Numbers of close contacts of individuals infected with SARS-CoV-2 and their association with government intervention strategies

Background

Contact tracing is conducted with the primary purpose of interrupting transmission from individuals who are likely to be infectious to others. Secondary analyses of data on the numbers of close contacts of confirmed cases could also: provide an early signal of increases in contact patterns that might precede larger than expected case numbers; evaluate the impact of government interventions on the number of contacts of confirmed cases; or provide data information on contact rates between age cohorts for the purpose of epidemiological modelling. We analysed data from 140,204 close contacts of 39,861 cases in Ireland from 1st May to 1st December 2020.

Results

Negative binomial regression models highlighted greater numbers of contacts within specific population demographics, after correcting for temporal associations. Separate segmented regression models of the number of cases over time and the average number of contacts per case indicated that a breakpoint indicating a rapid decrease in the number of contacts per case in October 2020 preceded a breakpoint indicating a reduction in the number of cases by 11 days.

Conclusions

We found that the number of contacts per infected case was overdispersed, the mean varied considerable over time and was temporally associated with government interventions. Analysis of the reported number of contacts per individual in contact tracing data may be a useful early indicator of changes in behaviour in response to, or indeed despite, government restrictions. This study provides useful information for triangulating assumptions regarding the contact mixing rates between different age cohorts for epidemiological modelling.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-12318-y.

Application of machine-learning methods to milk mid-infrared spectra for discrimination of cow milk from pasture or total mixed ration diets

The prevalence of "grass-fed" labeled food products on the market has increased in recent years, often commanding a premium price. To date, the majority of methods used for the authentication of grass-fed source products are driven by auditing and inspection of farm records. As such, the ability to verify grass-fed source claims to ensure consumer confidence will be important in the future. Mid-infrared (MIR) spectroscopy is widely used in the dairy industry as a rapid method for the routine monitoring of individual herd milk composition and quality. Further harnessing the data from individual spectra offers a promising and readily implementable strategy to authenticate the milk source at both farm and processor levels. Herein, a comprehensive comparison of the robustness, specificity, and accuracy of 11 machine-learning statistical analysis methods were tested for the discrimination of grass-fed versus non-grass-fed milks based on the MIR spectra of 4,320 milk samples collected from cows on pasture or indoor total mixed ration-based feeding systems over a 3-yr period. Linear discriminant analysis and partial least squares discriminant analysis (PLS-DA) were demonstrated to offer the greatest level of accuracy for the prediction of cow diet from MIR spectra. Parsimonious strategies for the selection of the most discriminating wavelengths within the spectra are also highlighted.

Integrating Serum Biomarkers into Prediction Models for Biochemical Recurrence Following Radical Prostatectomy

This study undertook to predict biochemical recurrence (BCR) in prostate cancer patients after radical prostatectomy using serum biomarkers and clinical features. Three radical prostatectomy cohorts were used to build and validate a model of clinical variables and serum biomarkers to predict BCR. The Cox proportional hazard model with stepwise selection technique was used to develop the model. Model evaluation was quantified by the AUC, calibration, and decision curve analysis. Cross-validation techniques were used to prevent overfitting in the Irish training cohort, and the Austrian and Norwegian independent cohorts were used as validation cohorts. The integration of serum biomarkers with the clinical variables (AUC = 0.695) improved significantly the predictive ability of BCR compared to the clinical variables (AUC = 0.604) or biomarkers alone (AUC = 0.573). This model was well calibrated and demonstrated a significant improvement in the predictive ability in the Austrian and Norwegian validation cohorts (AUC of 0.724 and 0.606), compared to the clinical model (AUC of 0.665 and 0.511). This study shows that the pre-operative biomarker PEDF can improve the accuracy of the clinical factors to predict BCR. This model can be employed prior to treatment and could improve clinical decision making, impacting on patients' outcomes and quality of life.

Predicting cow milk quality traits from routinely available milk spectra using statistical machine learning methods

Numerous statistical machine learning methods suitable for application to highly correlated features, as those that exist for spectral data, could potentially improve prediction performance over the commonly used partial least squares approach. Milk samples from 622 individual cows with known detailed protein composition and technological trait data accompanied by mid-infrared spectra were available to assess the predictive ability of different regression and classification algorithms. The regression-based approaches were partial least squares regression (PLSR), ridge regression (RR), least absolute shrinkage and selection operator (LASSO), elastic net, principal component regression, projection pursuit regression, spike and slab regression, random forests, boosting decision trees, neural networks (NN), and a post-hoc approach of model averaging (MA). Several classification methods (i.e., partial least squares discriminant analysis (PLSDA), random forests, boosting decision trees, and support vector machines (SVM)) were also used after stratifying the traits of interest into categories. In the regression analyses, MA was the best prediction method for 6 of the 14 traits investigated [curd firmness at 60 min, α_S1-casein (CN), α_S2-CN, κ-CN, α-lactalbumin, and β-lactoglobulin B], whereas NN and RR were the best algorithms for 3 traits each (rennet coagulation time, curd-firming time, and heat stability, and curd firmness at 30 min, β-CN, and β-lactoglobulin A, respectively), PLSR was best for pH, and LASSO was best for CN micelle size. When traits were divided into 2 classes, SVM had the greatest accuracy for the majority of the traits investigated. Although the well-established PLSR-based method performed competitively, the application of statistical machine learning methods for regression analyses reduced the root mean square error compared with PLSR from between 0.18% (κ-CN) to 3.67% (heat stability). The use of modern statistical machine learning methods for trait prediction from mid-infrared spectroscopy may improve the prediction accuracy for some traits.

Glycosylation in Indolent, Significant and Aggressive Prostate Cancer by Automated High-Throughput N-Glycan Profiling

The diagnosis and treatment of prostate cancer (PCa) is a major health-care concern worldwide. This cancer can manifest itself in many distinct forms and the transition from clinically indolent PCa to the more invasive aggressive form remains poorly understood. It is now universally accepted that glycan expression patterns change with the cellular modifications that accompany the onset of tumorigenesis. The aim of this study was to investigate if differential glycosylation patterns could distinguish between indolent, significant, and aggressive PCa. Whole serum N-glycan profiling was carried out on 117 prostate cancer patients’ serum using our automated, high-throughput analysis platform for glycan-profiling which utilizes ultra-performance liquid chromatography (UPLC) to obtain high resolution separation of N-linked glycans released from the serum glycoproteins. We observed increases in hybrid, oligomannose, and biantennary digalactosylated monosialylated glycans (M5A1G1S1, M8, and A2G2S1), bisecting glycans (A2B, A2(6)BG1) and monoantennary glycans (A1), and decreases in triantennary trigalactosylated trisialylated glycans with and without core fucose (A3G3S3 and FA3G3S3) with PCa progression from indolent through significant and aggressive disease. These changes give us an insight into the disease pathogenesis and identify potential biomarkers for monitoring the PCa progression, however these need further confirmation studies.

A risk calculator to inform the need for a prostate biopsy: a rapid access clinic cohort

BACKGROUND

Prostate cancer (PCa) represents a significant healthcare problem. The critical clinical question is the need for a biopsy. Accurate risk stratification of patients before a biopsy can allow for individualised risk stratification thus improving clinical decision making. This study aims to build a risk calculator to inform the need for a prostate biopsy.

METHODS

Using the clinical information of 4801 patients an Irish Prostate Cancer Risk Calculator (IPRC) for diagnosis of PCa and high grade (Gleason ≥7) was created using a binary regression model including age, digital rectal examination, family history of PCa, negative prior biopsy and Prostate-specific antigen (PSA) level as risk factors. The discrimination ability of the risk calculator is internally validated using cross validation to reduce overfitting, and its performance compared with PSA and the American risk calculator (PCPT), Prostate Biopsy Collaborative Group (PBCG) and European risk calculator (ERSPC) using various performance outcome summaries. In a subgroup of 2970 patients, prostate volume was included. Separate risk calculators including the prostate volume (IPRCv) for the diagnosis of PCa (and high-grade PCa) was created.

RESULTS

IPRC area under the curve (AUC) for the prediction of PCa and high-grade PCa was 0.6741 (95% CI, 0.6591 to 0.6890) and 0.7214 (95% CI, 0.7018 to 0.7409) respectively. This significantly outperforms the predictive ability of cancer detection for PSA (0.5948), PCPT (0.6304), PBCG (0.6528) and ERSPC (0.6502) risk calculators; and also, for detecting high-grade cancer for PSA (0.6623) and PCPT (0.6804) but there was no significant improvement for PBCG (0.7185) and ERSPC (0.7140). The inclusion of prostate volume into the risk calculator significantly improved the AUC for cancer detection (AUC = 0.7298; 95% CI, 0.7119 to 0.7478), but not for high-grade cancer (AUC = 0.7256; 95% CI, 0.7017 to 0.7495). The risk calculator also demonstrated an increased net benefit on decision curve analysis.

CONCLUSION

The risk calculator developed has advantages over prior risk stratification of prostate cancer patients before the biopsy. It will reduce the number of men requiring a biopsy and their exposure to its side effects. The interactive tools developed are beneficial to translate the risk calculator into practice and allows for clarity in the clinical recommendations.

mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models

Finite mixture models are being used increasingly to model a wide variety of random phenomena for clustering, classification and density estimation. mclust is a powerful and popular package which allows modelling of data as a Gaussian finite mixture with different covariance structures and different numbers of mixture components, for a variety of purposes of analysis. Recently, version 5 of the package has been made available on CRAN. This updated version adds new covariance structures, dimension reduction capabilities for visualisation, model selection criteria, initialisation strategies for the EM algorithm, and bootstrap-based inference, making it a full-featured R package for data analysis via finite mixture modelling.

European Randomised Study of Screening for Prostate Cancer (ERSPC) risk calculators significantly outperform the Prostate Cancer Prevention Trial (PCPT) 2.0 in the prediction of prostate cancer: a multi-institutional study

OBJECTIVE

To analyse the performance of the Prostate Cancer Prevention Trial Risk Calculator (PCPT-RC) and two iterations of the European Randomised Study of Screening for Prostate Cancer (ERSPC) Risk Calculator, one of which incorporates prostate volume (ERSPC-RC) and the other of which incorporates prostate volume and the prostate health index (PHI) in a referral population (ERSPC-PHI).

PATIENTS AND METHODS

The risk of prostate cancer (PCa) and significant PCa (Gleason score ≥7) in 2001 patients from six tertiary referral centres was calculated according to the PCPT-RC and ERSPC-RC formulae. The calculators' predictions were analysed using the area under the receiver-operating characteristic curve (AUC), calibration plots, Hosmer-Lemeshow test for goodness of fit and decision-curve analysis. In a subset of 222 patients for whom the PHI score was available, each patient's risk was calculated as per the ERSPC-RC and ERSPC-PHI risk calculators.

RESULTS

The ERSPC-RC outperformed the PCPT-RC in the prediction of PCa, with an AUC of 0.71 compared with 0.64, and also outperformed the PCPT-RC in the prediction of significant PCa (P<0.001), with an AUC of 0.74 compared with 0.69. The ERSPC-RC was found to have improved calibration in this cohort and was associated with a greater net benefit on decision-curve analysis for both PCa and significant PCa. The performance of the ERSPC-RC was further improved through the addition of the PHI score in a subset of 222 patients. The AUCs of the ERSPC-PHI were 0.76 and 0.78 for PCa and significant PCa prediction, respectively, in comparison with AUC values of 0.72 in the prediction of both PCa and significant PCa for the ERSPC-RC (P = 0.12 and P = 0.04, respectively). The ERSPC-PHI risk calculator was well calibrated in this cohort and had an increase in net benefit over that of the ERSPC-RC.

CONCLUSIONS

The performance of the risk calculators in the present cohort shows that the ERSPC-RC is a superior tool in the prediction of PCa; however the performance of the ERSPC-RC in this population does not yet warrant its use in clinical practice. The incorporation of the PHI score into the ERSPC-PHI risk calculator allowed each patient's risk to be more accurately quantified. Individual patient risk calculation using the ERSPC-PHI risk calculator can be undertaken in order to allow a systematic approach to patient risk stratification and to aid in the diagnosis of PCa.

The identification and internal validation of a preoperative serum biomarker panel to determine extracapsular extension in patients with prostate cancer

BACKGROUND

Accurate preoperative staging of prostate cancer (PCa) is important but current diagnostic methods cannot accurately determine extracapsular extension (ECE), resulting in the possible triage of patients towards a less appropriate arm of therapy. This has consequences to patient care and better methods of preoperatively determining ECE are required.

METHODS

We followed a biomarker development pathway and compared the preoperative serum expressions of VEGF-D, PEDF, IGF-I, IGFBP3, and CD14 in patients from the Irish Prostate Cancer Research Consortium (PCRC) with radical prostatectomy determined ECE against patients with nonECE.

RESULTS

The expression measurements of five proteins were fitted into a logistic regression model and backwards variable elimination methods were applied which resulted in a model with IGFBP3 and CD14 as the best combination biomarker panel. This panel was tested in an independent cohort of patients using an optimized multiplex electrochemiluminescence assay. Receiver operating characteristic curves were generated and the areas under the curve (AUC) were calculated as an estimation of prediction accuracy. The biomarker panel was validated with an AUC of 76.6%, and a sensitivity and specificity of 80% and 75% was obtained.

CONCLUSIONS

This is the first internally validated, preoperative serum biomarker panel that identifies ECE in patients with Gleason score 7 PCa with AUC 76.6%. The panel surpasses the routinely used diagnostic standards in accuracy and may help to improve preoperative cancer staging, better inform treatment options, and improve the referral patterns of patients with urgently treatable cancers towards more appropriate arms of therapy.

Treating chronic food refusal in young children: home-based parent training

We evaluated the effects of behavioral parent training program on parent and child feeding-related behaviors in the home. We trained mothers to initiate regular offerings of previously rejected (target) foods and to provide contingent attention (i.e., specific prompts, positive reinforcement) to increase their child's acceptance of nonpreferred foods. For 1 subject, we also directed training at increasing self-eating. Results of a nonconcurrent multiple baseline design across 3 mother-child dyads demonstrated that, with training, all mothers increased offerings of target foods and use of specific prompts, and 2 mothers increased levels of positive attention. In turn, children increased their acceptance of target foods and self-eating, thus demonstrating the functional effects of parent training on in-home meal times. Temporary increases in food refusals occurred when treatment was initiated but declined as treatment continued. We discuss the results in terms of the potential benefits and limitations of a home-based treatment model.

Psychosocial concomitants of children's feeding disorders

Compared behavior problems, child developmental skills, home environment, and parent emotional distress for 50 families of children aged 11-70 months with differing etiologies of feeding disorders (FD). Results showed that psychosocial functioning differed across FD classifications. Children with nonorganic characteristics to FD had more behavior problems compared to those with only organic FD. Children with primarily or only organic FD displayed lower developmental skills and their parents had higher emotional distress than children with primarily nonorganic FD. High parent distress was associated with older children who had poor feeding skills, less positive disciplinary practices, and higher social status. The findings imply that clinical services to families often are warranted and that service needs vary depending on the nature of FD. Mixed organic and nonorganic FD occurred in the majority of children, which supports the need for more detailed classification than an organic-nonorganic dichotomy used in prior research.

An in vivo evaluation of iron-chelating drugs derived from pyridoxal and its analogs

Chelating agents related to the newly described iron binding drug, pyridoxal isonicotinoyl hydrazone, were screened for their effects on iron excretion in the rat, employing a new and highly sensitive radioisotopic procedure. Pyridoxal itself induced significant iron excretion when given either parenterally or orally, the excreted iron being derived from the same pool as that tapped by the drug currently used in treating iron overload in man, desferrioxamine B. Schiff base derivatives of pyridoxal produced varying amounts of iron excretion, the hydrazone derivatives being much more effective than pyridoxal alone. These data suggested a number of possible mechanisms for the chelation of endogenous iron by such agents. Pyridoxal benzoyl hydrazone induced approximately 50% more iron excretion than did an equivalent dose of the parent isonicotinoyl analog and bile cannulation studies showed this difference to be associated with a prolonged duration of action of the benzoyl derivative. When give i.v., the benzoyl and isonicotinoyl hydrazones of pyridoxal and the benzoyl hydrazone of salicylaldehyde all produced levels of iron excretion which exceeded that seen with an equivalent dose of desferrioxamine B. It is concluded that the range of active Schiff base derivatives is likely to be large and that some of these, although not necessarily any of the particular compounds described here, may prove to be of clinical use.