Joint spatio-temporal modelling of adverse pregnancy outcomes sharing common risk factors at sub-county level in Kenya, 2016–2019

Background Adverse pregnancy outcomes jointly account for a high proportion of mortality and morbidity among pregnant women and their infants. Furthermore, the burden attributed to adverse pregnancy outcomes remains high and inadequately characterised due to the intricate interplay of its etiology and shared set of important risk factors. This study sought to quantify and map the underlying risk of multiple adverse pregnancy outcomes in Kenya at sub-county level using a shared component space-time modelling framework. Methods Reported sub-county level adverse pregnancy outcomes count from January 2016 – December 2019 were obtained from the Kenyan District Health Information System. A Bayesian hierarchical spatio-temporal model was used to estimate the joint burden of adverse pregnancy outcomes in space (sub-county) and time (year). To improve the precision of our estimates over time and space, information across the outcomes were combined via the shared and the outcome-specific components using a shared component model with spatio-temporal interactions. Results Overall, the total number of adverse outcomes in pregnancy increased by 14.2% (95% UI: 14.0–14.5) from 88,816 cases in 2016 to 101,455 cases in 2019. Between 2016 and 2019, the estimated low birth weight rate and the pre-term birth rate were 4.5 (95% UI: 4.4–4.7) and 2.3 (95% UI: 2.2–2.5) per 100 live births. The stillbirth and neonatal death rates were estimated to be 18.7 (95% UI: 18.0–19.4) and 6.9 (95% UI: 6.4–7.4) per 1000 live births. The magnitude of the spatio-temporal variation attributed to shared risk was high for pre-term births, low birth weight, neonatal deaths, stillbirths and neonatal deaths, respectively. The shared risk patterns were dominant in sub-counties located along the Indian ocean coastline, central and western Kenya. Conclusions This study demonstrates the usefulness of a Bayesian joint spatio-temporal shared component model in exploiting specific and shared risk of adverse pregnancy outcomes sub-nationally. By identifying sub-counties with elevated risks and data gaps, our estimates not only assert the need for bolstering maternal health programs in the identified high-risk sub-counties but also provides a baseline against which to assess the progress towards the attainment of Sustainable Development Goals.

Shared components of heritability across genetically correlated traits

Most disease-associated genetic variants are pleiotropic, affecting multiple genetically correlated traits. Their pleiotropic associations can be mechanistically informative: if many variants have similar patterns of association, they may act via similar pleiotropic mechanisms, forming a shared component of heritability. We developed Pleiotropic Decomposition Regression (PDR) to identify shared components and their underlying genetic variants. We validated PDR on simulated data and identified limitations of existing methods in recovering the true components. We applied PDR to three clusters of 5-6 traits genetically correlated with coronary disease, asthma, and type II diabetes respectively, producing biologically interpretable components. For CAD, PDR identified components related to BMI, hypertension and cholesterol, and it clarified the relationship among these highly correlated risk factors. We assigned variants to components, calculated their posterior-mean effect sizes, and performed out-of-sample validation. Our posterior-mean effect sizes pool statistical power across traits and substantially boost the correlation (r2) between true and estimated effect sizes compared with the original summary statistics: by 94% and 70% for asthma and T2D out of sample, and by a predicted 300% for CAD.

Pediatric Intensive Care Hybrid-Style Clinical Round During COVID-19 Pandemic: A Pilot Study

Objectives: With the evolving COVID-19 pandemic and the emphasis on social distancing to decrease the spread of SARS-CoV-2 among healthcare workers (HCWs), our pediatric intensive care unit (PICU) piloted the integration of Zoom meetings into clinical rounds. We aimed to explore the feasibility of these hybrid virtual and physical clinical rounds for PICU patients.Design: Mixed quantitative and qualitative deductive thematic content analysis of narrative responses.Setting: PICU, single tertiary-care academic center.Participants: Multidisciplinary PICU HCWs.Interventions: Integration of Zoom meeting into clinical daily PICU rounds.Measurements: For the quantitative part, we gathered the details of daily PICU hybrid rounds in terms of times, number of HCWs, and type of files shared through Zoom. For the qualitative part, open-ended questions were used.Main Results: The physical round took statistically significantly less time (34.68 ± 14.842 min) as compared with the Zoom round (72.45 ± 22.59 min), p < 0.001. The most shared component in the virtual round was chest X-rays (93.5%). Thirty-one HCWs participated in focus group discussions and were included in the analysis. Some of the HCWs' perceived advantages of the hybrid rounds were enabling multidisciplinary discussions, fewer round interruptions, and practicality of virtual discussions. The perceived challenges were the difficulty of the bedside nurse attending the virtual round, decreased teaching opportunities for the trainees, and decreased interactions among the team members, especially if video streaming was not utilized.Conclusions: Multidisciplinary hybrid virtual and physical clinical rounds in the PICU were perceived as feasible by HCWs. The virtual rounds decreased the physical contact between the HCWs, which could decrease the possibility of SARS-CoV-2 spread among the treating team. Still, several components of the hybrid round should be optimized to facilitate the virtual team-members' interactions and enhance the teaching experience.

Dual Use of E-Cigarettes with Conventional Tobacco is Associated with Increased Sleep Latency

RationaleHealth effects of e-cigarettes remain relatively unknown, including impact on sleep quality. We previously showed in a pilot study that females who both smoke conventional tobacco and vape e-cigarettes (dual users) had decreased sleep quality and more difficulty falling asleep, suggesting an effect of gender. We undertook this study in a larger cohort to assess the impact of e-cigarette, conventional tobacco, and dual use on sleep quality, cough, and drug use.MethodsParticipants (n = 1198) were recruited through online surveys posted to social media sites with a monetary incentive. Participants were grouped by inhalant use, with 8% e-cigarette users, 12% conventional tobacco users, 30% dual users, and 51% non-smokers/non-vapers.ResultsDual use of e-cigarettes and conventional tobacco was associated with increased sleep latency relative to non-smokers/non-vapers (p = 0.012). Dual use was also associated with a higher reporting of cough (p = 0.034), as well as increased marijuana (p < 0.001) and cocaine usage (p < 0.001).DiscussionDual use is associated with longer sleep latency, suggesting that the shared component nicotine may be a driver. Because sleep broadly impacts multiple aspects of human health, defining the effects of e-cigarettes and vaping devices on sleep is critical to further our understanding of the effects of vaping on health.

Pediatric intensive care hybrid-style clinical round during COVID-19 pandemic: A pilot study

Background: With the evolving COVID-19 pandemic and the emphasis on social distancing to decrease the spread of SARS-CoV-2 among healthcare workers (HCWs), our pediatric intensive care unit (PICU) piloted utilization of Zoom online into the clinical rounds to enhance communication among the treating team. We aimed to explore the feasibility of these hybrid virtual and physical clinical rounds for PICU patients from the HCWs' perspective. Methods: A mixed quantitative and qualitative deductive thematic content analysis of narrative responses from pediatric intensive care HCWs were analyzed, descriptive statistics were used Results: A total of 31 HCW were included in the analysis; the mean time of the virtual round was 72.45 minutes vs. 34.68 for physical rounds, the most shared component in the virtual round was CXR (93.5%). Some of the HCWs' perceived advantages of the hybrid rounds were enabling the multidisciplinary discussions, lesser round interruptions, and practicality of the virtual discussions. The perceived challenges were the difficulty of the bedside nurse to attend the virtual round, decreased teaching opportunities for the trainees, and decreased interactions among the team members, especially if the video streaming was not utilized. Conclusion: Hybrid virtual and physical clinical rounds in PICU were perceived as feasible by HCWs. The virtual rounds decreased the physical contact between the HCWs, which could decrease the possibility of SARS-CoV-2 spread among the treating team. Still, several components of the hybrid round could be optimized to facilitate the virtual team-members' interactions and enhance the teaching experience.

The Spatial Overlap of Police Calls Reporting Street-Level and Behind-Closed-Doors Crime: A Bayesian Modeling Approach

Traditionally, intimate-partner violence has been considered a special type of crime that occurs behind closed doors, with different characteristics from street-level crime. The aim of this study is to analyze the spatial overlap of police calls reporting street-level and behind-closed-doors crime. We analyzed geocoded police calls in the 552 census-block groups of the city of Valencia, Spain, related to street-level crime (N = 26,624) and to intimate-partner violence against women (N = 11,673). A Bayesian joint model was run to analyze the spatial overlap. In addition, two Bayesian hierarchical models controlled for different neighborhood characteristics to analyze the relative risks. Results showed that 66.5% of the total between-area variation in risk of reporting street-level crime was captured by a shared spatial component, while for reporting IPVAW the shared component was 91.1%. The log relative risks showed a correlation of 0.53, with 73.6% of the census-block groups having either low or high values in both outcomes, and 26.4% of the areas with mismatched risks. Maps of the shared component and the relative risks are shown to detect spatial differences. These results suggest that although there are some spatial differences between police calls reporting street-level and behind-closed-doors crime, there is also a shared distribution that should be considered to inform better-targeted police interventions.

Bivariate Joint Spatial Modeling to Identify Shared Risk Patterns of Hypertension and Diabetes in South Africa: Evidence from WHO SAGE South Africa Wave 2

Recent studies have suggested the common co-occurrence of hypertension and diabetes in South Africa. Given that hypertension and diabetes are known to share common socio-demographic, anthropometric and lifestyle risk factors, the aim of this study was to jointly model the shared and disease-specific geographical variation of hypertension and diabetes. The current analysis used the Study on Global Ageing and Adult Health (SAGE) South Africa Wave 2 (2014/15) data collected from 2761 participants. Of the 2761 adults (median age = 56 years), 641 (23.2%) had high blood pressure on measurement and 338 (12.3%) reported being diagnosed with diabetes. The shared component has distinct spatial patterns with higher values of odds in the eastern districts of Kwa-Zulu Natal and central Gauteng province. The shared component may represent unmeasured health behavior characteristics or the social determinants of health in our population. Our study further showed how a shared component (latent and unmeasured health behavior characteristics or the social determinants of health) is distributed across South Africa among the older adult population. Further research using similar shared joint models may focus on extending these models for multiple diseases with ecological factors and also incorporating sampling weights in the spatial analyses.

Shared Component Analysis

This paper proposes Shared Component Analysis (SCA) as an alternative to Principal Component Analysis (PCA) for the purpose of dimensionality reduction of neuroimaging data. The trend towards larger numbers of recording sensors, pixels or voxels leads to richer data, with finer spatial resolution, but it also inflates the cost of storage and computation and the risk of overfitting. PCA can be used to select a subset of orthogonal components that explain a large fraction of variance in the data. This implicitly equates variance with relevance, and for neuroimaging data such as electroencephalography (EEG) or magnetoencephalography (MEG) that assumption may be inappropriate if (latent) sources of interest are weak relative to competing sources. SCA instead assumes that components that contribute to observable signals on multiple sensors are of likely interest, as may be the case for deep sources within the brain as a result of current spread. In SCA, steps of normalization and PCA are applied iteratively, linearly transforming the data such that components more widely shared across channels appear first in the component series. The paper explains the motivation, defines the algorithm, evaluates the outcome, and sketches a wider strategy for dimensionality reduction of which this algorithm is an example. SCA is intended as a plug-in replacement for PCA for the purpose of dimensionality reduction.