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Abstract Background Indigenous people are insightful and informed about their own health and wellness, yet their visions, strengths and knowledge are rarely incorporated into health research. This can lead to subpar engagement or irrelevant research practices, which exacerbates the existing health inequities Indigenous people experience compared to the non-Indigenous population. Data consistently underscores the importance of Indigenous self-determination in research as a means to address health inequities. However, there are few formal methods to support this goal within the existing research context, which is dominated by Western perspectives. Main text Canadians Seeking Solutions and Innovations to Overcome Chronic Kidney Disease (Can-SOLVE CKD) is a patient-oriented research network in Canada that recognizes the need to create the space to facilitate Indigenous self-determination in research. Indigenous members of the network therefore created and evolved a unique group, called the Indigenous Peoples' Engagement and Research Council (IPERC). IPERC plays a critical role in informing Can-SOLVE CKD research priorities, as well as creating tools to support Indigenous-specific research and engagement. This approach ensures that Indigenous voices and knowledge are critical threads within the fabric of the network’s operations and research projects. Here, we describe the methods taken to create a council such as IPERC, and provide examples of initiatives by the council that aim to increase Indigenous representation, participation and partnership in research. We share lessons learned on what factors contribute to the success of IPERC, which could be valuable for other organizations interested in creating Indigenous-led research councils. Conclusion Indigenous self-determination in research is critical for addressing health inequities. Here, we present a unique model, led by a council of diverse Indigenous people, which could help reduce health equities and lead to a better era of research for everyone.

Objective: To determine the incidence, outcomes, and evaluate diagnostic modalities for postoperative vocal cord dysfunction (VCD) following cardiothoracic surgery in children. Methods: A prospective mixed-methods study using principles of implementation science was completed. All patients undergoing surgery involving the aortic arch, ductus, or ligamentum arteriosum and vascular rings from September 2019 to December 2020 were enrolled. Patients underwent speech pathology assessment, laryngeal ultrasound, and flexible direct laryngoscopy. Results: Ninety-five patients were eligible for inclusion. The incidence of VCD ranged from 18% to 56% and varied according to procedure group. VCD occurred in 42% of neonates. Repair of hypoplastic aortic arch was associated with increased risk of VCD (57%; P = .002). There was no significant difference in duration of intubation, pediatric intensive care unit stay, or hospital stay. Forty percent children were able to achieve full oral feeding. Children with VCD were more likely to require nasogastric supplementary feeding at discharge (60% vs 36%; P = .044). Sixty-eight percent of patients demonstrated complete resolution of VCD at a median of 97 days postoperatively. Laryngeal ultrasound and speech pathology assessment combined had a sensitivity of 91% in comparison to flexible direct laryngoscopy. Conclusions: VCD occurred in one-third and resolved in two-thirds of patients at a median of 3 months following cardiac surgery. Aortic arch repair carried the highest risk of VCD. VCD adversely influenced feeding. Forty percent of patients achieved full oral feeding before discharge. VCD did not delay intensive care unit or hospital discharge. Speech pathology assessment and laryngeal ultrasound combined was reliable for diagnosis in most patients and was more patient friendly than flexible direct laryngoscopy.

Abstract Background High mammographic density is associated with both risk of cancers being missed at mammography, and increased risk of developing breast cancer. Stratification of breast cancer prevention and screening requires mammographic density measures predictive of cancer. This study compares five mammographic density measures to determine the association with subsequent diagnosis of breast cancer and the presence of breast cancer at screening. Methods Women participating in the “Predicting Risk Of Cancer At Screening” (PROCAS) study, a study of cancer risk, completed questionnaires to provide personal information to enable computation of the Tyrer-Cuzick risk score. Mammographic density was assessed by visual analogue scale (VAS), thresholding (Cumulus) and fully-automated methods (Densitas, Quantra, Volpara) in contralateral breasts of 366 women with unilateral breast cancer (cases) detected at screening on entry to the study (Cumulus 311/366) and in 338 women with cancer detected subsequently. Three controls per case were matched using age, body mass index category, hormone replacement therapy use and menopausal status. Odds ratios (OR) between the highest and lowest quintile, based on the density distribution in controls, for each density measure were estimated by conditional logistic regression, adjusting for classic risk factors. Results The strongest predictor of screen-detected cancer at study entry was VAS, OR 4.37 (95% CI 2.72–7.03) in the highest vs lowest quintile of percent density after adjustment for classical risk factors. Volpara, Densitas and Cumulus gave ORs for the highest vs lowest quintile of 2.42 (95% CI 1.56–3.78), 2.17 (95% CI 1.41–3.33) and 2.12 (95% CI 1.30–3.45), respectively. Quantra was not significantly associated with breast cancer (OR 1.02, 95% CI 0.67–1.54). Similar results were found for subsequent cancers, with ORs of 4.48 (95% CI 2.79–7.18), 2.87 (95% CI 1.77–4.64) and 2.34 (95% CI 1.50–3.68) in highest vs lowest quintiles of VAS, Volpara and Densitas, respectively. Quantra gave an OR in the highest vs lowest quintile of 1.32 (95% CI 0.85–2.05). Conclusions Visual density assessment demonstrated a strong relationship with cancer, despite known inter-observer variability; however, it is impractical for population-based screening. Percentage density measured by Volpara and Densitas also had a strong association with breast cancer risk, amongst the automated measures evaluated, providing practical automated methods for risk stratification.

Abstract Background The differences between breast cancer risk factors in white British/Irish and Asian women attending screening in the UK are not well documented. Methods Between 2009-15 ethnicity and traditional breast cancer risk factors were self-identified by a screening cohort from Greater Manchester, with follow up to 2016. Risk factors and incidence rates were compared using age-standardised statistics (European standard population). Results Eight hundred and seventy-nine Asian women and 51,779 unaffected white British/Irish women aged 46-73 years were recruited. Asian women were at lower predicted breast cancer risk from hormonal and reproductive risk factors than white British/Irish women (mean 10 year risk 2.6% vs 3.1%, difference 0.4%, 95%CI 0.3-0.5%). White British/Irish women were more likely to have had a younger age at menarche, be overweight or obese, taller, used hormone replacement therapy and not to have had children.. However, despite being less overweight Asian women had gained more weight from age 20 years and were less likely to undertake moderate physical activity. Asian women also had a slightly higher mammographic density. Asian age-standardised incidence was 3.2 (95%CI 1.6-5.2, 18 cancers) per thousand women/year vs 4.5 (95%CI 4.2-4.8, 1076 cancers) for white British/Irish women. Conclusions Asian women attending screening in Greater Manchester are likely to have a lower risk of breast cancer than white British/Irish women, but they undertake less physical activity and have more adult weight gain.

Background: In the UK, women are invited for 3-yearly mammography screening, through the NHS Breast Screening Programme (NHSBSP), from the ages of 47–50 years to the ages of 69–73 years. Women with family histories of breast cancer can, from the age of 40 years, obtain enhanced surveillance and, in exceptionally high-risk cases, magnetic resonance imaging. However, no NHSBSP risk assessment is undertaken. Risk prediction models are able to categorise women by risk using known risk factors, although accurate individual risk prediction remains elusive. The identification of mammographic breast density (MD) and common genetic risk variants [single nucleotide polymorphisms (SNPs)] has presaged the improved precision of risk models. Objectives: To (1) identify the best performing model to assess breast cancer risk in family history clinic (FHC) and population settings; (2) use information from MD/SNPs to improve risk prediction; (3) assess the acceptability and feasibility of offering risk assessment in the NHSBSP; and (4) identify the incremental costs and benefits of risk stratified screening in a preliminary cost-effectiveness analysis. Design: Two cohort studies assessing breast cancer incidence. Setting: High-risk FHC and the NHSBSP Greater Manchester, UK. Participants: A total of 10,000 women aged 20–79 years [Family History Risk Study (FH-Risk); UK Clinical Research Network identification number (UKCRN-ID) 8611] and 53,000 women from the NHSBSP [aged 46–73 years; Predicting the Risk of Cancer At Screening (PROCAS) study; UKCRN-ID 8080]. Interventions: Questionnaires collected standard risk information, and mammograms were assessed for breast density by a number of techniques. All FH-Risk and 10,000 PROCAS participants participated in deoxyribonucleic acid (DNA) studies. The risk prediction models Manual method, Tyrer–Cuzick (TC), BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm) and Gail were used to assess risk, with modelling based on MD and SNPs. A preliminary model-based cost-effectiveness analysis of risk stratified screening was conducted. Main outcome measures: Breast cancer incidence. Data sources: The NHSBSP; cancer registration. Results: A total of 446 women developed incident breast cancers in FH-Risk in 97,958 years of follow-up. All risk models accurately stratified women into risk categories. TC had better risk precision than Gail, and BOADICEA accurately predicted risk in the 6268 single probands. The Manual model was also accurate in the whole cohort. In PROCAS, TC had better risk precision than Gail [area under the curve (AUC) 0.58 vs. 0.54], identifying 547 prospective breast cancers. The addition of SNPs in the FH-Risk case–control study improved risk precision but was not useful in BRCA1 (breast cancer 1 gene) families. Risk modelling of SNPs in PROCAS showed an incremental improvement from using SNP18 used in PROCAS to SNP67. MD measured by visual assessment score provided better risk stratification than automatic measures, despite wide intra- and inter-reader variability. Using a MD-adjusted TC model in PROCAS improved risk stratification (AUC = 0.6) and identified significantly higher rates (4.7 per 10,000 vs. 1.3 per 10,000; p

This study simulates an increased thermal capacitance (ITC) and thermal storage management (TSM) system to reduce the energy consumed by air conditioning and heating systems. The ITC/TSM is coupled with phase change materials (PCM), which enable tank volume reduction. The transient energy modeling software, the Transient System Simulation Tool (TRNSYS), is used to simulate the buildings’ thermal response and energy consumption, as well as the ITC/TSM system and controls. Four temperature-controlled operating regimes are used for the tank: building shell circulation, heat exchanger circulation, solar panel circulation, and storage. This study also explores possible energy-saving benefits from tank volume reduction such as losses associated with the environment temperature due to tank location. Three different tank locations are considered in this paper: outdoor, buried, and indoor. The smallest tank size (five gallons) is used for indoor placement, while the large tank (50 gallons) is used either for outdoor placement or buried at a depth of 1 m. Results for Atlanta, Georgia show an average 48% required energy decrease for cold months (October–April) and a 3% decrease for warm months (May–September) for the ITC/TSM system with PCM when compared with the reference case. A system with PCM reduces the tank size by 90% while maintaining similar energy savings.