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Young people's health has emerged as a neglected yet pressing issue in global development. Changing patterns of young people's health have the potential to undermine future population health as well as global economic development unless timely and effective strategies are put into place. We report the past, present, and anticipated burden of disease in young people aged 10–24 years from 1990 to 2013 using data on mortality, disability, injuries, and health risk factors. The Global Burden of Disease Study 2013 (GBD 2013) includes annual assessments for 188 countries from 1990 to 2013, covering 306 diseases and injuries, 1233 sequelae, and 79 risk factors. We used the comparative risk assessment approach to assess how much of the burden of disease reported in a given year can be attributed to past exposure to a risk. We estimated attributable burden by comparing observed health outcomes with those that would have been observed if an alternative or counterfactual level of exposure had occurred in the past. We applied the same method to previous years to allow comparisons from 1990 to 2013. We cross-tabulated the quantiles of disability-adjusted life-years (DALYs) by quintiles of DALYs annual increase from 1990 to 2013 to show rates of DALYs increase by burden. We used the GBD 2013 hierarchy of causes that organises 306 diseases and injuries into four levels of classification. Level one distinguishes three broad categories: first, communicable, maternal, neonatal, and nutritional disorders; second, non-communicable diseases; and third, injuries. Level two has 21 mutually exclusive and collectively exhaustive categories, level three has 163 categories, and level four has 254 categories. The leading causes of death in 2013 for young people aged 10–14 years were HIV/AIDS, road injuries, and drowning (25·2%), whereas transport injuries were the leading cause of death for ages 15–19 years (14·2%) and 20–24 years (15·6%). Maternal disorders were the highest cause of death for young women aged 20–24 years (17·1%) and the fourth highest for girls aged 15–19 years (11·5%) in 2013. Unsafe sex as a risk factor for DALYs increased from the 13th rank to the second for both sexes aged 15–19 years from 1990 to 2013. Alcohol misuse was the highest risk factor for DALYs (7·0% overall, 10·5% for males, and 2·7% for females) for young people aged 20–24 years, whereas drug use accounted for 2·7% (3·3% for males and 2·0% for females). The contribution of risk factors varied between and within countries. For example, for ages 20–24 years, drug use was highest in Qatar and accounted for 4·9% of DALYs, followed by 4·8% in the United Arab Emirates, whereas alcohol use was highest in Russia and accounted for 21·4%, followed by 21·0% in Belarus. Alcohol accounted for 9·0% (ranging from 4·2% in Hong Kong to 11·3% in Shandong) in China and 11·6% (ranging from 10·1% in Aguascalientes to 14·9% in Chihuahua) of DALYs in Mexico for young people aged 20–24 years. Alcohol and drug use in those aged 10–24 years had an annual rate of change of >1·0% from 1990 to 2013 and accounted for more than 3·1% of DALYs. Our findings call for increased efforts to improve health and reduce the burden of disease and risks for diseases in later life in young people. Moreover, because of the large variations between countries in risks and burden, a global approach to improve health during this important period of life will fail unless the particularities of each country are taken into account. Finally, our results call for a strategy to overcome the financial and technical barriers to adequately capture young people's health risk factors and their determinants in health information systems. Bill & Melinda Gates Foundation.

Numerous centrality measures exist to quantify the influence of edges within a network, with edge betweenness being one of the more well-known measures. However, such measures are inadequate in network percolation scenarios (e.g., the transmission of a disease over a transportation network of highways) as they fail to consider the changing percolation states of edges over time. This paper addresses this limitation by extending percolation centrality, a measure originally developed to evaluate the influence of vertices during a percolation process (i.e., a dynamic spread of a contagion) in the network, to the edge level. The proposed measure, edge percolation centrality, captures both the topological connectivity of the network as well as the percolation states of the edges. Although the algorithm’s observed complexity of O ( | V | 3.57 ) makes it computationally intensive, the utility of the proposed edge measure is evident in its application to both synthetic and real-world networks undergoing percolation processes.

There is extensive evidence that network structure (e.g., air transport, rivers, or roads) may significantly enhance the spread of epidemics into the surrounding geographical area. A new compartmental modeling framework is proposed which couples well-mixed (ODE in time) population centers at the vertices, 1D travel routes on the graph’s edges, and a 2D continuum containing the rest of the population to simulate how an infection spreads through a population. The edge equations are coupled to the vertex ODEs through junction conditions, while the domain equations are coupled to the edges through boundary conditions. A numerical method based on spatial finite differences for the edges and finite elements in the 2D domain is described to approximate the model, and numerical verification of the method is provided. The model is illustrated on two simple and one complex example geometries, and a parameter study example is performed. The observed solutions exhibit exponential decay after a certain time has passed, and the cumulative infected population over the vertices, edges, and domain tends to a constant in time but varying in space, i.e., a steady state solution.

In this paper, we apply data-driven optimization to estimate key parameters in a metric graph-based epidemiological model, with the aim of analyzing the effect of road networks on the geographic spread of epidemics. As a case study, we fit our model to data from the COVID-19 pandemic in Poland during 2021. Our dataset integrates county-level daily case reports, national census information, and traffic flow studies. This framework allows us to examine the relative contribution of specific travel routes over time and infer unobserved transmission patterns in the presence of incomplete or unreliable case reporting. The optimization problem that arises from the model fitting yields an objective function resembling the Rosenbrock \"banana\" or \"valley\" function, a classical difficult benchmark for optimization algorithms. To our knowledge, this represents the first appearance of a Rosenbrock-type function in a real-world epidemiological context. We demonstrate that such a structure can emerge naturally from a simple uncoupled SIR model under specific conditions: a low initial incidence rate and a prolonged infectious period. This suggests that the Rosenbrock behavior is an intrinsic feature of fitting compartmental models to approximately Gaussian epidemiological data, providing a realistic yet simple scenario with which to test optimization algorithms. We explore optimization strategies suited to the Rosenbrock-type structure and identify a feasible parameter set for modeling the spread of COVID-19 in Poland. We use this set of parameters to identify discrepancies between the model and the data, explore how reducing traffic flow into urban areas can help flatten the infection curve, and identify some patterns in the distribution of intra- versus inter-city incidence rates. While recognizing the complex interplay of social and behavioral elements that cannot be fully captured in a high-level geographic model, our findings highlight the usefulness of metric graph-based models for understanding large-scale disease transmission in structured transportation networks.

Metric graphs may be used to describe a variety of physical occurrences from motor vehicle traffic to random network lasers. Partial differential equations (PDEs) are a useful tool for describing these phenomena. PDEs are defined on the edges of the metric graph and are coupled to the other edges through junction conditions. In this dissertation, some introductory metric graph terminology is defined and the construction of three example metric graphs is discussed. Methods for solving the wave equation on a metric graph are then presented, in particular a spectral method and a finite difference method are developed. Splitting solutions by their frequencies using the spectral method reveals that for some frequencies solutions get trapped in specific shapes. This process is known as localization. Conditions are derived for exact localization of a selection of shapes, and a criterion for localization is developed. Finally, the metric graph model is extended and applied to a susceptible-infected-removed (SIR) model for infectious disease. In this model the edges of the metric graph represent 1D approximations of travel routes and are coupled to vertices representing population centers. The metric graph is then embedded in and coupled to a 2D region with exchange occurring along the edges. This model is run on two example regions.

The discrepancy sum \\(D_N(x,\\rho)\\) for irrational rotations has been of interest to mathematicians for over a century. While historically studied in an ``almost-everywhere'' or asymptotic sense, \\(D_N\\) for finite N is increasingly an object of interest for its nontrivial properties that depend on the Diophantine properties of \\(\\rho\\). This behavior is periodic in N with respect to the quotients of the continued fraction convergents, which grow quickly for some irrationals. Thus the stable computation of the sum is necessary for forming conjectures about its properties. However, computing the exact value of the sum and its corresponding probability density function (pdf) is notoriously difficult due to numerical instability in the sum itself and the failure of sampling methods to capture its jump discontinuities. This paper presents a novel computational algorithm that fully defines the discrepancy function and its associated pdf through its discontinuities. This allows the calculation of \\(D_N(x,\\rho)\\) to machine precision with minimal storage in O(N) time. This vast improvement in computability over the O(N^2) naive version enables, for the first time, the direct computation of the exact pdf up to machine precision in O(N log N) time, and with it, key properties of the discrepancy: \\( \\|D_N \\|_{\\infty}\\) (half of the support of the pdf), \\( \\|D_N \\|_{2}^2\\) (the variance of the pdf), and the kurtosis of the pdf. A key strength of the algorithm lies in its ability to produce clear, exact figures, allowing the development of mathematical intuition and the quick testing of conjectures. As an example, a newly conjectured pattern is presented: when \\(\\rho\\) is well-approximated by rational \\(\\frac{p_n}{q_n}\\), the pdf exhibits a predictable spiked-trapezoidal pattern when \\(N=kq_n\\). These shapes degrade as \\(k\\) increases, at a speed depending on how well \\(\\frac{p_n}{q_n}\\) approximates \\(\\rho\\).

Background There are not enough data on the epidemiology of asthma in the Kingdom of Saudi Arabia (KSA). We analyzed data from a national household survey conducted in KSA in 2013 to estimate prevalence, associated risk factors and control measurements of asthma. Methods The Saudi Health Interview Survey was a cross-sectional national multistage survey of 10,735 individuals aged 15 years or older. The survey included a detailed household questionnaire and a physical exam. We used self-reported clinical diagnosis of asthma to assess prevalence of asthma. Results The prevalence of asthma in KSA was 4.05 % (95 % confidence interval [CI]: 3.54–4.62 %). Asthma was less frequent in individuals with higher education but higher in former smokers and obese individuals. Around 76.7 % of asthma patients (95 % CI: 70.6–82.0 %) experienced an asthmatic attack, and 61.6 % (95 % CI: 54.4–68.4 %) visited a hospital/emergency room because of asthma during the past year. Asthma attack was less frequent in older patients (odds ratio [OR] = 0.78, 95 %CI: 0.59–0.96 for each decade of life). Current use of medication for asthma was highly associated with asthma attacks (OR = 9.14, 95 % CI: 3.29–25.38). Asthma attack was also more frequent in individuals who were exposed to secondhand smoking (OR = 2.17, 95 %CI: 1.05–4.45) and those who were obese (OR = 3.01, 95 %CI: 1.34–6.78). Conclusion Saudi Arabia has a relatively low prevalence of diagnosed asthma; however, many of the patients with known asthma do not have it under good control. Our study calls for programs to inform patients about the importance and proper means of controlling their condition. Implementing and monitoring of clinical guidelines can also help to improve asthma control among patients as well as identify undiagnosed cases.

ObjectivesWe analysed data from a large household survey to identify barriers to healthcare in the Kingdom of Saudi Arabia.MethodsThe Saudi Health Interview Survey (SHIS) is a national multistage survey of individuals aged 15 years or older. The survey combined a household questionnaire and a laboratory blood analysis. We used a backward elimination multivariate logistic regression model to measure association between (1) diagnosis, (2) treatment, and (3) control of hypertension or diabetes and sociodemographic factors, history of diagnosis with chronic conditions, and type of, and distance travelled to, the clinic last visited.ResultsBetween April and June 2013, a total of 10 735 participants completed SHIS and were invited to the local health clinics. Among hypertensive individuals, women, older individuals, and those previously diagnosed with diabetes and hypercholesterolaemia were more likely to have been diagnosed with hypertension than their counterparts. Among participants diagnosed with hypertension, the likelihood of being treated increased with age and education. The likelihood of having uncontrolled blood pressure despite treatment increased with education and a history of diagnosis with hypercholesterolaemia. Type of clinic visited and distance travelled to last clinic visit were not associated with diagnosis or treatment of hypertension or control of blood pressure. Similar factors were associated with the likelihood of diagnosis and treatment among individuals with diabetes. Having uncontrolled glycated haemoglobin levels, despite treatment, was less common among those who visited governmental clinics other than those of the Ministry of Health, compared with those who visited Ministry clinics.ConclusionsOur findings highlight the importance of individual characteristics in healthcare-seeking practices rather than system-based potential barriers. Saudis seem to mostly seek healthcare when sick. Hence, the Saudi Ministry of Health needs to implement a comprehensive plan including health education and investigations, to understand the barriers and bottlenecks to healthcare-seeking behaviour.

Cytomegalovirus, while relatively non-pathogenic in an immunocompetent host, is a significant cause of morbidity and mortality in the immunocompromised individual. Persons at risk for CMV disease include AIDS patients, organ transplant recipients, chemotherapy patients, fetuses and neonates. However, few animal models exist to study key issues of cytomegalovirus pathogenesis and persistence. This dissertation describes the molecular characterization of the rhesus cytomegalovirus (RhCMV) genome. Molecular definition of the RhCMV genome serves as a key component of developing infection of rhesus macaques with RhCMV as a non-human primate model for HCMV infection of humans. A literature review of simian cytomegalovirus research to date is provided, along with important parallels to HCMV research. Genomic characterization includes descriptions of homologies between the non-human primate CMVs within a variety of open reading frames located throughout the genome, as well as focus on two areas of the genome important for regulation of the virus life cycle and host immune response to CMV infection. First, direct genetic analysis of several open reading frames will be discussed. While the RhCMV genome has not been sequenced in its entirety, sequence information representative of approximately 20% of the genome will be analyzed. Each region of the genome has demonstrated a high degree of genetic relatedness and colinearity between the non-human primate CMV genomes. Next, a detailed characterization of the glycoprotein B (gB) gene will be provided. Glycoprotein B, in addition to being the major envelope glycoprotein of the virus, is also the main target of the host neutralizing antibody response to CMV. Sequence analysis and comparisons to HCMV gB are also provided in the analysis. Lastly, novel regulatory mechanisms for human and RhCMV immediate-early 1 and 2 (IE1 & 2) gene expression are discussed. IE 1 and 2 control the viral replication cycle, and are the crucial elements for establishing a productive versus a latent infection. A novel mechanism for the differential regulation of IE1 and 2 expression at late times of infection is proposed. A better understanding of the biology of RhCMV will enable researchers to answer key questions about HCMV pathogenesis, latency, treatment, and vaccination strategies.

Although overall air quality has improved in the United States, air pollution remains unevenly distributed across neighborhoods, producing disproportionate environmental burdens for minoritized and socioeconomically disadvantaged residents for whom greater exposure to other structurally rooted neighborhood stressors is also more frequent. These interrelated dynamics and layered vulnerabilities each have well-documented associations with physical and psychological health outcomes; however, much remains unknown about the joint effects of environmental hazards and neighborhood socioeconomic factors on self-reported health status. We examined the nexus of air pollution exposure, neighborhood socioeconomic disadvantage, and self-rated health (SRH) among adults in the United States. This observational study used individual-level data from the Panel Study of Income Dynamics merged with contextual information, including neighborhood socioeconomic and air pollution data at the census tract and census block levels, spanning the period of 1999-2015. We estimated ordinary least squares regression models predicting SRH by 10-y average exposures to fine particulate matter [particles in aerodynamic diameter ( )] and neighborhood socioeconomic disadvantage while controlling for individual-level correlates of health. We also investigated the interaction effects of air pollution and neighborhood socioeconomic disadvantage on SRH. On average, respondents in our sample rated their health as 3.41 on a scale of 1 to 5. Respondents in neighborhoods with higher 10-y average concentrations or socioeconomic disadvantage rated their health more negatively after controlling for covariates [ (95% CI: , ); (95% CI: , ), respectively]. We also found that the deleterious associations of exposure with SRH were weaker in the context of greater neighborhood socioeconomic disadvantage ( ; 95% CI: 0.002, 0.011). Study results indicate that the effects of air pollution on SRH may be less salient in socioeconomically disadvantaged neighborhoods compared with more advantaged areas, perhaps owing to the presence of other more proximate structurally rooted health risks and vulnerabilities in disinvested areas (e.g., lack of economic resources, health access, healthy food options). This intersection may further underscore the importance of meaningful involvement and political power building among community stakeholders on issues concerning the nexus of environmental and socioeconomic justice, particularly in structurally marginalized communities. https://doi.org/10.1289/EHP11268.