SARS-CoV-2 in Mexico: An Association Among Geography, Comorbidities, and Vitamin D Deficiency.

Objective: The aim of this study was to determine the association among temperature, relative humidity, latitude, vitamin D content and comorbidities in the spread of SAR-CoV-2 in Mexico in 2 different waves. Methods: The data on SARS-CoV-2 infections and comorbidities were obtained from the Mexican entities with the highest number of positive cases and deaths in the 2 waves that have most damaged the population. Results: Low temperature, high relative humidity, vitamin D deficiency and high percentage of comorbidities were factors that correlated with a high spread of SARS-CoV-2. Interestingly, 73.8% of the population had one of the most common comorbidities that favor the spread of the virus. Conclusion: The high percentage of comorbidities and the deficient concentration of vitamin D were determining factors in the high number of infections and deaths in Mexico. Furthermore, weather conditions could contribute to and alert to the spread of SARS-CoV-2.

Exploring the Spatial Relative Risk of COVID-19 in Berlin-Neukölln.

Identifying areas with high and low infection rates can provide important etiological clues. Usually, areas with high and low infection rates are identified by aggregating epidemiological data into geographical units, such as administrative areas. This assumes that the distribution of population numbers, infection rates, and resulting risks is constant across space. This assumption is, however, often false and is commonly known as the modifiable area unit problem. This article develops a spatial relative risk surface by using kernel density estimation to identify statistically significant areas of high risk by comparing the spatial distribution of address-level COVID-19 cases and the underlying population at risk in Berlin-Neukölln. Our findings show that there are varying areas of statistically significant high and low risk that straddle administrative boundaries. The findings of this exploratory analysis further highlight topics such as, e.g., Why were mostly affluent areas affected during the first wave? What lessons can be learned from areas with low infection rates? How important are built structures as drivers of COVID-19? How large is the effect of the socio-economic situation on COVID-19 infections? We conclude that it is of great importance to provide access to and analyse fine-resolution data to be able to understand the spread of the disease and address tailored health measures in urban settings.

The utility of Zip4 codes in spatial epidemiological analysis.

There are many public health situations within the United States that require fine geographical scale data to effectively inform response and intervention strategies. However, a condition for accessing and analyzing such data, especially when multiple institutions are involved, is being able to preserve a degree of spatial privacy and confidentiality. Hospitals and state health departments, who are generally the custodians of these fine-scale health data, are sometimes understandably hesitant to collaborate with each other due to these concerns. This paper looks at the utility and pitfalls of using Zip4 codes, a data layer often included as it is believed to be "safe", as a source for sharing fine-scale spatial health data that enables privacy preservation while maintaining a suitable precision for spatial analysis. While the Zip4 is widely supplied, researchers seldom utilize it. Nor is its spatial characteristics known by data guardians. To address this gap, we use the context of a near-real time spatial response to an emerging health threat to show how the Zip4 aggregation preserves an underlying spatial structure making it potentially suitable dataset for analysis. Our results suggest that based on the density of urbanization, Zip4 centroids are within 150 meters of the real location almost 99% of the time. Spatial analysis experiments performed on these Zip4 data suggest a far more insightful geographic output than if using more commonly used aggregation units such as street lines and census block groups. However, this improvement in analytical output comes at a spatial privy cost as Zip4 centroids have a higher potential of compromising spatial anonymity with 73% of addresses having a spatial k anonymity value less than 5 when compared to other aggregations. We conclude that while offers an exciting opportunity to share data between organizations, researchers and analysts need to be made aware of the potential for serious confidentiality violations.

Predicting COVID-19 positivity and hospitalization with multi-scale graph neural networks.

The pandemic of COVID-19 is undoubtedly one of the biggest challenges for modern healthcare. In order to analyze the spatio-temporal aspects of the spread of COVID-19, technology has helped us to track, identify and store information regarding positivity and hospitalization, across different levels of municipal entities. In this work, we present a method for predicting the number of positive and hospitalized cases via a novel multi-scale graph neural network, integrating information from fine-scale geographical zones of a few thousand inhabitants. By leveraging population mobility data and other features, the model utilizes message passing to model interaction between areas. Our proposed model manages to outperform baselines and deep learning models, presenting low errors in both prediction tasks. We specifically point out the importance of our contribution in predicting hospitalization since hospitals became critical infrastructure during the pandemic. To the best of our knowledge, this is the first work to exploit high-resolution spatio-temporal data in a multi-scale manner, incorporating additional knowledge, such as vaccination rates and population mobility data. We believe that our method may improve future estimations of positivity and hospitalization, which is crucial for healthcare planning.

Fractal dimension based geographical clustering of COVID-19 time series data.

Understanding the local dynamics of COVID-19 transmission calls for an approach that characterizes the incidence curve in a small geographical unit. Given that incidence curves exhibit considerable day-to-day variation, the fractal structure of the time series dynamics is investigated for the Flanders and Brussels Regions of Belgium. For each statistical sector, the smallest administrative geographical entity in Belgium, fractal dimensions of COVID-19 incidence rates, based on rolling time spans of 7, 14, and 21 days were estimated using four different estimators: box-count, Hall-Wood, variogram, and madogram. We found varying patterns of fractal dimensions across time and location. The fractal dimension is further summarized by its mean, variance, and autocorrelation over time. These summary statistics are then used to cluster regions with different incidence rate patterns using k-means clustering. Fractal dimension analysis of COVID-19 incidence thus offers important insight into the past, current, and arguably future evolution of an infectious disease outbreak.

Breast exam use during the protracted COVID-19 pandemic, by age, race, and geography.

In this study we analyzed data collected from the onset of the COVID-19 pandemic through March 31, 2022, to identify temporal shifts in breast exam volume. Screening mammography volume stabilized toward the end of the study period, and diagnostic exam volume varied over time and by age. Older women experienced a decline in diagnostic exam volume between August 2020 and April 2021 that was not observed among women aged younger than 50 years (50-69 years: monthly percentage change [MPC] = -6.5%; and 70 years and older: MPC = -15.7%). With respect to breast biopsy volume, women aged younger than 70 years had increased exam volume beginning in April 2020 and June 2020, whereas a corresponding increase among older women was delayed until April 2021 (70 years and older: MPC = 9.3%). Findings from our study suggest a temporal shift in the use of breast exams that could result in differential detection of breast cancer by age.

Clinical characteristics, treatment, and prognosis of 74 2019 novel coronavirus disease patients in Hefei: A single-center retrospective study.

ABSTRACT: Since December 2019, pneumonia caused by a novel coronavirus (SARS-CoV-2), namely 2019 novel coronavirus disease (COVID-19), has rapidly spread from Wuhan city to other cities across China. The present study was designed to describe the epidemiology, clinical characteristics, treatment, and prognosis of 74 hospitalized patients with COVID-19.Clinical data of 74 COVID-19 patients were collected to analyze the epidemiological, demographic, laboratory, radiological, and treatment data. Thirty-two patients were followed up and tested for the presence of the viral nucleic acid and by pulmonary computed tomography (CT) scan at 7 and 14 days after they were discharged.Among all COVID-19 patients, the median incubation period for patients and the median period from symptom onset to admission was all 6 days; the median length of hospitalization was 13 days. Fever symptoms were presented in 83.78% of the patients, and the second most common symptom was cough (74.32%), followed by fatigue and expectoration (27.03%). Inflammatory indicators, erythrocyte sedimentation rate (ESR), and C-reactive protein (CRP) of the intensive care unit (ICU) patients were significantly higher than that of the non-ICU patients (P < .05). However, 50.00% of the ICU patients had their the ratio of T helper cells to cytotoxic T cells (CD4/CD8) ratio lower than 1.1, whose proportion is much higher than that in non-ICU patients (P < .01).Compared with patients in Wuhan, COVID-19 patients in Anhui Province seemed to have milder symptoms of infection, suggesting that there may be some regional differences in the transmission of SARS-CoV-2 between different cities.

Regional differences in the quality of maternal and neonatal care during the COVID-19 pandemic in Portugal: Results from the IMAgiNE EURO study.

OBJECTIVE: To compare women's perspectives on the quality of maternal and newborn care (QMNC) around the time of childbirth across Nomenclature of Territorial Units for Statistics 2 (NUTS-II) regions in Portugal during the COVID-19 pandemic. METHODS: Women participating in the cross-sectional IMAgiNE EURO study who gave birth in Portugal from March 1, 2020, to October 28, 2021, completed a structured questionnaire with 40 key WHO standards-based quality measures. Four domains of QMNC were assessed: (1) provision of care; (2) experience of care; (3) availability of human and physical resources; and (4) reorganizational changes due to the COVID-19 pandemic. Frequencies for each quality measure within each QMNC domain were computed overall and by region. RESULTS: Out of 1845 participants, one-third (33.7%) had a cesarean. Examples of high-quality care included: low frequencies of lack of early breastfeeding and rooming-in (8.0% and 7.7%, respectively) and informal payment (0.7%); adequate staff professionalism (94.6%); adequate room comfort and equipment (95.2%). However, substandard practices with large heterogeneity across regions were also reported. Among women who experienced labor, the percentage of instrumental vaginal births ranged from 22.3% in the Algarve to 33.5% in Center; among these, fundal pressure ranged from 34.8% in Lisbon to 66.7% in Center. Episiotomy was performed in 39.3% of noninstrumental vaginal births with variations between 31.8% in the North to 59.8% in Center. One in four women reported inadequate breastfeeding support (26.1%, ranging from 19.4% in Algarve to 31.5% in Lisbon). One in five reported no exclusive breastfeeding at discharge (22.1%; 19.5% in Lisbon to 28.2% in Algarve). CONCLUSION: Urgent actions are needed to harmonize QMNC and reduce inequities across regions in Portugal.

Using survey data to estimate the impact of the omicron variant on vaccine efficacy against COVID-19 infection.

Symptoms-based detection of SARS-CoV-2 infection is not a substitute for precise diagnostic tests but can provide insight into the likely level of infection in a given population. This study uses symptoms data collected in the Global COVID-19 Trends and Impact Surveys (UMD Global CTIS), and data on variants sequencing from GISAID. This work, conducted in January of 2022 during the emergence of the Omicron variant (subvariant BA.1), aims to improve the quality of infection detection from the available symptoms and to use the resulting estimates of infection levels to assess the changes in vaccine efficacy during a change of dominant variant; from the Delta dominant to the Omicron dominant period. Our approach produced a new symptoms-based classifier, Random Forest, that was compared to a ground-truth subset of cases with known diagnostic test status. This classifier was compared with other competing classifiers and shown to exhibit an increased performance with respect to the ground-truth data. Using the Random Forest classifier, and knowing the vaccination status of the subjects, we then proceeded to analyse the evolution of vaccine efficacy towards infection during different periods, geographies and dominant variants. In South Africa, where the first significant wave of Omicron occurred, a significant reduction of vaccine efficacy is observed from August-September 2021 to December 2021. For instance, the efficacy drops from 0.81 to 0.30 for those vaccinated with 2 doses (of Pfizer/BioNTech), and from 0.51 to 0.09 for those vaccinated with one dose (of Pfizer/BioNTech or Johnson & Johnson). We also extended the study to other countries in which Omicron has been detected, comparing the situation in October 2021 (before Omicron) with that of December 2021. While the reduction measured is smaller than in South Africa, we still found, for instance, an average drop in vaccine efficacy from 0.53 to 0.45 among those vaccinated with two doses. Moreover, we found a significant negative (Pearson) correlation of around - 0.6 between the measured prevalence of Omicron in several countries and the vaccine efficacy in those same countries. This prediction, in January of 2022, of the decreased vaccine efficacy towards Omicron is in line with the subsequent increase of Omicron infections in the first half of 2022.

Observational cross-sectional study of the association of poor broadband provision with demographic and health outcomes: the Wolverhampton Digital ENablement (WODEN) programme.

OBJECTIVES: The association between impaired digital provision, access and health outcomes has not been systematically studied. The Wolverhampton Digital ENablement programme (WODEN) is a multiagency collaborative approach to determine and address digital factors that may impact on health and social care in a single deprived multiethnic health economy. The objective of this study is to determine the association between measurable broadband provision and demographic and health outcomes in a defined population. DESIGN: An observational cross-sectional whole local population-level study with cohorts defined according to broadband provision. SETTING/PARTICIPANTS: Data for all residents of the City of Wolverhampton, totalling 269 785 residents. PRIMARY OUTCOMES: Poor broadband provision is associated with variation in demographics and with increased comorbidity and urgent care needs. RESULTS: Broadband provision was measured using the Broadband Infrastructure Index (BII) in 158 City localities housing a total of 269 785 residents. Lower broadband provision as determined by BII was associated with younger age (p<0.001), white ethnic status (p<0.001), lesser deprivation as measured by Index of Multiple Deprivation (p<0.001), a higher number of health comorbidities (p<0.001) and more non-elective urgent events over 12 months (p<0.001). CONCLUSION: Local municipal and health authorities are advised to consider the variations in broadband provision within their locality and determine equal distribution both on a geographical basis but also against demographic, health and social data to determine equitable distribution as a platform for equitable access to digital resources for their residents.

COVID-19 and deprivation amplification: An ecological study of geographical inequalities in mortality in England.

'Deprivation amplification' is used to understand the relationship between deprivation, scale and COVID-19 mortality rates. We found that more deprived Middle Super Output Areas (MSOAs) in the more deprived northern regions suffered greater COVID-19 mortality rates. Across England, the most deprived 20% of MSOAs had higher mortality than the least deprived (44.1% more COVID-19 deaths/10,000). However, the most deprived MSOAs in the north fared worse than equally deprived areas in the rest of England (14.5% more deaths/10,000, beta = 0.136, p < 0.01). There was also strong evidence of spatial clustering and spill-overs. We discuss these findings in relation to 'deprivation amplification', the 'syndemic pandemic', and the health and place literature.

Collaborative and individual learning of geography in immersive virtual reality: An effectiveness study.

Many university-taught courses moved to online form since the outbreak of the global pandemic of coronavirus disease (COVID-19). Distance learning has become broadly used as a result of the widely applied lockdowns, however, many students lack personal contact in the learning process. A classical web-based distance learning does not provide means for natural interpersonal interaction. The technology of immersive virtual reality (iVR) may mitigate this problem. Current research has been aimed mainly at specific instances of collaborative immersive virtual environment (CIVE) applications for learning. The fields utilizing iVR for knowledge construction and skills training with the use of spatial visualizations show promising results. The objective of this study was to assess the effectiveness of collaborative and individual use of iVR for learning geography, specifically training in hypsography. Furthermore, the study's goals were to determine whether collaborative learning would be more effective and to investigate the key elements in which collaborative and individual learning were expected to differ-motivation and use of cognitive resources. The CIVE application developed at Masaryk University was utilized to train 80 participants in inferring conclusions from cartographic visualizations. The collaborative and individual experimental group underwent a research procedure consisting of a pretest, training in iVR, posttest, and questionnaires. A statistical comparison between the geography pretest and posttest for the individual learning showed a significant increase in the score (p = 0.024, ES = 0.128) and speed (p = 0.027, ES = 0.123), while for the collaborative learning, there was a significant increase in the score (p<0.001, ES = 0.333) but not in speed (p = 1.000, ES = 0.000). Thus, iVR as a medium proved to be an effective tool for learning geography. However, comparing the collaborative and individual learning showed no significant difference in the learning gain (p = 0.303, ES = 0.115), speed gain (p = 0.098, ES = 0.185), or performance motivation (p = 0.368, ES = 0.101). Nevertheless, the collaborative learning group had significantly higher use of cognitive resources (p = 0.046, ES = 0.223) than the individual learning group. The results were discussed in relation to the cognitive load theories, and future research directions for iVR learning were proposed.

Severe acute respiratory syndrome coronavirus-2: implications for blood safety and sufficiency.

BACKGROUND AND OBJECTIVE: Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a novel coronavirus, first identified in China at the end of 2019 and has now caused a worldwide pandemic. In this review, we provide an overview of the implications of SARS-CoV-2 for blood safety and sufficiency. MATERIAL AND METHOD: We searched the PubMed database, the preprint sites bioRxiv and medRxiv, the websites of the World Health Organization, European Centre for Disease Prevention and Control, the US Communicable Diseases Center and monitored ProMed updates. RESULTS: An estimated 15%-46% of SARS-CoV-2 infections are asymptomatic. The reported mean incubation period is 3 to 7 days with a range of 1-14 days. The blood phase of SARS-CoV-2 appears to be brief and low level, with RNAaemia detectable in only a small proportion of patients, typically associated with more severe disease and not demonstrated to be infectious virus. An asymptomatic blood phase has not been demonstrated. Given these characteristics of SARS-CoV-2 infection and the absence of reported transfusion transmission (TT), the TT risk is currently theoretical. To mitigate any potential TT risk, but more importantly to prevent respiratory transmission in donor centres, blood centres can implement donor deferral policies based on travel, disease status or potential risk of exposure. CONCLUSION: The TT risk of SARS-CoV-2 appears to be low. The biggest risk to blood services in the current COVID-19 pandemic is to maintain the sufficiency of the blood supply while minimizing respiratory transmission of SARS-CoV-19 to donors and staff while donating blood.

Understanding risk factors of a new variant outburst through global analysis of Omicron transmissibility.

The emergence of a new virus variant is generally recognized by its usually sudden and rapid spread (outburst) in a certain world region. Due to the near-exponential rate of initial expansion, the new strain may not be detected at its true geographical origin but in the area with the most favorable conditions leading to the fastest exponential growth. Therefore, it is crucial to understand better the factors that promote such outbursts, which we address in the example of analyzing global Omicron transmissibility during its global emergence/outburst in November 2021-February 2022. As predictors, we assemble a number of potentially relevant factors: vaccinations (both full and boosters), different measures of population mobility (provided by Google), estimated stringency of measures, the prevalence of chronic diseases, population age, the timing of the outburst, and several other socio-demographic variables. As a proxy for natural immunity (prevalence of prior infections in population), we use cumulative numbers of COVID-19 deaths. As a response variable (transmissibility measure), we use the estimated effective reproduction number (Re) averaged in the vicinity of the outburst maxima. To select significant predictors of Re, we use machine learning regressions that employ feature selection, including methods based on ensembles of decision trees (Random Forest and Gradient Boosting). We identify the young population, earlier infection onset, higher mobility, low natural immunity, and low booster prevalence as likely direct risk factors. Interestingly, we find that all these risk factors were significantly higher for Africa, though curiously somewhat lower in Southern African countries (where the outburst emerged) compared to other African countries. Therefore, while the risk factors related to the virus transmissibility clearly promote the outburst of a new virus variant, specific regions/countries where the outburst actually happens may be related to less evident factors, possibly random in nature.

Wildlife susceptibility to infectious diseases at global scales.

Disease transmission prediction across wildlife is crucial for risk assessment of emerging infectious diseases. Susceptibility of host species to pathogens is influenced by the geographic, environmental, and phylogenetic context of the specific system under study. We used machine learning to analyze how such variables influence pathogen incidence for multihost pathogen assemblages, including one of direct transmission (coronaviruses and bats) and two vector-borne systems (West Nile Virus [WNV] and birds, and malaria and birds). Here we show that this methodology is able to provide reliable global spatial susceptibility predictions for the studied host-pathogen systems, even when using a small amount of incidence information (i.e., [Formula: see text] of information in a database). We found that avian malaria was mostly affected by environmental factors and by an interaction between phylogeny and geography, and WNV susceptibility was mostly influenced by phylogeny and by the interaction between geographic and environmental distances, whereas coronavirus susceptibility was mostly affected by geography. This approach will help to direct surveillance and field efforts providing cost-effective decisions on where to invest limited resources.

Geographic microtargeting of social assistance with high-resolution poverty maps.

Hundreds of millions of poor families receive some form of targeted social assistance. Many of these antipoverty programs involve some degree of geographic targeting, where aid is prioritized to the poorest regions of the country. However, policy makers in many low-resource settings lack the disaggregated poverty data required to make effective geographic targeting decisions. Using several independent datasets from Nigeria, this paper shows that high-resolution poverty maps, constructed by applying machine learning algorithms to satellite imagery and other nontraditional geospatial data, can improve the targeting of government cash transfers to poor families. Specifically, we find that geographic targeting relying on machine learning-based poverty maps can reduce errors of exclusion and inclusion relative to geographic targeting based on recent nationally representative survey data. This result holds for antipoverty programs that target both the poor and the extreme poor and for initiatives of varying sizes. We also find no evidence that machine learning-based maps increase targeting disparities by demographic groups, such as gender or religion. Based in part on these findings, the Government of Nigeria used this approach to geographically target emergency cash transfers in response to the COVID-19 pandemic.

Geographic disparities in disruptions to abortion care in Louisiana at the onset of the COVID-19 pandemic.

OBJECTIVES: Prior research identified a significant decline in the number of abortions in Louisiana at the onset of the COVID-19 pandemic, as well as increases in second-trimester abortions and decreases in medication abortions. This study examines how service disruptions in particular areas of the state disparately affected access to abortion care based on geography. STUDY DESIGN: We collected monthly service data from Louisiana's abortion clinics (January 2018-May 2020) and conducted mystery client calls to determine whether clinics were scheduling appointments at pandemic onset (April-May 2020). We used segmented regression to assess whether service disruptions modified the main pandemic effects on the number, timing, and type of abortions using stratified models and interaction terms. Additionally, we calculated the median distance that Louisiana residents traveled to the clinic where they obtained care. RESULTS: For residents whose closest clinic was consistently scheduling appointments at the onset of the pandemic, the number of monthly abortions did not change (IRR = 1.07, 95% CI: 0.84-1.36). For those whose closest clinic services were disrupted, the number of monthly abortions decreased by 46% (IRR = 0.54, 95% CI: 0.45-0.65). Similarly, increases in second-trimester abortions and decreases in medication abortions were concentrated in areas where residents experienced service disruptions (AOR = 2.25, 95% CI: 1.21-4.56 and AOR = 0.59, 95% CI: 0.29-0.87, respectively) and were not seen elsewhere in the state. CONCLUSION: Changes in the number, timing and type of abortions were concentrated among residents in particular areas of Louisiana. The early stages of the COVID-19 pandemic exacerbated geographic disparities in access to abortion care. IMPLICATIONS: Disruptions in services at the beginning of the COVID-19 pandemic in Louisiana meaningfully affected pregnant people's ability to obtain an abortion at their nearest clinic. These findings reinforce the importance of developing mechanisms to support pregnant people during emergency situations when traveling to a nearby clinic is no longer possible.