES, less-educated, Black, Hispanic, and younger populations, each of which were associated independently with decreased vaccination.

Rural Health Disparities in Allergy, Asthma, and Immunologic Diseases: The Current State and Future Direction for Clinical Care and Research

Rural health disparities are well documented and continue to jeopardize the long-term health and wellness for the millions of individuals who live in rural America. The disparities observed between urban and rural residents encompass numerous morbidity and mortality measures for several chronic diseases and have been referred to as the "rural mortality penalty." Although the unmet health needs of rural communities are widely acknowledged, little is known about rural health disparities in allergies, asthma, and immunologic diseases. Furthermore, the intersection between rural health disparities and social determinants of health has not been fully explored. To achieve a more complete understanding of the factors that perpetuate rural health disparities, greater research efforts followed by improved practice and policy are needed that account for the complex social context within rural communities rather than a general comparison between urban and rural environments or focusing on biomedical factors. Moreover, research efforts must prioritize community inclusion throughout rural areas through meaningful engagement of stakeholders in both clinical care and research. In this review, we examine the scope of health disparities in the rural United States and the impact of social determinants of health. We then detail the current state of rural health disparities in the field of allergy, asthma, and immunology. To close, we offer future considerations to address knowledge gaps and unmet needs for both clinical care and research in addressing rural health disparities.

Bridging geographical disparities across 368 townships with healthcare system and socioeconomic factors in Taiwan

A universal health insurance program such as the National Health Insurance in Taiwan offers a wide coverage and increased access to healthcare services. Despite its ongoing efforts to enhance healthcare accessibility, differences in health for people living in urban and resource-deprived areas remain substantial. To investigate the longitudinal impact of the healthcare system and other potential structural drivers such as education and economic development on geographical disparities in health, we designed a panel study with longitudinal open secondary data, covering all 368 townships in Taiwan between 2013 and 2017. Our findings indicated higher mortality rates in the mountainous and rural areas near the east and south regions of the island in both years. Multivariate analyses showed an increase in the density of primary care physicians (PCP) was associated with lower all-cause mortality (β = - 0.72, p < 0.0001) and cardiovascular disease mortality (β = - 0.41, p < 0.0001). Effect of PCP is evident, but merely focusing on access to healthcare is still not enough. Additional measures are warranted to address the health disparities existing between urban and underprivileged areas.

Sauver J. Rates of Asymptomatic COVID-19 Infection and Associated Factors in Olmsted County, Minnesota, in the Prevaccination Era

Objective

To estimate rates and identify factors associated with asymptomatic COVID-19 in the population of Olmsted County during the prevaccination era.

Patients and Methods

We screened first responders (n=191) and Olmsted County employees (n=564) for antibodies to SARS-CoV-2 from November 1, 2020 to February 28, 2021 to estimate seroprevalence and asymptomatic infection. Second, we retrieved all polymerase chain reaction (PCR)-confirmed COVID-19 diagnoses in Olmsted County from March 2020 through January 2021, abstracted symptom information, estimated rates of asymptomatic infection and examined related factors.

Results

Twenty (10.5%; 95% CI, 6.9%-15.6%) first responders and 38 (6.7%; 95% CI, 5.0%-9.1%) county employees had positive antibodies; an additional 5 (2.6%) and 10 (1.8%) had prior positive PCR tests per self-report or medical record, but no antibodies detected. Of persons with symptom information, 4 of 20 (20%; 95% CI, 3.0%-37.0%) first responders and 10 of 39 (26%; 95% CI, 12.6%-40.0%) county employees were asymptomatic. Of 6020 positive PCR tests in Olmsted County with symptom information between March 1, 2020, and January 31, 2021, 6% (n=385; 95% CI, 5.8%-7.1%) were asymptomatic. Factors associated with asymptomatic disease included age (0-18 years [odds ratio {OR}, 2.3; 95% CI, 1.7-3.1] and >65 years [OR, 1.40; 95% CI, 1.0-2.0] compared with ages 19-44 years), body mass index (overweight [OR, 0.58; 95% CI, 0.44-0.77] or obese [OR, 0.48; 95% CI, 0.57-0.62] compared with normal or underweight) and tests after November 20, 2020 ([OR, 1.35; 95% CI, 1.13-1.71] compared with prior dates).

Conclusion

Asymptomatic rates in Olmsted County before COVID-19 vaccine rollout ranged from 6% to 25%, and younger age, normal weight, and later tests dates were associated with asymptomatic infection.

Assessing socioeconomic bias in machine learning algorithms in health care: a case study of the HOUSES index

OBJECTIVE

Artificial intelligence (AI) models may propagate harmful biases in performance and hence negatively affect the underserved. We aimed to assess the degree to which data quality of electronic health records (EHRs) affected by inequities related to low socioeconomic status (SES), results in differential performance of AI models across SES.

MATERIALS AND METHODS

This study utilized existing machine learning models for predicting asthma exacerbation in children with asthma. We compared balanced error rate (BER) against different SES levels measured by HOUsing-based SocioEconomic Status measure (HOUSES) index. As a possible mechanism for differential performance, we also compared incompleteness of EHR information relevant to asthma care by SES.

RESULTS

Asthmatic children with lower SES had larger BER than those with higher SES (eg, ratio = 1.35 for HOUSES Q1 vs Q2-Q4) and had a higher proportion of missing information relevant to asthma care (eg, 41% vs 24% for missing asthma severity and 12% vs 9.8% for undiagnosed asthma despite meeting asthma criteria).

DISCUSSION

Our study suggests that lower SES is associated with worse predictive model performance. It also highlights the potential role of incomplete EHR data in this differential performance and suggests a way to mitigate this bias.

CONCLUSION

The HOUSES index allows AI researchers to assess bias in predictive model performance by SES. Although our case study was based on a small sample size and a single-site study, the study results highlight a potential strategy for identifying bias by using an innovative SES measure.

Heterogeneity of the COVID-19 Pandemic in the United States of America: A Geo-Epidemiological Perspective

The spread of the COVID-19 pandemic has shown great heterogeneity between regions of countries, e. g., in the United States of America (USA). With the growing of the worldwide COVID-19 pandemic, there is a need to better highlight the variability in the trajectory of this disease in different worldwide geographic areas. Indeed, the epidemic trends across areas can display completely different evolution at a given time. Geo-epidemiological analyses using data, that are publicly available, could be a major topic to help governments and public administrations to implement health policies. Geo-epidemiological analyses could provide a basis for the implementation of relevant public health policies. With the COVID-19 pandemic, geo-epidemiological analyses can be readily utilized by policy interventions and USA public health authorities to highlight geographic areas of particular concern and enhance the allocation of resources.

Has COVID-19 changed how people think about the drivers of health? If so, does it matter?

Background

Could the COVID-19 pandemic prompt shifts in Americans' basic views on health mindset and policy solutions to health crises?

Methods

A sample of 1,637 individuals rated the extent to which items (e.g., the role of environmental vs. individual factors) "may affect people's health and wellbeing," both before (2018) and during the pandemic. In summer 2020 and fall 2021 they responded to questions about vaccination status and perceptions of COVID-19 related policies. We assessed changes in health mindset using repeated measures logistic regression, and used cross-sectional logistic regressions to assess whether variations in mindset explain COVID-19 related attitudes and behavior.

Results

Between 2018 and 2021 respondents gave increasing weight to where people live and genetic factors and less weight to the role of individual health choices. Views on the importance of access to healthcare did not change appreciably. Those who reported that health care and place have a strong effect on health and wellbeing were significantly more likely to get vaccinated. Moreover, those who strongly believed that place is important were significantly less likely to agree that their local government went too far in restricting their freedom and that the local economy should have been left alone.

Conclusion

Respondents were more likely in 2021 than in 2018 to recognize social determinants of health, and this is associated with a greater openness to pandemic-control measures. It remains to be seen, however, whether the changes in health mindset will persist over time and contribute to changes in policy and practice.

Predicting COVID-19 future trends for different European countries using Pearson correlation

The ability to accurately forecast the number of COVID-19 cases and future case trends would certainly assist governments and various organisations in strategising and preparing for the newly infected cases well in advance. Many predictions have failed to foresee future COVID-19 cases due to the lack of reliable data; however, such data are now widely available for predicting future trends in COVID-19 after more than one and a half years of the pandemic. Also, various countries are closely monitoring other countries that are experiencing a surge in COVID-19 cases in the expectation of similar scenarios, but this does not always produce correct results, as no research has identified specific correlations between different countries in terms of COVID-19 cases. During the past 18 months, many nations have watched countries whose COVID-19 cases have risen sharply, in anticipation of handling the situation themselves. However, this did not provide accurate results, as no research was conducted that compared countries to determine if their COVID-19 case trends were correlated. As official data on COVID-19 cases has become increasingly available, using the Pearson correlation technique to pinpoint the countries that should be closely monitored will help governments plan and prepare for the number of infections that are expected in the future at an early stage. In this study, a simple and real-time prediction of COVID-19 cases incorporating existing variables of coronavirus variants was used to explore the correlation among different European countries in terms of the number of COVID-19 cases officially recorded on a daily basis. Data from selected countries over the past 76 weeks were analysed using a Pearson correlation technique to determine if there were correlations between case trends and geographical position. The correlation coefficient (r) was employed for identifying whether the different countries in Europe were interrelated, with r > 0.85 indicating they were very strongly correlated, 0.85 > r > 0.8 indicating that they were strongly correlated, 0.8 > r > 0.7 indicating that they were moderately correlated, and r < 0.7 indicating that the examined countries were either weakly correlated or that a correlation did not exist. The results showed that although some neighbouring countries are strongly correlated, other countries that are not geographically close are also correlated. In addition, some countries on opposite sides of Europe (Belgium and Armenia) are also correlated. Other countries (France, Iceland, Israel, Kosovo, San Marino, Spain, Sweden and Turkey) were either weakly correlated or had no relationship at all.