Precision Public Health Research Articles

Not All Carcinogens Are Directly Mutagenic: A Holistic View of the Environmental Exposure–Cancer Connection

Cancers, a leading cause of morbidity and mortality worldwide, are undergoing notable shifts in incidence patterns. Cancer subtypes generally associated with advancing age are now increasingly being diagnosed in younger populations, while gender disparities in cancer susceptibility have fluctuated across various malignancies. Some cancer subtypes previously confined to specific geographical populations are now globally prevalent. Remarkably, these changing patterns of cancer incidence have occurred despite relatively stable mutational landscapes specific to each cancer type, highlighting the role of environmental exposures (exposomes) as potential drivers of these trends. Intriguingly, evidence suggests that the exposome may exert some of its influence through nonmutagenic mechanisms, although the precise ways in which environmental carcinogens trigger or promote cancer development remain poorly understood. This review summarizes the current understanding of the mutagenic and nonmutagenic mechanisms through which carcinogens, including tobacco smoke, air pollutants, metals, diet, and alcohol, impact cancer development, as this may help inform precision prevention and public health policy.

Double edged tech: navigating the public health and legal challenges of digital twin technology

Abstract This research addresses the legal challenges posed by the implementation of digital twin technology in public health. Digital twins are advanced computational models which aim to replicate human organ functions and biological processes. Their goal is to enable the simulation of the entire human body and to personalize the outputs with individual patient’s health data. Digital twin technology promises to bridge gap between personalized medicine and precision public health. This research is based on a comprehensive review of the current European and Swiss legal frameworks for medical devices and data protection that apply to digital twin technology. Within these frameworks, digital twins are in a grey area, as they are only considered as medical device software when applied to individuals. They also incorporate artificial intelligence, machine learning, and data analytics, which are already a source of legal concern in a healthcare setting, but their effective use in public health raises additional issues. Preliminary findings suggest significant shortcomings in the legal frameworks regarding data privacy, and the potential for increased surveillance by health insurance systems, which could lead to human rights violations. The public health application of digital twins would require global data analysis of sensitive personal information, raising further questions about the secure storage, transmission, and processing of this data. The study concludes that current legal frameworks must be future proofed to manage the complexities introduced by digital twins in public health. In particular, it suggests that regulatory oversight of medical devices needs to be significantly improved in order to safely harness the potential of digital twin technologies for public health. Finally, the new European Union health data space and the forthcoming EU AI act will also play important roles in the implementation of digital twin technologies. Key messages • Applying digital twin technology in public health faces considerable challenges, particularly the issues around data privacy, potential discrimination, and increased surveillance. • We need to adapt and strengthen existing legal frameworks to effectively manage the complexities introduced by digital twins, ensuring that these technologies can be safely used for public health.

Application of artificial intelligence to study the causality in public health: a systematic review

Abstract Introduction In epidemiology, establishing causation requires meeting rigorous criteria beyond mere association. Traditional methodologies often struggle to provide definitive causal conclusions due to confounding variables. However, leveraging artificial Intelligence (AI) and machine Learning (ML) offers a promising solution by modeling complex interactions among relevant factors. This systematic review explores the AI techniques utilized to uncover causal relationships in public health applications. Methods A literature search was conducted in Pubmed, Web of Science, and Scopus, employing the following search strategy: (Causalit*[Title/abstract] OR Causation*[Title/abstract] OR Causal[Title/abstract]) AND (‘Machine Learning’[Title/abstract] OR “Deep Learning” [Title/abstract] OR ‘Artificial Intelligence’ [Title/abstract] OR Algorithm* [Title/abstract]). The search aimed to identify studies encompassing causal inference techniques utilizing ML and AI methodologies in public health. Results From a total of 28,230 articles published up to August 2023, after removing duplicates (11,158) and articles that did not meet the inclusion criteria (16,168 post title/abstract screening, and 856 post full-text screening), the systematic review included 48 articles. Bayesian additive regression trees, Bayesian network models, causal forests analysis, and graphical causal models emerged as the prevailing causal methodologies. AI-driven causal methodologies have been investigated across various domains within public health, encompassing disease prediction, evaluation of treatment efficacy, identification of risk factors, analysis of health behaviors, precision Public Health initiatives, and environmental health assessment. Discussion While our systematic review affirms the potential of AI-driven causal approaches across several public health domains, further research is warranted before these emerging techniques can be effectively integrated into real settings. Key messages • In epidemiology, establishing causation requires meeting rigorous criteria beyond mere association and traditional methodologies often struggle to provide definitive causal conclusions. • This systematic review explores the AI techniques utilized to uncover causal relationships in public health applications.

Mapping barriers and bottlenecks for personalized preventive approaches in health systems worldwide

Abstract Over the past decades, the growing prevalence of chronic disease globally raised the public health need to plan tailored personalized preventive approaches, mainly through big data and -omic sciences. However, a conspicuous body of literature highlighted the existence of many barriers and bottlenecks hindering their actual implementation in real-word settings. From here, this scoping review of reviews aims to map all known barriers and bottlenecks to the implementation of personalized preventive approaches in European health systems and beyond. PubMed, Web of Science, Scopus and gray literature sources were consulted from 2017 to January 2023, identified barriers and bottlenecks were analyzed against the Consolidated Framework for Implementation Research (CFIR). Out of 11,602 records, 220 were deemed eligible for full-text screening, and a final sample of 34 review studies were extracted. Studies were mainly performed in USA 15 (44%) and UK 3 (9%), followed by Canada 2 (6%), India 2 (6%) and Italy 2 (6%). From our results, the lack of evidence on clinical utility, guidelines, specialized professionals, citizen trust and cultural issues are the most frequently reported barriers to the implementation of personalized preventive approaches worldwide. These in turn affect country specific policies and the applicability of such innovations across different populations, raising the risk of increasing health inequalities and discrimination concerns. Findings confirmed that most translational challenges pertain to primary and secondary prevention levels across several chronic diseases, with particular concerns for non-European ancestry individuals. Key messages • The identification of bottlenecks are informative of future precision public health interventions. • Barriers reported in most of the studies suggest the need to establish a targeted agenda.

The Chilean COVID-19 Genomics Network Biorepository: A Resource for Multi-Omics Studies of COVID-19 and Long COVID in a Latin American Population.

Although a lack of diversity in genetic studies is an acknowledged obstacle for personalized medicine and precision public health, Latin American populations remain particularly understudied despite their heterogeneity and mixed ancestry. This gap extends to COVID-19 despite its variability in susceptibility and clinical course, where ethnic background appears to influence disease severity, with non-Europeans facing higher hospitalization rates. In addition, access to high-quality samples and data is a critical issue for personalized and precision medicine, and it has become clear that the solution lies in biobanks. The creation of the Chilean COVID-19 Biorepository reported here addresses these gaps, representing the first nationwide multicentric Chilean initiative. It operates under rigorous biobanking standards and serves as one of South America's largest COVID cohorts. A centralized harmonization strategy was chosen and included unified standard operating procedures, a sampling coding system, and biobanking staff training. Adults with confirmed SARS-CoV-2 infection provided broad informed consent. Samples were collected to preserve blood, plasma, buffy coat, and DNA. Quality controls included adherence to the standard preanalytical code, incident reporting, and DNA concentration and absorbance ratio 260/280 assessments. Detailed sociodemographic, health, medication, and preexisting condition data were gathered. In five months, 2262 participants were enrolled, pseudonymized, and sorted by disease severity. The average Amerindian ancestry considering all participant was 44.0% [SD 15.5%], and this value increased to 61.2% [SD 19.5%] among those who self-identified as Native South Americans. Notably, 279 participants self-identified with one of 12 ethnic groups. High compliance (>90%) in all assessed quality controls was achieved. Looking ahead, our team founded the COVID-19 Genomics Network (C19-GenoNet) focused on identifying genetic factors influencing SARS-CoV-2 outcomes. In conclusion, this bottom-up collaborative effort aims to promote the integration of Latin American populations into global genetic research and welcomes collaborations supporting this endeavor. Interested parties are invited to explore collaboration opportunities through our catalog, accessible online.

Ethnic-Specific Threshold Analysis and BMI and Waist Circumference Cutoffs for Cardiovascular Disease and Subjective Wellbeing: Results using Data from the UK Biobank.

We aimed to identify ethnicity-specific BMI and waist circumference cutoffs for cardiovascular disease (CVD) and to define optimal thresholds for CVD risk and subjective wellbeing (SWB) through predictive modelling, to inform precise public health initiatives. We used data from 296,767 UK Biobank participants and adjusted logistic and linear regression models for CVD and SWB, respectively, complemented by receiver operating characteristic analysis, to explore optimal risk thresholds of CVD in six different ethnic groups and to calculate ethnicity-specific cutoffs of BMI and waist circumference (WC) to further elucidate the relationships between demographic factors and cardiovascular risk among diverse populations. The logistic regression model of CVD revealed moderate discriminative ability (AUROC ~ 64-65%) across ethnicities for CVD status, with sensitivity and specificity values indicating the model's predictive accuracy. For SWB, the model demonstrated moderate performance with an AUROC of 63%, supported by significant variables that included age, BMI, WC, physical activity, and alcohol intake. Adjusted-incidence rates of CVD revealed the evidence ethnic-specific CVD risk profiles with Whites, South Asians and Blacks demonstrating higher predicted CVD events compared to East Asians, mixed and other ethnic groups. Alterations of ethnicity-specific BMI and waist circumference are required to ensure ethnic minorities are provided with proper mitigation of cardiovascular risk, addressing the disparities observed in CVD prevalence and outcomes across diverse populations. This tailored approach to risk assessment can facilitate early detection, intervention and management of CVD, ultimately improving health outcomes and promoting health equity. The moderate accuracy of predictive models underscores the need for further research to identify additional variables that may enhance predictive accuracy and refine risk assessment strategies.

Efficient detection of data entry errors in large-scale public health surveys: an unsupervised machine learning approach

Data entry errors in large-scale public health surveys can undermine the effectiveness of data-driven interventions. Therefore, identifying these data entry errors is crucial for public health experts. In large-scale public health surveys, manually verifying the accuracy of every data point by domain experts is nearly impossible. This study evaluates unsupervised machine learning algorithms for detecting these errors, focusing on the 'weight' parameter in the Annual Health Survey (AHS) dataset. The AHS, conducted by the Ministry of Health and Family Welfare, Government of India, in collaboration with the Registrar General of India, is a large-scale, stratified, household-level survey targeting maternal and child health across nine states in India. The dataset is freely available on the Open Government Data (OGD) Platform of India for public health research. In this study, five algorithms—DBSCAN, K-Means, Gaussian Mixture Model (GMM), Isolation Forest (IF), and One-Class SVM (1C-SVM) were applied to detect erroneous data entries. The evaluation process involved comprehensive preprocessing and feature engineering to optimize detection capabilities. Performance metrics such as precision, recall, accuracy, false anomaly, and missed anomaly rates were used to assess each algorithm. Among these, DBSCAN demonstrated superior performance, achieving a recall of 94.7% and a precision of 81.9%, making it highly effective for this task. The findings underscore the potential of unsupervised machine learning in automating the detection of data entry errors, thereby improving the integrity of public health data. This research contributes to the advancement of precision public health, supporting more accurate and reliable evidence-based decision-making and policy formulation.

AI Governance: A Challenge for Public Health.

The rapid evolution of artificial intelligence (AI) is structuralizing social, political, and economic determinants of health into the invisible algorithms that shape all facets of modern life. Nevertheless, AI holds immense potential as a public health tool, enabling beneficial objectives such as precision public health and medicine. Developing an AI governance framework that can maximize the benefits and minimize the risks of AI is a significant challenge. The benefits of public health engagement in AI governance could be extensive. Here, we describe how several public health concepts can enhance AI governance. Specifically, we explain how (1) harm reduction can provide a framework for navigating the governance debate between traditional regulation and "soft law" approaches; (2) a public health understanding of social determinants of health is crucial to optimally weigh the potential risks and benefits of AI; (3) public health ethics provides a toolset for guiding governance decisions where individual interests intersect with collective interests; and (4) a One Health approach can improve AI governance effectiveness while advancing public health outcomes. Public health theories, perspectives, and innovations could substantially enrich and improve AI governance, creating a more equitable and socially beneficial path for AI development.

Abstract A062: Glycemic control and risk of total and advanced prostate cancer in Veterans with diabetes: analysis of disparities by race/ethnicity

Abstract Introduction Access to quality healthcare is considered a key driver in the multifactorial process giving rise to racial inequities in two of the most common complex diseases in adult men in the US - prostate cancer and diabetes. Whether inequities in diabetes treatment and outcomes, and/or related metabolic and hormone signaling pathways contribute to racial differences in prostate cancer outcomes in an equal access setting is unknown. Our objective is to determine if associations between glycemic control assessed by routine clinically measured hemoglobin A1c (A1c), among men with diabetes and risk of total and advanced prostate are similar in Black and White men receiving care from the Veterans Health Administration (VHA). Methods We used clinical data from the VHA to construct a longitudinal cohort of male Veterans >=45 years of age with diagnosed diabetes free of any cancer from 01/01/2010 to 12/31/2019. We classified men with total prostate cancer if the date of prostate cancer diagnosis preceded any other cancer diagnosis. Advanced prostate cancer was classified as prostate cancer diagnosis with Gleason score >=8, or PSA >=100 ng/mL, or metastasis, or death from prostate cancer. We used Cox regression to estimate hazard ratios (HRs) and 95% confidence intervals (CI) for time-updated annual mean A1c (<7% [ref]; 7-8%; >8%) and total and advanced prostate cancer risk adjusted for age; marital status; rurality, smoking; annual primary care visits; service-connected disability; comorbidities; statin use; and oral diabetes medication use for non-Hispanic Black (NHB) and non-Hispanic White (NHW) men separately. Results Among 736,964 Veterans with diabetes, we observed 31,330 prostate cancer events including 6,779 advanced prostate cancers. For NHW men, poor glycemic control (A1c >8%) and moderate control (A1c 7-8%) were associated with a 19% (HR=0.81; 95%CI=0.78-0.84) and 8% lower risk (HR=0.92; 95%CI=0.89-95) of total prostate cancer incidence (p-trend<0.001). Among NHB men, inverse associations were attenuated for poor (HR=0.90; 95% CI=0.86-0.95) and moderate (HR=1.01; 95% CI=0.96-1.06) glycemic control and total prostate cancer risk (p-trend <0.001). For advanced prostate cancer, associations were inverse for poor (HR=0.90; 95% CI=0.83-0.97) and moderate glycemic control (HR=0.90; 95% CI=0.84-0.97) and advanced prostate cancer risk in NHW men, while non-statistically significant associations were observed in NHB men with poor HR=1.06; 95%CI=0.96-1.18) and moderate glycemic control (HR=1.04; 95%CI=0.94-1.16). Conclusion In the largest equal access healthcare system in the US, inverse associations for glycemic control and risk of total and advanced prostate cancer varied by race/ethnicity with stronger inverse associations observed in NHW men compared to NHB men. Whether the racial differences in diabetes treatment and care contribute to the observed disparities in total and advanced prostate cancer risk could inform precision public health efforts aimed at reducing inequities in both diabetes and prostate cancer outcomes. Citation Format: Michael T. Marrone, Kinfe G. Bishu, Neal Axon, Hermes Florez, Robert L. Grubb, Mulugeta Gebregziabher. Glycemic control and risk of total and advanced prostate cancer in Veterans with diabetes: analysis of disparities by race/ethnicity [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A062.

Precision public health - hopes and threats.

The article formulates the following theses and conclusion: (1) the development processes of precision medicine and precision public health (PPH) are correlated; (2) the development of public health genomics depends not only on an increasingly complete exploration of the principles of the human genome, but also on a fundamentally better investigation of how broad environmental factors can alter gene expression or structure; (3) a necessary condition for the development of both precision medicine and precision public health is the collection of increasingly broader and more complete information about pro- and anti-health factors; (4) the above necessary condition is only possible in a situation of strong authoritarianism with regard to taking health-promoting measures. It is likely that in the near future public health will be shaped by health-promoting authoritarianism. There are two main problems in the development of precise public health (PPH). The first, is maintaining the right proportions between actions based on the genomic data, and actions determined by better understanding of the effect of environmental factors. The second, is the necessity to subject individuals and populations to increasingly closer pro-health surveillance, which must be increasingly more effective, forcing specific health-promoting behaviours on individual and social scales. PPH in full can only exist in conditions of complete information concerning health determinants of an individual, and each distinguished group of individuals, as well as fully pro-health organization of individual and social life, i.e. in conditions of full individual and social medicalization. However, such a full medicalization cannot be achieved in any way other than authoritarian.

Integration of digital twins to simulate community health scenarios and evaluate the effectiveness of public health interventions led by pharmacists

Public health emergencies such as the 2019 coronavirus disease (COVID-19) have increased the focus on digital technology. A digital twin (DT) is a cybernetic representation of a physical object (e.g., a person or a community) that helps us comprehend the complexity of the real thing and makes it easier to foresee, avoid, track, and improve its actual performance. To prepare for a new age of reliable and precise public health and medical care, DT technology can propel a much-needed radical transformation of conventional electronic health and medical records, which centre on individuals and their aggregates covering populations. Hence, this paper presents the Digital Twin-based Public Health Intervention Model (DT-PHIM) to simulate community health led by pharmacists. The use of digital twins in healthcare has the potential to revolutionize workflow analysis and resource allocation, ultimately resulting in better patient care and more efficient operations. In addition, healthcare providers may hone their abilities and rehearse intricate treatments in a secure and realistic environment with the help of digital twins. There is great promise for digital twin technology to revolutionize healthcare by boosting innovation, patient safety, and overall patient outcomes. On top of that, DTs may help doctors and other medical staff, such as hospital pharmacists, make decisions by simulating how therapy would affect these patients. The experimental outcomes demonstrate that the suggested DT-PHIM model increases the patient data access ratio by 97.4%, the monitoring efficiency ratio by 96.2% and the decision-making ratio by 98.2% compared to other existing models.

Artificial Intelligence and Health Inequities in Dietary Interventions on Atherosclerosis: A Narrative Review.

Poor diet is the top modifiable mortality risk factor globally, accounting for 11 million deaths annually with half being from diet-linked atherosclerotic cardiovascular disease (ASCVD). Yet, most of the world cannot afford a healthy diet-as the hidden costs of the inadequate global food system total over USD 13 trillion annually-let alone the much more clinically, financially, and ecologically costly and resource-intensive medical interventions required to address the disease progression and acute complications of ASCVD. Yet, AI is increasingly understood as a force multiplying revolutionary technology which may catalyze multi-sector efforts in medicine and public health to better address these significant health challenges. This novel narrative review seeks to provide the first known overview of the state-of-the-art in clinical interventions and public health policies in healthy diets for ASCVD, accelerated by health equity-focused AI. It is written from the first-hand practitioner perspective to provide greater relevance and applicability for health professionals and data scientists. The review summarizes the emerging trends and leading use cases in population health risk stratification and precision public health, AI democratizing clinical diagnosis, digital twins in precision nutrition, and AI-enabled culinary medicine as medical education and treatment. This review may, therefore, help inform and advance the evidence-based foundation for more clinically effective, financially efficient, and societally equitable dietary and nutrition interventions for ASCVD.

Reducing Breast Cancer Disparities with Precision Public Health: A New Strategy to Improve Prevention and Advance Health Equity in Delaware Hotspots.

While Black and White women are diagnosed with breast cancer (BC) at similar rates, Black women die from BC at a 40% higher rate. This disparity is even more pronounced for younger Black women, who die from BC at nearly twice the rate as younger White women. Black-White differences in BC mortality are largely attributable to health care and tumor biology factors. Black women face greater barriers to accessing BC screening and are twice as likely to be diagnosed with the aggressive triple-negative breast cancer (TNBC) subtype. Delaware leads the US for the incidence of late-stage BC diagnosed among younger women and TNBC. This commentary begins with a discussion of precision public health, an emerging framework that builds on and complements recent advances in precision medicine. Next, a new precision public health initiative designed to reduce BC disparities in Delaware by targeting local hotspots with prevention interventions is presented. Finally, next steps are considered for implementation, evaluation, and new research activities.

The Application of Precision Public Health to Vaccinology

The Application of Precision Public Health to Vaccinology

Spatial variation of short birth intervals and their determinant factors among reproductive women in Ethiopia using a geographically weighted regression model.

In low- and middle-income nations, a significant proportion of maternal and infant deaths are caused by a short birth interval (SBI). In Ethiopia, it is the main factor contributing to maternal and infant mortality. Understanding the spatial distribution of SBIs, i.e., birth intervals of less than 33 months, and the factors that influence them is important for categorizing and promoting targeted interventions. This study used a geographically weighted regression model to evaluate the factors associated with SBIs in hot areas of Ethiopia. The 2019 Ethiopian Mini Demographic and Health Survey, which is nationally representative, provided the data for this study. The first step in the two-stage cluster design used to collect the data was enumeration areas, and the second stage was households. The survey was conducted between 21 March 2019 and 28 June 2019. A hot spot analysis (local Getis-Ord Gi* statistics) was initially used to investigate spatial variation in SBIs. Geographically weighted regression was used to examine the regional variation in the relationship between SBIs and the factors that cause them. The study indicated that the overall proportion of SBIs among women in Ethiopia was 43.2%. The values for Global Moran's I (Moran's I = 0.773 and p < 0.001) showed the presence of significant SBIs clustering in Ethiopian administrative zones in Ethiopia. High-risk areas of the SBIs include Jarar, Doolo, Shabelle, Afder, Liben, Korahe, Nogob, West Harerge, Guji, Sidama, and Assosa zones. Living in a geographic region with a high proportion of uneducated women, women lacking breastfeeding practices, and followers of Orthodox religions increased the proportion of SBIs. Our full map of hot spots for short birth spacing and the factors that affect them helps in the implementation of precise public health measures for decision-makers.

The Relationship between Exposome and Microbiome.

Currently, exposome studies include a raft of different monitoring tools, including remote sensors, smartphones, omics analyses, distributed lag models, etc. The similarity in structure between the exposome and the microbiota plus their functions led us to pose three pertinent questions from this viewpoint, looking at the actual relationship between the exposome and the microbiota. In terms of the exposome, a bistable equilibrium between health and disease depends on constantly dealing with an ever-changing totality of exposures that together shape an individual from conception to death. Regarding scientific knowledge, the exposome is still lagging in certain areas, like the importance of microorganisms in the equation. The human microbiome is defined as an aggregate assemblage of gut commensals that are hosted by our surfaces related to the external environment. Commensals' resistance to a variety of environmental exposures, such as antibiotic administration, confirms that a layer of these organisms is protected within the host. The exposome is a conceptual framework defined as the environmental component of the science-inspired systems ideology that shifts from a specificity-based medical approach to reasoning in terms of complexity. A parallel concept in population health research and precision public health is the human flourishing index, which aims to account for the numerous environmental factors that affect individual and population well-being beyond ambient pollution.

A Dynamic and Effective Peptide-Based Strategy for Promptly Addressing Emerging SARS-CoV-2 Variants of Concern.

Genomic surveillance based on sequencing the entire genetic code of SARS-CoV-2 involves monitoring and studying genetic changes and variations in disease-causing organisms such as viruses and bacteria. By tracing the virus, it is possible to prevent epidemic spread in the community, ensuring a 'precision public health' strategy. A peptide-based design was applied to provide an efficacious strategy that is able to counteract any emerging viral variant of concern dynamically and promptly to affect the outcomes of a pandemic at an early stage while waiting for the production of the anti-variant-specific vaccine, which require longer times. The inhibition of the interaction between the receptor-binding domain (RBD) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and one of the cellular receptors (DPP4) that its receptors routinely bind to infect human cells is an intriguing therapeutic approach to prevent the virus from entering human cells. Among the other modalities developed for this purpose, peptides surely offer unique advantages, including ease of synthesis, serum stability, low immunogenicity and toxicity, and small production and distribution chain costs. Here, we obtained a potent new inhibitor based on the rearrangement of a previously identified peptide that has been rationally designed on a cell dipeptidyl peptidase 4 (DPP4) sequence, a ubiquitous membrane protein known to bind the RBD-SPIKE domain of the virus. This novel peptide (named DPP4-derived), conceived as an endogenous "drug", is capable of targeting the latest tested variants with a high affinity, reducing the VSV* DG-Fluc pseudovirus Omicron's infection capacity by up to 14%, as revealed by in vitro testing in human Calu-3 cells. Surface plasmon resonance (SPR) confirmed the binding affinity of the new DPP4-derived peptide with Omicron variant RBD.

Precision public health in the era of genomics and big data.

Precision public health (PPH) considers the interplay between genetics, lifestyle and the environment to improve disease prevention, diagnosis and treatment on a population level-thereby delivering the right interventions to the right populations at the right time. In this Review, we explore the concept of PPH as the next generation of public health. We discuss the historical context of using individual-level data in public health interventions and examine recent advancements in how data from human and pathogen genomics and social, behavioral and environmental research, as well as artificial intelligence, have transformed public health. Real-world examples of PPH are discussed, emphasizing how these approaches are becoming a mainstay in public health, as well as outstanding challenges in their development, implementation and sustainability. Data sciences, ethical, legal and social implications research, capacity building, equity research and implementation science will have a crucial role in realizing the potential for 'precision' to enhance traditional public health approaches.

Decoding the Future: Genomic Sequencing's Vital Role in Communicable Disease Prevention within Public Health Practice - A Scoping Review

Precision public health using genomic sequencing is a new field that has gained interest from public health practitioners for prevention and control measures. This study aims to identify the various applications of genomic sequencing for the prevention of communicable diseases in public health practice. Articles containing relevant keywords were determined using a systematic search strategy applied in Medline, Scopus, and Springer electronic databases. Full text included in the study was retrieved and categorised. A total of 24 articles were included in the final review. The main themes regarding the application of genomic sequencing in the prevention of communicable disease that were found in the articles were describing transmission patterns, investigating outbreaks, diagnosing infection, developing and evaluating interventions including vaccines, outcomes response treatment, and monitoring antimicrobial resistance. In conclusion, genomic sequencing has the potential to enhance the prevention and control of communicable diseases globally.

Joinpoint regression analysis of recent trends in desmoplastic malignant melanoma incidence and mortality: 15-year multicentre retrospective study.

Recent data reveal a marked rise in the detection and mortality rates of Desmoplastic Malignant Melanoma (DMM). This trend underscores the imperative for an in-depth analysis of DMM's epidemiology, which is crucial for the formulation of precise medical and public health strategies. This investigation seeks to elucidate the variations in the incidence and mortality of DMM over a 15-year period (2005-2019). Data on DMM patients was sourced from the Surveillance, Epidemiology, and End Results (SEER) database. Both incidence and incidence-based mortality rates (IBM) were directly extracted from the SEER database. Joinpoint regression was used to analyze and calculate the average annual percent change (AAPC) and its 95% confidence interval (CI). Between 2005 and 2019, 3,384 DMM cases were identified, boasting an age-adjusted incidence rate of 36.3 cases per 1000,000 person-years (95% CI 3.51-3.76) and an IBM of 1.65cases per 1000,000 person-years (95% CI 1.57-1.74). Of these, 2,353 were males (69.53%) and 1,031 were females (30.47%). There were 1894 patients (55.97%) who were over 70years old. Predominantly, DMM lesions manifested in exposed areas: Limbs (955, 28.22%), Face (906, 26.77%), and Scalp and Neck (865, 25.56%). The incidence of DMM increased significantly at a rate of APC = 0.9% during 2005-2019, while the incidence-based mortality showed a significant upward trend (APC = 7%) during 2005-2012, and slowly increasing trend (APC = 0.6%) during 2012-2019. In contrast to the modest upward trajectory in female incidence and mortality, male incidence initially surged, later declining, while male mortality peaked and stabilized post-2012. The primary sites for incidence and mortality were chronically sun-exposed areas: Face, Scalp and Neck, and Limbs. In recent years, the incidence and incidence-based mortality of DMM have significantly increased. Each subgroup analysis has different trends, and these trends can provide better support for our exploration of DMM.