Inform Public Health Policy Research Articles

Effect of human H3N2 influenza virus reassortment on influenza incidence and severity during the 2017–18 influenza season in the USA: a retrospective observational genomic analysis

During the 2017-18 influenza season in the USA, there was a high incidence of influenza illness and mortality. However, no apparent antigenic change was identified in the dominant H3N2 viruses, and the severity of the season could not be solely attributed to a vaccine mismatch. We aimed to investigate whether the altered virus properties resulting from gene reassortment were underlying causes of the increased case number and disease severity associated with the 2017-18 influenza season. Samples included were collected from patients with influenza who were prospectively recruited during the 2016-17 and 2017-18 influenza seasons at the Johns Hopkins Hospital Emergency Departments in Baltimore, MD, USA, as well as from archived samples from Johns Hopkins Health System sites. Among 647 recruited patients with influenza A virus infection, 411 patients with whole-genome sequences were available in the Johns Hopkins Center of Excellence for Influenza Research and Surveillance network during the 2016-17 and 2017-18 seasons. Phylogenetic trees were constructed based on viral whole-genome sequences. Representative viral isolates of the two seasons were characterised in immortalised cell lines and human nasal epithelial cell cultures, and patients' demographic data and clinical outcomes were analysed. Unique H3N2 reassortment events were observed, resulting in two predominant strains in the 2017-18 season: HA clade 3C.2a2 and clade 3C.3a, which had novel gene segment constellations containing gene segments from HA clade 3C.2a1 viruses. The reassortant re3C.2a2 viruses replicated with faster kinetics and to a higher peak titre compared with the parental 3C.2a2 and 3C.2a1 viruses (48 h vs 72 h). Furthermore, patients infected with reassortant 3C.2a2 viruses had higher Influenza Severity Scores than patients infected with the parental 3C.2a2 viruses (median 3·00 [IQR 1·00-4·00] vs 1·50 [1·00-2·00]; p=0·018). Our findings suggest that the increased severity of the 2017-18 influenza season was due in part to twointrasubtypes, cocirculating H3N2 reassortant viruses with fitness advantages over the parental viruses. This information could help inform future vaccine development and public health policies. The Center of Excellence for Influenza Research and Response in the US, National Science and Technology Council, and Chang Gung Memorial Hospital in Taiwan.

Discovering Subgroups of Children With High Mortality in Urban Guinea-Bissau: Exploratory and Validation Cohort Study.

The decline in global child mortality is an important public health achievement, yet child mortality remains disproportionally high in many low-income countries like Guinea-Bissau. The persisting high mortality rates necessitate targeted research to identify vulnerable subgroups of children and formulate effective interventions. This study aimed to discover subgroups of children at an elevated risk of mortality in the urban setting of Bissau, Guinea-Bissau, West Africa. By identifying these groups, we intend to provide a foundation for developing targeted health interventions and inform public health policy. We used data from the health and demographic surveillance site, Bandim Health Project, covering 2003 to 2019. We identified baseline variables recorded before children reached the age of 6 weeks. The focus was on determining factors consistently linked with increased mortality up to the age of 3 years. Our multifaceted methodological approach incorporated spatial analysis for visualizing geographical variations in mortality risk, causally adjusted regression analysis to single out specific risk factors, and machine learning techniques for identifying clusters of multifactorial risk factors. To ensure robustness and validity, we divided the data set temporally, assessing the persistence of identified subgroups over different periods. The reassessment of mortality risk used the targeted maximum likelihood estimation (TMLE) method to achieve more robust causal modeling. We analyzed data from 21,005 children. The mortality risk (6 weeks to 3 years of age) was 5.2% (95% CI 4.8%-5.6%) for children born between 2003 and 2011, and 2.9% (95% CI 2.5%-3.3%) for children born between 2012 and 2016. Our findings revealed 3 distinct high-risk subgroups with notably higher mortality rates, children residing in a specific urban area (adjusted mortality risk difference of 3.4%, 95% CI 0.3%-6.5%), children born to mothers with no prenatal consultations (adjusted mortality risk difference of 5.8%, 95% CI 2.6%-8.9%), and children from polygamous families born during the dry season (adjusted mortality risk difference of 1.7%, 95% CI 0.4%-2.9%). These subgroups, though small, showed a consistent pattern of higher mortality risk over time. Common social and economic factors were linked to a larger share of the total child deaths. The study's results underscore the need for targeted interventions to address the specific risks faced by these identified high-risk subgroups. These interventions should be designed to work to complement broader public health strategies, creating a comprehensive approach to reducing child mortality. We suggest future research that focuses on developing, testing, and comparing targeted intervention strategies unraveling the proposed hypotheses found in this study. The ultimate aim is to optimize health outcomes for all children in high-mortality settings, leveraging a strategic mix of targeted and general health interventions to address the varied needs of different child subgroups.

Correlation of Socioeconomic and Environmental Factors With Incidence of Crohn Disease in Children and Adolescents: Systematic Review and Meta-Regression.

The worldwide incidence of Crohn disease (CD) in childhood and adolescence has an increasing trend, with significant differences between different geographic regions and individual countries. This includes an increase in the incidence of CD in countries and geographic regions where CD was not previously prevalent. In response to the increasing incidence, the pediatric care landscape is facing growing challenges. This systematic review and meta-analysis were undertaken to comprehensively delineate the incidence rates of CD in pediatric populations across different countries and to explore potential influencing factors. We performed a systematic review of PubMed and Embase (via Ovid) for studies from January 1, 1970, to December 31, 2019. In addition, a manual search was performed in relevant and previously published reviews. The results were evaluated quantitatively. For this purpose, random effects meta-analyses and meta-regressions were performed to investigate the overall incidence rate and possible factors influencing the incidence. A qualitative synthesis of 74 studies was performed, with 72 studies included in the meta-analyses and 52 in the meta-regressions. The results of our meta-analysis showed significant heterogeneity between the individual studies, which cannot be explained by a sample effect alone. Our findings showed geographical differences in incidence rates, which increased with increasing distance from the equator, although no global temporal trend was apparent. The meta-regression analysis also identified geographic location, UV index, and Human Development Index as significant moderators associated with CD incidence. Our results suggest that pediatric CD incidence has increased in many countries since 1970 but varies widely with geographic location, which may pose challenges to the respective health care systems. We identified geographic, environmental, and socioeconomic factors that contribute to the observed heterogeneity in incidence rates. These results can serve as a basis for future research. To this end, implementations of internationally standardized and interoperable registries combined with the dissemination of health data through federated networks based on a common data model, such as the Observational Medical Outcomes Partnership, would be beneficial. This would deepen the understanding of CD and promote evidence-based approaches to preventive and interventional strategies as well as inform public health policies aimed at addressing the increasing burden of CD in children and adolescents. PROSPERO International prospective register of systematic reviews CRD42020168644; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=168644. RR2-10.1136/bmjopen-2020-037669.

MOBILE HEALTH (MHEALTH) INNOVATIONS FOR PUBLIC HEALTH FEEDBACK: A GLOBAL PERSPECTIVE

In recent years, Mobile Health (mHealth) innovations have emerged as a transformative force in the realm of public health, revolutionizing the way healthcare is delivered and monitored worldwide. This review presents a comprehensive overview of the latest advancements in mHealth technologies, focusing particularly on their role in soliciting and utilizing public health feedback on a global scale. The proliferation of smartphones and mobile applications has paved the way for novel approaches to healthcare delivery, monitoring, and data collection. Through leveraging the ubiquity and connectivity of mobile devices, mHealth initiatives have facilitated enhanced communication between healthcare providers and patients, enabling real-time monitoring of health metrics, adherence to treatment regimens, and timely interventions. One of the key aspects of mHealth innovations is their capacity to engage diverse populations and solicit feedback regarding various aspects of public health, ranging from disease outbreaks to healthcare service quality. Through interactive platforms and user-friendly interfaces, individuals can provide valuable insights, report symptoms, and participate in surveys, thereby contributing to the generation of actionable data for public health interventions. Furthermore, mHealth solutions have demonstrated significant promise in overcoming barriers to healthcare access, particularly in underserved and remote communities. By delivering health information, diagnostic tools, and remote consultations via mobile platforms, these innovations have expanded the reach of essential healthcare services, thereby bridging gaps in healthcare delivery and improving health outcomes on a global scale. However, challenges such as privacy concerns, technological literacy, and disparities in digital access persist, underscoring the importance of equitable deployment and user-centered design in mHealth initiatives. Moreover, the integration of mHealth solutions into existing healthcare infrastructure requires careful coordination among stakeholders, including policymakers, healthcare providers, and technology developers. Mobile Health (mHealth) innovations hold immense potential to revolutionize public health feedback mechanisms on a global scale. By harnessing the power of mobile technologies, these initiatives can empower individuals, enhance healthcare delivery, and inform evidence-based public health policies for a healthier future.
 Keywords: Mobile Health, mHealth, Public Health, Healthcare, Innovation, Review.

COVID-19 outcome trends by vaccination status in Canada, December 2020-January 2022.

The coronavirus disease 2019 (COVID-19) pandemic in Canada has evolved rapidly. Since late 2020, COVID-19 vaccines have been relied on to protect against severe outcomes in the presence of circulating variants of concern (VOC). This surveillance report provides a retrospective descriptive analysis of national trends in COVID-19 cases and severe outcomes by vaccination status, contextualizing trends against case demographics and circulating VOCs, from December 2020 to January 2022. Case and vaccination coverage surveillance data were obtained from the National COVID-19 Case Dataset and the Canadian COVID-19 Vaccination Coverage Surveillance System for 12 of 13 provinces and territories. Descriptive analyses were produced to describe trends over time among individuals aged 12 years and older by COVID-19 outcome, vaccination status, and demographics. Age-standardized and age-stratified incidence rates and incidence rate ratios were computed for cases, hospitalizations, and deaths. From mid to late-2021, incidence rates for cases and severe outcomes were consistently lowest among those with a completed primary series and highest among those who were unvaccinated. Unvaccinated individuals were much more likely to be hospitalized or to die compared to those with a completed primary series in all variant periods. Age-specific rates of severe outcomes were consistently highest among those aged 80 years and older across all vaccination statuses. Vaccination remains one of the most important public health interventions, particularly among older adults, to protect against COVID-19 severe outcomes as the pandemic evolves. Routine monitoring of COVID-19 outcomes by vaccination status can identify changes in COVID-19 epidemiology and inform public health action and policy.

Prenatal stress and externalizing behaviors in childhood and adolescence: A systematic review and meta-analysis.

Accumulating evidence suggests that psychological distress during pregnancy is linked to offspring risk for externalizing outcomes (e.g., reactive/aggressive behaviors, hyperactivity, and impulsivity). Effect sizes across studies have varied widely, however, due to differences in study design and methodology, including control for the confounding continuation of distress in the postnatal period. Clarifying these inconsistencies is necessary to guide the precision of prevention efforts and inform public health policies. A meta-analysis was conducted with 55 longitudinal studies to investigate the association between prenatal psychological distress (anxiety, depression, and perceived stress) and offspring externalizing behaviors. Results revealed a significant but small effect (r = .160) of prenatal distress on externalizing behaviors. The magnitude of the prenatal effect size remained largely unchanged after adjusting for postnatal distress (r = .159), implicating a unique effect of psychological distress during the prenatal period in the etiology of externalizing behaviors. Moderation tests showed that prenatal effects did not vary based on type and timing of psychological distress during pregnancy. Greater instability of distress from prenatal to postnatal periods predicted larger effects. Prenatal effects were comparable across most externalizing outcomes, consistent with the common comorbidity of externalizing spectrum disorders, although effects appeared smaller for nonaggressive rule-breaking (vs. aggressive) behaviors. Significant associations persisted across all developmental periods, appearing slightly larger in early childhood. We discuss these results in the context of developmental and psychobiological theories of externalizing behavior, offer preliminary clinical and public health implications, and highlight directions for future research including the need for longitudinal studies with more racially and socioeconomically diverse families. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Types and Brands of Derived Psychoactive Cannabis Products: An Online Retail Assessment, 2023.

Background: The 2018 Farm Bill led to new types of derived psychoactive cannabis products (DPCPs) being sold throughout the United States. This study describes the new types and brands of DPCPs sold online. Materials and Methods: In May 2023, data were recorded from three top-trafficked U.S.-based DPCP retail websites, including information about each product (N=804). Results: DPCP modalities included disposable vapes (43%), edibles (29%), vape carts (18%), pre-rolls (7%), flower (2%), dabs (1%), and vape pods (<1%). Among the 118 brands, the most common were Exhale, Delta Extrax, Cake, URB, Looper, and TRE House. There were 26 different intoxicating compounds overall, the most prevalent being: Delta-8 tetrahydrocannabinol (THC), THC-P, Delta-9 THC, HHC, THC-A, Delta-10 THC, THC-H, THC-B, THC-JD, THC-X, HHC-P, and Delta-11 THC. Overall, 54% of products were blends, containing two to eight different intoxicating compounds in a single product. Discussion: This is the first study to systematically assess DPCPs sold online. Most of the DPCP market is comprised of vapes and edibles, but these products contain a wide array of compounds and blends. Data from this diverse, rapidly evolving market are needed to examine its consumer impact and inform public health policies and programs.

Machine learning drug discovery based on graph neural network and large language model

The COVID-19 pandemic has presented an urgent need to understand the long-term health implications faced by survivors. Post-COVID-19 complications, such as acute kidney injury, arrhythmia, and stroke, pose significant challenges to public health. Despite extensive research on COVID-19 complications, a comprehensive understanding of the risk factors remains elusive due to the potential confounding variables present in the data. Traditional statistical models, while insightful, may not fully capture the causal relationships between these risk factors and post-COVID-19 complications. Motivated by this gap in the literature, we propose a novel approach using causal inference models to predict the likelihood of post-COVID-19 complications based on patient demographics and pre-existing conditions. Our model, trained on a dataset of COVID-19 inpatients in Wuhan Province, China, estimates the causal effect of these factors on the likelihood of patients experiencing post-COVID-19 complications. This approach allows us to isolate the causal impact of each factor while accounting for potential confounders, providing a more accurate understanding of the underlying mechanisms driving these relationships. Unlike traditional models that predict the probability of certain outcomes, our model provides insights into the causal relationships between risk factors and complications, offering a more reliable and comprehensive understanding of the underlying mechanisms. This approach can help identify at-risk patients, inform targeted interventions, and contribute to the development of effective prevention and treatment strategies. Our work aims to contribute to the current understanding of the virus and inform public health policies and interventions.

Assessing the impact of human behavior towards preventative measures on COVID-19 dynamics for Gauteng, South Africa: a simulation and forecasting approach

&lt;abstract&gt;&lt;p&gt;Globally, the COVID-19 pandemic has claimed millions of lives. In this study, we develop a mathematical model to investigate the impact of human behavior on the dynamics of COVID-19 infection in South Africa. Specifically, our model examined the effects of positive versus negative human behavior. We parameterize the model using data from the COVID-19 fifth wave of Gauteng province, South Africa, from May 01, 2022, to July 23, 2022. To forecast new cases of COVID-19 infections, we compared three forecasting methods: exponential smoothing (ETS), long short-term memory (LSTM), and gated recurrent units (GRUs), using the dataset. Results from the time series analysis showed that the LSTM model has better performance and is well-suited for predicting the dynamics of COVID-19 compared to the other models. Sensitivity analysis and numerical simulations were also performed, revealing that noncompliant infected individuals contribute more to new infections than those who comply. It is envisaged that the insights from this work can better inform public health policy and enable better projections of disease spread.&lt;/p&gt;&lt;/abstract&gt;

Complementing traditional care with digital therapeutics for chronic non-communicable disease management

Abstract We will examine the role of digital therapeutics in diabetes care, where we will discuss how Digital Therapeutics can supplement face-to-face consultations by facilitating data exchange between patients and healthcare professionals, improving disease monitoring, and promoting patient empowerment. In the context of mental health, we will present evidence supporting the effectiveness of computer-assisted cognitive behaviour therapy (CCBT) as a valuable adjunct to treatment as usual (TAU) in primary care settings. Throughout the presentation, we will highlight the importance of integrating digital therapeutics into existing healthcare systems and addressing barriers to their adoption, such as socio-demographic disparities and healthcare professional attitudes through tools such as regulatory frameworks and reimbursement pathways. By exploring the complementary role of digital therapeutics in chronic NCD management, we aim to encourage dialogue on the potential synergies between traditional care and digital interventions to improve patient outcomes and inform public health policy.

Building public trust and confidence in secondary use of health data in Ireland

Abstract Background Secondary use of health data can drive research and healthcare improvements with implications for policy and practice. However, relevant legislation, implementation of technical safeguards, and ethical data management are essential. Existing evidence suggests general public support for secondary data use, but with specific concerns about privacy, confidentiality, misuse of data and trust. The aim of this study is to explore key stakeholders’ perspectives on secondary health data use in Ireland, and mechanisms to build a culture of trust. Methods The study adopts a qualitative cross-sectional approach. Participants include academics and researchers, healthcare professionals, data controllers and ethics experts, industry, and patients and public, recruited through purposive sampling. Data is collected through focus groups discussions, and reflexive thematic analysis is employed. Results The preliminary results suggest that benefits of secondary data use include access to large datasets, streamlined research, reduced bias, and cost-effectiveness, which may lead to improved patient outcomes. Barriers include an underdeveloped national data infrastructure, difficulties with access and linkage, complex ethical regulation, and narrow predetermined indices of health. Concerns related to data sharing are context-dependent, and influenced by patients’ socio-demographic characteristics. Trust appears to be both a bottom-up and top-down process, facilitated through clear communication, organizational openness, transparency, accountability, fairness, respect, and patient and public involvement (PPI). Conclusions The study identifies strong support for secondary health data use among the stakeholders, notwithstanding an identified need for structural improvements, strong safeguards, education and training, and meaningful PPI. This research helps inform public health practice and policies towards building a culture of trust pertaining to secondary data use. Key messages • This study produces novel insights to help advance research and policy on the barriers and facilitators to building trust and confidence in the access and use of health data for secondary purposes. • This study contributes to qualitative research methods related to eliciting people's views and perspectives in a safe, collaborative and efficient manner.

Development of the TrAnsparent ReportinG of observational studies Emulating a Target trial (TARGET) guideline

BackgroundObservational studies are increasingly used to inform health decision-making when randomised trials are not feasible, ethical or timely. The target trial approach provides a framework to help minimise common biases...

Assessment of Hospital-Onset SARS-CoV-2 Infection Rates and Testing Practices in the US, 2020-2022.

Characterizing the scale and factors associated with hospital-onset SARS-CoV-2 infections could help inform hospital and public health policies regarding prevention and surveillance needs for these infections. To evaluate associations of hospital-onset SARS-CoV-2 infection rates with different periods of the COVID-19 pandemic, hospital characteristics, and testing practices. This cohort study of US hospitals reporting SARS-CoV-2 testing data in the PINC AI Healthcare Database COVID-19 special release files was conducted from July 2020 through June 2022. Data were collected from hospitals that reported at least 1 SARS-CoV-2 reverse transcription-polymerase chain reaction or antigen test during hospitalizations discharged that month. For each hospital-month where the hospital reported sufficient data, all hospitalizations discharged in that month were included in the cohort. SARS-CoV-2 viral tests and results reported in the microbiology files for all hospitalizations in the study period by discharge month were identified. Data analysis was conducted from September 2022 to March 2023. Hospitalizations discharged in an included hospital-month. Multivariable generalized estimating equation negative-binomial regression models were used to assess associations of monthly rates of hospital-onset SARS-CoV-2 infections per 1000 patient-days (defined as a first positive SARS-CoV-2 test during after hospitalization day 7) with the phase of the pandemic (defined as the predominant SARS-CoV-2 variant in circulation), admission testing rates, and hospital characteristics (hospital bed size, teaching status, urban vs rural designation, Census region, and patient distribution variables). A total of 5687 hospital-months from 288 distinct hospitals were included, which contributed 4 421 268 hospitalization records. Among 171 564 hospitalizations with a positive SARS-CoV-2 test, 7591 (4.4%) were found to be hospital onset and 6455 (3.8%) were indeterminate onset. The mean monthly hospital-onset infection rate per 1000 patient-days was 0.27 (95 CI, 0.26-0.29). Hospital-onset infections occurred in 2217 of 5687 hospital-months (39.0%). The monthly percentage of discharged patients tested for SARS-CoV-2 at admission varied; 1673 hospital-months (29.4%) had less than 25% of hospitalizations tested at admission; 2199 hospital-months (38.7%) had 25% to 50% of all hospitalizations tested, and 1815 hospital months (31.9%) had more than 50% of all hospitalizations tested at admission. Postadmission testing rates and community-onset infection rates increased with admission testing rates. In multivariable models restricted to hospital-months testing at least 25% of hospitalizations at admission, a 10% increase in community-onset SARS-CoV-2 infection rate was associated with a 178% increase in the hospital-onset infection rate (rate ratio, 2.78; 95% CI, 2.52-3.07). Additionally, the phase of the COVID-19 pandemic, the admission testing rate, Census region, and bed size were all significantly associated with hospital-onset SARS-CoV-2 infection rates. In this cohort study of hospitals reporting SARS-CoV-2 infections, there was an increase of hospital-onset SARS-CoV-2 infections when community-onset infections were higher, indicating a need for ongoing and enhanced surveillance and prevention efforts to reduce in-hospital transmission of SARS-CoV-2 infections, particularly when community-incidence of SARS-CoV-2 infections is high.

Persistence of antibody responses to COVID-19 vaccines among participants in the COVID-19 Community Research Partnership

IntroductionHigh levels of immunity to SARS-CoV-2 in the community correlate with protection from COVID-19 illness. Measuring COVID-19 antibody seroprevalence and persistence may elucidate the level and length of protection afforded by vaccination and infection within a population. MethodsWe measured the duration of detectable anti-spike antibodies following COVID-19 vaccination in a multistate, longitudinal cohort study of almost 13,000 adults who completed daily surveys and submitted monthly dried blood spots collected at home. ResultsOverall, anti-spike antibodies persisted up to 284 days of follow-up with seroreversion occurring in only 2.4% of the study population. In adjusted analyses, risk of seroreversion increased with age (adults aged 55–64: adjusted hazard ratio [aHR] 2.19 [95% confidence interval (CI): 1.22, 3.92] and adults aged > 65: aHR 3.59 [95% CI: 2.07, 6.20] compared to adults aged 18–39). Adults with diabetes had a higher risk of seroreversion versus nondiabetics (aHR 1.77 [95% CI: 1.29, 2.44]). Decreased risk of seroreversion was shown for non-Hispanic Black versus non-Hispanic White (aHR 0.32 [95% CI: 0.13, 0.79]); college degree earners versus no college degree (aHR 0.61 [95% CI: 0.46, 0.81]); and those who received Moderna mRNA-1273 vaccine versus Pfizer-BioNTech BNT162b2 (aHR 0.35 [95% CI: 0.26, 0.47]). An interaction between healthcare worker occupation and sex was detected, with seroreversion increased among male, non-healthcare workers. ConclusionWe established that a remote, longitudinal, multi-site study can reliably detect antibody durability following COVID-19 vaccination. The survey platform and measurement of antibody response using at-home collection at convenient intervals allowed us to explore sociodemographic factors and comorbidities and identify predictors of antibody persistence, which has been demonstrated to correlate with protection against disease. Our findings may help inform public health interventions and policies to protect those at highest risk for severe illness and assist in determining the optimal timing of booster doses.Clinical trials registry: NCT04342884.

Breast Cancer Prediction Based on RF-SVM

Breast cancer prediction is crucial in identifying women who may be at risk for developing the disease. By doing the prediction, doctors can make the rapid diagnosis. Additionally, breast cancer prediction can also help guide research efforts and inform public health policies aimed at reducing the incidence and mortality of breast cancer. SVM (Support Vector Machine)is a classic method in machine learning, Random Forest is also widely used but they all have some shortcomings. Random Forest dont have high accuracy. So RF-SVM(Random Forest and Random Forest) is be chosen to do the prediction. The goal of this research is to train a model that can achieve high accuracy in a relatively short time. As for the result, it shows that RF-SVM has achieved a high accuracy(0.95), compared with other method although RF(Random Forest) has the highest accuracy(0.97), it has the lowest precision(0.95). Over all, RF-SVM has the best overall performance. After trial, traditional machine learning methods turns out to be more stable.

IS BREASTFEEDING SUPERIOR TO FORMULA FEEDING IN IMPACTING CHILD COGNITIVE DEVELOPMENT? A SYSTEMATIC REVIEW

Background: Past research evidence has shown that breastfeeding has significant benefits with regard to cognitive development. However, emerging evidence has shown how breastfeeding may lead to equally beneficial outcomes with regard to cognitive development. Therefore, the present study was undertaken to assess the relevant evidence. Aims: The aim of this systematic review was to compare the impact of breastfeeding and formula feeding on cognitive development by analyzing prospective cohort studies. Methods: This research conducted a thorough search of several databases such as AMED, PUBMED in addition to British Nursing Index and CINAHL. The goal was to utilize all sources of information with regard to identification of prospective studies. The search was limited to articles published between 2000 and 2016, resulting in a total of 15 studies, including 10 quantitative and 5 qualitative studies, that were deemed relevant for review. To ensure that the studies selected for review were of high quality and free from biases, two critical appraisal tools were used. The first tool, the Critical Appraisal Skills Program (CASP), is a widely recognized tool for evaluating the quality and rigor of qualitative and quantitative research studies. The second tool, the Quality Assessment Tool for Quantitative Studies developed by the Effective Public Health Project (EPHPP), is specifically designed to assess the quality of quantitative studies. Results: The review findings revealed that breastfeeding may provide cognitive advantages to children when compared to formula feeding. However, it is pertinent to note how these results are often confounding by factors such as socioeconomic status, parental education etc. Consequently, when evaluating the effects of breastfeeding and formula feeding on cognitive development, it is necessary to consider the role of environmental, biological, social, and cultural factors. Conclusion: Overall, this review underscores the need for further research to better understand the complex relationships between breastfeeding, formula feeding, and cognitive development and to help inform public health policies and interventions aimed at promoting optimal cognitive development in children. Keywords: breast feeding, formula feeding, cognitive development, CASP, Medline, EMBASE

Rates of potentially actionable germline variants in homologous recombination repair genes in a large international cohort outside of the United States.

10590 Background: Identification of pathogenic germline variants (PGVs) in homologous recombination repair (HRR) genes among patients (pts) with breast (BC), ovarian (OC), pancreatic (PaC), and prostate cancer (PrC) is critical to determine potential eligibility for targeted therapies such as PARP inhibitors (PARPi). It is important to evaluate the frequency and patterns of PGVs across different regions of the World to help inform the counseling of pts and implementation of precision medicine. Methods: A retrospective analysis of pts with BC, OC, PaC, and/or PrC cancer undergoing GGT through a single laboratory was conducted. The location of the ordering provider determined the assigned region, which included: Asia-Pacific (APAC), Latin America (LATAM), Middle East and African (MEA), and United Kingdom and Europe (UKEU). Analyses were limited to pts tested for PARPi-eligible (in the US) HRR genes and PGV analysis to the 10 most frequent genes for that cancer type. Descriptive statistics and Chi square with Yates correction were utilized. Significance was set at p&lt;0.05. Results: The cohort consisted of 40,539 pts with an average age at testing of 51 years, the majority of whom had BC (81%) (Table). The median number of genes tested ranged from 14-85 (Table). BRCA1 and BRCA2 were the most frequent genes with PGVs (3,358 pts, 8%). LATAM BC pts had significantly higher rates of BRCA1/2 PGVs (10%) than the other 3 regions ( p &lt;0.0007). OC BRCA1/2 rates were high, and statistically similar, across regions (15-17%, p&gt;0.08). Rates of BRCA1/2 and PALB2 in PaC pts did not differ between regions (4-7%, p&gt;0.08), nor did rates of BRCA1/2, CHEK2, ATM, and PALB2 in PrC pts (7-11%, p&gt;0.16) (Table). In sum, 3,434 pts (9% of those tested for the eligible genes) had PGVs in the above PARPi-eligible HRR genes. A notable limitation is that pts were ascertained by cancer type but information on other PARPi eligibility, namely metastatic/advanced disease, was not available. Conclusions: 1 in 12 pts were found to carry PARPi-eligible PGVs across the 4 cancer types and regions analyzed. Knowledge of these PGVs can help inform public health policies, current and future treatment strategies, and identify at-risk family members. In some countries, GGT may represent a more cost-effective and accessible pathway to targeted therapies compared to tumor profiling due to ease of sample collection and lower cost of testing. [Table: see text]

Numerical Analysis of Fractional-Order Dynamic Dengue Disease Epidemic in Sudan

The main idea of this work is numerical simulation and stability analysis for the fractional-order dynamics of the dengue disease outbreak in Sudan. This research uses a computer technique based on the Adams-Bashforth approach to numerically resolve a fractional-order dengue epidemic in Sudan. Analyses of numerical and dynamic stability show that the fractional-order dengue fever model is sensitive to initial conditions for those parameters. Therefore, the parameters’ values are critical in establishing how many individuals will get better from their sickness and how many will become ill. The proposed method is effective in providing an illustration of the solution’s dynamics over a very long horizon of time, which is crucial for making accurate predictions about the spread of dengue in Sudan. In addition, this method can be utilized to assess the efficacy of various intervention strategies and inform public health policies aimed at reducing the burden of dengue fever in Sudan. It can also assist in identifying areas most susceptible to dengue infestations and prioritizing disease control resources.

105 What is the Association Between Smoking or Vaping and Covid-19 Susceptibility?

Abstract Currently, any association between smoking and COVID-19 susceptibility is conflicted as there is limited research especially on the impact of vaping. This systematic literature review will identify genes and signalling pathways that are affected by smoking or vaping that may play a role in altering the susceptibility of an individual to COVID-19. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, OVID and Web of Science databases, were searched (1946 to Nov 2021). Abstracts and subsequent full texts were screened for eligibility by RB, with EM and AB screening 5% each. Discrepancies were discussed and agreed between all. Papers were excluded for reasons such as focussing on comorbidities, immune cells or those with no exposure. Data from included papers detailing the exposure (type, duration and dose if applicable), pathway or gene of interest, sample type (species and cell type) and the overall findings, were extracted. Pathway analysis was undertaken to identify those pathways in common between smoking, vaping, nicotine and COVID-19 (g:Profiler and Cytoscape). We identified genes within the respiratory tract involved in both the initial viral response and those affected by smoking, vaping or nicotine exposure. This enabled us to determine signalling pathways of interest and therefore areas for further study. This systematic review will inform further research to identify any altered susceptibility and the risk of severe COVID-19 posed to smokers and vapers. This will inform Public Health Policies, highlighting vulnerable groups and provide guidance of relevance to both COVID-19 and other respiratory viruses.

Socioeconomic inequalities of Long COVID: a retrospective population-based cohort study in the United Kingdom.

To estimate the risk of Long COVID by socioeconomic deprivation and to further examine the inequality by sex and occupation. We conducted a retrospective population-based cohort study using data from the ONS COVID-19 Infection Survey between 26 April 2020 and 31 January 2022. This is the largest nationally representative survey of COVID-19 in the UK with longitudinal data on occupation, COVID-19 exposure and Long COVID. Community-based survey in the UK. A total of 201,799 participants aged 16 to 64 years and with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. The risk of Long COVID at least 4 weeks after SARS-CoV-2 infection by index of multiple deprivation (IMD) and the modifying effects of socioeconomic deprivation by sex and occupation. Nearly 10% (n = 19,315) of participants reported having Long COVID. Multivariable logistic regression models, adjusted for a range of variables (demographic, co-morbidity and time), showed that participants in the most deprived decile had a higher risk of Long COVID (11.4% vs. 8.2%; adjusted odds ratio (aOR): 1.46; 95% confidence interval (CI): 1.34, 1.59) compared to the least deprived decile. Significantly higher inequalities (most vs. least deprived decile) in Long COVID existed in healthcare and patient-facing roles (aOR: 1.76; 95% CI: 1.27, 2.44), in the education sector (aOR: 1.68; 95% CI: 1.31, 2.16) and in women (aOR: 1.56; 95% CI: 1.40, 1.73) than men (aOR: 1.32; 95% CI: 1.15, 1.51). This study provides insights into the heterogeneous degree of inequality in Long COVID by deprivation, sex and occupation. These findings will help inform public health policies and interventions in incorporating a social justice and health inequality lens.