scholarly journals Hybrid prevalence estimation: Method to improve intervention coverage estimations

2018 ◽  
Vol 115 (51) ◽  
pp. 13063-13068 ◽  
Author(s):  
Caroline Jeffery ◽  
Marcello Pagano ◽  
Janet Hemingway ◽  
Joseph J. Valadez
Keyword(s):  
Estimation Method ◽  
Weighted Average ◽  
Primary Source ◽  
Poor Quality ◽  
Quality Data ◽  
Prevention Services ◽  
Polio Vaccination ◽  
Survey Estimates ◽  
Combined Data ◽  
Hybrid Estimator

Delivering excellent health services requires accurate health information systems (HIS) data. Poor-quality data can lead to poor judgments and outcomes. Unlike probability surveys, which are representative of the population and carry accuracy estimates, HIS do not, but in many countries the HIS is the primary source of data used for administrative estimates. However, HIS are not structured to detect gaps in service coverage and leave communities exposed to unnecessary health risks. Here we propose a method to improve informatics by combining HIS and probability survey data to construct a hybrid estimator. This technique provides a more accurate estimator than either data source alone and facilitates informed decision-making. We use data from vitamin A and polio vaccination campaigns in children from Madagascar and Benin to demonstrate the effect. The hybrid estimator is a weighted average of two measurements and produces SEs and 95% confidence intervals (CIs) for the hybrid and HIS estimators. The estimates of coverage proportions using the combined data and the survey estimates differ by no more than 3%, while decreasing the SE by 1–6%; the administrative estimates from the HIS and combined data estimates are very different, with 3–25 times larger CI, questioning the value of administrative estimates. Estimators of unknown accuracy may lead to poorly formulated policies and wasted resources. The hybrid estimator technique can be applied to disease prevention services for which population coverages are measured. This methodology creates more accurate estimators, alongside measured HIS errors, to improve tracking the public’s health.

The Impact of Missing and Error-Prone Auxiliary Information on Sparse-Matrix Sub-Population Parameter Estimates

Methodology ◽  
2015 ◽  
Vol 11 (3) ◽  
pp. 89-99 ◽  
Author(s):  
Leslie Rutkowski ◽  
Yan Zhou
Keyword(s):  
Sparse Matrix ◽  
Small Body ◽  
Auxiliary Information ◽  
Poor Quality ◽  
Quality Data ◽  
Estimation Methods ◽  
Parameter Estimates ◽  
Population Parameter ◽  
Conditioning Model ◽  
The Impact

Abstract. Given a consistent interest in comparing achievement across sub-populations in international assessments such as TIMSS, PIRLS, and PISA, it is critical that sub-population achievement is estimated reliably and with sufficient precision. As such, we systematically examine the limitations to current estimation methods used by these programs. Using a simulation study along with empirical results from the 2007 cycle of TIMSS, we show that a combination of missing and misclassified data in the conditioning model induces biases in sub-population achievement estimates, the magnitude and degree to which can be readily explained by data quality. Importantly, estimated biases in sub-population achievement are limited to the conditioning variable with poor-quality data while other sub-population achievement estimates are unaffected. Findings are generally in line with theory on missing and error-prone covariates. The current research adds to a small body of literature that has noted some of the limitations to sub-population estimation.

scholarly journals R factors in Rietveld analysis: How good is good enough?

2006 ◽  
Vol 21 (1) ◽  
pp. 67-70 ◽  
Author(s):  
Brian H. Toby
Keyword(s):  
Rietveld Analysis ◽  
Poor Quality ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data ◽  
Poor Quality Data ◽  
Error Index ◽  
R Factors ◽  
Very High

The definitions for important Rietveld error indices are defined and discussed. It is shown that while smaller error index values indicate a better fit of a model to the data, wrong models with poor quality data may exhibit smaller values error index values than some superb models with very high quality data.

scholarly journals SMOS Brightness Temperature Monitoring Quality Control Review and Enhancements

2021 ◽  
Vol 13 (20) ◽  
pp. 4081
Author(s):  
Peter Weston ◽  
Patricia de Rosnay
Keyword(s):  
Quality Control ◽  
Brightness Temperature ◽  
Weather Prediction ◽  
European Space Agency ◽  
Poor Quality ◽  
Quality Data ◽  
Space Agency ◽  
Control Procedures ◽  
Land Data Assimilation System ◽  
Nwp Model

Brightness temperature (Tb) observations from the European Space Agency (ESA) Soil Moisture Ocean Salinity (SMOS) instrument are passively monitored in the European Centre for Medium-range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS). Several quality control procedures are performed to screen out poor quality data and/or data that cannot accurately be simulated from the numerical weather prediction (NWP) model output. In this paper, these quality control procedures are reviewed, and enhancements are proposed, tested, and evaluated. The enhancements presented include improved sea ice screening, coastal and ambiguous land-ocean screening, improved radio frequency interference (RFI) screening, and increased usage of observation at the edge of the satellite swath. Each of the screening changes results in improved agreement between the observations and model equivalent values. This is an important step in advance of future experiments to test the direct assimilation of SMOS Tbs into the ECMWF land data assimilation system.

scholarly journals Meta-Learning PAC-Bayes Priors in Model Averaging

2020 ◽  
Vol 34 (04) ◽  
pp. 4198-4205
Author(s):  
Yimin Huang ◽  
Weiran Huang ◽  
Liang Li ◽  
Zhenguo Li
Keyword(s):  
Model Averaging ◽  
Selection Procedure ◽  
Real Data ◽  
Poor Quality ◽  
Quality Data ◽  
Main Challenge ◽  
Meta Learning ◽  
Model Set ◽  
Base Learner ◽  
Proper Priors

Nowadays model uncertainty has become one of the most important problems in both academia and industry. In this paper, we mainly consider the scenario in which we have a common model set used for model averaging instead of selecting a single final model via a model selection procedure to account for this model's uncertainty in order to improve reliability and accuracy of inferences. Here one main challenge is to learn the prior over the model set. To tackle this problem, we propose two data-based algorithms to get proper priors for model averaging. One is for meta-learner, the analysts should use historical similar tasks to extract the information about the prior. The other one is for base-learner, a subsampling method is used to deal with the data step by step. Theoretically, an upper bound of risk for our algorithm is presented to guarantee the performance of the worst situation. In practice, both methods perform well in simulations and real data studies, especially with poor quality data.

The creation and use of big administrative data

2019 ◽  
pp. 23-34
Author(s):  
Harvey Goldstein ◽  
Ruth Gilbert
Keyword(s):  
Administrative Data ◽  
Poor Quality ◽  
Quality Data ◽  
Data Repositories ◽  
Public And Private ◽  
The Public ◽  
Public Benefit ◽  
Public Benefits ◽  
Poor Quality Data ◽  
Few Data

his chapter addresses data linkage which is key to using big administrative datasets to improve efficient and equitable services and policies. These benefits need to weigh against potential harms, which have mainly focussed on privacy. In this chapter we argue for the public and researchers to be alert also to other kinds of harms. These include misuses of big administrative data through poor quality data, misleading analyses, misinterpretation or misuse of findings, and restrictions limiting what questions can be asked and by whom, resulting in research not achieved and advances not made for the public benefit. Ensuring that big administrative data are validly used for public benefit requires increased transparency about who has access and whose access is denied, how data are processed, linked and analysed, and how analyses or algorithms are used in public and private services. Public benefits and especially trust require replicable analyses by many researchers not just a few data controllers. Wider use of big data will be helped by establishing a number of safe data repositories, fully accessible to researchers and their tools, and independent of the current monopolies on data processing, linkage, enhancement and uses of data.

scholarly journals Deterioration in the water quality of an urbanised estuary with recommendations for improvement

Water SA ◽  
2019 ◽  
Vol 45 (1 January) ◽  
Author(s):  
Adams JB ◽  
L Pretorius ◽  
GC Snow
Keyword(s):  
Water Quality ◽  
Inorganic Nitrogen ◽  
Vertical Mixing ◽  
Primary Source ◽  
Inorganic Phosphorus ◽  
Quality Data ◽  
Spatial And Temporal Variability ◽  
Phytoplankton Blooms ◽  
Water Quality Data

Water quality characteristics of the heavily urbanised and industrialised Swartkops River and Estuary in the Eastern Cape have been the focus of several studies since the 1970s. Overloaded and poorly maintained wastewater treatment works (WWTWs), polluted stormwater runoff and solid waste have all contributed to the deterioration in the water quality of the river and estuary. The objective of this study was to determine the current water quality status of the Swartkops Estuary, by investigating spatial and temporal variability in physico-chemical parameters and phytoplankton biomass and where possiblerelate this to historical water quality data. The present study found evidence suggesting that water is not flushed as efficiently from the upper reaches of the estuary as was previously recorded. Reduced vertical mixing results in strong stratification and persistent eutrophic conditions with phytoplankton blooms (> 20 μg chl a·L−1), extending from the middle reaches to the tidal head of the estuary. The Motherwell Canal was and still is a major source of nitrogen (particularly ammonium) to the estuary, but the Swartkops River is the primary source of phosphorus with excessive inputs from the cumulative effectof three WWTWs upstream. An analysis of historical water quality data in the Swartkops Estuary (1995 to 2013) shows that all recorded dissolved inorganic phosphorus measurements were classified as hypertrophic (> 0.1 mg P·L−1), whereas 41% of dissolved inorganic nitrogen measurements were either mesotrophic or eutrophic. If nutrient removal methods at the three WWTWs were improved and urban runoff into the Motherwell Canal better managed, it is likely that persistent phytoplankton blooms and health risks associated with eutrophication could be reduced.

Uncertainty Estimation During the Process of Flood Risk Assessment in Developing Countries – Case Study in the Pampanga River Basin –

2014 ◽  
Vol 9 (1) ◽  
pp. 69-77 ◽  
Author(s):  
Toshio Okazumi ◽  
◽  
Mamoru Miyamoto ◽  
Badri Bhakta Shrestha ◽  
Maksym Gusyev
Keyword(s):  
Risk Assessment ◽  
Developing Countries ◽  
Data Collection ◽  
Flood Risk ◽  
Poor Quality ◽  
Quality Data ◽  
Flood Risk Assessment ◽  
Assessment Task ◽  
Damage Data ◽  
Assessment Results

Flood risk assessment should be one of the basic methods for disaster damage mitigation to identify and estimate potential damage before disasters and to provide appropriate information for countermeasures. Existing methods usually do not account for uncertainty in risk assessment results. The concept of uncertainty is especially important for developing countries where risk assessment results may often be unreliable due to inadequate and poor quality data. We focus on three questions concerning risk assessment results in this study: a) How much does lack of data in developing countries influence flood risk assessment results? b) Which datamost influence the results? and c) Which data should be prioritized in data collection to improve risk assessment effectiveness? We found the largest uncertainty in the damage data among observation, model, and agricultural damage calculations. We conclude that reliable disaster damage data collection must be emphasized to obtain reliable flood risk assessment results and prevent uncertainty where possible. We propose actions to improve assessment task efficiency and investment effectiveness for developing countries.

Lichen elements as pollution indicators: evaluation of methods for large monitoring programmes

2017 ◽  
Vol 49 (4) ◽  
pp. 415-424 ◽  
Author(s):  
Susan WILL-WOLF ◽  
Sarah JOVAN ◽  
Michael C. AMACHER
Keyword(s):  
Poor Quality ◽  
Community Context ◽  
Quality Data ◽  
Permanent Plots ◽  
Target Species ◽  
Element Analysis ◽  
Lichen Community ◽  
Poor Quality Data ◽  
Specialist Field ◽  
Flavoparmelia Caperata

AbstractLichen element content is a reliable indicator for relative air pollution load in research and monitoring programmes requiring both efficiency and representation of many sites. We tested the value of costly rigorous field and handling protocols for sample element analysis using five lichen species. No relaxation of rigour was supported; four relaxed protocols generated data significantly different from rigorous protocols for many of the 20 validated elements. Minimally restrictive site selection criteria gave quality data from 86% of 81 permanent plots in northern Midwest USA; more restrictive criteria would likely reduce indicator reliability. Use of trained non-specialist field collectors was supported when target species choice considers the lichen community context. Evernia mesomorpha, Flavoparmelia caperata and Physcia aipolia/stellaris were successful target species. Non-specialists were less successful at distinguishing Parmelia sulcata and Punctelia rudecta from lookalikes, leading to few samples and some poor quality data.

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