Beyond the waves: Unraveling pandemic outcomes with genomic insights and immunity analysis - Evidence from 14 countries.

OBJECTIVE: Although the World Health Organization and many governments have recategorized COVID-19 as a generally mild to moderately severe disease, consecutive pandemic waves driven by immune escape variants have underscored the need for timely and accurate prediction of the next outbreak. Nevertheless, little attention has been paid to translating genomic data and infection- and vaccine-induced immunity into direct estimates. METHODS: We retrieved epidemiologic and genomic data shortly before pandemic waves across 14 developed countries from late 2021 to mid-2022 and examined associations between early-stage variant competition, infection- and vaccine-induced immunity, and the time intervals between wave peaks. We applied regression analysis and the generalized estimating equation method to construct an inferential model. RESULTS: Each per cent increase in the proportion of a new variant was associated with a 1.0% reduction in interpeak intervals on average. Curvilinear associations between vaccine-induced immunity and outcome variables were observed, suggesting that reaching a critical vaccine distribution rate may decrease the caseload of the upcoming wave. CONCLUSIONS: By leveraging readily accessible pre-outbreak genomic and epidemiologic data, our results not only substantiate the predictive potential of early variant fractions but also propose that immunity acquired through infection alone may not sufficiently mitigate transmission. Conversely, a rapid and widespread vaccination initiative appears to be correlated with a decrease in disease incidence.

Power calculation for detecting interaction effect in cross-sectional stepped-wedge cluster randomized trials: an important tool for disparity research.

BACKGROUND: The stepped-wedge cluster randomized trial (SW-CRT) design has become popular in healthcare research. It is an appealing alternative to traditional cluster randomized trials (CRTs) since the burden of logistical issues and ethical problems can be reduced. Several approaches for sample size determination for the overall treatment effect in the SW-CRT have been proposed. However, in certain situations we are interested in examining the heterogeneity in treatment effect (HTE) between groups instead. This is equivalent to testing the interaction effect. An important example includes the aim to reduce racial disparities through healthcare delivery interventions, where the focus is the interaction between the intervention and race. Sample size determination and power calculation for detecting an interaction effect between the intervention status variable and a key covariate in the SW-CRT study has not been proposed yet for binary outcomes. METHODS: We utilize the generalized estimating equation (GEE) method for detecting the heterogeneity in treatment effect (HTE). The variance of the estimated interaction effect is approximated based on the GEE method for the marginal models. The power is calculated based on the two-sided Wald test. The Kauermann and Carroll (KC) and the Mancl and DeRouen (MD) methods along with GEE (GEE-KC and GEE-MD) are considered as bias-correction methods. RESULTS: Among three approaches, GEE has the largest simulated power and GEE-MD has the smallest simulated power. Given cluster size of 120, GEE has over 80% statistical power. When we have a balanced binary covariate (50%), simulated power increases compared to an unbalanced binary covariate (30%). With intermediate effect size of HTE, only cluster sizes of 100 and 120 have more than 80% power using GEE for both correlation structures. With large effect size of HTE, when cluster size is at least 60, all three approaches have more than 80% power. When we compare an increase in cluster size and increase in the number of clusters based on simulated power, the latter has a slight gain in power. When the cluster size changes from 20 to 40 with 20 clusters, power increases from 53.1% to 82.1% for GEE; 50.6% to 79.7% for GEE-KC; and 48.1% to 77.1% for GEE-MD. When the number of clusters changes from 20 to 40 with cluster size of 20, power increases from 53.1% to 82.1% for GEE; 50.6% to 81% for GEE-KC; and 48.1% to 79.8% for GEE-MD. CONCLUSIONS: We propose three approaches for cluster size determination given the number of clusters for detecting the interaction effect in SW-CRT. GEE and GEE-KC have reasonable operating characteristics for both intermediate and large effect size of HTE.

PrEP Uptake and Methamphetamine Use Patterns in a 4-Year U.S. National Prospective Cohort Study of Sexual and Gender Minority People, 2017-2022.

Methamphetamine use is on the rise among sexual and gender minority people who have sex with men (SGMSM), escalating their HIV risk. Despite pre-exposure prophylaxis (PrEP) being an effective biomedical HIV prevention tool, its uptake in relation to methamphetamine use patterns in SGMSM has not been studied. In a U.S. cohort study from 2017 to 2022, 6,253 HIV-negative SGMSM indicated for but not using PrEP were followed for four years. Methamphetamine use was categorized (i.e., newly initiated, persistently used, never used, used but quit), and PrEP uptake assessed using generalized estimating equation (GEE), adjusted for attrition. Participants had a median age of 29, with 51.9% White, 11.1% Black, 24.5% Latinx, and 12.5% other races/ethnicities. Over the four years, PrEP use increased from 16.3 to 27.2%. GEE models identified risk factors including housing instability and food insecurity. In contrast, older age, health insurance, clinical indications, and prior PrEP use increased uptake. Notably, Latinx participants were more likely to use PrEP than Whites. Regarding methamphetamine use, those who newly initiated it were more likely to use PrEP compared to non-users. However, those who quit methamphetamine and those who persistently used it had PrEP usage rates comparable to those of non-users. Though PrEP uptake increased, it remained low in SGMSM. Methamphetamine use was associated with PrEP uptake. Healthcare providers should assess methamphetamine use for harm reduction. Prioritizing younger, uninsured SGMSM and addressing basic needs can enhance PrEP uptake and reduce HIV vulnerabilities.

Effect of nutrition education integrating the health belief model and theory of planned behavior on dietary diversity of pregnant women in Southeast Ethiopia: a cluster randomized controlled trial.

BACKGROUND: Maternal anemia, miscarriage, low birth weight (LBW), preterm birth (PTB), intrauterine growth restriction (IUGR), prenatal and infant mortality, morbidity, and the risk of chronic disease later in life are all increased by a lack of dietary diversity during pregnancy. However, evidence for the effect of nutrition education on the dietary diversity score (DDS) among pregnant women was sparse in Ethiopia, particularly in the study areas. This study aimed to assess the effect of nutrition education on dietary diversity among pregnant women in urban settings in Southeast Ethiopia. METHODS: A community-based two-arm parallel cluster randomized controlled trial was conducted among 447 randomly selected pregnant women attending antenatal care (224 intervention group and 223 control group) at health facilities from February to December 2021. A multistage cluster sampling technique, followed by systematic sampling, was used to select the pregnant women. Pregnant women who participated in the interventions were given nutrition education starting at 16 weeks of gestation and continuing for 6 months. We used a pre-tested, interviewer-administered, structured questionnaire to collect the data. A 24-hour qualitative dietary recall was used to calculate the dietary diversity score (DDS). A multivariable generalized estimating equation (GEE) model was conducted to evaluate the intervention effect. RESULTS: After the intervention, the proportion of adequate dietary diversity was 14.15% higher in the intervention arm compared to the control group (45.09% versus 30.94%, P = 0.002). The overall difference in adequate dietary diversity between the two groups was 8.5%. After adjusting for background characteristics, the multivariable GEE binary logistic model revealed that having received intervention [(AOR = 1.89, 95% CI: 1.27, 2.79)], being literate [(AOR = 3.41, 95% CI: 1.13, 10.23)], and having high wealth [(AOR = 1.60, 95% CI: 1.09, 2.35)] significantly improved adequate dietary diversity. CONCLUSION: The findings indicated that having received the intervention, being literate, and having a high level of wealth significantly improved maternal dietary diversity. Efforts should be made to increase nutrition education using the health belief model (HBM) and the theory of planned behavior (TPB). Moreover, there is a need to improve literacy and economic empowerment through income-generating activities to enhance adequate dietary diversification during pregnancy. TRIAL REGISTRATION: Clinicaltrials.gov (PACTR202201731802989, Retrospectively registered on 24 January 2022).

Optimal ICSI timing on immature oocytes for low prognosis patients under the POSEIDON classification.

BACKGROUND: The optimal timing of performing ICSI on immature oocytes for POSEIDON patients is still unknown to get better early embryonic development outcomes. The purpose of this study was to implore the most appropriate time to carry out ICSI on in vitro maturation GV and MI oocytes for POSEIDON patients. METHODS: Two hundred thirty-nine immature oocytes from 163 POSEIDON patients were prospectively performed ICSI at different timings: P-ICSI (ICSI was performed on in vitro matured oocytes 4-6 h after the first polar body extrusion, N = 81), R-ICSI (ICSI was performed on in vitro matured oocytes less than 4 h after the first polar body extrusion, N = 80), and E-ICSI (ICSI was performed on in vitro matured oocytes the next day after oocytes retrieval, N = 78). Fertilization and embryonic development outcomes were collected and statistically analyzed. Mitochondria distribution of cytoplasm of in vitro matured oocytes with different time cultures after the first polar body (PB1) extrusion was stained. RESULTS: Compared to the E-ICSI group, more day 3 embryos from P-ICSI became blastocysts after sequential culture though without statistical significance (OR = 3.71, 95% CI: 0.94-14.63, P = 0.061). Compared to the E-ICSI group, more embryos from both P-ICSI and R-ICSI groups were clinically used with statistical significance (OR = 5.67, 95% CI: 2.24-14.35, P = 0.000 for P-ICSI embryos; OR = 3.23, 95% CI: 1.23-8.45, P = 0.017 for R-ICSI embryos). Compared to the E-ICSI group, transferred embryos from P-ICSI and R-ICSI had a higher implantation rate though without statistical significance (35.3% for P-ICSI embryos; 9.1% or R-ICSI embryos and 0% for E-ICSI embryos, P = 0.050). Among the three group, there were most healthy babies delivered from the P-ICSI group (5, 1 and 0 for P-ICSI, R-ICSI and E-ICSI respectively). The mitochondria in the cytoplasm of in vitro matured oocytes with a less than 4 h and 4-6 h culture after PB1 extrusion presented semiperipheral and diffused distribution patterns, respectively. CONCLUSIONS: Our results revealed P-ICSI (ICSI was performed on in vitro matured oocytes 4-6 h after the first polar body extrusion) provided the most efficient method to utilize the immaturation oocytes basing on embryos utilization and live birth outcome for low prognosis patients under the POSEIDON classification. The mitochondria distribution of the in vitro matured oocytes' cytoplasm from P-ICSI varied that from R-ICSI.

Assessing the use of GEE methods for analyzing binary outcomes in family studies: the Strong Heart Family Study.

The generalized estimating equations method (GEE) is commonly applied to analyze data obtained from family studies. GEE is well known for its robustness on misspecification of correlation structure. However, the unbalanced distribution of family sizes and complicated genetic relatedness structure within each family may challenge GEE performance. We focused our research on binary outcomes. To evaluate the performance of GEE, we conducted a series of simulations, on data generated adopting the kinship matrix (correlation structure within each family) from the Strong Heart Family Study (SHFS). We performed a fivefold cross-validation to further evaluate the GEE predictive power on data from the SHFS. A Bayesian modeling approach, with direct integration of the kinship matrix, was also included to contrast with GEE. Our simulation studies revealed that GEE performs well on a binary outcome from families having a relatively simple kinship structure. However, data with a binary outcome generated from families with complex kinship structures, especially with a large genetic variance, can challenge the performance of GEE.

High prevalence of zero-dose children in underserved and special setting populations in Ethiopia using a generalize estimating equation and concentration index analysis.

BACKGROUND: Globally, according to the World Health Organization (WHO) 2023 report, more than 14.3 million children in low- and middle-income countries, primarily in Africa and South-East Asia, are not receiving any vaccinations. Ethiopia is one of the top ten countries contributing to the global number of zero-dose children. OBJECTIVE: To estimate the prevalence of zero-dose children and associated factors in underserved populations of Ethiopia. METHODS: A cross-sectional vaccine coverage survey was conducted in June 2022. The study participants were mothers of children aged 12-35 months. Data were collected using the CommCare application system and later analysed using Stata version 17. Vaccination coverage was estimated using a weighted analysis approach. A generalized estimating equation model was fitted to determine the predictors of zero-dose children. An adjusted odds ratio (AOR) with 95% confidence interval (CI) and a p-value of 0.05 or less was considered statistically significant. RESULTS: The overall prevalence of zero-dose children in the study settings was 33.7% (95% CI: 34.9%, 75.7%). Developing and pastoralist regions, internally displaced peoples, newly formed regions, and conflict-affected areas had the highest prevalence of zero-dose children. Wealth index (poorest [AOR = 2.78; 95% CI: 1.70, 4.53], poorer [AOR = 1.96; 95% CI: 1.02, 3.77]), single marital status [AOR = 2.4; 95% CI: 1.7, 3.3], and maternal age (15-24 years) [AOR = 1.2; 95% CI: 1.1, 1.3] were identified as key determinant factors of zero-dose children in the study settings. Additional factors included fewer than four Antenatal care visits (ANC) [AOR = 1.3; 95% CI: 1.2, 1.4], not receiving Postnatal Care (PNC) services [AOR = 2.1; 95% CI: 1.5, 3.0], unavailability of health facilities within the village [AOR = 3.7; 95% CI: 2.6, 5.4], women-headed household [AOR = 1.3; 95% CI:1.02, 1.7], low gender empowerment [AOR = 1.6; 95% CI: 1.3, 2.1], and medium gender empowerment [AOR = 1.7; 95% CI: 1.2, 2.5]. CONCLUSION: In the study settings, the prevalence of zero-dose children is very high. Poor economic status, disempowerment of women, being unmarried, young maternal age, and underutilizing antenatal or post-natal services are the important predictors. Therefore, it is recommended to target tailored integrated and context-specific service delivery approach. Moreover, extend immunization sessions opening hours during the evening/weekend in the city administrations to meet parents' needs.

Weight stigma is a predictor of disordered eating in Brazilian college students during the COVID-19 pandemic: A 16-month cohort follow-up.

This study examined the relationship between weight stigma and disordered eating behaviors in university students during the COVID-19 pandemic, considering individuals with and without overweight. A national sample of 738 college students completed an online questionnaire at three time points between July 2020 and December 2021, reporting their experiences of weight stigma, perceived increase in weight stigma during the pandemic, internalized weight stigma, and disordered eating behaviors. The findings showed that the trajectories of disordered eating behaviors varied depending on participants' anthropometric status. Among those with overweight, there was a decrease over time in binge eating, food restriction, and purging. Conversely, the non-overweight group experienced a slight increase in binge eating from T2 to T3. Using the Generalized Estimating Equations model, the study revealed that weight stigma predicted disordered eating behaviors in both weight spectrums during the pandemic. Experiences of weight stigma and the perceived increase in weight stigma significantly increased the likelihood of engaging in binge eating, food restriction, and purging among individuals with overweight. Furthermore, the internalization of weight stigma was longitudinally associated with all examined eating behaviors, regardless of anthropometric status. These findings highlight the importance of addressing weight stigma to prevent disordered eating in college students.

Impact of Land Use and Land Cover (LULC) Changes on Carbon Stocks and Economic Implications in Calabria Using Google Earth Engine (GEE).

Terrestrial ecosystems play a crucial role in global carbon cycling by sequestering carbon from the atmosphere and storing it primarily in living biomass and soil. Monitoring terrestrial carbon stocks is essential for understanding the impacts of changes in land use on carbon sequestration. This study investigates the potential of remote sensing techniques and the Google Earth Engine to map and monitor changes in the forests of Calabria (Italy) over the past two decades. Using satellite-sourced Corine land cover datasets and the InVEST model, changes in Land Use Land Cover (LULC), and carbon concentrations are analyzed, providing insights into the carbon dynamics of the region. Furthermore, cellular automata and Markov chain techniques are used to simulate the future spatial and temporal dynamics of LULC. The results reveal notable fluctuations in LULC; specifically, settlement and bare land have expanded at the expense of forested and grassland areas. These land use and land cover changes significantly declined the overall carbon stocks in Calabria between 2000 and 2024, resulting in notable economic impacts. The region experienced periods of both decline and growth in carbon concentration, with overall losses resulting in economic impacts up to EUR 357.57 million and carbon losses equivalent to 6,558,069.68 Mg of CO 2 emissions during periods of decline. Conversely, during periods of carbon gain, the economic benefit reached EUR 41.26 million, with sequestered carbon equivalent to 756,919.47 Mg of CO 2 emissions. This research aims to highlight the critical role of satellite data in enhancing our understanding and development of comprehensive strategies for managing carbon stocks in terrestrial ecosystems.

Leveraging Google Earth Engine for a More Effective Grassland Management: A Decision Support Application Perspective.

Grasslands cover a substantial portion of the earth's surface and agricultural land and is crucial for human well-being and livestock farming. Ranchers and grassland management authorities face challenges in effectively controlling herders' grazing behavior and grassland utilization due to underdeveloped infrastructure and poor communication in pastoral areas. Cloud-based grazing management and decision support systems (DSS) are needed to address this issue, promote sustainable grassland use, and preserve their ecosystem services. These systems should enable rapid and large-scale grassland growth and utilization monitoring, providing a basis for decision-making in managing grazing and grassland areas. In this context, this study contributes to the objectives of the EU LIFE IMAGINE project, aiming to develop a Web-GIS app for conserving and monitoring Umbria's grasslands and promoting more informed decisions for more sustainable livestock management. The app, called "Praterie" and developed in Google Earth Engine, utilizes historical Sentinel-2 satellite data and harmonic modeling of the EVI (Enhanced Vegetation Index) to estimate vegetation growth curves and maturity periods for the forthcoming vegetation cycle. The app is updated in quasi-real time and enables users to visualize estimates for the upcoming vegetation cycle, including the maximum greenness, the days remaining to the subsequent maturity period, the accuracy of the harmonic models, and the grassland greenness status in the previous 10 days. Even though future additional developments can improve the informative value of the Praterie app, this platform can contribute to optimizing livestock management and biodiversity conservation by providing timely and accurate data about grassland status and growth curves.

Spatiotemporal Evolution and Spatial Analysis of Ecological Environmental Quality in the Longyangxia to Lijiaxia Basin in China Based on GEE.

The upper reaches of the Yellow River are critical ecological barriers within the Yellow River Basin (YRB) that are crucial for source conservation. However, environmental challenges in this area, from Longyangxia to Lijiaxia, have emerged in recent years. To assess the ecological environment quality (EEQ) evolution from 1991 to 2021, we utilized remote sensing ecological indices (RSEIs) on the Google Earth Engine (GEE) platform. Spatial autocorrelation and heterogeneity impacting EEQ changes were examined. The results of this study show that the mean value of the RSEIs fluctuated over time (1991: 0.70, 1996: 0.77, 2001: 0.67, 2006: 0.71, 2011: 0.68, 2016: 0.65, and 2021: 0.66) showing an upward, downward, and then upward trend. The mean values of the overall RSEI are all at 0.65 and above. Most regions showed no significant EEQ change during 1991-2021 (68.59%, 59.23%, and 55.78%, respectively). Global Moran's I values (1991-2021) ranged from 0.627 to 0.412, indicating significant positive correlation between EEQ and spatial clustering, and the LISA clustering map (1991-2021) shows that the area near Longyangxia Reservoir shows a pattern of aggregation, dispersion, and then aggregation again. The factor detection results showed that heat was the most influential factor, and the interaction detection results showed that greenness and heat had a significant effect on regional ecosystem distribution. Our study integrates spatial autocorrelation and spatial heterogeneity and combines them with reality to provide an in-depth discussion and analysis of the Longyangxia to Lijiaxia Basin. These findings offer guidance for ecological governance, vegetation restoration, monitoring, and safeguarding the upper Yellow River's ecological integrity.

A comprehensive research on open surface drinking water resources in Istanbul using remote sensing technologies.

Istanbul is a megacity with a population of 15.5 million and is one of the fastest-growing cities in Europe. Due to the rapidly increasing population and urbanization, Istanbul's daily water needs are constantly increasing. In this study, eight drinking water basins that supply water to Istanbul were comprehensively examined using remote sensing observations and techniques. Water surface area changes were determined monthly, and their relationships with meteorological parameters and climate change were investigated. Monthly water surface areas of natural lakes and dams were determined with the Normalized Difference Water Index (NDWI) applied to Sentinel-2 satellite images. Sentinel-1 Synthetic Aperture Radar (SAR) images were used in months when optical images were unavailable. The study was carried out using 3705 optical and 1167 SAR images on the Google Earth Engine (GEE) platform. Additionally, to determine which areas of water resources are shrinking, water frequency maps of the major drinking water resources were produced. Land use/land cover (LULC) changes that occurred over time were determined, and the effects of the increase in urbanization, especially on drinking water surface areas, were investigated. ESRI LULC data was used to determine LULC changes in watersheds, and the increase in urbanization areas from 2017 to 2022 ranged from 1 to 91.43%. While the basin with the least change was in Istranca, the highest increase in the artificial surface was determined to be in the Büyükçekmece basin with 1833.03 ha (2.89%). While there was a 1-12.35% decrease in the surface areas of seven water resources from 2016 to 2022, an increase of 2.65-93% was observed in three water resources (Büyükçekmece, Sazlidere, and Elmali), each in different categories depending on their size. In the overall analysis, total WSA decreased by 62.33 ha from 2016 to 2022, a percentage change of 0.70%. Besides the areal change analysis, the algae contents of the drinking water resources over the years were examined for the major water basins using the Normalized Difference Chlorophyll Index (NDCI) and revealed their relationship with meteorological factors and urbanization.

Assessing Tunisia's urban air quality using combined lichens and Sentinel-5 satellite integration.

In Tunisia, urban air pollution is becoming a bigger problem. This study used a combined strategy of biomonitoring with lichens and satellite mapping with Sentinel-5 satellite data processed in Google Earth Engine (GEE) to assess the air quality over metropolitan Tunis. Lichen diversity was surveyed across the green spaces of the Faculty of Science of Tunisia sites, revealing 15 species with a predominance of pollution-tolerant genera. The Index of Atmospheric Purity (IAP) calculated from the lichen data indicated poor air quality. Spatial patterns of pollutants sulfur dioxide (SO2), ozone (O3), nitrogen dioxide (NO2), carbon monoxide (CO), and aerosol index across Greater Tunis were analyzed from Sentinel-5 datasets on the GEE platform. The higher values of these indices in the research area indicate that it may be impacted by industrial activity and highlight the considerable role that vehicle traffic plays in air pollution. The results of the IAP, IBL, and the combined ground-based biomonitoring and satellite mapping techniques confirm poor air quality and an environment affected by atmospheric pollutants which will enable proactive air quality management strategies to be put in place in Tunisia's rapidly expanding cities.

CoCoA: conditional correlation models with association size.

Many scientific questions can be formulated as hypotheses about conditional correlations. For instance, in tests of cognitive and physical performance, the trade-off between speed and accuracy motivates study of the two variables together. A natural question is whether speed-accuracy coupling depends on other variables, such as sustained attention. Classical regression techniques, which posit models in terms of covariates and outcomes, are insufficient to investigate the effect of a third variable on the symmetric relationship between speed and accuracy. In response, we propose a conditional correlation model with association size, a likelihood-based statistical framework to estimate the conditional correlation between speed and accuracy as a function of additional variables. We propose novel measures of the association size, which are analogous to effect sizes on the correlation scale while adjusting for confound variables. In simulation studies, we compare likelihood-based estimators of conditional correlation to semiparametric estimators adapted from genomic studies and find that the former achieves lower bias and variance under both ideal settings and model assumption misspecification. Using neurocognitive data from the Philadelphia Neurodevelopmental Cohort, we demonstrate that greater sustained attention is associated with stronger speed-accuracy coupling in a complex reasoning task while controlling for age. By highlighting conditional correlations as the outcome of interest, our model provides complementary insights to traditional regression modeling and partitioned correlation analyses.

Marginal structural models for multilevel clustered data.

Marginal structural models (MSMs), which adopt inverse probability treatment weighting in the estimating equations, are powerful tools to estimate the causal effects of time-varying exposures in the presence of time-dependent confounders. Motivated by the Conservation of Hearing Study (CHEARS) Audiology Assessment Arm (AAA) where repeated hearing measurements were clustered by study participants, time, and testing sites, we propose two methods to account for the multilevel correlation structure when fitting the MSMs. The first method directly models the covariance of the repeated outcomes when solving the weighted generalized estimating equations for MSMs, while the second two-stage analysis approach fits cluster-specific MSMs first and then combines the estimated parameters using mixed-effects meta-analysis. Finite sample simulation results suggest that our methods can obtain less biased and more efficient estimates of the parameters by accounting for the multilevel correlation. Moreover, we explore the effects of using fixed- or mixed-effects model to estimate the treatment probability on the parameter estimates of the MSMs in the presence of unmeasured cluster-level confounders. Lastly, we apply our methods to the CHEARS AAA data set, to estimate the causal effects of aspirin use on hearing loss.

How to analyze continuous and discrete repeated measures in small-sample cross-over trials?

To optimize the use of data from a small number of subjects in rare disease trials, an at first sight advantageous design is the repeated measures cross-over design. However, it is unclear how these within-treatment period and within-subject clustered data are best analyzed in small-sample trials. In a real-data simulation study based upon a recent epidermolysis bullosa simplex trial using this design, we compare non-parametric marginal models, generalized pairwise comparison models, GEE-type models and parametric model averaging for both repeated binary and count data. The recommendation of which methodology to use in rare disease trials with a repeated measures cross-over design depends on the type of outcome and the number of time points the treatment has an effect on. The non-parametric marginal model testing the treatment-time-interaction effect is suitable for detecting between group differences in the shapes of the longitudinal profiles. For binary outcomes with the treatment effect on a single time point, the parametric model averaging method is recommended, while in the other cases the unmatched generalized pairwise comparison methodology is recommended. Both provide an easily interpretable effect size measure, and do not require exclusion of periods or subjects due to incompleteness.

MALDI Peptide Mapping for Fast Analysis in Protein Footprinting.

Although protein footprinting results are commonly obtained by ESI-based LC-MS/MS, a more rapid-turnaround alternative approach is desirable to expand the scope of protein footprinting and facilitate routine analysis such as monitoring protein high order structure in quality control or checking epitope maps. Considering that MALDI is a faster procedure that can be easily adapted for high-throughput analysis, we explore here the feasibility of developing a MALDI-based analysis "portfolio" of bottom-up peptide mass mapping for footprinting. The approach was applied to several model proteins that were submitted to two footprinting strategies, FPOP and GEE labeling, and their performance was evaluated. We found adequate coverage that can be improved with automatic off-line separation and spotting, demonstrating the capability to footprint accurately protein conformational change, showing that MALDI may be useful for selected applications in protein footprinting.

Independent-effect comparison of five crosslinking procedures for Progressive Keratoconus based on Keratometry and the ABCD Grading System using Generalized Estimating Equations (GEE).

PURPOSE: Corneal collagen crosslinking (CXL) is an effective treatment for progressive keratoconus. Multiple CXL modalities are clinically available. The present study compared the 1 year outcomes of five types of CXL procedures for progressive keratoconus in a Chinese population using generalized estimating equations (GEE). METHODS: This retrospective study included 239 eyes in 171 patients with keratoconus who underwent CXL and were followed up for 1 year. Five CXL procedures were assessed, including Accelerated Transepithelial CXL, Iontophoresis CXL for 10 min, CXL plus phototherapeutic keratectomy (CXL-plus-PTK), High-Fluence Accelerated CXL, and Accelerated CXL. Patients treated with the Accelerated CXL procedure represented the reference group. Primary outcomes were visual acuity change, spherical equivalence, endothelial cell density, mean keratometry (Kmean), maximum keratometry (Kmax), minimum corneal thickness (MCT), and the ABCD Grading System, consisting of A (staging index for ARC; ARC = anterior radius of curvature), B (staging index for PRC, PRC = posterior radius of curvature), and C (staging index for MCT) values 1 year postoperatively compared to baseline. Secondary outcomes were corrected GEE comparisons from each procedure versus the Accelerated CXL group. RESULTS: The Accelerated Transepithelial CXL group had lower performance than the Accelerated CXL group according to Kmean and Kmax. The CXL-plus-PTK group performed significantly better than the reference group as reflected by Kmax (ß = -0.935, P = 0.03). However, the CXL-plus-PTK group did not perform as well for B and C, and the Iontophoresis CXL group performed better for C. CONCLUSIONS: The CXL-plus-PTK procedure was more effective than the Accelerated CXL procedure based on Kmax, and the Iontophoresis CXL procedure performed better on the C value based on the ABCD Grading System.

Analysis and forecasting of air quality index based on satellite data.

OBJECTIVE: The air quality index (AQI) forecasts are one of the most important aspects of improving urban public health and enabling society to remain sustainable despite the effects of air pollution. Pollution control organizations deploy ground stations to collect information about air pollutants. Establishing a ground station all-around is not feasible due to the cost involved. As an alternative, satellite-captured data can be utilized for AQI assessment. This study explores the changes in AQI during various COVID-19 lockdowns in India utilizing satellite data. Furthermore, it addresses the effectiveness of state-of-the-art deep learning and statistical approaches for forecasting short-term AQI. MATERIALS AND METHODS: Google Earth Engine (GEE) has been utilized to capture the data for the study. The satellite data has been authenticated against ground station data utilizing the beta distribution test before being incorporated into the study. The AQI forecasting has been explored using state-of-the-art statistical and deep learning approaches like VAR, Holt-Winter, and LSTM variants (stacked, bi-directional, and vanilla). RESULTS: AQI ranged from 100 to 300, from moderately polluted to very poor during the study period. The maximum reduction was recorded during the complete lockdown period in the year 2020. Short-term AQI forecasting with Holt-Winter was more accurate than other models with the lowest MAPE scores. CONCLUSIONS: Based on our findings, air pollution is clearly a threat in the studied locations, and it is important for all stakeholders to work together to reduce it. The level of air pollutants dropped substantially during the different lockdowns.

Prediction of rehospitalization and mortality risks for skilled nursing facilities using a dimension reduction approach.

BACKGROUND: Hospitals are incentivized to reduce rehospitalization rates, creating an emphasis on skilled nursing facilities (SNFs) for post-hospital discharge. How rehospitalization rates vary depending on patient and SNF characteristics is not well understood, in part because these characteristics are high-dimensional. We sought to estimate rehospitalization and mortality risks by patient and skilled nursing facility (SNF) leveraging high-dimensional characteristics. METHODS: Using 1,060,337 discharges from 13,708 SNFs of Medicare patients residing or visiting a provider in Wisconsin, Iowa, and Illinois, factor analysis was performed to reduce the number of patient and SNF characteristics. K-means clustering was applied to SNF factors to categorize SNFs into groups. Rehospitalization and mortality risks within 60 days of discharge was estimated by SNF group for various values of patient factors. RESULTS: Patient and SNF characteristics (616 in total) were reduced to 12 patient factors and 4 SNF groups. Patient factors reflected broad conditions. SNF groups differed in beds and staff capacity, off-site services, and physical and occupational therapy capacity; and in mortality and rehospitalization rates for some patients. Patients with cardiac, orthopedic, and neuropsychiatric conditions are associated with better outcomes when assigned to SNFs with greater on-site capacity (i.e. beds, staff, physical and occupational therapy), whereas patients with conditions related to cancer or chronic renal failure are associated with better outcomes when assigned to SNFs with less on-site capacity. CONCLUSIONS: Risks of rehospitalization and mortality appear to vary significantly by patient and SNF, with certain SNFs being better suited for some patient conditions over others.