276,811 research outputs found
The Network Survival Method for Estimating Adult Mortality: Evidence From a Survey Experiment in Rwanda.
Adult death rates are a critical indicator of population health and well-being. Wealthy countries have high-quality vital registration systems, but poor countries lack this infrastructure and must rely on estimates that are often problematic. In this article, we introduce the network survival method, a new approach for estimating adult death rates. We derive the precise conditions under which it produces consistent and unbiased estimates. Further, we develop an analytical framework for sensitivity analysis. To assess the performance of the network survival method in a realistic setting, we conducted a nationally representative survey experiment in Rwanda (n = 4,669). Network survival estimates were similar to estimates from other methods, even though the network survival estimates were made with substantially smaller samples and are based entirely on data from Rwanda, with no need for model life tables or pooling of data from other countries. Our analytic results demonstrate that the network survival method has attractive properties, and our empirical results show that this method can be used in countries where reliable estimates of adult death rates are sorely needed
User's Guide to the ROI Forecasting Calculator: Estimating ROI for Medicaid Quality Improvement Programs
Explains the online tool for state Medicaid agencies, health plans, and stakeholders to assess the cost-savings potential of quality improvement measures. Outlines analytical issues and best practices for each component and how to interpret the results
Age grading \u3cem\u3eAn. gambiae\u3c/em\u3e and \u3cem\u3eAn. arabiensis\u3c/em\u3e using near infrared spectra and artificial neural networks
Background
Near infrared spectroscopy (NIRS) is currently complementing techniques to age-grade mosquitoes. NIRS classifies lab-reared and semi-field raised mosquitoes into \u3c or ≥ 7 days old with an average accuracy of 80%, achieved by training a regression model using partial least squares (PLS) and interpreted as a binary classifier. Methods and findings
We explore whether using an artificial neural network (ANN) analysis instead of PLS regression improves the current accuracy of NIRS models for age-grading malaria transmitting mosquitoes. We also explore if directly training a binary classifier instead of training a regression model and interpreting it as a binary classifier improves the accuracy. A total of 786 and 870 NIR spectra collected from laboratory reared An. gambiae and An. arabiensis, respectively, were used and pre-processed according to previously published protocols. The ANN regression model scored root mean squared error (RMSE) of 1.6 ± 0.2 for An. gambiae and 2.8 ± 0.2 for An. arabiensis; whereas the PLS regression model scored RMSE of 3.7 ± 0.2 for An. gambiae, and 4.5 ± 0.1 for An. arabiensis. When we interpreted regression models as binary classifiers, the accuracy of the ANN regression model was 93.7 ± 1.0% for An. gambiae, and 90.2 ± 1.7% for An. arabiensis; while PLS regression model scored the accuracy of 83.9 ± 2.3% for An. gambiae, and 80.3 ± 2.1% for An. arabiensis. We also find that a directly trained binary classifier yields higher age estimation accuracy than a regression model interpreted as a binary classifier. A directly trained ANN binary classifier scored an accuracy of 99.4 ± 1.0 for An. gambiae and 99.0 ± 0.6% for An. arabiensis; while a directly trained PLS binary classifier scored 93.6 ± 1.2% for An. gambiae and 88.7 ± 1.1% for An. arabiensis. We further tested the reproducibility of these results on different independent mosquito datasets. ANNs scored higher estimation accuracies than when the same age models are trained using PLS. Regardless of the model architecture, directly trained binary classifiers scored higher accuracies on classifying age of mosquitoes than regression models translated as binary classifiers. Conclusion
We recommend training models to estimate age of An. arabiensis and An. gambiae using ANN model architectures (especially for datasets with at least 70 mosquitoes per age group) and direct training of binary classifier instead of training a regression model and interpreting it as a binary classifier
Modeling of the HIV infection epidemic in the Netherlands: A multi-parameter evidence synthesis approach
Multi-parameter evidence synthesis (MPES) is receiving growing attention from
the epidemiological community as a coherent and flexible analytical framework
to accommodate a disparate body of evidence available to inform disease
incidence and prevalence estimation. MPES is the statistical methodology
adopted by the Health Protection Agency in the UK for its annual national
assessment of the HIV epidemic, and is acknowledged by the World Health
Organization and UNAIDS as a valuable technique for the estimation of adult HIV
prevalence from surveillance data. This paper describes the results of
utilizing a Bayesian MPES approach to model HIV prevalence in the Netherlands
at the end of 2007, using an array of field data from different study designs
on various population risk subgroups and with a varying degree of regional
coverage. Auxiliary data and expert opinion were additionally incorporated to
resolve issues arising from biased, insufficient or inconsistent evidence. This
case study offers a demonstration of the ability of MPES to naturally integrate
and critically reconcile disparate and heterogeneous sources of evidence, while
producing reliable estimates of HIV prevalence used to support public health
decision-making.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS488 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Social group size affects Mycobacterium bovis infection in European badgers (Meles meles)
1. In most social animals, the prevalence of directly transmitted pathogens increases in larger groups and at higher population densities. Such patterns are predicted by models of Mycobacterium bovis infection in European badgers (Meles meles). 2. We investigated the relationship between badger abundance and M. bovis prevalence, using data on 2696 adult badgers in 10 populations sampled at the start of the Randomized Badger Culling Trial. 3. M. bovis prevalence was consistently higher at low badger densities and in small social groups. M. bovis prevalence was also higher among badgers whose genetic profiles suggested that they had immigrated into their assigned social groups. 4. The association between high M. bovis prevalence and small badger group size appeared not to have been caused by previous small-scale culling in study areas, which had been suspended, on average, 5 years before the start of the current study. 5. The observed pattern of prevalence might occur through badgers in smaller groups interacting more frequently with members of neighbouring groups; detailed behavioural data are needed to test this hypothesis. Likewise, longitudinal data are needed to determine whether the size of infected groups might be suppressed by disease-related mortality. 6. Although M. bovis prevalence was lower at high population densities, the absolute number of infected badgers was higher. However, this does not necessarily mean that the risk of M. bovis transmission to cattle is highest at high badger densities, since transmission risk depends on badger behaviour as well as on badger density
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