Trends in decision-making by primary care physicians regarding common infectious complaints

Primary care physicians played an important role in the global response during the COVID-19 pandemic, but with the absence of laboratory and diagnostics services, the move to telehealth and the focus on respiratory assessment, they faced increased uncertainty when making clinical decisions. This paper aims to examine the impact of the pandemic on decisions made by primary care physicians, as measured by referrals to chest X-ray and laboratory tests and by prescriptions of antibiotics. We conducted a retrospective study of all visits recorded with fever or cough, presenting to 209 community clinics in Southern Israel during the years 2018–2022. We describe changes in outcome rates across time and use multivariate generalised linear mixed effects model to compare the odds of referrals and prescriptions between periods, while accounting for gender, age, clinic sector, visit type, diagnosis, and season. In total, 609,823 visits to primary care physicians complied with the cohort definitions. Social restrictions were associated with a decline in all measured outcomes for primary care physician decisions, most prominently among ages 20-59, for throat culture referral during the first lockdown (OR = 0.46) and for cephalosporine prescription during the second lockdown (OR = 0.55). This trend persisted following the cancellation of the restrictions. Despite higher uncertainty during the COVID-19 social restrictions, the overall course of clinical decision-making processes was maintained, and was associated with a reduction in the use of auxiliary resources, which can improve the quality of patient care by lowering costs and supporting prevention of future antibiotics resistance.</p

Two-Stage Genome-Wide Search for Epistasis with Implementation to Recombinant Inbred Lines (RIL) Populations

<div><p>Objective and Methods</p><p>This paper proposes an inegrative two-stage genome-wide search for pairwise epistasis on expression quantitative trait loci (eQTL). The traits are clustered into multi-trait complexes that account for correlations between them that may result from common epistasis effects. The search is done by first screening for epistatic regions and then using dense markers within the identified regions, resulting in substantial reduction in the number of tests for epistasis. The FDR is controlled using a hierarchical procedure that accounts for the search structure. Each combination of trait and marker-pair is tested using a model that accounts for both statistical and functional interpretations of epistasis and considers orthogonal effects, such that their contributions to heritability can be estimated individually. We examine the impact of using multi-trait complexes rather than single traits, and of using a hierarchical search for epistasis rather than skipping the initial screen for epistatic regions. We apply the proposed algorithm on <i>Arabidopsis</i> transcription data.</p><p>Principal Findings</p><p>Both epistasis detection power and heritability contributed by epistasis increased when using multi-trait complexes rather than single traits. Epistatic effects common to the eQTLs included in the complexes have higher chance of being identified by analysis of multi-trait complexes, particularly when epistatic effects on individual traits are small. Compared to direct testing for all potential epistatic effects, the hierarchical search was substantially more powerful in detecting epistasis, while controlling the FDR at the desired level. Association in functional roles within genomic regions was observed, supporting an initial screen for epistatic QTLs.</p></div

Q–Q-plot of –log(p-values) in single-trait analysis, assuming exponential distribution under the null.

<p>The p-values are for significance of epistatic interaction in the NOIA model. As expected, the p-values corresponding the null cases (marked by diamonds) follow a straight line that represents their distribution under the null. The p-values corresponding the presumably weak epistatic effects on meta-traits (marked by squares) generate p-values that are distinctly lower than the null p-values. The p-values corresponding the presumably strong epistatic effects on single-traits (marked by triangles) generate the lowest p-values.</p

Power comparison between QTLBIM and the hierarchical testing approach.

<p>Simulation results. Standard error is <0.02 for power, <0.0007 for single traits and <0.0009 for meta-traits.</p><p>Power comparison between QTLBIM and the hierarchical testing approach.</p

Arabidopsis genome - detected epistasis effects.

<p>Arabidopsis genome - detected epistasis effects.</p

FDR comparison between QTLBIM and the hierarchical testing approach for meta-traits analysis – simulation results.

<p>FDR, estimated by the average proportion of erroneously identified epistatic effects among all identified epistatic effects, is plotted against the epistatic effect size. The solid lines mark the FDR obtained by the QTLBIM method, and the dashed lines mark the FDR obtained by the proposed hierarchical approach. Standard error was <0.025 for single traits and <0.003 for meta-traits.</p

A schematic description of the two-stage epistasis detection algorithm.

<p>Meta-traits with the corresponding traits are presented as ellipses with dots. Below them a partial genetic map of five <i>Arabidopsis</i> chromosomes is zoomed in. Filled circles and short vertical lines denote “framework” and “secondary” markers, respectively, and long vertical lines denote borders of sparse marker regions. The figure describes discovery of epistasis for the combination of meta-trait <i>g</i>, sparse marker pair and in the first step, and their “secondary” markers (in grey) in the second step.</p

Epistasis detection power – simulation results.

<p>Power, estimated by the proportion of epistatic effects detected by the procedure, is plotted against the epistatic effect size. Standard error was <0.003 for single traits and <0.025 for meta-traits. (a) Power levels are saturated for the multi-trait complexes analysis on models with main effects, and thus lower epistatic effects are used in Fig. 3b. (b) Reduced sizes of epistatic effects for the multi-trait complexes analysis, which generate non-saturated power levels, are examined on models with main effects. b. Epistasis detection power for the meta-traits analysis on models with main effects - simulation results. Power, estimated by the proportion of epistatic effects detected by the procedure, is plotted against the epistatic effect size. Standard error was <0.003 for single traits and <0.025 for meta-traits.</p