The Effects of Cranial Orientation on Forensic Frontal Sinus Identification as Assessed by Outline Analyses

The utility of frontal sinuses for personal identification is widely recognized, but potential factors affecting its reliability remain uncertain. Deviations in cranial position between antemortem and postmortem radiographs may affect sinus appearance. This study investigates how slight deviations in orientations affect sinus size and outline shape and potentially impact identification. Frontal sinus models were created from CT scans of 21 individuals and digitally oriented to represent three clinically relevant radiographic views. From each standard view, model orientations were deviated at 5° intervals in horizontal, vertical, and diagonal (e.g., left-up) directions (27 orientations per individual). For each orientation, sinus dimensions were obtained, and outline shape was assessed by elliptical Fourier analyses and principal component (PC) analyses. Wilcoxon sign rank tests indicated that sinus breadth remained relatively stable (p > 0.05), while sinus height was significantly affected with vertical deviations (p < 0.006). Mann–Whitney U tests on Euclidean distances from the PC scores indicated consistently lower intra- versus inter-individual distances (p < 0.05). Two of the three orientations maintained perfect (100%) outline identification matches, while the third had a 98% match rate. Smaller and/or discontinuous sinuses were most problematic, and although match rates are high, practitioners should be aware of possible alterations in sinus variables when conducting frontal sinus identifications.

Prescription Patterns and Relationship to Pharmacogenomics Testing in the Military Health System

ABSTRACT Introduction Clinical utilization of pharmacogenomics (PGx) testing is highly institutionally dependent, and little information is known about provider practices of PGx testing in the Military Health System (MHS). In this study, we aimed to characterize Clinical Pharmacogenetics Implementation Consortium (CPIC) actionable prescription (Rx) patterns and their temporal relationship with PGx testing in the MHS. Methods Using data from the Military Health System Management Analysis and Reporting Tool (M2) database, this retrospective cohort study included all patients receiving at least one PGx test and at least one CPIC actionable Rx from January 2015 to August 2020 (845 patients, 1,471 PGx, 7,725 index CPIC actionable Rxs). Rx patterns and temporal relationships with PGx testing were characterized via descriptive statistics. Binomial regression was used to determine which patient and provider characteristics were associated with a patient receiving a PGx test within 30 days of an index Rx. Results Patients had a median of 9 index CPIC actionable Rx’s (range 1–26). Pain medications were most commonly prescribed (N = 794, 94% patients with at least 1 Rx). However, pain medication had the lowest Rx–PGx match rate (40%) compared to an average of 62% Rx–PGx match rate for all CPIC drugs. Antidepressants were also commonly prescribed (N = 668, 79.1% patients with at least 1 Rx), and antidepressants had the highest Rx–PGx match rate of 86.7%. A minority of providers (20%, N = 249) ordered the majority of PGx tests (86.1%, N = 1,266) and only 8.3% of PGx tests (N = 398) matched to a CPIC actionable drug within 30 days of the test (defined by Rxs ordered within 30 days before or after the PGx test). However, approximately 39.8% of patients (N = 317) had at least one drug match to a PGx test within 30 days. The largest predictor of whether a patient received a PGx test within 30 days of any index Rx was whether or not a specific psychiatry provider ordered the PGx test (odds ratio; OR 3.7, 95% CI 2.13–6.54, P &lt; 0.001). Neither the CPIC level of evidence nor FDA PGx actionable or informative labels had a significant effect on PGx test timing. Conclusions PGx testing was generally limited to high Rx-drug users and was found to be an under-utilized resource. PGx testing did not typically follow CPIC guidelines. Implementing PGx testing protocols, simplifying PGx test-ordering by incorporating at minimum CYP2D6, CYP2C19, and CYP2C9 into PGx-testing panels, and unifying providers’ PGx knowledgebase in the MHS are feasible and would improve the clinical utilization of PGx tests in the MHS.

Evaluation of the ASSIGN open-source deterministic address-matching algorithm for allocating Unique Property Reference Numbers to general practitioner-recorded patient addresses

IntroductionLinking places to people is a core element of the UK government's geospatial strategy. Matching patient addresses in electronic health records to their Unique Property Reference Numbers (UPRNs) enables spatial linkage for research, innovation and public benefit. Available algorithms are not transparent or evaluated for use with addresses recorded by health care providers. ObjectivesTo describe and quality assure the open-source deterministic ASSIGN address-matching algorithm applied to general practitioner-recorded patient addresses. MethodsBest practice standards were used to report the ASSIGN algorithm match rate, sensitivity and positive predictive value using gold-standard datasets from London and Wales. We applied the ASSIGN algorithm to the recorded addresses of a sample of 1,757,018 patients registered with all general practices in north east London. We examined bias in match results for the study population using multivariable analyses to estimate the likelihood of an address-matched UPRN by demographic, registration, and organisational variables. ResultsWe found a 99.5% and 99.6% match rate with high sensitivity (0.999,0.998) and positive predictive value (0.996,0.998) for the Welsh and London gold standard datasets respectively, and a 98.6% match rate for the study population. The 1.4% of the study population without a UPRN match were more likely to have changed registered address in the last 12 months (match rate: 95.4%), be from a Chinese ethnic background (95.5%), or registered with a general practice using the SystmOne clinical record system (94.4%). Conversely, people registered for more than 6.5 years with their general practitioner were more likely to have a match (99.4%) than those with shorter registration durations. ConclusionsASSIGN is a highly accurate open-source address-matching algorithm with a high match rate and minimal biases when evaluated against a large sample of general practice-recorded patient addresses. ASSIGN has potential to be used in other address-based datasets including those with information relevant to the wider determinants of health.

Factors Influencing Rural Households’ Decision-Making Behavior on Residential Relocation: Willingness and Destination

All the traditional models of centralized residence based on “building a new socialist countryside” and “maintaining a balance between the increase and the decrease” are top-down in nature and require farmers to make responses and readjustment to all possible policies and changes. Therefore, it’s important to understand farmers’ preferences and take their willingness and needs into account when designing and implementing the relative planning programs of centralized residence. In this paper, with the numerical value 10 as the criterion of Events Per Variable (EPV) and Variance Inflation Factor (VIF), four different types of binary logistic regression were respectively applied to analyze factors that may influence farmer households’ relocation willingness and relocation destination in the following five aspects: Individual characteristics, household characteristics, housing characteristics, farmland characteristics, and implementation environment of centralized residence. As indicated in the results, people would show more willingness to relocate when they were younger, had higher household income, lived in an older building, possessed a bigger building area, owned farmland with higher quality, or lived in an environment with a higher infrastructure match rate. In addition, household income was a common factor influencing households’ choice between nearby relocation sites (NRS) and urban areas as their relocation destinations. The building area and occupancy rate negatively affected households’ choice of NRS, while building age negatively affected that of urban areas. Based on these influencing factors, some policy suggestions are proposed in this paper in terms of job creation, implementation of zoning and classification strategies, improvement of the quality of land transfer services, and reconstruction of the rural landscapes.

Mutability of Druggable Kinases and Pro-inflammatory Cytokines by Their Proximity to Telomeres and A+T Content

Mutations of protein kinases are common and can cause cancer and other diseases. However, our understanding of the mutability in genes encoding kinases remains rudimentary. Given previously proposed factors associated with high mutation rates, we analyzed how many genes encoding druggable kinases match with (i) proximity to telomeres or (ii) high A+T content. We extracted genomic information using National Institute of Health Genome Data Viewer. Among 129 druggable human kinase genes studied, 106 genes satisfied either factors (i) or (ii), resulting in an 82% match. A similar 85% match rate was found in 73 genes encoding pro-inflammatory cytokines of multisystem inflammatory syndrome in children. As we further compared these two factors in 20 de novo mutations of mice exposed to space-like ionizing radiation, however, 10 of 20 murine genetic loci met (i) or (ii), leading to only a 50% match. When compared with the top selling approved drugs, the data suggest that matching rate analysis on factor-druggable genome is feasible to systematically prioritize the relative mutability of the novel druggable candidates. These two factors not only predict how mutations of the disease phenotype are attributed to genetic and/or environmental factors but also sort out druggable proteins by their relative mutability.

Comparative analysis of alignment tools for application on Nanopore sequencing data

INTRODUCTION: Long-read sequencing techniques such as Oxford Nanopore sequencing, are representing a promising novel approach in molecular-biological methodology, enabling potential facilitation in mapping and de novo assembly. In comparison to conventional sequencing methods, novel alignment tools are mandated to compensate differing data structures (especially high error rate) to achieve acceptably accurate analysis results. METHODS: In this study, benchmarking for long read aligners BLASR, GraphMap, LAST, minimap2, NGMLR and the short-read aligner BWA MEM on three experimental datasets was conducted. Obtained alignment results were compared for various quality and performance criteria, such as match rate, mismatch rate, error rate, working memory usage and computational time. RESULTS: The comparison yielded differences in alignment quality and performance of tools under test. Tool LAST showed the largest differences among all tools. Minimap2 achieved constant quality with good performance. BLASR, GraphMap, BWA MEM and NGMLR showed slight differences only. CONCLUSION: Differences among the tools could be reasoned with dataset characteristics and algorithm approaches of individual tools. All tools except BLASR seem applicable for Nanopore sequencing data. Therefore, selection of the tool should be done under consideration of the experimental design and the further downstream analysis

15 2020 CaRMS Residency Match Confirms Popularity of Pediatrics

Primary Subject area Medical Education Background Longitudinal data about the interest in, and competitiveness of, pediatric postgraduate training in Canada has not been reported. Objectives 1. To describe the results of the 2020 CaRMS pediatric residency match with respect to application rates, first-choice discipline choices, and succesful match rates by gender. 2. To examine the trend of these indices over the past decade. Design/Methods Data from the 2020 Canadian Residency Matching Service (CaRMS) pediatric residency match was evaluated and compared over the past decade. Residency match data from other programs was also used for some comparison reporting. Results Of a total pool of 2998 Canadian medical graduate (CMG) applicants in 2020, 305 (10.2%) applied to pediatrics, and 17 of these latter applicants (5.6%) applied solely to pediatrics. In the first iteration CaRMS match, pediatrics was the first-choice discipline for 177 CMG applicants (6.0% of all first choices). Pediatrics has been consistent as a first-choice discipline over the years: 5.9% (2017), 5.5% (2015), and 6.1% (2013). Of the 155 first-year positions offered in pediatrics this year, all were filled. Of those CMGs who matched to pediatrics in 2020, the specialty was the first-choice discipline for 128 applicants (92.8%) and the second-choice discipline for 9 applicants (6.5%). There were clear gender differences noted. Pediatrics accounted for 8.3% of female and 3.2% of male first-choice disciplines. Of the 135 females whose first-choice discipline was pediatrics, 101 matched to that first choice (74.8%). Of the 41 males whose first-choice discipline was pediatrics, 26 matched to that first choice (63.4%). Since 1995 (at CaRMS’ inception), the rates of first-choice discipline choice by gender have been quite stable (Table 1), with females consistently higher than males, while the first-choice discipline matching rate by gender have varied (Figure 1). Forty CMG applicants whose first-choice discipline was pediatrics matched to an alternate discipline choice and nine went unmatched, suggesting that pediatrics continues to be a competitive discipline. The pediatric rate of first-choice discipline matching to another alternate choice of 22.6% (40/177) is comparable to Anesthesia (22.1%; 34/154), Ophthalmology (26.7%; 20/75), and Otolaryngology (20.9%; 9/43). Conclusion Pediatrics continues to be a top specialty choice for graduates of Canadian medical schools, according to data from the 2020 CaRMS match. There are gender differences noted in the choice of pediatrics as a first-choice discipline, and in the successful match rate to pediatrics programs. The rate of successful first-choice discipline matching by gender have varied over time, with the past two years showing significantly greater matching success for females. These trends in the CaRMS pediatric data have implications on discipline recruitment and the pediatric workforce in Canada, and merit further exploration.

1369Probabilistic data linkage of health facility and individual data using PMA2020 surveys

Background Most studies rely on clustered analyses to study how the characteristics of health facilities influence individual outcomes. Our aim was to perform a probabilistic linkage between individual and health facility data to enable individual-level analyses. Methods We linked data from the most recent female questionnaire from 11 countries monitored by the Performance Monitoring for Action 2020 to a master health facility dataset (appending all rounds of surveys). Only women that reported which type of facility they visited were considered in the analysis. A probabilistic linkage was performed using 13 blocking variables (e.g., facility type and cluster of residence/location of the woman/facility) and 11 matching variables (e.g., types of contraceptive methods used/offered by the women/facility). Each concordant matching variable received a + 1 score, or a 0 score otherwise. We assessed linkage quality by pooled odds ratio of non-matches according to wealth tertiles (richest vs. poorest) and area of residence (urban vs. rural) using a meta-analytical approach. Results A total of 21,102 women and 7,056 facilities were considered in the linkage process. The average match rate was 57.9%, ranging from 42.5% in Indonesia to 69.1% in Burkina Faso. The pooled odds of non-match were 74% higher for the richest women compared to the poorest, and 67% higher for women living in urban areas compared to rural areas. Conclusions High match rates were achieved in countries with sufficient information on public and private facilities. The lack of information about private facilities contributed to the higher odds of non-match among the better off. Key messages We performed a probabilistic linkage approach to link individual and health facility data, making it possible to understand how the characteristics of health facilities can influence individual-level outcomes. Our findings also bring light to the importance of sampling both public and private facilities, aiming to maximise match rates and reduce differences on match rates according to socio demographic characteristics of the sample.

Automated Linking of Historical Data

The recent digitization of complete count census data is an extraordinary opportunity for social scientists to create large longitudinal datasets by linking individuals from one census to another or from other sources to the census. We evaluate different automated methods for record linkage, performing a series of comparisons across methods and against hand linking. We have three main findings that lead us to conclude that automated methods perform well. First, a number of automated methods generate very low (less than 5 percent) false positive rates. The automated methods trace out a frontier illustrating the trade-off between the false positive rate and the (true) match rate. Relative to more conservative automated algorithms, humans tend to link more observations but at a cost of higher rates of false positives. Second, when human linkers and algorithms use the same linking variables, there is relatively little disagreement between them. Third, across a number of plausible analyses, coefficient estimates and parameters of interest are very similar when using linked samples based on each of the different automated methods. We provide code and Stata commands to implement the various automated methods. (JEL C81, C83, N01, N31, N32)