County-level longitudinal clustering of COVID-19 mortality to incidence ratio in the United States

As of November 12, 2020, the mortality to incidence ratio (MIR) of COVID-19 was 5.8 in the US. A longitudinal model-based clustering system on the disease trajectories over time was used to identify �vulnerable� clusters of counties that would benefit from allocating additional resources by federal, state and county policymakers. County-level COVID-19 cases and deaths, together with a set of potential risk factors were collected for 3050 U.S. counties during the 1st wave of COVID-19 (Mar25�Jun3, 2020), followed by similar data for 1344 counties (in the �sunbelt� region of the country) during the 2nd wave (Jun4�Sep2, 2020), and finally for 1055 counties located broadly in the great plains region of the country during the 3rd wave (Sep3�Nov12, 2020). We used growth mixture models to identify clusters of counties exhibiting similar COVID-19 MIR growth trajectories and risk-factors over time. The analysis identifies �more vulnerable� clusters during the 1st, 2nd and 3rd waves of COVID-19. Further, tuberculosis (OR 1.3�2.1�3.2), drug use disorder (OR 1.1), hepatitis (OR 13.1), HIV/AIDS (OR 2.3), cardiomyopathy and myocarditis (OR 1.3), diabetes (OR 1.2), mesothelioma (OR 9.3) were significantly associated with increased odds of being in a more vulnerable cluster. Heart complications and cancer were the main risk factors increasing the COVID-19 MIR (range 0.08�0.52 MIR�). We identified �more vulnerable� county-clusters exhibiting the highest COVID-19 MIR trajectories, indicating that enhancing the capacity and access to healthcare resources would be key to successfully manage COVID-19 in these clusters. These findings provide insights for public health policymakers on the groups of people and locations they need to pay particular attention while managing the COVID-19 epidemic. © 2021, The Author(s)

Malaria infection and the risk of epilepsy: a meta-analysis

Epilepsy, a chronic disease of the central nervous system, is highly prevalent in malaria-endemic regions. Therefore, several studies have evaluated the associations between malaria infection and epilepsy development. A meta-analysis of observational studies published from inception to 10 May 2022 has been conducted to synthesize and pool the existing data on this topic. The relevant publications were systematically searched in PubMed/Medline, Scopus, Embase and Web of Science database collections. A random-effects meta-analysis model (REM) was utilized to generate the pooled odds ratio (OR) at 95% confidence intervals (CIs). The between-studies heterogeneity was assessed with I2, as well as several subgroups, meta-regression and sensitivity analysis were performed to identify the source of heterogeneity. Overall, 17 eligible studies containing 6285 cases and 13 909 healthy controls were included. The REM showed a significant positive association between malaria infection and epilepsy development (OR 2.36; 95% CI 1.44–3.88). In subgroup analyses, significant positive associations were observed in studies that: epilepsy was the outcome in the follow-up of patients with cerebral malaria (OR 7.10; 95% CI 3.50–14.38); used blood smear to diagnose malaria (OR 4.80; 95% CI 2.36–9.77); included only children (OR 3.92; 95% CI 1.81–8.50); published before 2010 (OR 6.39; 95% CI 4.25–9.62). Our findings indicated that patients with malaria, especially those with cerebral malaria, are at a high risk of epilepsy development; however, further well-designed and controlled studies are needed to verify the strength of the association

Spatial Modeling of COVID-19 Vaccine Hesitancy in the United States

Vaccine hesitancy refers to delay in acceptance or refusal of vaccines despite the availability of vaccine services. Despite the efforts of United States healthcare providers to vaccinate the bulk of its population, vaccine hesitancy is still a severe challenge that has led to the resurgence of COVID-19 cases to over 100,000 people during early August 2021. To our knowledge, there are limited nationwide studies that examined the spatial distribution of vaccination rates, mainly based on the social vulnerability index (SVI). In this study, we compiled a database of the percentage of fully vaccinated people at the county scale across the continental United States as of 29 July 2021, along with SVI data as potential significant covariates. We further employed multiscale geographically weighted regression to model spatial nonstationarity of vaccination rates. Our findings indicated that the model could explain over 79% of the variance of vaccination rate based on Per capita income and Minority (%) (with positive impacts), and Age 17 and younger (%), Mobile homes (%), and Uninsured people (%) (with negative effects). However, the impact of each covariate varied for different counties due to using separate optimal bandwidths. This timely study can serve as a geospatial reference to support public health decision-makers in forming region-specific policies in monitoring vaccination programs from a geographic perspective

Spatial Analysis of COVID-19 Vaccination: A Scoping Review

Spatial analysis of COVID-19 vaccination research is increasing in recent literature due to the availability of COVID-19 vaccination data that usually contain location components. However, to our knowledge, no previous study has provided a comprehensive review of this research area. Therefore, in this scoping review, we examined the breadth of spatial and spatiotemporal vaccination studies to summarize previous findings, highlight research gaps, and provide guidelines for future research. We performed this review according to the five-stage methodological framework developed by Arksey and O’Malley. We screened all articles published in PubMed/MEDLINE, Scopus, and Web of Science databases, as of 21 September 2021, that had employed at least one form of spatial analysis of COVID-19 vaccination. In total, 36 articles met the inclusion criteria and were organized into four main themes: disease surveillance (n = 35); risk analysis (n = 14); health access (n = 16); and community health profiling (n = 2). Our findings suggested that most studies utilized preliminary spatial analysis techniques, such as disease mapping, which might not lead to robust inferences. Moreover, few studies addressed data quality, modifiable areal unit problems, and spatial dependence, highlighting the need for more sophisticated spatial and spatiotemporal analysis techniques