From Population Databases to Research and Informed Health Decisions and Policy

Blockchain technology and federated machine learning for collaborative initiatives in orthodontics and craniofacial health

There is a paucity of largescale collaborative initiatives in orthodontics and craniofacial health. Such nationally representative projects would yield findings that are generalizable. The lack of large-scale collaborative initiatives in the field of orthodontics creates a deficiency in study outcomes that can be applied to the population at large. The objective of this study is to provide a narrative review of potential applications of blockchain technology and federated machine learning to improve collaborative care. We conducted a narrative review of articles published from 2018 to 2023 to provide a high level overview of blockchain technology, federated machine learning, remote monitoring, and genomics and how they can be leveraged together to establish a patient centered model of care. To strengthen the empirical framework for clinical decision making in healthcare, we suggest use of blockchain technology and integrating it with federated machine learning. There are several challenges to adoption of these technologies in the current healthcare ecosystem. Nevertheless, this may be an ideal time to explore how best we can integrate these technologies to deliver high quality personalized care. This article provides an overview of blockchain technology and federated machine learning and how they can be leveraged to initiate collaborative projects that will have the patient at the center of care.

Air Quality, Pollution and Sustainability Trends in South Asia: A Population-Based Study

INTRODUCTION

Worsening air quality and pollution lead to numerous environmental health and sustainability issues in the South Asia region. This study analyzes India, Nepal, Bangladesh, Pakistan, Sri Lanka, and Nepal for air quality data trends and sustainability indicators.

METHODOLOGY

By using a population-based study design, six South Asian countries were analyzed using a step-wise approach. Data were obtained from government websites and publicly available repositories for region dynamics and key variables.

RESULTS

Between 1990 and 2020, air quality data indicated the highest rise in CO2 emissions in India (578.5 to 2441.8 million tons) (MT), Bangladesh, Nepal, and Pakistan. Greenhouse gas emissions, from 1990 to 2018, nearly tripled in India (1990.4 to 3346.6 MT of CO2-equivalents), Nepal (20.6 to 54.6 MT of CO2-equivalents), and Pakistan, and doubled in Bangladesh. Methane emissions rose the highest in Pakistan (70.4 to 151 MT of CO2-equivalents), followed by Nepal (17 to 31 MT of CO2-equivalents) and India (524.8 to 669.3 MT of CO2-equivalents). Nitrous oxide nearly doubled in Bangladesh (16.5 to 29.3 MT of CO2-equivalents), India (141.6 to 256.9 MT of CO2-equivalents), Nepal (17 to 31 MT of CO2-equivalents), and more than doubled in Pakistan (27 to 61 MT of CO2-equivalents). On noting particulate matter 2,5 annual exposure, India saw the highest rise from 81.3 µg/m3 (in 1990) to 90.9 µg/m3 (2017), whereas trends were steady in Pakistan (60.34 to 58.3 µg/m3). The highest rise was noted in Nepal (87.6 to 99.7 µg/m3) until 2017. During the coronavirus disease 19 pandemic, the pre-and post-pandemic changes between 2018 and 2021 indicated the highest PM2.5 concentration in Bangladesh (76.9 µg/m3), followed by Pakistan (66.8 µg/m3), India (58.1 µg/m3), Nepal (46 µg/m3) and Sri Lanka (17.4 µg/m3). Overall, South Asian countries contribute to the worst air quality and sustainability trends regions worldwide.

CONCLUSIONS

Air pollution is prevalent across a majority of South Asia countries. Owing to unsustainable industrial practices, pollution trends have risen to hazardous levels. Economic, environmental, and human health impacts have manifested and require urgent, concerted efforts by governing bodies in the region.

Artificial Intelligence-Driven Prediction Modeling and Decision Making in Spine Surgery Using Hybrid Machine Learning Models

Healthcare systems worldwide generate vast amounts of data from many different sources. Although of high complexity for a human being, it is essential to determine the patterns and minor variations in the genomic, radiological, laboratory, or clinical data that reliably differentiate phenotypes or allow high predictive accuracy in health-related tasks. Convolutional neural networks (CNN) are increasingly applied to image data for various tasks. Its use for non-imaging data becomes feasible through different modern machine learning techniques, converting non-imaging data into images before inputting them into the CNN model. Considering also that healthcare providers do not solely use one data modality for their decisions, this approach opens the door for multi-input/mixed data models which use a combination of patient information, such as genomic, radiological, and clinical data, to train a hybrid deep learning model. Thus, this reflects the main characteristic of artificial intelligence: simulating natural human behavior. The present review focuses on key advances in machine and deep learning, allowing for multi-perspective pattern recognition across the entire information set of patients in spine surgery. This is the first review of artificial intelligence focusing on hybrid models for deep learning applications in spine surgery, to the best of our knowledge. This is especially interesting as future tools are unlikely to use solely one data modality. The techniques discussed could become important in establishing a new approach to decision-making in spine surgery based on three fundamental pillars: (1) patient-specific, (2) artificial intelligence-driven, (3) integrating multimodal data. The findings reveal promising research that already took place to develop multi-input mixed-data hybrid decision-supporting models. Their implementation in spine surgery may hence be only a matter of time.

Impact of the Fourth Industrial Revolution on the Health Sector: A Qualitative Study

Objectives

The Fourth Industrial Revolution is changing the way health is understood, transforming the methods of treatment and diagnosis as well as the relationship between health professionals and patients and altering the management and organization of health systems. The main objective of this study was to explore the impact that the Fourth Industrial Revolution is having on the health sector.

Methods

Conducting interviews consisting of four questions with 10 professionals who had experience working in the health sector to gain their insights and to obtain information to meet the general objective of the study as well as its specific objectives.

Results

From the analysis of the respondents’ responses, it was possible to create five dimensions developed by the topics most addressed by respondents, namely, impact on healthcare efficiency and effectiveness, impact on government action, impact on human resources, impact on health system organization, and financial impact on the health sector.

Conclusions

Although the Fourth Industrial Revolution is still at an early stage, it has been concluded that it is having a major positive impact on the health sector. For the effective and efficient implementation of these disruptive technologies, a global interaction between governments, health professionals, stakeholders, and society is essential to make this change possible.

Gender medicine: Lessons from COVID-19 and other medical conditions for designing health policy

Gender-specific differences in the prevalence, incidence, comorbidities, prognosis, severity, risk factors, drug-related aspects and outcomes of various medical conditions are well documented. We present a literature review on the extent to which research in this field has developed over the years, and reveal gaps in gender-sensitive awareness between the clinical portrayal and the translation into gender-specific treatment regimens, guidelines and into gender-oriented preventive strategies and health policies. Subsequently, through the lens of gender, we describe these domains in detail for four selected medical conditions: Asthma, obesity and overweight, chronic kidney disease and coronavirus disease 2019. As some of the key gender differences become more apparent during adolescence, we focus on this developmental stage. Finally, we propose a model which is based on three influential issues: (1) Investigating gender-specific medical profiles of related health conditions, rather than a single disease; (2) The dynamics of gender disparities across developmental stages; and (3) An integrative approach which takes into account additional risk factors (ethnicity, socio-demographic variables, minorities, lifestyle habits etc.). Increasing the awareness of gender-specific medicine in daily practice and in tailored guidelines, already among adolescents, may reduce inequities, facilitate the prediction of future trends and properly address the characteristics and needs of certain subpopulations within each gender.

Family Study Designs Informed by Tumor Heterogeneity and Multi-Cancer Pleiotropies: The Power of the Utah Population Database

BACKGROUND

Previously, family-based designs and high-risk pedigrees have illustrated value for the discovery of high- and intermediate-risk germline breast cancer susceptibility genes. However, genetic heterogeneity is a major obstacle hindering progress. New strategies and analytic approaches will be necessary to make further advances. One opportunity with the potential to address heterogeneity via improved characterization of disease is the growing availability of multisource databases. Specific to advances involving family-based designs are resources that include family structure, such as the Utah Population Database (UPDB). To illustrate the broad utility and potential power of multisource databases, we describe two different novel family-based approaches to reduce heterogeneity in the UPDB.

METHODS

Our first approach focuses on using pedigree-informed breast tumor phenotypes in gene mapping. Our second approach focuses on the identification of families with similar pleiotropies. We use a novel network-inspired clustering technique to explore multi-cancer signatures for high-risk breast cancer families.

RESULTS

Our first approach identifies a genome-wide significant breast cancer locus at 2q13 [P = 1.6 × 10-8, logarithm of the odds (LOD) equivalent 6.64]. In the region, IL1A and IL1B are of particular interest, key cytokine genes involved in inflammation. Our second approach identifies five multi-cancer risk patterns. These clusters include expected coaggregations (such as breast cancer with prostate cancer, ovarian cancer, and melanoma), and also identify novel patterns, including coaggregation with uterine, thyroid, and bladder cancers.

CONCLUSIONS

Our results suggest pedigree-informed tumor phenotypes can map genes for breast cancer, and that various different cancer pleiotropies exist for high-risk breast cancer pedigrees.

IMPACT

Both methods illustrate the potential for decreasing etiologic heterogeneity that large, population-based multisource databases can provide.See all articles in this CEBP Focus section, "Modernizing Population Science."

Screening for Myopia-Related Retinal Changes Among Teenagers

BACKGROUND AND OBJECTIVE

To evaluate the relationship between spherical equivalent (SE) refractive error and myopia-related peripheral retinal changes (MPRCs) and to find the optimal cutoff for screening myopic teenagers.

PATIENTS AND METHODS

The study included 2,286 myopic subjects who had a dilated fundus examination between 1998 and 2011 and SE of -4.0 diopters (D) or higher in both eyes.

RESULTS

Ninety-five subjects (4.16%) had MPRCs. Two subjects (0.09%) had moderate-to-severe MPRCs. A significant correlation (P < .0001) between SE and MPRC was found. The highest sum of sensitivity (72.63%) and specificity (73.44%) was achieved when a SE of -6.25 D in the more myopic eye was utilized as the cutoff.

CONCLUSIONS

SE and MPRCs are highly associated. The optimal cutoff to detect MPRC in teenagers is -6.25 D. However, using a SE of -4.0 D as the cutoff may be the practical choice for screening due to the noninvasiveness nature and low cost of the examination. [Ophthalmic Surg Lasers Imaging Retina. 2019;50:e311-e319.].

Impact of chemotherapy on subsequent generations

Chemotherapy is extensively used in healthcare and its usage is only increasing. Since DNA and DNA modifiers (epigenetics) are altered by chemotherapy, the long-term effects in exposed individuals are important to clinicians and researchers. For example, animal studies have shown evidence of both genetic and epigenetic changes in progeny several generations downstream from the initial exposure. At present, there is extremely limited available research in humans but the study of the generational effects of chemotherapy could prove to be significant.