The first genetically confirmed cohort of Facioscapulohumeral Muscular Dystrophy from Northern India.

Facioscapulohumeral muscular dystrophy (FSHD) is the third most common form of hereditary myopathy. Sixty per cent of the world's population lives in Asia, so a significant percentage of the world's FSHD participants is expected to live there. To date, most FSHD studies have involved individuals of European descent, yet small-scale studies of East-Asian populations suggest that the likelihood of developing FSHD may vary. Here, we present the first genetically confirmed FSHD cohort of Indian ancestry, which suggests a pathogenic FSHD1 allele size distribution intermediate between European and North-East Asian populations and more asymptomatic carriers of 4 unit and 5 unit FSHD1 alleles than observed in European populations. Our data provides important evidence of differences relevant to clinical diagnostics and underscores the need for global FSHD participation in research and trial-ready Indian FSHD cohorts.

The mutational profile in a South African cohort with inherited neuropathies and spastic paraplegia.

Introduction: Limited diagnostics are available for inherited neuromuscular diseases (NMD) in South Africa and (excluding muscle disease) are mainly aimed at the most frequent genes underlying genetic neuropathy (GN) and spastic ataxias in Europeans. In this study, we used next-generation sequencing to screen 61 probands with GN, hereditary spastic paraplegia (HSP), and spastic ataxias for a genetic diagnosis. Methods: After identifying four GN probands with PMP22 duplication and one spastic ataxia proband with SCA1, the remaining probands underwent whole exome (n = 26) or genome sequencing (n = 30). The curation of coding/splice region variants using gene panels was guided by allele frequencies from internal African-ancestry control genomes (n = 537) and the Clinical Genome Resource's Sequence Variant Interpretation guidelines. Results: Of 32 GN probands, 50% had African-genetic ancestry, and 44% were solved: PMP22 (n = 4); MFN2 (n = 3); one each of MORC2, ATP1A1, ADPRHL2, GJB1, GAN, MPZ, and ATM. Of 29 HSP probands (six with predominant ataxia), 66% had African-genetic ancestry, and 48% were solved: SPG11 (n = 3); KIF1A (n = 2); and one each of SPAST, ATL1, SPG7, PCYT2, PSEN1, ATXN1, ALDH18A1, CYP7B1, and RFT1. Structural variants in SPAST, SPG11, SPG7, MFN2, MPZ, KIF5A, and GJB1 were excluded by computational prediction and manual visualisation. Discussion: In this preliminary cohort screening panel of disease genes using WES/WGS data, we solved ~50% of cases, which is similar to diagnostic yields reported for global cohorts. However, the mutational profile among South Africans with GN and HSP differs substantially from that in the Global North.

Geriatric Telehealth: A Standardized Patient Case for Medical Students.

Introduction: The COVID-19 pandemic has necessitated the rapid expansion of telemedicine. However, there has been minimal coverage of telemedicine in traditional undergraduate medicine curricula. Telemedicine presents specific challenges in the geriatric population, including unfamiliarity with technology, cognitive and sensory barriers, inclusion of family and/or caregivers, multimorbidity, and a high degree of medical complexity. Methods: We developed a workshop to allow rising third-year medical students to practice a telemedicine patient encounter while developing skills for assessing and communicating with geriatric patients. This 90-minute workshop consisted of an introductory didactic presentation and a standardized patient activity for small groups of two to five students. Students' level of comfort with telemedicine for assessment of geriatric patients was evaluated with a pre- and postsurvey. Results: Fifty-eight students participated in the workshop and completed the surveys (presurvey = 58, postsurvey = 40), with roughly half (52%) reporting prior experience with telemedicine. A 5-point Likert-type scale (1 = very uncomfortable, 5 = very comfortable) was used. Students reported statistically significant increases in comfort using telemedicine (presurvey = 3.1, postsurvey = 3.9, p < .001) and using telemedicine for patients ≥65 years (presurvey = 2.8, postsurvey = 3.9, p < .001) after completing the workshop. Discussion: Medical students' comfort levels using telemedicine and caring for patients ages 65 and older with a telehealth visit improved after participating in this workshop. To help prepare students for telehealth practice in their future careers, educators should provide them with opportunities to practice and develop this critical skill set.

The Human Mpox Global Outbreak: Available Control Tools and the Opportunity to Break a Cycle of Neglect in Endemic Countries.

The 2022 global outbreak of human Mpox (formerly monkeypox) virus (MPXV) infection outside of the usual endemic zones in Africa challenged our understanding of the virus's natural history, transmission dynamics, and risk factors. This outbreak has highlighted the need for diagnostics, vaccines, therapeutics, and implementation research, all of which require more substantial investments in equitable collaborative partnerships. Global multidisciplinary networks need to tackle MPXV and other neglected emerging and reemerging zoonotic pathogens to address them locally and prevent or quickly control their worldwide spread. Political endorsement from individual countries and financial commitments to maintain control efforts will be essential for long-term sustainability.

Neuromuscular disease genetics in under-represented populations: increasing data diversity.

Neuromuscular diseases (NMDs) affect ∼15 million people globally. In high income settings DNA-based diagnosis has transformed care pathways and led to gene-specific therapies. However, most affected families are in low-to-middle income countries (LMICs) with limited access to DNA-based diagnosis. Most (86%) published genetic data is derived from European ancestry. This marked genetic data inequality hampers understanding of genetic diversity and hinders accurate genetic diagnosis in all income settings. We developed a cloud-based transcontinental partnership to build diverse, deeply-phenotyped and genetically characterized cohorts to improve genetic architecture knowledge, and potentially advance diagnosis and clinical management. We connected 18 centres in Brazil, India, South Africa, Turkey, Zambia, Netherlands and the UK. We co-developed a cloud-based data solution and trained 17 international neurology fellows in clinical genomic data interpretation. Single gene and whole exome data were analysed via a bespoke bioinformatics pipeline and reviewed alongside clinical and phenotypic data in global webinars to inform genetic outcome decisions. We recruited 6001 participants in the first 43 months. Initial genetic analyses 'solved' or 'possibly solved' ∼56% probands overall. In-depth genetic data review of the four commonest clinical categories (limb girdle muscular dystrophy, inherited peripheral neuropathies, congenital myopathy/muscular dystrophies and Duchenne/Becker muscular dystrophy) delivered a ∼59% 'solved' and ∼13% 'possibly solved' outcome. Almost 29% of disease causing variants were novel, increasing diverse pathogenic variant knowledge. Unsolved participants represent a new discovery cohort. The dataset provides a large resource from under-represented populations for genetic and translational research. In conclusion, we established a remote transcontinental partnership to assess genetic architecture of NMDs across diverse populations. It supported DNA-based diagnosis, potentially enabling genetic counselling, care pathways and eligibility for gene-specific trials. Similar virtual partnerships could be adopted by other areas of global genomic neurological practice to reduce genetic data inequality and benefit patients globally.

Opioid Agonist Therapies and Pregnancy Outcomes for Pregnant People With Opioid Use Disorder: Protocol for a Systematic Review.

BACKGROUND: Opioid use disorder (OUD) during pregnancy presents a significant risk to maternal, fetal, and neonatal health, increasing the likelihood of adverse events, such as maternal overdose, pregnancy loss, stillbirth, preterm birth, low birth weight, and neonatal abstinence syndrome. In order to reduce the risk of these outcomes, the standard of care for OUD during pregnancy in many jurisdictions within the United States and Canada is opioid agonist therapy (OAT). OAT refers to prescription medications that alleviate or eliminate opioid withdrawal symptoms, so that opioid use can be managed more safely. Although OAT has been recognized as a safe option for pregnant people with OUD, many jurisdictions do not have treatment guidelines regarding pharmacological options, dosing recommendations, side effect management, and individual preferences. There is currently a lack of systematic evidence on the impacts of different OAT regimens on pregnancy outcomes. OBJECTIVE: We aim to evaluate the impacts of specific OAT agents on pregnancy outcomes and inform recommendations for practitioners treating pregnant people with OUD. METHODS: The MEDLINE, Embase, CINAHL, and PsycINFO databases will be searched for published quantitative studies assessing pregnancy outcomes for individuals on OAT. Given the substantially increased risk of preterm birth, low birth weight, small for gestational age, and stillbirth among pregnant people with OUD, these four end points will comprise our primary outcomes. Database searches will not be restricted by date, and conference abstracts will be restricted to the past 2 years. Titles, abstracts, and full-text articles will be independently screened by 2 reviewers. Data will be extracted independently and in duplicate, using a data extraction form to reduce the risk of reviewer bias. The risk of bias within individual studies will be assessed by using the appropriate CASP (Critical Appraisal Skills Programme) checklists. For studies that consider the same research questions, interventions, or outcomes, meta-analyses will be conducted to synthesize the pooled effect size. In the event that studies cannot be compared directly, results will be synthesized in a narrative account. Between-study heterogeneity will be measured by using the τ2 statistic. If more than 10 studies are available for pooling, publication bias will be evaluated by using the Egger regression test. RESULTS: As of January 2023, a total of 3266 abstracts have been identified for screening. Data extraction is expected to commence in February 2023. CONCLUSIONS: The topic of OAT and its effect on pregnancy is an understudied area that has the potential to improve health outcomes, clinical practice, education, and community advocacy. The results of our review will be used to inform clinical practice guidelines and improve health outcomes for pregnant people. Findings will be disseminated to diverse groups of stakeholders, including policy makers, clinicians, community partners, and individuals with lived experience of drug use. TRIAL REGISTRATION: PROSPERO CRD42022332082; https://tinyurl.com/2p94pkx5. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/42417.

Oral Fluvoxamine With Inhaled Budesonide for Treatment of Early-Onset COVID-19 : A Randomized Platform Trial.

BACKGROUND: Previous trials have demonstrated the effects of fluvoxamine alone and inhaled budesonide alone for prevention of disease progression among outpatients with COVID-19. OBJECTIVE: To determine whether the combination of fluvoxamine and inhaled budesonide would increase treatment effects in a highly vaccinated population. DESIGN: Randomized, placebo-controlled, adaptive platform trial. (ClinicalTrials.gov: NCT04727424). SETTING: 12 clinical sites in Brazil. PARTICIPANTS: Symptomatic adults with confirmed SARS-CoV-2 infection and a known risk factor for progression to severe disease. INTERVENTION: Patients were randomly assigned to either fluvoxamine (100 mg twice daily for 10 days) plus inhaled budesonide (800 mcg twice daily for 10 days) or matching placebos. MEASUREMENTS: The primary outcome was a composite of emergency setting retention for COVID-19 for more than 6 hours, hospitalization, and/or suspected complications due to clinical progression of COVID-19 within 28 days of randomization. Secondary outcomes included health care attendance (defined as hospitalization for any cause or emergency department visit lasting >6 hours), time to hospitalization, mortality, patient-reported outcomes, and adverse drug reactions. RESULTS: Randomization occurred from 15 January to 6 July 2022. A total of 738 participants were allocated to oral fluvoxamine plus inhaled budesonide, and 738 received placebo. The proportion of patients observed in an emergency setting for COVID-19 for more than 6 hours or hospitalized due to COVID-19 was lower in the treatment group than the placebo group (1.8% [95% credible interval {CrI}, 1.1% to 3.0%] vs. 3.7% [95% CrI, 2.5% to 5.3%]; relative risk, 0.50 [95% CrI, 0.25 to 0.92]), with a probability of superiority of 98.7%. No relative effects were found between groups for any of the secondary outcomes. More adverse events occurred in the intervention group than the placebo group, but no important differences between the groups were detected. LIMITATION: Low event rate overall, consistent with contemporary trials in vaccinated populations. CONCLUSION: Treatment with oral fluvoxamine plus inhaled budesonide among high-risk outpatients with early COVID-19 reduced the incidence of severe disease requiring advanced care. PRIMARY FUNDING SOURCE: Latona Foundation, FastGrants, and Rainwater Charitable Foundation.

Development and external validation of machine learning algorithms for postnatal gestational age estimation using clinical data and metabolomic markers.

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.

Early Treatment with Pegylated Interferon Lambda for Covid-19.

BACKGROUND: The efficacy of a single dose of pegylated interferon lambda in preventing clinical events among outpatients with acute symptomatic coronavirus disease 2019 (Covid-19) is unclear. METHODS: We conducted a randomized, controlled, adaptive platform trial involving predominantly vaccinated adults with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in Brazil and Canada. Outpatients who presented with an acute clinical condition consistent with Covid-19 within 7 days after the onset of symptoms received either pegylated interferon lambda (single subcutaneous injection, 180 µg) or placebo (single injection or oral). The primary composite outcome was hospitalization (or transfer to a tertiary hospital) or an emergency department visit (observation for >6 hours) due to Covid-19 within 28 days after randomization. RESULTS: A total of 933 patients were assigned to receive pegylated interferon lambda (2 were subsequently excluded owing to protocol deviations) and 1018 were assigned to receive placebo. Overall, 83% of the patients had been vaccinated, and during the trial, multiple SARS-CoV-2 variants had emerged. A total of 25 of 931 patients (2.7%) in the interferon group had a primary-outcome event, as compared with 57 of 1018 (5.6%) in the placebo group, a difference of 51% (relative risk, 0.49; 95% Bayesian credible interval, 0.30 to 0.76; posterior probability of superiority to placebo, >99.9%). Results were generally consistent in analyses of secondary outcomes, including time to hospitalization for Covid-19 (hazard ratio, 0.57; 95% Bayesian credible interval, 0.33 to 0.95) and Covid-19-related hospitalization or death (hazard ratio, 0.59; 95% Bayesian credible interval, 0.35 to 0.97). The effects were consistent across dominant variants and independent of vaccination status. Among patients with a high viral load at baseline, those who received pegylated interferon lambda had lower viral loads by day 7 than those who received placebo. The incidence of adverse events was similar in the two groups. CONCLUSIONS: Among predominantly vaccinated outpatients with Covid-19, the incidence of hospitalization or an emergency department visit (observation for >6 hours) was significantly lower among those who received a single dose of pegylated interferon lambda than among those who received placebo. (Funded by FastGrants and others; TOGETHER ClinicalTrials.gov number, NCT04727424.).

Digital Immunization Tracking in Long-Term Care and Assisted Living Facilities.

A disproportionate share of the health impacts of COVID-19 has been borne by older adults, particularly those in long-term care facilities (LTCs). Vaccination has been critical to efforts to combat this issue, but as we begin to emerge from this pandemic, questions remain about how to protect the health of residents of LTC and assisted living facilities proactively in order to prevent such a disaster from occurring again. Vaccination, not just against COVID-19, but also against other vaccine-preventable illness, will be a key component of this effort. However, there are currently substantial gaps in the uptake of vaccines recommended for older adults. Technology offers an opportunity to assist in filling these vaccination gaps. Our experiences in Fredericton, New Brunswick suggest that a digital immunization solution would facilitate better uptake of adult vaccines for older adults in assisted and independent living facilities and would help policy and decision makers to identify coverage gaps and develop interventions to protect these individuals.

Advancing detection and response capacities for emerging and re-emerging pathogens in Africa.

Recurrent disease outbreaks caused by a range of emerging and resurging pathogens over the past decade reveal major gaps in public health preparedness, detection, and response systems in Africa. Underlying causes of recurrent disease outbreaks include inadequacies in the detection of new infectious disease outbreaks in the community, in rapid pathogen identification, and in proactive surveillance systems. In sub-Saharan Africa, where 70% of zoonotic outbreaks occur, there remains the perennial risk of outbreaks of new or re-emerging pathogens for which no vaccines or treatments are available. As the Ebola virus disease, COVID-19, and mpox (formerly known as monkeypox) outbreaks highlight, a major paradigm shift is required to establish an effective infrastructure and common frameworks for preparedness and to prompt national and regional public health responses to mitigate the effects of future pandemics in Africa.

Early Treatment with Fluvoxamine among Patients with COVID-19: A Cost-Consequence Model.

To date, two published randomized trials have indicated a clinical benefit of early treatment with fluvoxamine versus placebo for adults with symptomatic COVID-19. Using the results of the largest of these trials, the TOGETHER trial, we conducted a cost-consequence analysis to assess the health system benefits of preventing progression to severe COVID-19 in outpatient populations in the United States. A decision-analytic model in the form of a decision tree was constructed to evaluate two treatment strategies for high-risk patients with confirmed, symptomatic COVID-19 in the primary analysis: treatment with a 10-day course of fluvoxamine (100 mg twice daily) and current standard-of-care. A secondary analysis comparing a 5-day course of nirmatrelvir-ritonavir was also conducted. We used a time horizon of 28 days. Reported outcomes included cost-savings and hospitalization days avoided. The results of our analysis indicated that administration of fluvoxamine to symptomatic outpatients at high risk of progressing to severe COVID-19 was substantially cost-saving, in the amount of $232 per eligible patient and prevented an average of 0.15 hospital days per patient treated, compared with standard of care. Nirmatrelvir-ritonavir was also shown to be cost-saving despite its higher acquisition cost and provided savings to the healthcare system of $625 per patient treated. These findings suggest that fluvoxamine is likely to be a cost-effective addition to frontline COVID-19 mitigation strategies in many settings, particularly where access to nirmaltrevir-ritonavir or monoclonal antibodies is limited.

Use of trained scent dogs for detection of COVID-19 and evidence of cost-saving.

Background: One of the lessons learned from the coronavirus disease 2019 (COVID-19) pandemic is the importance of early, flexible, and rapidly deployable disease detection methods. Currently, diagnosis of COVID-19 requires the collection of oro/nasopharyngal swabs, nasal turbinate, anterior nares and saliva but as the pandemic continues, disease detection methods that can identify infected individuals earlier and more quickly will be crucial for slowing the spread of the virus. Previous studies have indicated that dogs can be trained to identify volatile organic compounds (VOCs) produced during respiratory infections. We sought to determine whether this approach could be applied for detection of COVID-19 in Rwanda and measured its cost-saving. Methods: Over a period of 5 months, four dogs were trained to detect VOCs in sweat samples collected from human subjects confirmed positive or negative for COVID-19 by reverse transcription polymerase chain reaction (RT-PCR) testing. Dogs were trained using a detection dog training system (DDTS) and in vivo diagnosis. Samples were collected from 5,253 participants using a cotton pad swiped in the underarm to collect sweat samples. Statistical analysis was conducted using R statistical software. Findings: From August to September 2021 during the Delta wave, the sensitivity of the dogs' COVID-19 detection ranged from 75.0 to 89.9% for the lowest- and highest-performing dogs, respectively. Specificity ranged from 96.1 to 98.4%, respectively. In the second phase coinciding with the Omicron wave (January-March 2022), the sensitivity decreased substantially from 36.6 to 41.5%, while specificity remained above 95% for all four dogs. The sensitivity and specificity by any positive sample detected by at least one dog was 83.9, 95% CI: 75.8-90.2 and 94.9%; 95% CI: 93.9-95.8, respectively. The use of scent detection dogs was also found to be cost-saving compared to antigen rapid diagnostic tests, based on a marginal cost of approximately $14,000 USD for testing of the 5,253 samples which makes 2.67 USD per sample. Testing turnaround time was also faster with the scent detection dogs, at 3 h compared to 11 h with routine diagnostic testing. Conclusion: The findings from this study indicate that trained dogs can accurately identify respiratory secretion samples from asymptomatic and symptomatic COVID-19 patients timely and cost-effectively. Our findings recommend further uptake of this approach for COVID-19 detection.

Toward a New Paradigm of North-South and South-South Partnerships for Pandemic Preparedness: Lessons Learned from COVID-19 and Other Outbreaks.

COVID-19 underscores the need to reimagine North-South partnerships and redefine best practices for building public health and research capacity to address emergent health threats and pandemic preparedness in low- and-middle income countries (LMICs). Historically, outbreak and emergency responses have failed to ensure that the Global South has the autonomy and capacity to respond to public health threats in a timely and equitable manner. The COVID-19 response, however, has demonstrated that innovations and solutions in the Global South can not only fill resource and capacity gaps in LMICs but can also provide solutions to challenges globally. These innovations offer valuable lessons about strengthening local manufacturing capacity to produce essential diagnostic, treatment, and prevention tools; implementing high-quality research studies; expanding laboratory and research capacity; and promoting effective cooperation and governance. We discuss specific examples of capacity-building from Rwanda, South Africa, and Senegal. To fulfill promises made to the Global South during the COVID-19 pandemic, restore and resume health service delivery, and effectively prevent and respond to the next health threat, we need to prioritize equitable access to local manufacturing of basic health tools while building health systems capacities in the Global South.

Interferon Treatments for SARS-CoV-2: Challenges and Opportunities.

Interferon (IFN) therapies are used to treat a variety of infections and diseases and could be used to treat SARS-CoV-2. However, optimal use and timing of IFN therapy to treat SARS-CoV-2 is not well documented. We aimed to synthesize available evidence to understand whether interferon therapy should be recommended for treatment compared to a placebo or standard of care in adult patients. We reviewed literature comparing outcomes of randomized control trials that used IFN therapy for adults diagnosed with SARS-CoV-2 between 2019 and 2021. Data were extracted from 11 of 669 screened studies. Evidence of IFN effectiveness was mixed. Five studies reported that IFN was a better therapy than the control, four found no or minimal difference between IFN and the control, and two concluded that IFN led to worse patient outcomes than the control. Evidence was difficult to compare because of high variability in outcome measures, intervention types and administration, subtypes of IFNs used and timing of interventions. We recommend standardized indicators and reporting for IFN therapy for SARS-CoV-2 to improve evidence synthesis and generation. While IFN therapy has the potential to be a viable treatment for SARS-CoV-2, especially when combined with antivirals and early administration, the lack of comparable of study outcomes prevents evidence synthesis and uptake.

Real world external validation of metabolic gestational age assessment in Kenya.

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.

Cost-effectiveness of a gestational age metabolic algorithm for preterm and small-for-gestational-age classification.

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.

Newcomer knowledge, attitudes, and beliefs about human papillomavirus (HPV) vaccination.

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.

A Global Pandemic Treaty Must Address Antimicrobial Resistance.

Antimicrobial resistance (AMR) is one of the defining global health threats of our time, but no international legal instrument currently offers the framework and mechanisms needed to address it. Fortunately, the actions needed to address AMR have considerable overlap with the actions needed to confront other pandemic threats.