Acceptable Level Of Precision Research Articles

Development of empirically derived brief program evaluation measures in Pennsylvania first-episode psychosis coordinated specialty care programs.

The Pennsylvania first episode psychosis program evaluation (PA-FEP-PE) core assessment battery was developed as a standard and comprehensive clinical assessment and data collection tool in Pennsylvania coordinated specialty care programs (CSC). To reduce administrative time and maximize clinical utility by maintaining acceptable levels of precision, we aimed to generate a short form using item response theory (IRT)-based computer-adaptive test (CAT) simulation and analyse the implementation and acceptability of the short form among providers from PA-CSC. FEP participants (n=759; age 14-36) from nine coordinated specialty care programs completed 156 items drawn from the PA-FEP-PE battery. Multidimensional IRT-based CAT simulations were used to select the best PA-FEP-PE items for abbreviated forms. A 67-item PA-FEP-PE short form was developed to capture six factors: (1) positive affect and surgency (with negative loadings on Anxious-Misery items); (2) psychiatric services satisfaction; (3) antipsychotic side effect severity; (4) family turmoil and associated traumas; (5) trauma load; and (6) psychosis. The total number of items was reduced more than 50% in the PA-FEP-PE shortened forms. The short form demonstrated good psychometric properties, and it was well accepted by our providers in the implementation. The empirical derivation and implementation of abbreviated measures of key domains and constructs in FEP care have streamlined and facilitated PA-FEP program evaluation. Our work supports potential application of IRT based methods to empirically reduce core assessment battery measures in large-scale data collection efforts such as in the Early Psychosis Intervention Network.

A new subsampling methodology to optimize the characterization of two-dimensional bedrock fracture networks

This paper introduces a new subsampling method to determine the empirical relationships between the areal sampling coverage and the topological-geometric parameters resulting from characterization of two-dimensional outcrop fracture networks. We further developed these relationships into correlations between the total sampling coverage and variance of the characterization result, hence providing insight to the objective uncertainties related to fracture network characterization. The analyses were conducted using two new open-source Python packages: fractopo and fractopo-subsampling, designed for fracture network analysis and subsampling, respectively. We conducted the study on the well-exposed crystalline outcrops of Getaberget, Åland Islands, Finland, where a total of 42499 fracture traces were manually digitized from 13 circular target areas. For the purposes of subsampling, we conducted fracture network characterization for randomly located and sized subsample areas, which locate within the larger target areas.Based on our subsampling results we provide recommendations for the preliminary optimization of areal coverage used in outcrop fracture sampling and the use of our subsampling method for assessing the precision related to the areal fracture network characterization in other previously uncharacterized areas. As an example, we recommend using a total sampling area of 8000 m2 with 8 circular sampling areas to define the power-law exponents of fracture traces when conducting outcrop fracture network characterization with drone-based methodology done with similar initial sampling setup in comparable geological environments, as this coverage has shown an acceptable level of precision.

The Automatic Classification of Pyriproxyfen-Affected Mosquito Ovaries.

Simple SummaryWith resistance to the pyrethroid family of insecticides increasing, it is more important than ever that tools are available to measure the efficacy of alternatives. Pyriproxyfen (PPF) is an alternative insecticide whose mode of action sterilises adult mosquitoes. Consequently, the efficacy of PPF-based tools can be measured through visual examination of egg development by trained experts. This, however, can be a time-consuming process for which the required expertise can be difficult to train and is not available in many contexts. As such, we propose that an objective machine learning program, which can automatically classify the fertility status of adult mosquito ovaries via a colour image, be developed to improve the speed, accuracy, and consistency of assessment. This study shows that a convolutional neural network, built in Python’s TensorFlow library, can quickly classify images of dissected ovaries into either ‘fertile’ or ‘infertile’ with a high accuracy rate. Such an application would be a practical and accessible tool available to all researchers studying the efficacy of PPF or other insecticides with a similar mode of action.Pyriproxyfen (PPF) may become an alternative insecticide for areas where pyrethroid-resistant vectors are prevalent. The efficacy of PPF can be assessed through the dissection and assessment of vector ovaries. However, this reliance on expertise is subject to limitations. We show here that these limitations can be overcome using a convolutional neural network (CNN) to automate the classification of egg development and thus fertility status. Using TensorFlow, a resnet-50 CNN was pretrained with the ImageNet dataset. This CNN architecture was then retrained using a novel dataset of 524 dissected ovary images from An. gambiae s.l. An. gambiae Akron, and An. funestus s.l., whose fertility status and PPF exposure were known. Data augmentation increased the training set to 6973 images. A test set of 157 images was used to measure accuracy. This CNN model achieved an accuracy score of 94%, and application took a mean time of 38.5 s. Such a CNN can achieve an acceptable level of precision in a quick, robust format and can be distributed in a practical, accessible, and free manner. Furthermore, this approach is useful for measuring the efficacy and durability of PPF treated bednets, and it is applicable to any PPF-treated tool or similarly acting insecticide.

Analysis of heterocyclic aromatic amines using selective extraction by magnetic molecularly imprinted polymers coupled with liquid chromatography - Mass spectrometry.

Heterocyclic aromatic amines (HCAs) are highly carcinogenic and mutagenic chemicals. This study reports on the development of magnetic molecularly imprinted polymers (MMIPs) for the purification and quantification of HCAs. A novel magnetic molecularly imprinted polymer was successfully prepared using a surface molecular imprinting method using functionalized Fe particles as the magnetic cores. 2-Amino-3-methylimidazo[4,5-f]quinoline (IQ) was used as a molecular template; methacrylic acid (MAA), ethylene glycol dimethyl acrylate (EGDMA), 2, 2'-Azobis (2-methylpropionitrile) were used as the functional monomer, crosslinker, and initiator, respectively. The use of the template/functional monomer/crosslinking agent at a ratio of 1:4:20 resulted in a product with better adsorption properties (3.24 mg/g). The HCAs were successfully detected and quantified in processed meat samples by MISPE and LC-MS/MS. Under the final optimized detection conditions, the proposed method offered good linearity (R > 0.995) for the five HCAs with an acceptable level of precision, and an LOQ of 0.05 ng/g was successfully achieved.

Numerical Pathology in Selected Kubelka–Munk Formulas, and Strategies for Mitigation

Causes of numerical pathology in formulas for reflectance factor (R), transmittance factor (T), and reflectance factor over a perfectly black background (R 0 ) under the Kubelka-Munk model are posited, and alternate formulas believed less prone to these pathologies are introduced. Suggestions are offered not only for R, T, and R 0 , but also for intermediate or adjunct quantities used in the main formulas. Computational experiments were performed to verify that the new models produce the same results as the existing ones under non-pathological conditions, exhibit acceptable levels of precision in a customary floating-point environment, and are more robust with respect to edge cases where an input quantity is zero. The new formulas performed well, with some evidence that the new hyperbolic forms provide better accuracy than their exponential counterparts.

Fast and Accurate 2D Analytical Subdomain Method for Coaxial Magnetic Coupling Analysis

Magnetic couplings (MCs) enable contactless speed/torque transmission via interactions between the magnetic fields of permanent magnets (PMs) rather than a physical mechanical connection. The contactless transmission of mechanical power leads to improvements in terms of efficiency and reliability due to the absence of wear between moving parts. One of the most common MC topologies is the coaxial type, also known as the radial configuration. This paper presents an analytical tool for the accurate and fast analysis of coaxial magnetic couplings (CMCs) using a two-dimensional subdomain approach. In particular, the proposed analytical tool resolves Laplace’s and Poisson’s equations for both air-gap and PM regions. The tool can be used to evaluate the impact of several design parameters on the performance of the CMC, enabling quick and accurate sensitivity analyses, which in turn guide the choice of design parameters. After discussing the building procedure of the analytical tool, its applicability and suitability for sensitivity analyses are assessed and proven with the analysis of a fully parameterized CMC geometry. The accuracy and the computational burden of the proposed analytical tool are compared against those of the finite element method (FEM), revealing faster solving times and acceptable levels of precision.

A surrogate virus neutralization test to quantify antibody-mediated inhibition of SARS-CoV-2 in finger stick dried blood spot samples

The spike protein of SARS-CoV-2 engages the human angiotensin-converting enzyme 2 (ACE2) receptor to enter host cells, and neutralizing antibodies are effective at blocking this interaction to prevent infection. Widespread application of this important marker of protective immunity is limited by logistical and technical challenges associated with live virus methods and venous blood collection. To address this gap, we validated an immunoassay-based method for quantifying neutralization of the spike-ACE2 interaction in a single drop of capillary whole blood, collected on filter paper as a dried blood spot (DBS) sample. Samples are eluted overnight and incubated in the presence of spike antigen and ACE2 in a 96-well solid phase plate. Competitive immunoassay with electrochemiluminescent label is used to quantify neutralizing activity. The following measures of assay performance were evaluated: dilution series of confirmed positive and negative samples, agreement with results from matched DBS-serum samples, analysis of results from DBS samples with known COVID-19 status, and precision (intra-assay percent coefficient of variation; %CV) and reliability (inter-assay; %CV). Dilution series produced the expected pattern of dose–response. Agreement between results from serum and DBS samples was high, with concordance correlation = 0.991. Analysis of three control samples across the measurement range indicated acceptable levels of precision and reliability. Median % surrogate neutralization was 46.9 for PCR confirmed convalescent COVID-19 samples and 0.1 for negative samples. Large-scale testing is important for quantifying neutralizing antibodies that can provide protection against COVID-19 in order to estimate the level of immunity in the general population. DBS provides a minimally-invasive, low cost alternative to venous blood collection, and this scalable immunoassay-based method for quantifying inhibition of the spike-ACE2 interaction can be used as a surrogate for virus-based assays to expand testing across a wide range of settings and populations.

A Systematic Review of Small Domain Estimation Research in Forestry During the Twenty-First Century From Outside the United States

Small domain estimation (SDE) research outside of the United States has been centered in Canada and Europe—both in transnational organizations, such as the European Union, and in the national statistics offices of individual countries. Support for SDE research is driven by government policy-makers responsible for core national statistics across domains. Examples include demographic information about provision of health care or education (a social domain) or business data for a manufacturing sector (economic domain). Small area estimation (SAE) research on forest statistics has typically studied a subset of core environmental statistics for a limited geographic domain. The statistical design and sampling intensity of national forest inventories (NFIs) provide population estimates of acceptable precision at the national level and sometimes for broad sub-national regions. But forest managers responsible for smaller areas—states/provinces, districts, counties—are facing changing market conditions, such as emerging forest carbon markets, and budgetary pressures that limit local forest inventories. They need better estimates of conditions and trends for small sub-sets of a national-scale domain than can be provided at acceptable levels of precision from NFIs. Small area estimation research is how forest biometricians at the science-policy interface build bridges to inform decisions by forest managers, landowners, and investors.

A highly sensitive multiplex immunoassay for inflammatory cytokines in dried blood spots.

Inflammatory cytokines are key regulators of inflammation, but current measurement approaches require venous blood to quantify low circulating concentrations associated with chronic, low-grade inflammation. This article describes a highly sensitive multiplex immunoassay protocol for the measurement of IL6, IL8, IL10, and TNFα in finger stick dried blood spot (DBS) samples. The protocol uses a multiplex electrochemiluminescent immunoassay platform. The following measures of assay performance were evaluated: reliability (inter-assay percent coefficient of variation; %CV), precision (intra-assay %CV), lower limit of detection (LLD), linearity of dilution, and agreement with results from matched plasma samples. Analysis of three control samples across the assay range indicated an acceptable level of precision and reliability for each cytokine. Linearity of dilution returned average values that ranged from 104.1 to 127.6% of expected. Lower limits of detection for IL6, IL8, and IL10 were <0.5, and <1.0pg/ml for TNFα. Level of agreement in results between matched DBS and plasma samples was high for all cytokines except for IL8. Finger stick DBS sampling provides a viable alternative to venipuncture for the quantification of IL6, IL10, and TNFα at low concentrations associated with chronic inflammation. The presence of red blood cells may interfere with the quantification of IL8 in DBS. In facilitating blood collection in nonclinical settings this method can advance scientific understandings of how social and ecological contexts shape immune function and health over the life course.

In-house validation of accelerated solvent extraction-gas chromatography-mass spectrometry for the determination of bound 3- and 2-monochloropropanediols (MCPD) and glycidol in food products

ABSTRACT The study aimed to establish the detection method for bound 3-, 2-MCPD, and glycidol using accelerated solvent extraction (ASE) and gas chromatography mass spectrometry (GC-MS). The ASE was modified for reduced solvent volume and process time to extract lipid from the chocolate spread, infant formula, potato chips, and sweetened creamer. The solvent selected for ASE was a mixture of iso-hexane and acetone at 100°C with the lipid and analyte recovery ranging from 96.9% to 98.6% and 84.1% to 107.5%, respectively. The derivatisation of analytes was adopted from the AOCS method Cd29a-13 for GC-MS analysis. The results showed that the coefficient of determination (R2) of all analytes was >0.99. The limit of detection (LOD) was 0.1 mg kg−1 expressed in lipid basis for both bound 3- and 2-MCPD and 0.2 mg kg−1 expressed in lipid basis for bound glycidol. The limit of quantitation (LOQ) was 0.3 mg kg−1 expressed in lipid basis for both bound 3- and 2-MCPD and 0.6 mg kg−1 expressed in lipid basis for bound glycidol. A blank spiked with 3-monochloropropanediols fatty acid esters (MCPDE) and 2-MCPDE (0.3, 2.1, and 7.2 mg kg−1) and glycidol esters (0.6, 4.7, and 16.6 mg kg−1) were chosen for accuracy and precision tests. The recoveries were 91.7% to 105.9%. Both repeatability and within-laboratory reproducibility of the analysis were within the acceptable level of precision ranging from 1.7% to 16%. This is the first time that a full validation procedure extending to both accuracy and precision tests has been carried out for sweetened creamer and chocolate spread. Overall, the combined protocol of ASE and AOCS Cd29a-13 was successfully validated for both solid and liquid food samples with lipid content from 10% to 30%.

Spatial hierarchical Bayes Small Area Model for disaggregated level crop acreage estimation

Crop area statistics in most of the states in India are provided based on complete enumeration or census approach. But, shortage of man power, failure of the primary and revenue staffs to devote adequate time and attention in collection and compilation of data has deteriorated the quality of area statistics as well as increased the time lag in availability of data in hand. In the view of above problem, a well-designed sample survey has the ability to cater the need of accurate and timely crop area information with utilization of limited resources. A pilot study conducted by ICAR- Indian Agricultural Statistics Research Institute attempts to estimate disaggregated level crop yield based on reduced number of Crop Cutting Experiments (CCEs) while crop acreage estimation has been done through sample survey approach. But, traditional sampling theory has also some limitations in providing reliable and valid estimates particularly for districts/areas with few or negligible sample sizes. To tackle this need Small Area Estimation (SAE) approach has been considered in this paper. In particular, using Hierarchical Bayes spatial small area model disaggregated level crop area has been estimated for two major crops, rice and wheat respectively in the state of Uttar Pradesh for Agriculture year 2015-16. Estimates produced using SAE technique has acceptable precision level.

District level crop yield estimation with reduced number of crop cutting experiments

It was observed that around 1300000 Crop Cutting Experiments (CCE) were conducted in India every year to find out the crop yield estimates of several major and minor crops under General Crop Estimation Surveys (GCES). Due to shortage of manpower and huge bulk of work day by day the data quality is becoming questionable. To tackle this problem, a pilot study was conducted by ICAR-IASRI, New Delhi sponsored by Directorate of Economics and Statistics (DES), Ministry of Agriculture and Farmers Welfare (MoA &amp; FW), Govt. of India to generate district level estimates of major crop yield from a reduced sample size of villages selected from the states. With the reduction in number of villages, the problem of no sample size in some districts were faced during the study where common design based estimates of crop yield cannot be generated. To tackle this problem Aggregate level Small Area Estimation (SAE) was used to tackle this problem. The results obtained from this study in the state of Uttar Pradesh for two major crops, i.e. rice and wheat for two seasons, i.e. kharif and rabi of Agriculture Year 2015-16 and for paddy in Assam for kharif of the Agriculture Year (AY) 2015-16 in India were discussed. The yield estimates were compared with the estimates released under GCES for AY 2015-16. It was found that the estimates obtained from reduced sample size of number of CCEs w.r.t. GCES, produced similar estimates with acceptable level of precision.

Triad system for object's 3D localization using low-resolution 2D ultrasonic sensor array

AbstractIn the recently published researches in the object localization field, 3D object localization takes the largest part of this research due to its importance in our daily life. 3D object localization has many applications such as collision avoidance, robotic guiding and vision and object surfaces topography modeling. This research study represents a novel localization algorithm and system design using a low-resolution 2D ultrasonic sensor array for 3D real-time object localization. A novel localization algorithm is developed and applied to the acquired data using the three sensors having the minimum calculated distances at each acquired sample, the algorithm was tested on objects at different locations in 3D space and validated with acceptable level of precision and accuracy. Polytope Faces Pursuit (PFP) algorithm was used for finding an approximate sparse solution to the object location from the measured three minimum distances. The proposed system successfully localizes the object at different positions with an error average of ±1.4 mm, ±1.8 mm, and ±3.7 mm in x-direction, y-direction, and z-direction, respectively, which are considered as low error rates.

Application of ISO standards on sampling and effects on the quality assessment of solid biofuel employed in a real power plant

Bioenergy is one of the main contributors in the renewable energy markets but the quality of solid biomass is often a concern. This study provides an application of the ISO 18135 on biomass sampling, evaluating how sampling procedure affects the analytical results and suggesting possible solutions to reach an acceptable level of precision for quality assessment.A sampling plan compliant with the aforementioned standard was carried out in a 21 MW biomass power plant located in Northern Italy in August and February, collecting samples of different typology from heaps stored in the plant and from trucks coming from suppliers. Moisture content analysis was performed for all the samples, and for those from heaps ash and nitrogen contents were also investigated.Considering moisture content, to achieve a good precision, a significantly different number of increments are needed in August and February. Regarding ash content, it would be impossible to obtain the precision suggested by the standard, because it would lead to a number of increments not in line with practical operations. Nitrogen content is not a concern in this case, because of the measured low values. In general terms, precision values suggested by ISO 18135 require a high effort by operator and resulted unsuitable in terms of practical application for moisture and ash contents. This is probably due to the different solid biomass considered during the standard development.For this reason, it is important to assess the quality of the different solid biofuels in order to tune the sampling procedure accordingly.

Rake Sampling to Estimate Biomass of Submersed Aquatic Vegetation in Coastal Wetlands

We evaluated two rake-enclosure sampling methods for estimating biomass of submersed aquatic vegetation (SAV) within managed tidal impoundments (MTIs) in the Ashepoo-Combahee-Edisto Rivers Basin, South Carolina. We collected SAV using a modified rake within 0.086 m2 cylindrical sampler (CS), a non-modified rake within 0.2-m2 quadrat (QS), and after raking, hand-collected remaining SAV within each enclosure (i.e., rake + hand = total SAV biomass). We used linear mixed models to evaluate rake sampling methods to estimate total (QS and CS) and species-specific SAV biomass (QS). The marginal coefficient of determination (R2) for both rake sampling methods was 0.95 for estimating total SAV biomass and model fitted values met our a priori acceptable level of precision (CV ≤ 15–20%). Furthermore, the QS-rake method explained 95% of the variation in total widgeongrass biomass (Ruppia maritima), 97% of dwarf spikerush biomass (Eleocharis parvula), and 97% of muskgrass (Chara spp.) biomass but model fitted values only met a priori precision for widgeongrass biomass. We conclude that our rake-SAV sampling estimates total SAV and widgeongrass biomass with little unexplained variation. Furthermore, abiotic variables had no significant effect when estimating SAV biomass. We suggest managers and researchers use our rake methods to estimate biomass of SAV in wetlands with similar species compositions, and resulting estimates may be used to estimate foraging carrying capacity for habitat conservation planning and management.

Nonparametric Bootstrap Technique to Improve Positional Accuracy in Mobile Robots With Differential Drive Mechanism

Wheeled mobile robots (WMRs) inevitably experience positional inaccuracy, which is caused by a number of factors including design imperfections, problems with component fabrication, sensor errors, and electromechanical malfunction. We have previously proposed an odometry-based technique that reduces positional inaccuracy in WMRs driven with standard wheels. This technique combines high sampling rates and short-term accuracy, and calculates necessary lateral and longitudinal corrections by using linear regression to model the relationship between positional inaccuracy and angular velocities of the robot's wheels. This technique can do so without the precision of measurement required by other techniques. In this paper, we discuss how a nonparametric bootstrap approach can be used to find both interval and point estimates of the modified angular velocities required to alleviate the positional inaccuracy of the WMR. First, the robot travels along a path recommended by the odometry-based error reduction technique. Then, the positional and angular errors of the robot at the stop point are measured. Next, these measurements are used to estimate angular velocities, providing the necessary confidence intervals (prediction). Results from these calculations could be incorporated into the robot program to modify the movement along a given path (validation). To show viability, the bootstrap technique was applied to a prototype mobile robot while the robot was programmed to move along an unseen trajectory. Results indicate that, for this typical unseen path, the bootstrap technique is capable of improving real-time positional systematic and non-systematic inaccuracy with acceptable levels of precision compared to the linear regression technique under the normality assumption. The bootstrap technique exhibited better efficacy than the linear regression, therefore, it may be a useful tool to conduct real-time calibration of differential drive WMRs.

Thermal characteristics of evacuated tube solar collectors with coil inside: An experimental study and evolutionary algorithms

In this paper, the thermal characteristics of an evacuated tube solar collector for different volumetric flow rates of the fluid (10, 30 and 50 l/h) was experimentally improved by using copper oxide/distilled water (Cu2O/DW) nanofluid, and parabolic concentrator. Moreover, the effect of different volume fractions of the utilized nanofluid on the fluid properties, such as convective heat transfer coefficient, Nusselt number, and the useful gain of the collector was experimented. Finally, three artificial intelligence (AI) techniques namely, multi-variate adaptive regression spline (MARS), model tree (MT) and gene-expression programming (GEP) have been employed to predict the energy efficiency (ηІ) and inlet-outlet water temperature difference (ΔT). The input variables were volume of the storage tank (V), volume fraction of the nanofluid (VF), and mass flow rate of the fluid (M˙). The proposed AI methods presented robust formulations for prediction of ηІ and ΔT with an acceptable level of precision. The statistical results of AI models demonstrated that the MARS method can make a more accurate prediction of the collector performance than GEP and MT. It was also concluded that increase in both flow rate, and concentration of the nanofluid, lead to an increase in the thermal performance of the solar collector.

Automatic detection of the Cyprideis torosa (Jones, 1856) sieve pores from backscattered Electron Scanning Electron Microscopy images and development of morphometric tools for their shape identification

Abstract Round-shaped sieve pores of Cyprideis torosa have negative correlation with salinity and are a useful proxy for reconstructing paleo-salinity trends, especially for oligohaline to mesohaline waters. However, given its time-consuming character, this method has only been used rarely. A protocol for the automatic detection of sieve pores from Backscattered Electron Scanning Electron Microscopy (SEM-BSE) images and ImageJ-FIJI software has been developed. The use of SEM-BSE images has optimized the sieve pore contrasts and extend the observation possibilities of sieve pores on less well-preserved valves. Automatic detection significantly reduces analysis times, by avoiding individual pore measurements and applying batch processes on samples. Rosenfeld and Vesper (1977) proposed an elongation index (length/beam) to discriminate round and elongated shapes, but the identification of irregular shapes only depends on the operator's appreciation. Due to the rapid acquisition of morphometric data with the automatic detection method, it is possible to systematize the use of metrical tools to discriminate sieve pore shapes. Two methods were developed from a repository based on metric variables obtained by manually digitalized 1490 sieve pores: (i) a Functional Discriminant analysis based on 5 variables (Feret's aspect ratio, roundness, aspect ratio and two ratios: Apore/AMinimum Bounding Circle and Ppore/PMinimum Bounding Circle) and (ii) an irregularity index (Roundness/(Apore/AMinimum Bounding Circle), as a complement to the elongation index. In addition, a bootstrap on 1774 sieve pores indicates that is useful to analyze over 10 valves per sample in order to estimate the proportions of these sieve pore shapes with an acceptable level of precision.

Comparing Scores From Full Length, Short Form, and Adaptive Tests of the Social Interaction Anxiety and Social Phobia Scales.

The current study developed and examined the performance of a computerized adaptive version of the Social Interaction Anxiety and Social Phobia Scales (SIAS/SPS) and compared results with a previously developed static short form (SIAS-6/SPS-6) in terms of measurement precision, concordance with the full forms, and sensitivity to treatment. Among an online sample of Australian adults, there were relatively minor differences in the performance of the adaptive tests and static short forms when compared with the full scales. Moreover, both adaptive and static short forms generated similar effect sizes across treatment in a clinical sample. This provides further evidence for the use of static or adaptive short forms of the SIAS/SPS rather than the lengthier 20-item versions. However, at the individual level, the adaptive tests were able to maintain an acceptable level of precision, using few items as possible, across the severity continua in contrast to the static short forms.

Sample size calculation for estimating key epidemiological parameters using serological data and mathematical modelling

BackgroundOur work was motivated by the need to, given serum availability and/or financial resources, decide on which samples to test in a serum bank for different pathogens. Simulation-based sample size calculations were performed to determine the age-based sampling structures and optimal allocation of a given number of samples for testing across various age groups best suited to estimate key epidemiological parameters (e.g., seroprevalence or force of infection) with acceptable precision levels in a cross-sectional seroprevalence survey.MethodsStatistical and mathematical models and three age-based sampling structures (survey-based structure, population-based structure, uniform structure) were used. Our calculations are based on Belgian serological survey data collected in 2001–2003 where testing was done, amongst others, for the presence of Immunoglobulin G antibodies against measles, mumps, and rubella, for which a national mass immunisation programme was introduced in 1985 in Belgium, and against varicella-zoster virus and parvovirus B19 for which the endemic equilibrium assumption is tenable in Belgium.ResultsThe optimal age-based sampling structure to use in the sampling of a serological survey as well as the optimal allocation distribution varied depending on the epidemiological parameter of interest for a given infection and between infections.ConclusionsWhen estimating epidemiological parameters with acceptable levels of precision within the context of a single cross-sectional serological survey, attention should be given to the age-based sampling structure. Simulation-based sample size calculations in combination with mathematical modelling can be utilised for choosing the optimal allocation of a given number of samples over various age groups.