Incidence Of COVID-19 Research Articles

Association of Area Deprivation Index With Mortality in Critically Ill Adults With COVID-19.

Various social determinants of health have been established as significant risk factors for COVID-19 transmission, prevalence, incidence, and mortality. Area deprivation index (ADI, a composite score made up of educational, housing, and poverty markers) is an accepted multidimensional social determinants of health measure. Little is known about how structural social determinants of health before hospitalization, including ADI, may affect mortality related to COVID-19 in critically ill patients. To examine the association of ADI with intensive care unit (ICU) mortality in patients with COVID-19 and compare its predictive power with that of clinical factors. This was a retrospective cohort study of critically ill adults with COVID-19 in 3 hospitals within a single health system. Multivariable logistic regression models (adjusted for demographic and clinical variables) were used to examine the association of ADI with ICU mortality. Data from 1784 patients hospitalized from 2020 to 2022 were analyzed. In multivariable models, no association was found between national ADI and ICU mortality. Notable factors associated with ICU mortality included treatment year, age, van Walraven weighted score, invasive mechanical ventilation, and body mass index. In this study, clinical factors were more predictive of mortality than ADI and other social determinants of health. The influence of ADI may be most relevant before hospital admission. These findings could serve as a foundation for shaping targeted public health strategies and hospital interventions, enhancing care delivery, and potentially contributing to better outcomes in future pandemics.

Identifying Points of Interest (POIs) as sentinels for infectious disease surveillance: a COVID-19 study

Traditional surveillance relies on medical facilities, such as clinics and laboratories, as sentinels to monitor disease activities. Few studies have investigated the feasibility of using Point of Interests (POIs) as sentinels for disease surveillance. POIs, such as restaurants, retail stores, and churches, are places where people often interact with one another and thus play a critical role in transmission of infectious diseases like influenza and COVID-19. To fill this gap, we proposed a method to estimate people's potential crowdedness at POIs and explored its utility as an early indicator to signal local disease outbreaks. In a case study in Florida, USA, we utilized weekly foot traffic data at 0.3 million POIs to calculate their weekly crowdedness, and tested local correlations between the crowdedness of each POI and its surrounding COVID-19 incidences with different time lags. We identified 261 POIs as potential sentinels that could signal the risk one to three weeks ahead of disease outbreaks. Most of these sentinel POIs provided food/drink services, ambulatory healthcare and religious/civic services. They were characterized by a relatively large group of customers and a stable patronization over time. This research provides new insights into improving current disease surveillance systems by incorporating more diverse and widely distributed POIs.

Intersectionality of sex and race in COVID-19 mortality and vaccination inequities in Massachusetts

BackgroundInequities in COVID-19 incidence, morbidity, and mortality between racial and ethnic groups in the United States (U.S.) have been documented since the start of the pandemic in early 2020. Similarly, disparities by sex for COVID-19 morbidity and mortality have emerged, with men dying at a higher rate than women. Little research has been done to understand how the intersection of sex and race impacts COVID-19 inequities in Massachusetts (MA). This cross-sectional study examined how COVID-19 mortality rates (February 2020- May 2023) and vaccination rates (December 2020-February 2023) varied by sex across racial groups in MA.MethodsUsing Massachusetts Department of Public Health data of all COVID-19 mortality cases and primary series vaccinations in MA from 2020 to 2023, we calculated both age-specific and age-adjusted COVID-19 mortality rates in order to account for differences in age distributions across sex-race groups.ResultsOverall, men across all age-race groups consistently had a higher mortality rate compared to their female counterparts. The age-standardized mortality rate difference between White men and White women is the smallest, with the rate for White men being 1.3 times higher than White women. The age-standardized mortality rate between Hispanic men and Hispanic women varies the largest, with the rate for Hispanic men being 1.7 times higher than Hispanic women. Notably, Black women and White women have similar vaccination rates, yet the age-standardized mortality rate for Black women is 1.4 times the rate of White women.ConclusionsOur findings show that there are disparities at the intersection of sex and race for COVID-19 mortality and vaccination in MA. This highlights the importance for targeted COVID-19 interventions at the intersection of sex and race and the need for detailed COVID-19 reporting by sex within race groups.

Revisiting COVID-19 Maps with Bayesian Hierarchical Modelling: Socioeconomic Insights from Istanbul

Abstract Objectives This study explores the spatial disparities in COVID-19 incidence across neighborhoods in Uskudar, Istanbul, and examines the relationship between these disparities and neighborhood-level socioeconomic status. Methods This ecological study analyzed cumulative COVID-19 case data for 33 neighborhoods in Uskudar, collected up to 31 December 2020. Socioeconomic status was assessed using the Socioeconomic Development Index (SEDI) scores from Istanbul Metropolitan Municipality. Spatial analysis included Moran’s I statistics and Local Indicators of Spatial Association (LISA) cluster analysis. Bayesian Hierarchical Modeling with the Besag-York-Mollie (BYM) model was employed to decompose spatially structured and unstructured variations, adjusting for SEDI scores and their sub-components. Model validation involved convergence diagnostics and Monte Carlo error assessments. Results A total of 29,834 COVID-19 cases were identified, with an incidence rate of 56.1 per 1000 residents. Spatial analysis revealed significant clustering of high incidence rates in peripheral and inner city neighborhoods. Moran’s I statistic for COVID-19 incidence was 0.331 (p = 0.003), indicating positive spatial autocorrelation. SEDI scores were also spatially autocorrelated (Moran’s I = 0.293; p = 0.005) and negatively correlated with COVID-19 incidence (r = -0.570; p = 0.001). Bayesian hierarchical models confirmed the association between lower socioeconomic status and higher COVID-19 incidence, even after adjusting for spatial autocorrelation. Conclusions The study highlights significant spatial and socioeconomic disparities in COVID-19 incidence in Uskudar, Istanbul. Lower socioeconomic status is associated with higher COVID-19 incidence, underscoring the need for region-specific public health strategies that consider socioeconomic factors. These findings can guide future interventions to mitigate the impact of the pandemic on vulnerable populations. Key messages • Study reveals COVID-19 incidence in Uskudar is significantly influenced by socioeconomic status, highlighting need for targeted health interventions. • Positive spatial autocorrelation found in COVID-19 cases and socioeconomic index suggests clustering in specific Uskudar neighborhoods.

The effect of 2 years COVID-19 pandemic on myocardial infarction and stroke admissions: nationwide registry study, sex differences and mortality

Abstract Background Throughout the COVID-19 pandemic many countries reported a decline in myocardial infarction (MI) and stroke admissions. Factors leading to the decline remain uncertain. Purpose In this nationwide study spanning the first two years of the COVID-19 pandemic we evaluated the changes in MI and stroke admissions as the effect of pandemic intensity and nationwide lockdown. We also investigated sex and age differences. We benefited from the fact that the Czech Republic had both, the lowest and one of the highest COVID-19 incidence. Methods We compared the first (March 1, 2020, to February 28, 2021) and the second (March 1, 2021, to February 28, 2022) pandemic years overall and during the COVID-19 waves with the preceding year (March 1, 2019, to February 28, 2020). The occurrence of MI and stroke, patient characteristics, and mortality data were obtained from two nationwide registers covering every hospitalization and every death in the Czech Republic. Results During the first and second pandemic year as compared to the preceding year there were 13,856 and 14,123 vs 15,499 MI admissions, representing a -10,6% (p<0.001) and -8,9% (p<0.001) decline; and 23,194 and 23,231 vs 25,046 stroke admissions, representing a -7.4% (p<0.001) and -7.2% (p<0.001) decline respectively. The significant decline appeared in both MI and stroke in all COVID-19 waves (spring 2020, spring 2021, autumn 2020/winter 2021, -autumn 2021/winter 2022). The greatest MI hospitalization decline (-16%) occurred during the first strict national lock down, during the first COVID-19 wave (spring 2020), the pandemic wave with the lowest COVID-19 incidence. Both, MI and stroke admissions reversed to pre-pandemic numbers during the summer (2020, 2021) when most lockdown restrictions were lifted. The decline in MI was greater in women than in men during both the first (-11.8%, -10%; p<0.001) and second (-10.8%, -7.9%; p<0.001) pandemic years, also for stroke in women greater than in men during the first (-9%, -5.9%; p<0.001) and second (-9.7%, -4.8%; p<0.001) pandemic years. The 30-day all-cause mortality in patients with MI was higher during the first (10.6%; p<0.001) but not the second (9.6%; p=0.34) pandemic year as compared to the preceding year (9.3%), whereas the 30-day mortality in patients with stroke remained unchanged. Conclusions There was a decline in both MI and stroke admissions during the first two years of the pandemic, but such decline was not related to COVID-19 incidence. The greatest reduction in both, MI and stroke admissions, appeared during the first lockdown in the first COVID-19 wave (spring 2020), the period of the lowest COVID-19 incidence. Moreover, the decline in MI and stroke admissions was greater in women. This highlights potential sex differences in health effect. Health authorities should be aware of these findings, in order to adapt their message and restriction measures in case of future major epidemics.Myocardial infarction casesStroke cases

Prevalence of COVID-19 Status among the People of Different Strata

Background: Coronavirus disease (COVID-19) is an irresistible illness caused by the SARS-CoV-2 infection. Most individuals who drop wiped out with COVID-19 will involvement gentle to direct side effects and recoup without uncommon treatment. Some will become seriously ill and require medical attention. However, it is preventable through obeying some measures of personal hygiene. Methods: This descriptive cross-sectional study was conducted to determine COVID-19 status among people of different strata in Uttara and Savar. A total of 1200 conveniently selected respondents were interviewed face-to-face using a semi-structured questionnaire. Results: Mean age was 38.59 years with standard deviation ±14.117 years. Majority (63.33%) were male. Among them 20.7% were housewives, 11.5% were students, 15.3% were businessmen and 9.3% were service holders. Most (98.6%) of the respondents mentioned to know about pandemic situation of COVID-19. Among the respondents 207 (17.3%) suffered from COVID-19. Among those who suffered from COVID-19 (207), 157 performed test for COVID-19 and was positive. The other 50 did not perform test and only assumed to be suffering from COVID-19 as they had symptoms. On the other hand, 993 respondents mentioned of not suffering from COVID-19. Among them, 803 had no symptom and 190 underwent COVID-19 test and were negative. Proportion of suffering from COVID-19 was found high among the doctors (64.1%), service holders (22.5%), students (21.7%) and teachers (21.1%). Conclusion: Measures to be undertaken to create awareness among the people to abide by the health rules so that we can reduce the incidence of COVID 19. EWMCJ Vol. 12, No. 1&2, January-July 2024: 80-87

Exploring demographic, healthcare, and socio-economic factors as predictors of COVID-19 incidence rate: A spatial regression analysis.

This study investigated the relationship between demographic, healthcare, and socio-economic factors, and COVID-19 incidence rate per 100,000 population in Thailand at the province level between January 2020 and March 2022, using a five-phase approach by spatial analysis. OLS models were initially used with significant variables: household, hospital, and industry density, nighttime light index (NTLI). Spatial dependency led to spatial error (SEM) and spatial lag models (SLM), performing better with similar significant variables being applied. SEM explains 58, 65 and, 70 percent in Wave 1, 4 and 5 of COVID-19 variation. SLM explains 25 and 76 percent in Wave 2 and 3 of incidence rate. Positive associations were found between incidence and household density, hospital/medical establishments with beds, Nighttime Light Index (NTLI), and negative with population, hospital, and industry density. Wave 5 showed significant changes with negative for household, hospital, and industry density, urban population; positive for hospital/medical establishments with beds, internet access, NTLI. The study showed that significant predictors of COVID-19 incidence rate vary across waves. Population, household and hospital density, urbanization, access to medical facilities, industrialization, internet access, and NTLI all play a role. The study suggests SEM and SLM models are more appropriate, providing useful information for policymakers and health officials in managing pandemic in Thailand.

An international comparison of Long-term Care Facility outcomes during the COVID-19 pandemic

Abstract Background The COVID-19 pandemic has had huge repercussions for public health internationally and different countries had different outcomes based on preconceived policy interventions. Lessons influencing future policy have been learned, notably how best to manage vulnerable populations, particularly those living in long-term care facilities (LTCFs). In Ireland LTCF residents accounted for 56% of deaths during the first wave of the pandemic and it is imperative to establish the causes of this. International research has highlighted how factors including county incidence rate, facility size, occupancy, LTCF ownership and compliance with health inspection regulations influenced LTCF COVID-19 outcomes. The aim of this research is to explore the impact of these factors in an Irish context and compare to best international standards. Methods The main outcome considered was deaths per 100 beds. Data on crude mortality were available for 580 Irish LTCFs up to 28 May 2020. Data regarding occupancy, ownership, facility size and compliance with regulations were obtained for these LTCFs from a novel dataset of 1130 available LTCF regulatory inspection reports between 2018- 2022. Regression analysis was carried out to show how LTCF mortality was predicted by examined variables. Results The most significant predictor of deaths from COVID-19 in Irish LTCFs resulting from this research was LTCF occupancy (Odds ratio:1.029, significance <0.001). Regional community incidence, in concordance with international literature was also an important factor. Other stated predictors in this model were either non-significant or only marginally significant. These results are analysed in conversation with international research of the same variables. Conclusions To protect LTCF residents for potential future pandemics, attention should be paid to monitoring and suppressing local incidence rates and moves should be implemented towards designing smaller facilities with individual rooms for vulnerable residents. Key messages • Irish long-term care pandemic mortality was predicted by high occupancy of facilities and high regional COVID-19 incidence. • Comparing International COVID-19 outcomes in long-term care facilities can provide guidelines for the future.

Equity-centered adaptive sampling in sub-sewershed wastewater surveillance using census data.

Sub-city, or sub-sewershed, wastewater monitoring for infectious diseases offers a data-driven strategy to inform local public health response and complements city-wide data from centralized wastewater treatment plants. Developing strategies for equitable representation of diverse populations in sub-city wastewater sampling frameworks is complicated by misalignment between demographic data and sampling zones. We address this challenge by: (1) developing a geospatial analysis tool that probabilistically assigns demographic data for subgroups aggregated by race and age from census blocks to sub-city sampling zones; (2) evaluating representativeness of subgroup populations for COVID-19 wastewater-based disease surveillance in Davis, California; and (3) demonstrating scenario planning that prioritizes vulnerable populations. We monitored SARS-CoV-2 in wastewater as a proxy for COVID-19 incidence in Davis (November 2021-September 2022). Daily city-wide sampling and thrice-weekly sub-city sampling from 16 maintenance holes covered nearly the entire city population. Sub-city wastewater data, aggregated as a population-weighted mean, correlated strongly with centralized treatment plant data (Spearman's correlation 0.909). Probabilistic assignment of demographic data can inform decisions when adapting sampling locations to prioritize vulnerable groups. We considered four scenarios that reduced the number of sampling zones from baseline by 25% and 50%, chosen randomly or to prioritize coverage of >65-year-old populations. Prioritizing representation increased coverage of >65-year-olds from 51.1% to 67.2% when removing half the zones, while increasing coverage of Black or African American populations from 67.5% to 76.7%. Downscaling had little effect on correlations between sub-city and centralized data (Spearman's correlations ranged from 0.875 to 0.917), with strongest correlations observed when prioritizing coverage of >65-year-old populations.

Spatial and Ethno-national Health Inequalities: Health and Mortality Gaps between Palestinians and Jews in Israel.

This research adopts an analytical spatial perspective to explain ethno-national health inequality between Palestinians and Jews in Israel. The work identifies the forces that instigated and maintained the spatial segregation of Palestinians and elaborates the role of segregation in generating health gaps between Palestinians and Jews. The analysis suggests a novel conceptualization of two types of segregation: (a) exclusion from the center and confinement to the periphery and (b) segregation within the geographic periphery. Using administrative data on COVID-19 incidence, hospitalization, and death and various health indicators for localities, I devise a decomposition method that evaluates the relative contribution of each type of segregation to the total health gap. The findings indicate that the segregation of Palestinians from the center and their confinement to peripheral regions are crucial determinants of their poor health outcomes and that the segregation of the Palestinian community within the geographic periphery also contributes to poorer health.

Influence of socioeconomic aspects on morbidity and mortality rates from COVID-19 in the state of Tocantins, Brazil

This study aimed to analyze the spatial correlation of COVID-19 with social vulnerability indices in the state of Tocantins. Methodology: An ecological and cross-sectional study was conducted, analyzing the temporal evolution of the pandemic using the local bivariate Moran's index and the Kernel intensity estimator for COVID-19 cases in Tocantins from 2020 to 2022. Results: The northern region of the state showed the highest concentration of cases and deaths, a concerning fact given the high social vulnerability of most municipalities in this region. Conclusion: The study identified a positive spatial correlation between the incidence and mortality rates of COVID-19 and the social vulnerability index of the municipalities. These findings highlight the need to strengthen preventive measures and expand the physical, technological, and human resources of the healthcare network, especially in areas serving populations with higher vulnerability and traditional communities. This is particularly relevant as the disease also affects more isolated areas, impacting indigenous, riverside, and quilombola populations.

How did COVID-19 case distribution associate with the urban built environment? A community-level exploration in Shanghai focusing on non-linear relationship.

Several associations between the built environment and COVID-19 case distribution have been identified in previous studies. However, few studies have explored the non-linear associations between the built environment and COVID-19 at the community level. This study employed the March 2022 Shanghai COVID-19 pandemic as a case study to examine the association between built-environment characteristics and the incidence of COVID-19. A non-linear modeling approach, namely the boosted regression tree model, was used to investigate this relationship. A multi-scale study was conducted at the community level based on buffers of 5-minute, 10-minute, and 15-minute walking distances. The main findings are as follows: (1) Relationships between built environment variables and COVID-19 case distribution vary across scales of analysis at the neighborhood level. (2) Significant non-linear associations exist between built-environment characteristics and COVID-19 case distribution at different scales. Population, housing price, normalized difference vegetation index, Shannon's diversity index, number of bus stops, floor-area ratio, and distance from the city center played important roles at different scales. These non-linear results provide a more refined reference for pandemic responses at different scales from an urban planning perspective and offer useful recommendations for a sustainable COVID-19 post-pandemic response.

COVID-19 incidence and cardiorespiratory fitness among first-year college students.

With the emergence of the COVID-19 virus, there was a widespread infection rate among college campuses, creating a need to understand the impact of COVID-19 infection on the health and wellbeing of adolescents. The aim of this study was to examine COVID-19 incidence and cardiorespiratory fitness (CRF) among undergraduate students in the 2 years post-COVID-19 pandemic lock-down. Participants (n = 151) included undergraduate college freshmen students during the 2021-2022 and 2022-2023 academic years. A series of annual surveys (n = 151) and laboratory measures among a sub-sample of participants (n = 28) were conducted to assess COVID-19 incidence, CRF, anthropometrics, and physical activity (PA). Over half of participants self-reported COVID-19 infection (59%), "good" or better CRF, a healthy body mass index (BMI), and 25% met PA recommendations. Nearly a quarter, (24%) perceived a negative impact of COVID-19 on their CRF and although not statistically significant, participants who contracted COVID-19 had 46% lower odds of having a positive perception of CRF than those who did not contract COVID-19 before. However, students who were more physically active were more likely to perceive a negative impact of COVID-19 incidence on their CRF (p = 0.035). Although no relationship was detected between COVID-19 and CRF, those who perceived a negative impact of COVID-19 on their CRF reported engaging in more physical activity.

Projections of anxiety disorder prevalence during and beyond the COVID-19 pandemic in Germany using the illness-death model.

Although there is now substantial evidence on the acute impacts of the COVID-19 pandemic on anxiety disorders, the long-term population impact of the pandemic remains largely unexplored. To quantify a possible longitudinal population-level impact of the pandemic by projecting the prevalence of anxiety disorders through 2030 among men and women aged up to 95 years in Germany under scenarios with varying impacts of the pandemic on the incidence of anxiety disorders. We used a three-state illness-death model and data from the Global Burden of Disease Study to model historical trends of the prevalence and incidence of anxiety disorders. The German population projections determined the initial values for projections. The COVID-19 incidence rate data informed an additional incidence model, which was parameterised with a wash-in period, delay, wash-out period, incidence increase level and decay constant. When no additional increase in the incidence during the pandemic waves during 2020-2022 was assumed, it was estimated that 3.86 million women (9.96%) and 2.13 million men (5.40%) would have anxiety disorders in 2030. When increases in incidence following pandemic waves were assumed, the most extreme scenario projected 5.67 million (14.02%) women and 3.30 million (8.14%) men with the mental disorder in 2030. Any increased incidence during the pandemic resulted in elevated prevalence over the projection period. Projection of anxiety disorder prevalence based on the illness-death model enables simulations with varying assumptions and provides insight for public health planning. These findings should be refined as trend data accumulate and become available.

The Mortality of Politics: An American Paradox.

The United States has the poorest health statistics of any high-income country. Political polarization has risen dramatically; newer safety net programs (the Affordable Care Act [ACA]) are unevenly provided because many Republican-leaning states refused expanded Federal coverage. Democratic programs have reduced physician leadership of medicine. Both have been deleterious. Here, we investigated associations among four key health measures two of which directly impact pregnancy outcomes and two that affect all patients by percentage of each state that voted for the Republican versus Democratic candidate in the 2020 presidential election. For each state, we used public, non-partisan databases to assess the incidence of COVID, maternal, and infant mortality per 100,000 population and average life expectancy. Correlations among these four outcome variables and percentage Republican vote were calculated (r), contextualized by measuring associations with related variables including COVID vaccination rates, access to medical care, and incidences of heart disease, obesity, diabetes, gunshot deaths, and automotive fatalities. COVID mortality, maternal and infant mortality, and life expectancy were highly correlated with percentage Republican ("red") vote per state. If "red" states had vaccination rates equivalent to Democratic-leaning ("blue") states, 72,000 deaths could have been avoided. Overall, "red" states have lower health metrics, reduced access to care, and higher comorbidities. The percent Republican vote was strongly associated, but not the whole answer, for worse health outcomes for multiple key measures of public health including mortality, access to care, and various comorbidities. Overall, the ACA has improved patient access to care but has also led to "maternity care deserts" disproportionately in rural areas in "red" states. Translating insurance coverage into improved care and outcomes requires further analysis and will require multi-pronged approaches including expanding coverage and incentivizing quality care.

Characteristics of Italian, German and Spanish Socio-Economic, Public Health and Long-Term Care Systems Associated with COVID-19 Incidence and Mortality in the First Pandemic Year: Lessons for Future Sustainability in an International Perspective

Background/Objectives: The main outcomes of the COVID-19 pandemic can be used to assess the capability and sustainability of public healthcare and Long-Term Care (LTC) systems. This study aims to identify the population’s demographic and socio-economic characteristics, as well as other national resources associated with the incidence and mortality of COVID-19, by comparing three European countries during the first pandemic period (Italy, Spain, and Germany). The results will identify possible strengths and weaknesses that could be considered as hints of the need for health and social intervention. Methods: Variables describing the countries’ core demographics, socio-economic characteristics, and national resources were collected from 2001–2021 from well-established international databases. COVID-19 incidence and death figures from 1 March 2020 to 31 March 2021 were extracted from national health databases. Analysis focused on bivariate and weighted multivariable linear regressions between incidence, mortality, and socio-economic covariates. Results: Findings show that both care models and socio-demographic characteristics influenced the capability of the first year’s response to the COVID-19 emergency. Formal public care appears to represent the most effective strategy against incidence and mortality regarding COVID-19, especially for older people, because it mitigates the adverse effects of socio-economic characteristics. Conclusions: Current strategies oriented towards privatizing care should, therefore, be considered critically, since they may result in weaker protection of vulnerable groups, such as frail older people, due to the unequal position of individuals with different socio-economic conditions in purchasing services from the care market.

Diagnosis from an Epidemiological Point of View. The Example of COVID-19

A diagnostic test is anything that provides data about the patient's health or disease. All such tests can be formally evaluated for their accuracy and precision (sensitivity and specificity). Once a test has been performed, whether positive or negative, sensitivity and specificity are not the priority issues; the physician's dilemma is whether or not the patient has the disease, once the test result is known. This is the positive and negative predictive value. However, the positive predictive value depends on the prevalence: when prevalence is high, the positive predictive value of the test increases and, as a consequence, there are fewer false positives and more false negatives. When prevalence is low, the opposite occurs: the positive predictive value of the test decreases, and there will be more false positives and fewer false negatives. Furthermore, diagnoses are generally not determined and labeled in isolation; rather, diagnoses are applied sequentially (Bayesian inference). Thus, prior knowledge facilitates rapid decision-making that is generally correct by increasing the pre-test diagnostic probability. When tests are performed in series (one after the other), specificity and positive predictive value are maximized, but sensitivity and negative predictive value are reduced. Thus, we have the example of COVID-19. For a low prevalence scenario (such as the current one), assuming a specificity of 98%, a positive test ensures the diagnosis in at least 2/3 of patients. And a negative result practically rules it out. When the incidence of COVID-19 is high. For example, with a prevalence of 30% or in practice when we are in some "COVID-19 wave", a positive result ensures the diagnosis in 94% but a negative result can occur in up to 10% of patients. On the other hand, a second test after the first gives a more reliable result if its results go in the same direction.

Preventive Measures and Handling of COVID-19 and Emerging Disease in the Border Areas of West Kalimantan Province Entikong District and Jagoi Babang District

One of the country's entry points is the National Border Post (PLBN), which is the main key in preventing the entry of the COVID-19 pandemic into Indonesia because many people come and go from various places so that the country's entrance in border areas becomes one of the places at risk of spread. COVID-19 virus such as in Entikong District and Jagoi Babang District. This research was conducted to determine the description of knowledge, attitudes, prevention efforts, health service facilities and health promotions in the Entikong and Jagoi Babang border areas, determine the influence of prevention efforts on the incidence of COVID-19, determine the influence of health service facilities and health promotions on the incidence of COVID-19 and find out the reality of implementing 3T. This research uses a mix method research design, namely a research step that combines two forms of research approaches, namely qualitative and quantitative. The sampling technique used purposive sampling with a total of 156 respondents who were divided into two groups, namely the case group and the control group. The following is a description of the research results, namely: 1) The case group in the Entikong border area has the highest presentation of low knowledge, poor attitude presentation, poor presentation of prevention efforts, and poor health facility presentation compared to the Jagoi Babang border area and the two regions combined, 2 ) There is a significant influence in efforts to prevent the incidence of COVID - 19, 3) There is a significant influence of health service facilities and health promotions in preventing the incidence of COVID - 19, 4) There are factors that encourage and hinder someone from carrying out a COVID - 19 test in the area borders, 5) There are obstacles in implementing the handling of COVID - 19 in border areas, and 6) There are problems in implementing handling of COVID - 19 in border areas..

Bayesian Nowcasting with Laplacian-P-Splines

During an epidemic, the daily number of reported infected cases, deaths or hospitalizations is often lower than the actual number due to reporting delays. Nowcasting aims to estimate the cases that have not yet been reported and combine it with the already reported cases to obtain an estimate of the daily cases. In this article, we present a fast and flexible Bayesian approach for nowcasting by combining P-splines and Laplace approximations. Laplacian-P-splines provide a flexible framework for nowcasting that is computationally less demanding as compared to traditional Markov chain Monte Carlo techniques. The proposed approach also permits to naturally quantify the prediction uncertainty. Model performance is assessed through simulations and the nowcasting method is applied to COVID-19 mortality and incidence cases in Belgium. Supplementary materials for this article are available online.

The Influence of Initial Immunosuppression on the Kinetics of Humoral Response after SARS-CoV-2 Vaccination in Patients Undergoing Kidney Transplantation.

Background: The effect of initial immunosuppressive therapy on the kinetics of the SARS-CoV-2 vaccine-induced humoral response is unknown. Here, we compared the kinetics of SARS-CoV-2 vaccine-induced humoral response in chronic kidney disease patients undergoing kidney transplantation (KTRs) and compared to patients remaining on dialysis during the Omicron circulation. Methods: This prospective, non-randomized, real-world study included 113 KTRs and 108 patients on dialysis. Those with previous COVID-19 or negative IgG at screening were excluded. Blood samples were collected to assess SARS-CoV-2 IgG titers and neutralizing antibodies at months (M) 1, 3, 6, and 12. Results: Seroreversion occurred in one KTR and in three patients on dialysis. KTRs had lower IgG titers over time (M1: 10,809.3 ± 12,621.7 vs. 15,267.8 ± 16,096.2 AU/mL; M3: 12,215.5 ± 12,885.8 vs. 15,016.2 ± 15,346.1 AU/mL; M6: 12,540.4 ± 13,010.7 vs. 18,503.5 ± 14,581.0 AU/mL; p = 0.005), but neutralizing antibodies were similar (M1: 94.0 vs. 90.3%; M3: 92.9 vs. 90.5%; M6: 99.0 vs. 95.5%; M12: 98.9 vs. 97.5%; p = 0.812). During follow-up, KTRs received more vaccines (141 vs. 73; p < 0.001) and contracted more COVID-19 (32.7% vs. 14.8%; p = 0.002). Conclusions: Compared to patients on dialysis, KTRs had lower SARS-CoV-2 IgG titers and similar rates of seroreversion and neutralizing antibodies over time. Although KTRs received more boosters, they had a higher incidence of COVID-19.