Community Characteristics and COVID-19 Outcomes: A Study of 159 Counties in Georgia, United States

Type 2 Diabetes Status, Diabetes Complication Severity Index Scores, and Their Relationship With COVID-19 Severity: A Retrospective Cohort Study of Hospitalized Patients in a Southwest Virginia Health System

Background Studies have shown that patients with type 2 diabetes mellitus (T2DM) tend to have poorer outcomes associated with COVID-19, including increased rates of hospitalization, ICU admission, need for ventilatory support, and mortality. Methods We performed a retrospective cohort study that included all non-pregnant adult patients who were hospitalized as a result of COVID-19 in a Southwest Virginia health system between March 18, 2020, and August 31, 2022. T2DM status was treated as a binary variable. T2DM severity was assessed using the Diabetes Complications Severity Index (DCSI). Multivariate logistic regression was used to assess the relationship between T2DM status and COVID-19 severity outcomes. Multivariate logistic regression was also used to assess the relationship between DCSI score and COVID-19 severity outcomes among patients with an established diagnosis of T2DM at the time of COVID-19 hospital admission. Results Patients with T2DM had 1.27 times the odds of experiencing a poor COVID-19 clinical outcome (95% CI: 1.13, 1.43) and 1.35 times the odds of in-hospital mortality (95% CI: 1.14, 1.59) compared to patients without diabetes. Among patients with T2DM, increasing DCSI score was significantly associated with increased odds of experiencing a poor COVID-19 clinical outcome and in-hospital mortality. Conclusions Diabetic patients in our sample were at increased odds of experiencing poor COVID-19 clinical outcomes and in-hospital mortality compared to individuals without diabetes. Amongst patients with T2DM, increasing DCSI score was associated with worse COVID-19 outcomes. Clinical decision support tools may be able to utilize DCSI scores as an indicator of COVID-19 severity risk to facilitate decisions regarding treatment aggressiveness and resource allocation.

Viral and host factors associated with SARS-CoV-2 disease severity in Georgia, USA

While SARS-CoV-2 vaccines have shown strong efficacy, their suboptimal uptake combined with the continued emergence of new viral variants raises concerns about the ongoing and future public health impact of COVID-19. We investigated viral and host factors, including vaccination status, that were associated with SARS-CoV-2 disease severity in a setting with low vaccination rates. We analyzed clinical and demographic data from 1,957 individuals in the state of Georgia, USA, coupled with viral genome sequencing from 1,185 samples. We found no difference in disease severity between individuals infected with Delta and Omicron variants among the participants in this study, after controlling for other factors, and we found no specific mutations associated with disease severity. Compared to those who were unvaccinated, vaccinated individuals experienced less severe SARS-CoV-2 disease, and the effect was similar for both variants. Vaccination within 270 days before infection was associated with decreased odds of moderate and severe outcomes, with the strongest association observed at 91-270 days post-vaccination. Older age and underlying health conditions, especially immunosuppression and renal disease, were associated with increased disease severity. Overall, this study provides insights into the impact of vaccination status, variants/mutations, and clinical factors on disease severity in SARS-CoV-2 infection when vaccination rates are low. Understanding these associations will help refine and reinforce messaging around the crucial importance of vaccination in mitigating the severity of SARS-CoV-2 disease.

Tokyo's COVID-19: An urban perspective on factors influencing infection rates in a global city

This research investigates the relationship between COVID-19 and urban factors in Tokyo. To understand the spread dynamics of COVID-19, the study examined 53 urban variables (including population density, socio-economic status, housing conditions, transportation, and land use) in 53 municipalities of Tokyo prefecture. Using spatial models, the study analysed the patterns and predictors of COVID-19 infection rates. The findings revealed that COVID-19 cases were concentrated in central Tokyo, with clustering levels decreasing after the outbreaks. COVID-19 infection rates were higher in areas with a greater density of retail stores, restaurants, health facilities, workers in those sectors, public transit use, and telecommuting. However, household crowding was negatively associated. The study also found that telecommuting rate and housing crowding were the strongest predictors of COVID-19 infection rates in Tokyo, according to the regression model with time-fixed effects, which had the best validation and stability. This study's results could be useful for researchers and policymakers, particularly because Japan and Tokyo have unique circumstances, as there was no mandatory lockdown during the pandemic.

Effects of the built environment and human factors on the spread of COVID-19: A systematic literature review

Soon after its emergence, COVID-19 became a global problem. While different types of vaccines and treatments are now available, still non-pharmacological policies play a critical role in managing the pandemic. The literature is enriched enough to provide comprehensive, practical, and scientific insights to better deal with the pandemic. This research aims to find out how the built environment and human factors have affected the transmission of COVID-19 on different scales, including country, state, county, city, and urban district. This is done through a systematic literature review of papers indexed on the Web of Science and Scopus. Initially, these databases returned 4264 papers, and after different stages of screening, we found 166 relevant papers and reviewed them. The empirical papers that had at least one case study and analyzed the effects of at least one built environment factor on the spread of COVID-19 were selected. Results showed that the driving forces can be divided into seven main categories: density, land use, transportation and mobility, housing conditions, demographic factors, socio-economic factors, and health-related factors. We found that among other things, overcrowding, public transport use, proximity to public spaces, the share of health and services workers, levels of poverty, and the share of minorities and vulnerable populations are major predictors of the spread of the pandemic. As the most studied factor, density was associated with mixed results on different scales, but about 58 % of the papers reported that it is linked with a higher number of cases. This study provides insights for policymakers and academics to better understand the dynamic roles of the non-pharmacological driving forces of COVID-19 at different levels.

The World-Wide Adaptations of Diabetic Management in the Face of COVID-19 and Socioeconomic Disparities: A Scoping Review

Diabetes is an increasingly prevalent chronic disease throughout the world. It is imperative for patients to have access to reliable treatment and resources in order to avoid long-term complications. Economic and social factors contribute to the accessibility of these resources and have a direct impact on diabetes management. Socioeconomic status (SES) presents challenges to diabetic management due to financial and geographical access to care, medications, educational resources, healthy food options, and physical activity. The coronavirus (COVID-19) pandemic exacerbated these challenges, especially during the height of lockdowns. Therefore, it is important to gain insight into how the pandemic challenged diabetes management, taking into consideration socioeconomic disparities. The objective is to assess how the COVID-19 pandemic has impacted the care of chronic diabetic patients internationally and determine how these outcomes vary between patients of different socioeconomic classes. The following study was designed as a scoping review and utilized PubMed, EMBASE, CINAHL, and Web of Science. A Boolean search strategy combined search terms as follows: (((COVID-19) AND (diabetes)) AND ((socioeconomic factors) OR (social inequality OR standard of living))) AND (treatment OR management). Inclusion criteria included studies addressing diabetic patients, socioeconomic variables (income, occupation, level of education, and ethnicity), glycemic control, and degree of access to quality healthcare. Studies exploring the pathophysiology of COVID-19 or diabetes mellitus were excluded. In addition, studies were chosen between the years 2020 and 2022. The search resulted in 214 articles. The full-text assessment was then conducted on the remaining 67 articles. After screening for eligibility and relevance, 19 articles were retained for this review. The results of this study indicate that 8 out of the 18 studies revealed worse outcomes for those with diabetes mellitus and concomitant COVID-19 infection. Patients with diabetes were more likely to be hospitalized and represent a larger percentage of COVID-19 fatalities. In addition, patients with diabetes and co-morbid COVID-19 infection were more likely to have a higher hemoglobin A1c (HbA1c), belong to a lower SES, and have worse glycemic control due to pandemic-associated lockdown. In order to combat the effects of the pandemic, many countries created novel and innovative management strategies. Overall, there are positive and negative effects from the pandemic on diabetic management strategies. This scoping review identified successes in diabetic treatment under pandemic conditions and areas that need optimization. The successful adaptations of many nations convey the capacity for new policy implementation to care for diabetic patients regardless of SES.

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.

The Association of COVID-19 Infection and Vaccination Rates in Florida

OBJECTIVE

This study investigates the association of COVID-19 infection and vaccination rates with 2020 presidential election voting preference in Florida counties and the moderating role of age, race, ethnicity, and other community characteristics.

METHODS

Florida county COVID-19 infection and vaccination counts through September 2021 were supplemented with socioeconomic characteristics and 2020 presidential election results. Poisson regression measured the association of infection and vaccination rates with county political preferences, race, ethnicity, and other county demographic and economic characteristics. For models of April through September 2021 infection rates, the same county characteristics were assessed alongside county vaccination levels.

RESULTS

Each 1% increase in county full vaccination rates was associated with 82.47 fewer infections per 100 000 during the span of April to September 2021. Vaccination rate was the largest and most statistically significant determinant of vaccine era infections. Each 1% increase in the county share of votes for the 2020 Republican presidential candidate was associated with 109.7 more COVID-19 infections per 100 000 through March 2021 and a 0.546% decrease in county vaccination rates through September 2021.

CONCLUSIONS

At the county level, COVID-19 vaccination rates are associated with infection rates, with a higher county population proportion of fully vaccinated associated with fewer infections per 100 000. County political preference in the 2020 presidential election is significantly associated with county-level COVID-19 infection and vaccination rates.

Geographic disparities and predictors of COVID-19 hospitalization risks in the St. Louis Area, Missouri (USA)

Background

There is evidence of geographic disparities in COVID-19 hospitalization risks that, if identified, could guide control efforts. Therefore, the objective of this study was to investigate Zip Code Tabulation Area (ZCTA)-level geographic disparities and identify predictors of COVID-19 hospitalization risks in the St. Louis area.

Methods

Hospitalization data for COVID-19 and several chronic diseases were obtained from the Missouri Hospital Association. ZCTA-level data on socioeconomic and demographic factors were obtained from the American Community Survey. Geographic disparities in distribution of COVID-19 age-adjusted hospitalization risks, socioeconomic and demographic factors as well as chronic disease risks were investigated using choropleth maps. Predictors of ZCTA-level COVID-19 hospitalization risks were investigated using global negative binomial and local geographically weighted negative binomial models.

Results

COVID-19 hospitalization risks were significantly higher in ZCTAs with high diabetes hospitalization risks (p < 0.0001), COVID-19 risks (p < 0.0001), black population (p = 0.0416), and populations with some college education (p = 0.0005). The associations between COVID-19 hospitalization risks and the first three predictors varied by geographic location.

Conclusions

There is evidence of geographic disparities in COVID-19 hospitalization risks that are driven by differences in socioeconomic, demographic and health-related factors. The impacts of these factors vary by geographical location implying that a ‘one-size-fits-all’ approach may not be appropriate for management and control. Using both global and local models leads to a better understanding of geographic disparities. These findings are useful for informing health planning to identify geographic areas likely to have high numbers of individuals needing hospitalization as well as guiding vaccination efforts.

Association Between Interorganizational Collaboration in Opioid Response and Treatment Capacity for Opioid Use Disorder in Counties of Five States: A Cross-Sectional Study

Background:

Local governments on the front lines of the opioid epidemic often collaborate across organizations to achieve a more comprehensive opioid response. Collaboration is especially important in rural communities, which can lack capacity for addressing health crises, yet little is known about how local collaboration in opioid response relates to key outputs like treatment capacity.

Purpose:

This cross-sectional study examined the association between local governments’ interorganizational collaboration activity and agonist treatment capacity for opioid use disorder (OUD), and whether this association was stronger for rural than for metropolitan communities.

Methods:

Data on the location of facilities providing buprenorphine and methadone were merged with a 2019 survey of all 358 counties in 5 states (CO, NC, OH, PA, and WA) that inquired about their collaboration activity for opioid response. Regression analysis was used to estimate the effect of a collaboration activity index and its constituent items on the capacity to provide buprenorphine or methadone in a county and whether this differed by urbanicity.

Results:

A response rate of 47.8% yielded an analytic sample of n = 171 counties, including 77 metropolitan, 50 micropolitan, and 44 rural counties. Controlling for covariates, a 1-unit increase in the collaboration activity index was associated with 0.155 (95% CI = 0.005, 0.304) more methadone facilities, ie, opioid treatment programs (OTPs), per 100 000 population. An interaction model indicated this association was stronger for rural (average marginal effect = 0.354, 95% CI = 0.110, 0.599) than for non-rural counties. Separate models revealed intergovernmental data and information sharing, formal agreements, and organizational reforms were driving the above associations. Collaboration activity did not vary with the capacity to provide buprenorphine at non-OTP facilities. Spatial models used to account for spatial dependence occurring with OUD treatment capacity showed similar results.

Conclusion:

Rural communities may be able to leverage collaborations in opioid response to expand treatment capacity through OTPs.