The Value of Evidence-Based Computer Simulation of Oral Health Outcomes for Management Analysis of the Alaska Dental Health Aide Program

Objectives: To create an evidence‐based research tool to inform and guide policy and program managers as they develop and deploy new service delivery models for oral disease prevention and intervention. Methods: A village‐level discrete event simulation was developed to project outcomes associated with different service delivery patterns. Evidence‐ based outcomes were associated with dental health aide activities, and projected indicators (DMFT, F+ST, T‐health, SiC, CPI, ECC) were proxy for oral health outcomes. Model runs representing the planned program implementation, a more intensive staffing scenario, and a more robust prevention scenario, generated 20‐year projections of clinical indicators; graphs and tallies were analyzed for trends and differences. Results: Outcomes associated with alternative patterns of service delivery indicate there is potential for substantial improvement in clinical outcomes with modest program changes. Not all segments of the population derive equal benefit when program variables are altered. Children benefit more from increased prevention, while adults benefit more from intensive staffing. Conclusions: Evidence‐ based simulation is a useful tool to analyze the impact of changing program variables on program outcome measures. This simulation informs dental managers of the clinical outcomes associated with policy and service delivery variables. Simulation tools can assist public health managers in analyzing and understanding the relationship between their policy decisions and long‐term clinical outcomes.The Ford Foundation

SimpactCyan 1.0 : an open-source simulator for individual-based models in HIV epidemiology with R and Python interfaces

SimpactCyan is an open-source simulator for individual-based models in HIV epidemiology. Its core algorithm is written in C++ for computational efficiency, while the R and Python interfaces aim to make the tool accessible to the fast-growing community of R and Python users. Transmission, treatment and prevention of HIV infections in dynamic sexual networks are simulated by discrete events. A generic “intervention” event allows model parameters to be changed over time, and can be used to model medical and behavioural HIV prevention programmes. First, we describe a more efficient variant of the modified Next Reaction Method that drives our continuous-time simulator. Next, we outline key built-in features and assumptions of individual-based models formulated in SimpactCyan, and provide code snippets for how to formulate, execute and analyse models in SimpactCyan through its R and Python interfaces. Lastly, we give two examples of applications in HIV epidemiology: the first demonstrates how the software can be used to estimate the impact of progressive changes to the eligibility criteria for HIV treatment on HIV incidence. The second example illustrates the use of SimpactCyan as a data-generating tool for assessing the performance of a phylodynamic inference framework

Analyzing Child Mortality in Nigeria with Geoadditive Survival Models

Child mortality reflects a country's level of socio-economic development and quality of life. In developing countries, mortality rates are not only influenced by socio-economic, demographic and health variables but they also vary considerably across regions and districts. In this paper, we analyze child mortality in Nigeria with flexible geoadditive survival models. This class of models allows to measure small-area district-specific spatial effects simultaneously with possibly nonlinear or time-varying effects of other factors. Inference is fully Bayesian and uses recent Markov chain Monte Carlo (MCMC) simulation. The application is based on the 1999 Nigeria Demographic and Health Survey. Our method assesses effects at a high level of temporal and spatial resolution not available with traditional parametric models

Modeling the Worldwide Spread of Pandemic Influenza: Baseline Case and Containment Interventions

We present a study of the worldwide spread of a pandemic influenza and its possible containment at a global level taking into account all available information on air travel. We studied a metapopulation stochastic epidemic model on a global scale that considers airline travel flow data among urban areas. We provided a temporal and spatial evolution of the pandemic with a sensitivity analysis of different levels of infectiousness of the virus and initial outbreak conditions (both geographical and seasonal). For each spreading scenario we provided the timeline and the geographical impact of the pandemic in 3,100 urban areas, located in 220 different countries. We compared the baseline cases with different containment strategies, including travel restrictions and the therapeutic use of antiviral (AV) drugs. We show that the inclusion of air transportation is crucial in the assessment of the occurrence probability of global outbreaks. The large-scale therapeutic usage of AV drugs in all hit countries would be able to mitigate a pandemic effect with a reproductive rate as high as 1.9 during the first year; with AV supply use sufficient to treat approximately 2% to 6% of the population, in conjunction with efficient case detection and timely drug distribution. For highly contagious viruses (i.e., a reproductive rate as high as 2.3), even the unrealistic use of supplies corresponding to the treatment of approximately 20% of the population leaves 30%-50% of the population infected. In the case of limited AV supplies and pandemics with a reproductive rate as high as 1.9, we demonstrate that the more cooperative the strategy, the more effective are the containment results in all regions of the world, including those countries that made part of their resources available for global use.Comment: 16 page

A COVID-19 Recovery Strategy Based on the Health System Capacity Modeling. Implications on Citizen Self-management

Versión preprint depositada sin articulo publicado dada la actualidad del tema. *Solicitud de los autoresConfinement ends, and recovery phase should be accurate planned. Health System (HS) capacity, specially ICUs and plants capacity and availability, will remain the key stone in this new Covid-19 pandemic life cycle phase. Until massive vaccination programs will be a real option (vaccine developed, world wield production capacity and effective and efficient administration process), date that will mark recovery phase end, important decisions should be taken. Not only by authorities. Citizen self-management and organizations self-management will be crucial. This means: citizen and organizations day a day decision in order to control their own risks (infecting others and being infected). This paper proposes a management tool that is based on a ICUs and plants capacity model. Principal outputs of this tool are, by sequential order and by last best data available: (i) ICUs and plants saturation estimation data (according to incoming rate of patients), (ii) with this results new local and temporal confinement measure can be planned and also a dynamic analysis can be done to estimate maximum Ro saturation scenarios, and finally (iii) provide citizen with clear and accurate data allow them adapting their behavior to authorities’ previous recommendations. One common objective: to accelerate as much as possible socioeconomic normalization with a strict control over HS relapses risk

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 290)

This bibliography lists 125 reports, articles and other documents introduced into the NASA scientific and technical information system in October 1986

Designing a package of sexual and reproductive health and HIV outreach services to meet the heterogeneous preferences of young people in Malawi: results from a discrete choice experiment.

BACKGROUND: This article examines young people's preferences for integrated family planning (FP) and HIV services in rural Malawi. Different hypothetical configurations for outreach services are presented using a Discrete Choice Experiment (DCE). Responses are analysed using Random Parameters Logit and Generalised Mixed Logit (GMXL) models in preference space and a GMXL model parameterised in willingness-to-pay space. Simulations are used to estimate the proportion of respondents expected to choose different service packages as elements are varied individually and in combination. RESULTS: Responses were collected from 537 young people aged 15-24. Results show that when considering attending an outreach service to access family planning young people value confidentiality and the availability of HIV services including HIV counselling and testing (HCT) and HIV treatment, though significant observable and unobservable heterogeneity is present. Female respondents and those aged 20-24 were less concerned with service confidentiality compared to male respondents and those aged 15-19; respondents who were in a relationship at the time of the survey valued confidentiality more than those who reported being single. The addition of sports and recreation for young people may also be an attractive feature of a youth-friendly service; however, preferences for this attribute vary according to respondent gender. Results of the simulation modelling indicate that the most preferred service package is one that offers confidential services, both HCT and HIV treatment and sports for youth, with up to 32% of respondents expected to choose this service over a service where clients may have concerns over confidentiality, only HCT is available and there are no additional activities for young people. Estimates of willingness-to-pay for service attributes indicate that respondents were willing to pay up to USD$1.76 for confidentiality, USD$0.65 for a service offering both HCT and HIV treatment and USD$0.26 for a service including sports for youth. CONCLUSIONS: Young people were able to complete a complex DCE and appeared to trade between the different characteristics used to describe the outreach services. These findings may offer important insight to policy makers designing youth friendly SRH outreach services and providers aiming to improve the acceptability and uptake of FP services

Dairy herd mastitis and reproduction: using simulation to aid interpretation of results from discrete time survival analysis

Probabilistic sensitivity analysis (PSA) is a simulation-based technique for evaluating the relative importance of different inputs to a complex process model. It is commonly employed in decision analysis and for evaluation of the potential impact of uncertainty in research findings on clinical practice, but has a wide variety of other possible applications. In this example, it was used to evaluate the association between herd-level udder health and reproductive performance in dairy herds. Although several recent studies have found relatively large associations between mastitis and fertility at the level of individual inseminations or lactations, the current study demonstrated that herd-level intramammary infection status is highly unlikely to have a clinically significant impact on the overall reproductive performance of a dairy herd under typical conditions. For example, a large increase in incidence rate of clinical mastitis (from 92 to 131 cases per 100 cows per year) would be expected to increase a herd's modified FERTEX score (a cost-based measure of overall reproductive performance) by just £4.501 per cow per year. The herd's background level of submission rate (proportion of eligible cows served every 21 days) and pregnancy risk (proportion of inseminations leading to a pregnancy) correlated strongly with overall reproductive performance and explained a large proportion of the between-herd variation in performance. PSA proved to be a highly useful technique to aid understanding of results from a complex statistical model, and has great potential for a wide variety of applications within the field of veterinary science