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This scoping review examines the role of digital solutions in active, participant-centered surveillance of adverse events following initial release of COVID-19 vaccines. The goals of this paper were to examine the existing literature surrounding digital solutions and technology used for active, participant centered, AEFI surveillance of novel COVID-19 vaccines approved by WHO. This paper also aimed to identify gaps in literature surrounding digital, active, participant centered AEFI surveillance systems and to identify and describe the core components of active, participant centered, digital surveillance systems being used for post-market AEFI surveillance of WHO approved COVID-19 vaccines, with a focus on the digital solutions and technology being used, the type of AEFI detected, and the populations under surveillance. The findings highlight the need for customized surveillance systems based on local contexts and the lessons learned to improve future vaccine monitoring and pandemic preparedness.

Vaccine certificates have been implemented worldwide, aiming to promote vaccination rates and to reduce the spread of COVID-19. However, their use during the COVID-19 pandemic was controversial and has been criticized for infringing upon medical autonomy and individual rights. We administered a national online survey exploring social and demographic factors predicting the degree of public approval of vaccine certificates in Canada. We conducted a multivariate linear regression which revealed which factors were predictive of vaccine certificate acceptance in Canada. Self-reported minority status ( p  < .001), rurality ( p  < .001), political ideology ( p  < .001), age ( p  < .001), having children under 18 in the household ( p  < .001), education ( p  = .014), and income status ( p  = .034) were significant predictors of attitudes toward COVID-19 vaccine certificates. We observed the lowest vaccine-certificate approval among participants who: self-identify as a visible minority; live in rural areas; are politically conservative; are 18-34 years of age; have children under age 18 living in the household; have completed an apprenticeship or trades education; and those with an annual income between $100,000-$159,999. The present findings are valuable for their ability to inform the implementation of vaccine certificates during future pandemic scenarios which may require targeted communication between public health agencies and under-vaccinated populations.

Background: Vaccine safety surveillance is a core component of vaccine pharmacovigilance. In Canada, active, participant-centered vaccine surveillance is available for influenza vaccines and has been used for COVID-19 vaccines., Objective: The objective of this study is to evaluate the effectiveness and feasibility of using a mobile app for reporting participant-centered seasonal influenza adverse events following immunization (AEFIs) compared to a web-based notification system., Methods: Participants were randomized to influenza vaccine safety reporting via a mobile app or a web-based notification platform. All participants were invited to complete a user experience survey., Results: Among the 2408 randomized participants, 1319 (54%) completed their safety survey 1 week after vaccination, with a higher completion rate among the web-based notification platform users (767/1196, 64%) than among mobile app users (552/1212, 45%; P<.001). Ease-of-use ratings were high for the web-based notification platform users (99% strongly agree or agree) and 88.8% of them strongly agreed or agreed that the system made reporting AEFIs easier. Web-based notification platform users supported the statement that a web-based notification-only approach would make it easier for public health professionals to detect vaccine safety signals (91.4%, agreed or strongly agreed)., Conclusions: Participants in this study were significantly more likely to respond to a web-based safety survey rather than within a mobile app. These results suggest that mobile apps present an additional barrier for use compared to the web-based notification-only approach., Trial Registration: ClinicalTrials.gov NCT05794113; https://clinicaltrials.gov/show/NCT05794113., (©A Brianne Bota, Julie A Bettinger, Shirley Sarfo-Mensah, Jimmy Lopez, David P Smith, Katherine M Atkinson, Cameron Bell, Kim Marty, Mohamed Serhan, David T Zhu, Anne E McCarthy, Kumanan Wilson. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 08.05.2023.)

Background: Accurate estimates of gestational age (GA) at birth are important for preterm birth surveillance but can be challenging to obtain in low income countries. Our objective was to develop machine learning models to accurately estimate GA shortly after birth using clinical and metabolomic data., Methods: We derived three GA estimation models using ELASTIC NET multivariable linear regression using metabolomic markers from heel-prick blood samples and clinical data from a retrospective cohort of newborns from Ontario, Canada. We conducted internal model validation in an independent cohort of Ontario newborns, and external validation in heel prick and cord blood sample data collected from newborns from prospective birth cohorts in Lusaka, Zambia and Matlab, Bangladesh. Model performance was measured by comparing model-derived estimates of GA to reference estimates from early pregnancy ultrasound., Results: Samples were collected from 311 newborns from Zambia and 1176 from Bangladesh. The best-performing model accurately estimated GA within about 6 days of ultrasound estimates in both cohorts when applied to heel prick data (MAE 0.79 weeks (95% CI 0.69, 0.90) for Zambia; 0.81 weeks (0.75, 0.86) for Bangladesh), and within about 7 days when applied to cord blood data (1.02 weeks (0.90, 1.15) for Zambia; 0.95 weeks (0.90, 0.99) for Bangladesh)., Conclusions: Algorithms developed in Canada provided accurate estimates of GA when applied to external cohorts from Zambia and Bangladesh. Model performance was superior in heel prick data as compared to cord blood data., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Hawken et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Globally, measures, such as lockdown, quarantining, and physical distancing, have been implemented to curb the spread of COVID-19. As the vaccines are now available and reintegration into society is beginning, measures such as vaccine certificates are being implemented around the world. We conducted a scoping review to identify the initial digital solutions for COVID-19 vaccine certificates and evaluate them on the basis of purpose and use case, technological architecture, and ethical and legal implications. Articles identified from a Google search and a search of MEDLINE, Ovid and preprint servers were reviewed in duplicate, and data were extracted using a data extraction form. Data were extracted for date, location, type of article, source, companies identified for creating vaccine certificates, technology used, type of evidence provided (article quoting research study or an expert opinion), digital architecture, security and privacy measures, and use cases. Technology emerged as the most dominant theme followed by ethics, travel, legal concerns, public policy, and scientific concerns. Our review identified eight solutions that are working toward COVID-19 vaccine certificates world-wide, all optimizing blockchain technology. COVID-19 vaccine certificates are being considered in 11 countries and are in place in 5 others. Many issues concerning the themes we identified remain to be addressed to facilitate successful implementation.

Using data from Ontario Canada, we previously developed machine learning-based algorithms incorporating newborn screening metabolites to estimate gestational age (GA). The objective of this study was to evaluate the use of these algorithms in a population of infants born in Siaya county, Kenya. Cord and heel prick samples were collected from newborns in Kenya and metabolic analysis was carried out by Newborn Screening Ontario in Ottawa, Canada. Postnatal GA estimation models were developed with data from Ontario with multivariable linear regression using ELASTIC NET regularization. Model performance was evaluated by applying the models to the data collected from Kenya and comparing model-derived estimates of GA to reference estimates from early pregnancy ultrasound. Heel prick samples were collected from 1,039 newborns from Kenya. Of these, 8.9% were born preterm and 8.5% were small for GA. Cord blood samples were also collected from 1,012 newborns. In data from heel prick samples, our best-performing model estimated GA within 9.5 days overall of reference GA [mean absolute error (MAE) 1.35 (95% CI 1.27, 1.43)]. In preterm infants and those small for GA, MAE was 2.62 (2.28, 2.99) and 1.81 (1.57, 2.07) weeks, respectively. In data from cord blood, model accuracy slightly decreased overall (MAE 1.44 (95% CI 1.36, 1.53)). Accuracy was not impacted by maternal HIV status and improved when the dating ultrasound occurred between 9 and 13 weeks of gestation, in both heel prick and cord blood data (overall MAE 1.04 (95% CI 0.87, 1.22) and 1.08 (95% CI 0.90, 1.27), respectively). The accuracy of metabolic model based GA estimates in the Kenya cohort was lower compared to our previously published validation studies, however inconsistency in the timing of reference dating ultrasounds appears to have been a contributing factor to diminished model performance., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2022 Hawken et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Accurate estimates of gestational age in pregnancy are important for the provision of optimal care. Although current guidelines generally recommend estimating gestational age via first-trimester ultrasound measurement of crown-rump length, error associated with this method can range from 3 to 8 days of gestation. In pregnancies resulting from assisted reproductive technology, estimated due date can be calculated on the basis of the age of the embryo and the date of embryo transfer, arguably providing the most accurate estimates possible. We have developed and extensively validated statistical models to estimate gestational age postnatally using metabolomic markers from blood samples in combination with clinical and demographic data. These models have shown high accuracy compared with first-trimester ultrasound, the recommended method for estimating gestational age in spontaneous pregnancies. We hypothesized that gestational age derived from date and stage of embryo at transfer in newborns conceived using assisted reproduction therapy would provide the most accurate reference standard possible to evaluate and compare the accuracy of both first-trimester ultrasound and metabolomic model-based gestational dating., Objective: This study aimed to validate both first-trimester ultrasound dating and postnatal metabolomic gestational age estimation models against gestational age derived from date and stage of embryo at transfer in a cohort of newborns conceived via assisted reproductive technology, both overall and in important subgroups of interest (preterm birth, small for gestational age, and multiple birth)., Study Design: This was a retrospective cohort study of infants born in Ontario, Canada between 2015 and 2017 and captured in the provincial birth registry. Spontaneous conceptions were randomly partitioned into a model derivation sample (80%) and a test sample (20%) for model validation. A cohort of assisted conceptions resulting from fresh embryo transfers was derived to evaluate the accuracy of both ultrasound and model-based gestational dating. Postnatal gestational age estimation models were developed with multivariable linear regression using elastic-net regularization. Gestational age estimates from dating ultrasound and from postnatal metabolomic models were compared with date of embryo transfer reference gestational age in the independent test cohorts. Accuracy was quantified by calculating mean absolute error and the square root of mean squared error., Results: Our model derivation cohort included 202,300 spontaneous conceptions, and the testing cohorts included 50,735 spontaneous conceptions and 1924 assisted conceptions. In the assisted conception cohort, first-trimester dating ultrasound was accurate to within approximately ±1.5 days compared with date of embryo transfer reference overall (mean absolute error, 0.21 [95% confidence interval, 0.20-0.23]). When compared with gestational age derived from date of embryo transfer, the metabolomic estimation models were accurate to within approximately ±5 days overall (0.79 [0.76-0.81] weeks). When ultrasound was used as the reference in validating the metabolomic model, the mean absolute error was slightly higher overall (0.81 [0.78-0.84] weeks). In general, the accuracy of gestational age estimates derived from ultrasound or metabolomic models was highest in term infants and lower in preterm and small-for-gestational-age newborns., Conclusion: Our findings support the accuracy of ultrasound as a gestational age dating tool. They also support the potential utility of metabolic gestational age dating algorithms in settings where ultrasound or other accurate methods of estimating gestational age are not available because of lack of infrastructure or specialized training (eg, low-income countries). However, the accuracy of metabolomic model-based dating was generally lower than that of ultrasound., (© 2022 Published by Elsevier Inc.)

Background: Newborn screening (NBS) for sickle cell disease incidentally identifies heterozygous carriers of hemoglobinopathy mutations. In Ontario, Canada, these carrier results are not routinely disclosed, presenting an opportunity to investigate the potential health implications of carrier status. We aimed to compare rates of health services use among children identified as carriers of hemoglobinopathy mutations and those who received negative NBS results., Methods: Eligible children underwent NBS in Ontario from October 2006 to March 2010 and were identified as carriers or as screen-negative controls, matched to carriers 5:1 based on neighbourhood and timing of birth. We used health care administrative data to determine frequencies of inpatient hospitalizations, emergency department (ED) visits, and physician encounters through March 2012, using multivariable negative binomial regression to compare rates of service use in the two cohorts. We analyzed data from 4987 carriers and 24,935 controls., Results: Adjusted incidence rate ratios (95% CI) for service use in carriers versus controls among children < 1 year of age were: 1.11 (1.06-1.17) for ED visits; 0.97 (0.89-1.06) for inpatient hospitalization; and 1.02 (1.00-1.04) for physician encounters. Among children ≥1 year of age, adjusted rate ratios were: 1.03 (0.98-1.07) for ED visits; 1.14 (1.03-1.25) for inpatient hospitalization and 0.92 (0.90-0.94) for physician encounters., Conclusions: While we identified statistically significant differences in health services use among carriers of hemoglobinopathy mutations relative to controls, effect sizes were small and directions of association inconsistent across age groups and health service types. Our findings are consistent with the assumption that carrier status is likely benign in early childhood.

Background:  Postnatal gestational age (GA) algorithms derived from newborn metabolic profiles have emerged as a novel method of acquiring population-level preterm birth estimates in low resource settings. To date, model development and validation have been carried out in North American settings. Validation outside of these settings is warranted.   Methods:  This was a retrospective database study using data from newborn screening programs in Canada, the Philippines and China. ELASTICNET machine learning models were developed to estimate GA in a cohort of infants from Canada using sex, birth weight and metabolomic markers from newborn heel prick blood samples. Final models were internally validated in an independent sample of Canadian infants, and externally validated in infant cohorts from the Philippines and China.  Results:  Cohorts included 39,666 infants from Canada, 82,909 from the Philippines and 4,448 from China.  For the full model including sex, birth weight and metabolomic markers, GA estimates were within ±5 days of ultrasound values in the Canadian internal validation (mean absolute error (MAE) 0.71, 95% CI: 0.71, 0.72), and within ±6 days of ultrasound GA in both the Filipino (0.90 (0.90, 0.91)) and Chinese cohorts (0.89 (0.86, 0.92)). Despite the decreased accuracy in external settings, our models incorporating metabolomic markers performed better than the baseline model, which relied on sex and birth weight alone. In preterm and growth-restricted infants, the accuracy of metabolomic models was markedly higher than the baseline model. Conclusions:  Accuracy of metabolic GA algorithms was attenuated when applied in external settings.  Models including metabolomic markers demonstrated higher accuracy than models using sex and birth weight alone. As innovators look to take this work to scale, further investigation of modeling and data normalization techniques will be needed to improve robustness and generalizability of metabolomic GA estimates in low resource settings, where this could have the most clinical utility., Competing Interests: No competing interests were disclosed., (Copyright: © 2021 Hawken S et al.)

Recent studies in alcohol use disorders (AUDs) have demonstrated some connections between carnitine metabolism and the pathophysiology of the disease. In this scoping review, we aimed to collate and examine existing research available on carnitine metabolism and AUDs and develop hypotheses surrounding the role carnitine may play in AUD. A scoping review method was used to search electronic databases in September 2019. The database search terms used included "alcohol, alcoholism, alcohol abuse, alcohol consumption, alcohol drinking patterns, alcohol-induced disorders, alcoholic intoxication, alcohol-related disorders, binge drinking, Wernicke encephalopathy, acylcarnitine, acetyl-l-carnitine, acetylcarnitine, carnitine and palmitoylcarnitine." The inclusion criteria included English language, human-based, AUD diagnosis and measured blood or tissue carnitine or used carnitine as a treatment. Of 586 studies that were identified and screened, 65 underwent abstract review, and 41 were fully reviewed. Eighteen studies were ultimately included for analysis. Data were summarized in an electronic data extraction form. We found that there is limited literature available. Alcohol use appears to impact carnitine metabolism, most clearly in the setting of alcoholic cirrhosis. Six studies found carnitine to be increased in AUD, of which 5 were conducted in patients with alcoholic cirrhosis. Only 3 placebo-controlled trials were identified and provide some support for the use of carnitine in AUD to decrease cravings, anhedonia, and withdrawal and improve cognition. The increase in plasma carnitine in alcoholic cirrhosis may be related to disordered fatty acid metabolism and oxidative stress that occurs in AUD. The multiple possible therapeutic effects carnitine could have on ethanol metabolism and the early evidence available for carnitine supplementation as a treatment for AUD provide a foundation for future randomized control trials of carnitine for treating AUD., (© 2021 The Authors. Alcoholism: Clinical & Experimental Research published by Wiley Periodicals LLC on behalf of Research Society on Alcoholism.)

Preterm birth is the leading global cause of neonatal morbidity and mortality. Reliable gestational age estimates are useful for quantifying population burdens of preterm birth and informing allocation of resources to address the problem. However, evaluating gestational age in low-resource settings can be challenging, particularly in places where access to ultrasound is limited. Our group has developed an algorithm using newborn screening analyte values derived from dried blood spots from newborns born in Ontario, Canada for estimating gestational age within one to two weeks. The primary objective of this study is to validate a program that derives gestational age estimates from dried blood spot samples (heel-prick or cord blood) collected from health and demographic surveillance sites and population representative health facilities in low-resource settings in Zambia, Kenya, Bangladesh and Zimbabwe. We will also pilot the use of an algorithm to identify birth percentiles based on gestational age estimates and weight to identify small for gestational age infants. Once collected from local sites, samples will be tested by the Newborn Screening Ontario laboratory at the Children's Hospital of Eastern Ontario (CHEO) in Ottawa, Canada. Analyte values will be obtained through laboratory analysis for estimation of gestational age as well as screening for other diseases routinely conducted at Ontario's newborn screening program. For select conditions, abnormal screening results will be reported back to the sites in real time to facilitate counseling and future clinical management. We will determine the accuracy of our existing algorithm for estimation of gestational age in these newborn samples. Results from this research hold the potential to create a feasible method to assess gestational age at birth in low- and middle-income countries where reliable estimation may be otherwise unavailable., Competing Interests: No competing interests were disclosed., (Copyright: © 2021 Bota AB et al.)

Background: Human Papillomavirus (HPV) is the most common sexually transmitted infection in Canada and around the world. Vaccination is an effective prevention strategy, but uptake is low, especially among newcomers to Canada. We sought to understand newcomers' knowledge, attitudes, and beliefs (KAB) on HPV and HPV vaccination and their role in HPV vaccine acceptance., Methods: Newcomers were defined as individuals born outside Canada, (i.e., individuals born in a different country, the majority of whom are immigrants or refugees, but also includes students and undocumented migrants). Eligible participants were newcomers, aged 16 or older and who could read or write in English, French or Arabic. Surveys were administered in two community health centres in Ottawa, Canada that primarily engage with newcomer populations. Follow-up interviews were conducted either at the community health centre or over the phone, depending on participants' preferences., Results: Fifty participants completed the survey, the majority of whom were women (74%) and spoke Arabic as their first language (54%). Seven participants completed supplemental interviews to complement their survey responses. The majority (70%) of participants had not previously heard of HPV. Less than half (46%) knew that the vaccine is effective in preventing certain types of cancer; nearly 40% incorrectly believed the vaccine could cure HPV. Qualitative interviews supported the survey findings., Conclusions: Despite a lack of HPV knowledge due to cultural and language barriers, there is still a strong desire among newcomers to receive the vaccine, especially when accompanied by a physician recommendation. Cultural and language-appropriate resources are needed to help newcomers make informed vaccination decisions and promote HPV vaccine uptake.

Background: Preterm birth complications are the leading cause of death among children under 5 years of age, and this imposes a heavy burden on healthcare and social systems, particularly in low- and middle-income countries where reliable estimates of gestational age may be difficult to obtain. Metabolic analyte data can aid in accurately estimating gestational age. However, important costs are associated with this approach, which are related to the collection and analysis of newborn samples, and its cost-effectiveness has yet to be determined., Objective: This study aimed to evaluate the cost-effectiveness of an internationally validated gestational age estimation algorithm based on neonatal blood spot metabolite data in combination with clinical and demographic variables (birthweight, sex, and multiple birth status) compared with a basic algorithm that uses only clinical and demographic variables in classifying infants as preterm or term (using a 37-week dichotomous preterm or term classification) and determining gestational age., Study Design: The cost per correctly classified preterm infant and per correctly classified small-for-gestational-age infant for the metabolic algorithm vs the basic algorithm were estimated with data from an implementation study in Bangladesh., Results: Over 1 year, the metabolic algorithm correctly classified an average of 8.7 (95% confidence interval, 1.3-14.7) additional preterm infants and 145.3 (95% confidence interval, 128.0-164.7) additional small-for-gestational-age infants per 1323 infants screened compared with the basic algorithm using only clinical and demographic variables. The incremental annual cost of adopting the metabolic algorithm was $100,031 (95% confidence interval, $86,354-$115,725). If setup costs were included, the cost was $120,496 (95% confidence interval, $106,322-$136,656). Compared with the basic algorithm, the incremental cost per preterm infant correctly classified by the metabolic algorithm is $11,542 ($13,903 with setup), and the incremental cost per small-for-gestational-age infant is $688 ($829 with setup)., Conclusion: This research quantifies the cost per detection of preterm or small-for-gestational-age infant in the implementation of a newborn screening program to aid in improved classification of preterm and, in particular, small-for-gestational-age infants in low- and middle-income countries., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: We aim to describe the general characteristics of how the Canadian newspaper The Globe and Mail reports on opioid-related news, the opioid crisis and its victims, and explore how Canadians' perceptions of the opioid crisis could have developed over time from this reporting. The Globe and Mail has the highest circulation among Canadian newspapers and is Canada's newspaper of record., Methods: Reviewers performed independent, blinded bibliometric searches of all The Globe and Mail articles archived in the Canadian Periodicals Index Quarterly spanning an 18-year period (1 January 2000-1 June 2018) related to the keywords "opioids" or "drugs and opioids" and "opiates". Independently and in duplicate, reviewers manually extracted qualitative data from articles and identified emergent themes. Articles were screened independently by both reviewers based on the inclusion criteria. Conflicts were resolved by discussion and consensus. Social representation theory was used as a framework for describing how the opioid crisis is portrayed in Canada., Results: Our search yielded 650 relevant opioid articles. The number of articles peaked in 2009, 2012, and in 2016, coinciding with major developments in the epidemic. The language used in this discourse has evolved over the years and has slowly shifted towards less stigmatizing language. Content analysis of the articles revealed common social representations attributing responsibility to pharmaceutical companies, physicians, and foreign countries., Conclusions: The Globe and Mail's coverage of the opioid crisis is focused on basic social representations and attributed responsibility for the crisis to a few collectives. A shift toward coverage of the root causes of the opioid epidemic could positively influence the general public's perception of the opioid crisis and promote deeper understanding of the issue. Journalists face several obstacles to achieve greater focus and framing of the opioid crisis; a closer working relationship between the media and the research community is needed.

Background:  Postnatal gestational age (GA) algorithms derived from newborn metabolic profiles have emerged as a novel method of acquiring population-level preterm birth estimates in low resource settings. To date, model development and validation have been carried out in North American settings. Validation outside of these settings is warranted.   Methods:  This was a retrospective database study using data from newborn screening programs in Canada, the Philippines and China. ELASTICNET machine learning models were developed to estimate GA in a cohort of infants from Canada using sex, birth weight and metabolomic markers from newborn heel prick blood samples. Final models were internally validated in an independent group of infants, and externally validated in cohorts of infants from the Philippines and China.  Results:  Cohorts included 39,666 infants from Canada, 82,909 from the Philippines and 4,448 from China.  For the full model including sex, birth weight and metabolomic markers, GA estimates were within 5 days of ultrasound values in the Canadian internal validation (mean absolute error (MAE) 0.71, 95% CI: 0.71, 0.72), and within 6 days of ultrasound GA in both the Filipino (0.90 (0.90, 0.91)) and Chinese cohorts (0.89 (0.86, 0.92)). Despite the decreased accuracy in external settings, our models incorporating metabolomic markers performed better than the baseline model, which relied on sex and birth weight alone. In preterm and growth-restricted infants, the accuracy of metabolomic models was markedly higher than the baseline model. Conclusions:  Accuracy of metabolic GA algorithms was attenuated when applied in external settings.  Models including metabolomic markers demonstrated higher accuracy than models using sex and birth weight alone. As innovators look to take this work to scale, further investigation of modeling and data normalization techniques will be needed to improve robustness and generalizability of metabolomic GA estimates in low resource settings, where this could have the most clinical utility., Competing Interests: No competing interests were disclosed., (Copyright: © 2020 Hawken S et al.)

Preterm birth is the leading global cause of neonatal morbidity and mortality. Reliable gestational age estimates are useful for quantifying population burdens of preterm birth and informing allocation of resources to address the problem. However, evaluating gestational age in low-resource settings can be challenging, particularly in places where access to ultrasound is limited. Our group has developed an algorithm using newborn screening analyte values derived from dried blood spots from newborns born in Ontario, Canada for estimating gestational age within one to two weeks. The primary objective of this study is to validate a program that derives gestational age estimates from dried blood spot samples (heel-prick or cord blood) collected from health and demographic surveillance sites and population representative health facilities in low-resource settings in Zambia, Kenya, Bangladesh and Zimbabwe. We will also pilot the use of an algorithm to identify birth percentiles based on gestational age estimates and weight to identify small for gestational age infants. Once collected from local sites, samples will be tested by the Newborn Screening Ontario laboratory at the Children's Hospital of Eastern Ontario (CHEO) in Ottawa, Canada. Analyte values will be obtained through laboratory analysis for estimation of gestational age as well as screening for other diseases routinely conducted at Ontario's newborn screening program. For select conditions, abnormal screening results will be reported back to the sites in real time to facilitate counseling and future clinical management. We will determine the accuracy of our existing algorithm for estimation of gestational age in these newborn samples. Results from this research hold the potential to create a feasible method to assess gestational age at birth in low- and middle-income countries where reliable estimation may be otherwise unavailable., Competing Interests: No competing interests were disclosed., (Copyright: © 2020 Bota AB et al.)

Importance: Systemic chemotherapy can be administered either through a peripheral vein (IV), or centrally through peripherally inserted central catheter (PICC), totally implanted vascular access devices (PORTs) or tunnelled cuffed catheters. Despite the widespread use of systemic chemotherapy in patients with breast cancer, the optimal choice of vascular access is unknown., Objective: This systematic review evaluated complication rates and patient satisfaction with different access strategies for administering neo/adjuvant chemotherapy for breast cancer., Evidence Reviewed: Ovid Medline, EMBASE and the Cochrane Central Register of Controlled Trials were searched from 1946 to September 2017. Two reviewers independently assessed each citation. The Newcastle-Ottawa scale was used to assess the quality of cohort and case-control studies., Findings: Of 1584 citations identified, 15 unique studies met the pre-specified eligibility criteria. There were no randomised studies comparing types of vascular access. Reports included six single-institution retrospective cohort studies, one retrospective multi-institution cohort, one retrospective cohort database study, five prospective single-institution studies, one prospective multi-institution study and one nested case-control study. Median complication rates were infection: 6.0% PICC (2 studies) versus 2.1% PORT (8 studies); thrombosis: 8.9% PICC (2 studies) versus 2.6% PORT (9 studies); extravasation: 0 PICC (1 study) versus 0.4% PORT (4 studies) and mechanical issues: PICC 3.8% (1 study) versus 1.8% PORT (9 studies). Satisfaction/quality of life appeared high with each device., Conclusion: In the absence of high-quality data comparing vascular access strategies, randomised, adequately powered, prospective studies would be required to help inform clinical practice and reduce variation.