Sampling Fraction Research Articles

Factors influencing the precision of species richness estimation in Japanese vascular plants

AbstractAimEstimating species richness from a series of samples is an important and widely debated issue in ecology and biodiversity conservation. Numerous tests of respective richness estimators gave insights into the precision, the limitations and the pitfalls of richness forecasting. However, few benchmark tests used almost complete empiric census data obtained at those spatial scales where richness estimation is most useful for conservation management.LocationJapan.MethodsWe use an extraordinary dataset on the spatial distribution of Japanese plants containing complete information on the occurrence of each Japanese plant species at the 10 × 10 km2 grid cell level. We link the estimates of four estimators representing different theoretical approaches, Chao2, rarefaction, species–area relationships (SAR) and species abundance distributions (SAD), to environmental data using a fully nested sampling design.ResultsChao2 and rarefaction behaved very similar in all tests and significantly underestimated true richness below 40% sampling fraction. SAR and SAD were less precise than Chao2 and rarefaction at higher sampling fraction but also less affected by low sample size. In general, SAD provided robust estimates over the whole range of sampling fraction and 67.4% of estimates ranged within the 10% error level. Higher species spatial turnover increased and high evenness in occurrence decreased the precision of the SAD estimator. Precision of the four estimators was largely unaffected by environmental variability but increased with increasing latitude.Main conclusionsOur results strongly indicate that the pattern of Japanese plant species spatial distribution is sufficiently scale invariant for richness estimators to provide precise forecasting results at the country level. The simplest process to generate such a spatial distribution is ecological drift.

Intercalation of 3X-pyridine with X = F, Cl, Br, I, in 2D ferrous nitroprusside. Thermal induced spin transition in Fe(3F-pyridine)2[Fe(CN)5NO

The series of 2D solids, Fe(3XPy)2[Fe(CN)5NO] with 3X ​= ​3-Fluoro, 3-Chloro, 3-Bromo, 3-Iodo and Py ​= ​Pyridine, was obtained by intercalation of the organic molecule (3XPy) between neighboring layers of 2D ferrous nitroprusside. The organic ligand was found coordinated to the axial positions for the iron atom linked at the equatorial N ends of the CN groups in the nitroprusside building block, [Fe(CN)5NO]. The axial CN and NO ligands remain unlinked and contribute to the solid stability through attractive dipole-dipole interactions. The formed hybrid solids preserve the 2D nature because no chemical bond is established between neighboring organic molecules in the interlayer region. For X ​= ​F, a thermal induced spin transition was observed, but not for the remaining derivatives. The nature of such behavior is discussed according to the recorded XRD, Raman and Mössbauer data and computational calculations. The spin crossover transition in Fe(3FPy)2[Fe(CN)5NO] is an incomplete process, a sample fraction of about 50% remains in paramagnetic state, even at 5 ​K. The spin transition is accompanied of an atypical small structural change, corresponding to a variation of 0.7% for the unit cell volume. Such behavior finds explanation in both, the bonding properties of the N end of the CN ligands in the nitroprusside pseudo-octahedral block, and in the intermolecular interactions in the interlayer region.

PAH sampling and quantification from woody biomass fast pyrolysis in a pyroprobe reactor with a modified tar sampling system

The present work focuses on the sampling procedure and quantification of the PAH yield from the fast pyrolysis of waste softwood. In particular, fast pyrolysis experiments were conducted using a CDS Pyroprobe 5200 at temperatures between 500 °C and 1000 °C, at a heating rate of 600 °C/s for a sample size of 30 mg. High performance liquid chromatography (HPLC) was used for the determination of the PAH compounds present in the liquid sample fraction, while a micro – GC was employed for the analysis of the main gaseous products (CO, CO2, CH4 and H2). An alternative tar sampling protocol was proposed, which employed the use of a cold trap (50 °C) and an isopropanol filled impinger bottle for the collection of the condensable products. The experiments were compared to heated foil reactor based pyrolysis tests within the same temperature range and heating rate, except for a slightly lower sample size (10 mg).The Pyroprobe and adapted sampling system proved to be more efficient regarding PAH capture and quantification compared to the heated foil reactor. Naphthalene, acenaphthylene and phenanthrene were the main PAH compounds detected. The PAH yields increased with pyrolysis temperature, up to values corresponding to roughly 0.2 wt% of the overall yield at 1000 °C. From the results it was derived that PAH evolution is mainly a product of secondary decomposition of primary tar, since the char yield stabilized for higher temperatures and the yields of CO, H2 and CH4 increased. Overall mass balance closure values were around 80 wt% on average. Char and gas yields were determined with high reproducibility, however gravimetric liquid analysis lacked due to the inability to gravimetrically measure the yield condensing in the impinger bottle. Future work is aimed on improving on this particular aspect. Overall, the alternative tar sampling system proposed was successful in the quantification of PAH from biomass fast pyrolysis experiments offering increased flexibility, accuracy and practicality of use.

Evaluation of Mass Drug Administration through Transmission Assessment Survey in Udupi District of Karnataka, India

Annual Mass Drug Administration (MDA) of single dose of antifilarial drugs to all the eligible population was initiated in 2004 in Udupi district, a known endemic district for bancroftian filariasis. Nine rounds were implemented in this Implementation Unit (IU) with drug coverage ranging from 83% to 90% in different MDA rounds and with drug compliance rate of more than 65% in each of the MDA round. The baseline mf prevalence of 1.3% in 2004 was brought down to 0.09% in the year 2013. The impact assessment performed after 9 MDA rounds revealed <1% mf prevalence in each of the sentinel and spot check sites. The mf prevalence was further measured in 10 additional sites, which recorded <1% mf prevalence in all the sites. Having qualified for Transmission Assessment Survey (TAS), school-based survey was conducted in March 2014 in EUvaluation unit (EU) targeting 6 to 7-year children. The number of clusters, sample size, sampling fraction, critical cut off value, clusters and additional clusters to be sampled were estimated using Survey Sample Builder tool. A total of 1579 children from 53 clusters were screened against the target of 1553 and 5 were found positive for filaria antigen (0.3%) against the critical cut off value of 18 and hence the decision of stoppage of further rounds of MDA was taken. The “Post MDA Surveillance” was implemented in the EU, wherein the mf prevalence among 5-9-year age group in sentinel and spot check sites was measured annually for two years. The mf prevalence of such surveys was less than 1%. The second TAS conducted in 2016 after a gap of 2.4 years has shown presence of 0.6% filarial antigenemia thus passing the second TAS also. How to cite this article:Srivastava PK, Dhariwal AC, Kumar BGP, Ashok H, Usha A, Hoti SL. Evaluation of Mass Drug Administration through Transmission Assessment Survey in Udupi District of Karnataka, India. J Commun Dis 2019; 51(4): 21-28. DOI: https://doi.org/10.24321/0019.5138.201933

Quality of life and its association with psychiatric symptoms and socio-demographic characteristics among people with schizophrenia: A hospital-based cross-sectional study.

ObjectivesTo identify sociodemographic and illness-related factors associated with quality of life among people with Schizophrenia.MethodsA hospital-based cross-sectional study design was employed among 351 people with schizophrenia and attending the followup service at Jimma University Medical Center, psychiatric clinic during the study period. Participants were recruited using a systematic random sampling method and a sample fraction of two was used after the first person was identified by a lottery method. Data entry was done using EpiData version 3.1 and then exported to Statistical Package for Social Sciences version 25 for analysis. Multiple regression analysis was used to determine the statistically significant association between quality of life and independent variables.ResultsAmong the four domains of quality of life, respondents scored the lowest mean in the social relationships domain (10.14 ± 3.12). Final adjusted multiple regression model revealed, being divorced was negatively associated with the physical domain (β = −0.72, p = 0.02), having no formal education was negatively associated with physical health domain (β = −0.69, p = 0.001) and age was positively associated with the psychological domain (β = 0.371, p = 0.071). Being rural resident was negatively associated with physical domain (β = −0.48, p = 0.01), with environmental domain (β = −0.64, p = 0.03), with social relationships domain (β = −0.45, p = 0.04), and with overall quality of life (β = −1.93, p = 0.006). Positive symptoms (β = −0.22, p = 0.001), negative symptoms (β = −0.36, p = 0.001), and general psychopathology (β = −0.098, p = 0.006) were inversely associated with overall quality of life.ConclusionIn this study, the social relationship domain of quality of life among people with schizophrenia has the lowest mean score. Some socio-demographic variables and psychiatric symptoms were found to be key significant associated factors of quality of life. Priority interventions to improve the social deficits and addressing psychiatric symptoms of people with schizophrenia is essential to improve their quality of life.

Robust inference under r-size-biased sampling without replacement from finite population

The case of size-biased sampling of known order from a finite population without replacement is considered. The behavior of such a sampling scheme is studied with respect to the sampling fraction. Based on a simulation study, it is concluded that such a sample cannot be treated either as a random sample from the parent distribution or as a random sample from the corresponding r-size weighted distribution and as the sampling fraction increases, the biasness in the sample decreases resulting in a transition from an r-size-biased sample to a random sample. A modified version of a likelihood-free method is adopted for making statistical inference for the unknown population parameters, as well as for the size of the population when it is unknown. A simulation study, which takes under consideration the sampling fraction, demonstrates that the proposed method presents better and more robust behavior compared to the approaches, which treat the r-size-biased sample either as a random sample from the parent distribution or as a random sample from the corresponding r-size weighted distribution. Finally, a numerical example which motivates this study illustrates our results.

Isolation of Two Isochlorogenic Acid Isomers from Phenolic Rich Fraction of Artemisia turanica Krasch.

Total phenolic content (TPC) and antioxidant capacity of five different extracts (petroleum ether (40-60), dichloromethane, ethyl acetate, ethanol and ethanol-water (1:1 v/v)) of Artemisia turanica (A. turanica) aerial parts were determined and phytochemical study on the most promising extract was carried out. Folin–Ciocalteu method, 1, 1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging test, β-carotene bleaching (BCB) method, and ferrous ion chelating (FIC) assay were performed. Vacuum liquid chromatography (VLC) and semi-preparative HPLC were used for bioassay-guided phytochemical isolation. Structures of isolated compounds were established using spectroscopic analysis including NMR and MS. Among all the extracts analyzed, the hydroethanolic extract exhibited the highest phenolic content and antioxidant activity. VLC of this extract yielded seven fractions (A to G) which were subjected to all antecedent experiments. The same sample (Fraction D) showed the highest total phenolic content and free radical scavenging activity but the only statistically significant correlation between TPC and EC50 values was observed for BCB. 3,5-dicaffeoylquinic acid (isochlorogenic acid A), and 4,5-dicaffeoylquinic acid (isochlorogenic acid C) was isolated from the most active fraction. Antioxidant activity of A. turanica is probably partly due to the presence of isomers of isochlorogenic acid.

GEANT4 simulation of energy resolution of the SPACAL electromagnetic calorimeter

Geant4 based simulation study of energy resolution is performed for Spaghetti electromagnetic calorimeter module (SPACAL). Several configurations of SPACAL module, with different fiber pitch and scintillation material, were simulated. For each configuration, the energy and angular dependencies of energy resolution were determined. The sampling fraction for electromagnetic showers for the selected SPACAL configuration was also determined

An Overview of Peripheral Artery Disease in the Elderly: A Study in a Tertiary Hospital Southern Nigeria.

Background:The incidence of Peripheral Artery Disease (PAD), one of the manifestations of atherosclerosis, is increasing worldwide with significant effects on the quality of life. The disease is a strong prognostic marker for future cardiovascular events in people over 60 years, but relatively little is known about the condition among the elderly in sub-Saharan Africa.Objective:The objective of this study was to determine the prevalence and pattern of peripheral artery disease in elderly patients. Materials and Methods: A cross-sectional hospital-based study was carried out among elderly patients aged 60 years and above attending the Geriatric Clinic of the Department of Family Medicine of University of Benin Teaching Hospital, Edo State, Nigeria, from September to November 2017. Using systematic random sampling technique, 370 respondents were recruited from a sampling frame of 2160 respondents and a sampling fraction of 6. A structured questionnaire was administered to collect data on sociodemographic characteristics, lifestyle variable, and medical history. The ankle-brachial index (ABI) and toe-brachial index (TBI) were used to assess for PAD. The analysis was done using descriptive statistics.Results:The mean age was 69.3 ± 7 years comprising 76.5% females, 50% of the respondents were married, while 47% were widowed. The ABI showed 35.4% prevalence of PAD, while TBI for respondents with elevated ABI was 45.0%.Conclusion:The study showed a point prevalence of PAD to be 37.8%. From the total respondents, 45.7% had mild, while 2.9% had severe PAD. Routine screening for PAD is recommended for the early detection and management.

Modelling multilevel data under complex sampling designs: An empirical likelihood approach

Data used in social, behavioural, health or biological sciences may have a hierarchical structure due to the population of interest or the sampling design. Multilevel or marginal models are often used to analyse such hierarchical data. These data are often selected with unequal probabilities from a clustered and stratified population. An empirical likelihood approach for the regression parameters of a multilevel model is proposed. It has the advantage of taking into account of the sampling design. This approach can be used for point estimation, hypothesis testing and confidence intervals for the sub-vector of parameters. It provides asymptotically valid inference for small and large sampling fractions. The simulation study shows the advantages of the empirical likelihood approach over alternative parametric approaches. The approach proposed is illustrated using the Programme for International Student Assessment (PISA) survey data.

Using alpha hulls to automatically and reproducibly detect edge clusters in atom probe tomography datasets

An automated way to accurately and reproducibly identify edge clusters within atom probe tomography datasets has been developed. The alpha-hull algorithm is used to generate a concave alpha-shape around an atom probe dataset. Information from core-linkage cluster searches is used, in combination with the calculated alpha-shape, to determine which clusters are on the edge of the dataset.The potential effects that not removing edge clusters may have on calculated cluster sizes, number densities and compositions is discussed. The viability of the methodology is demonstrated via application to real datasets, one of which was a non-standard shape.The sensitivity of the method to user parameter selection is explored. Sampling fractions >0.1% and an alpha value, used to make the alpha shape, greater than twice the maximum measured nearest neighbour distance were found to be suitable.

Estimating Diversification Rates on Incompletely Sampled Phylogenies: Theoretical Concerns and Practical Solutions.

Molecular phylogenies are a key source of information about the tempo and mode of species diversification. However, most empirical phylogenies do not contain representatives of all species, such that diversification rates are typically estimated from incompletely sampled data. Most researchers recognize that incomplete sampling can lead to biased rate estimates, but the statistical properties of methods for accommodating incomplete sampling remain poorly known. In this point of view, we demonstrate theoretical concerns with the widespread use of analytical sampling corrections for sparsely sampled phylogenies of higher taxonomic groups. In particular, corrections based on "sampling fractions" can lead to low statistical power to infer rate variation when it is present, depending on the likelihood function used for inference. In the extreme, the sampling fraction correction can lead to spurious patterns of diversification that are driven solely by unbalanced sampling across the tree in concert with low overall power to infer shifts. Stochastic polytomy resolution provides an alternative to sampling fraction approaches that avoids some of these biases. We show that stochastic polytomy resolvers can greatly improve the power of common analyses to estimate shifts in diversification rates. We introduce a new stochastic polytomy resolution method (Taxonomic Addition for Complete Trees [TACT]) that uses birth-death-sampling estimators across an ultrametric phylogeny to estimate branching times for unsampled taxa, with taxonomic information to compatibly place new taxa onto a backbone phylogeny. We close with practical recommendations for diversification inference under several common scenarios of incomplete sampling. [Birth-death process; diversification; incomplete sampling; phylogenetic uncertainty; rate heterogeneity; rate shifts; stochastic polytomy resolution.].

A new variance estimator for unequal probability sampling without replacement in the presence of non-response

A new variance estimator for estimating the population total has been proposed under unequal probability sampling without replacement in the presence of nonresponse. The new variance estimator does not require the joint inclusion probability in the estimation under the reverse framework where the sampling fraction is negligible and equal response probabilities for all units. The efficiency of the new estimator is compared with the existing estimators7 via a simulation study.

Sampling-based Markov regression model for multistate disease progression: Applications to population-based cancer screening program.

To develop personalized screening and surveillance strategies, the information required to superimpose state-specific covariates into the multi-step progression of disease natural history often relies on the entire population-based screening data, which are costly and infeasible particularly when a new biomarker is proposed. Following Prentice's case-cohort concept, a non-standard case-cohort design from a previous study has been adapted for constructing multistate disease natural history with two-stage sampling. Nonetheless, the use of data only from first screens may invoke length-bias and fail to consider the test sensitivity. Therefore, a new sampling-based Markov regression model and its variants are proposed to accommodate additional subsequent follow-up data on various detection modes to construct state-specific covariate-based multistate disease natural history with accuracy and efficiency. Computer simulation algorithms for determining the required sample size and the sampling fraction of each detection mode were developed either through power function or the capacity of screening program. The former is illustrated with breast cancer screening data from which the effect size and the required sample size regarding the effect of BRCA on multistate outcome of breast cancer were estimated. The latter is applied to population-based colorectal cancer screening data to identify the optimal sampling fraction of each detection mode.

Compact fluidic system for functional assessment of pancreatic islets.

Transplantation of pancreatic islets is becoming a promising therapy for people with type I diabetes. In this study, we present a compact fluidic system that enables assessment of islet functionality ex vivo for efficient islet transplantation. The fluidic system includes a micromesh sheet-embedded chip. Islets can be loaded easily on the micromesh sheet and observed clearly by microscopy. Islets on the mesh sheet mainly remained in place during perfusion and did not get damaged by hydraulic pressure because of high porosity of the micromesh sheet. The fluidic system was assembled with a sample fraction chip of polydimethylsiloxane. The chip includes a channel and columns, both having surfaces that were super-hydrophilized so that solutions could flow smoothly within the chip by gravity. Using mouse pancreatic islets, a dynamic glucose-stimulated insulin secretion test was performed to examine the performance of the fluidic system. The system successfully analyzed levels and patterns of insulin secretion upon exposure of the islets to low- and high-glucose solutions in turns, thus demonstrating its capacity to assess islet functions more easily and cost-effectively.

Identification of sampling patterns for high-resolution compressed sensing MRI of porous materials: 'learning' from X-ray microcomputed tomography data.

There exists a strong motivation to increase the spatial resolution of magnetic resonance imaging (MRI) acquisitions so that MRI can be used as a microscopy technique in the study of porous materials. This work introduces a method for identifying novel data sampling patterns to achieve undersampling schemes for compressed sensing MRI (CS-MRI) acquisitions, enabling 3D spatial resolutions of 17.6 µm to be achieved. A data-driven learning approach is used to derive k-space undersampling schemes for 3D MRI acquisitions from 3D X-ray microcomputed tomography (µCT) datasets acquired at a higher spatial resolution than can be acquired using MRI. The performance of the new sampling approach was compared to other, well-established sampling strategies using simulated MRI data obtained from high-resolution µCT images of rock core plugs. These simulations were performed for a range of different k-space sampling fractions (0.125-0.375) using images of Ketton limestone. The method was then extended to consideration of imaging Estaillades limestone and Fontainebleau sandstone. The results show that the new sampling approach performs as well as or better than conventional variable density sampling and without need for time-consuming parameter optimisation. Further, a bespoke sampling pattern is produced for each rock type. The novel undersampling strategy was employed to acquire 3D magnetic resonance images of a Ketton limestone rock at spatial resolutions of 35 and 17.6 µm. The ability of the k-space sampling scheme produced using the new approach in enabling reconstruction of the pore space characteristics of the rock was then demonstrated by benchmarking against the pore space statistics obtained from high-resolution µCT data. The MRI data acquired at 17.6 µm resolution gave excellent agreement with the pore size distribution obtained from the X-ray microcomputed tomography dataset, while the pore coordination number distribution obtained from the MRI data was slightly skewed to lower coordination numbers. This approach provides a method of producing a k-space undersampling pattern for MRI acquisition at a spatial resolution for which a fully sampled acquisition at that spatial resolution would be impractically long. The approach can be easily extended to other CS-MRI techniques, such as spatially resolved flow and relaxation time mapping. LAY DESCRIPTION: Magnetic resonance imaging (MRI) is widely used to study the microstructure of, and fluid transport phenomena in porous media relevant for engineering applications. A major application is the study of water and hydrocarbon transport in porous sedimentary rocks, which typically have pore sizes smaller than 100 µm. The spatial resolution of routine MRI acquisitions, however, is limited to several hundred µm due to the relatively low sensitivity of the magnetic resonance method. Therefore, there exists a strong motivation to increase the spatial resolution of MRI by one to two orders of magnitude to be able to study these rocks at a pore scale. This work reports the initial step towards achieving this. Three-dimensional images of rock pore structure are acquired at both 35 and 17.6 µm spatial resolution. In ongoing work, these methods are now being incorporated into magnetic resonance velocity imaging methods, thereby enabling imaging of both pore structure and hydrodynamics at these much higher spatial resolutions than were hitherto possible. Although X-ray microcomputed tomography (µCT) produces high spatial resolution images, it is far more limited in being able to spatially map transport processes (i.e. flow) in porous media. This work reports a strategy for accelerating the image acquisition time such that sufficient signal-to-noise ratio (SNR) is achieved to increase the spatial resolution, that is, the voxel size within which there is sufficient SNR within the resulting image. To achieve this, a technique known as compressed sensing is used which exploits undersampling of the acquired data relative to the standard fully sampled image. In MRI, data are acquired in so-called k-space and Fourier transformed to yield the real space image. The challenge, when undersampling, is to optimise the specific points in k-space that are acquired because these will influence the quality of the resulting image. This work reports a straightforward, robust strategy for identifying the optimal sets of k-space points to acquire. The method introduced uses simulated MRI images calculated from high-resolution µCT images of the rocks of interest, from which optimised MRI sampling patterns are obtained. The method does not require any optimisation of parameters for its implementation, which is a significant advantage compared to other strategies. Moreover, we show that the pore space characteristics of the acquired MRI images are in excellent agreement with the same characteristics obtained from a high-resolution µCT image.

Bayesian Empirical Likelihood Inference with Complex Survey Data

SummaryWe propose a Bayesian empirical likelihood approach to survey data analysis on a vector of finite population parameters defined through estimating equations. Our method allows overidentified estimating equation systems and is applicable to both smooth and non-differentiable estimating functions. Our proposed Bayesian estimator is design consistent for general sampling designs and the Bayesian credible intervals are calibrated in the sense of having asymptotically valid design-based frequentist properties under single-stage unequal probability sampling designs with small sampling fractions. Large sample properties of the Bayesian inference proposed are established for both non-informative and informative priors under the design-based framework. We also propose a Bayesian model selection procedure with complex survey data and show that it works for general sampling designs. An efficient Markov chain Monte Carlo procedure is described for the required computation of the posterior distribution for general vector parameters. Simulation studies and an application to a real survey data set are included to examine the finite sample performances of the methods proposed as well as the effect of different types of prior and different types of sampling design.

Exposure to Bisphenol A and Bisphenol S and Incident Type 2 Diabetes: A Case-Cohort Study in the French Cohort D.E.S.I.R.

Background:The question of whether exposure to bisphenol A (BPA) contributes to the development of type 2 diabetes is still unresolved. Most epidemiological evidence on the association between BPA and diabetes is from cross-sectional studies or longitudinal studies with single urinary measurements. No prospective study has examined exposure to BPA analogs such as bisphenol S (BPS) in relation to incident type 2 diabetes.Objectives:We aimed to investigate whether exposure to BPA and BPS, assessed at up to two time points, was associated with the incidence of type 2 diabetes.Methods:We performed a case–cohort study on 755 participants without diabetes at baseline and followed-up over 9 y as part of the French prospective cohort Data from an Epidemiological Study on the Insulin Resistance Syndrome (D.E.S.I.R.). BPA-glucuronide (BPA-G) and BPS-glucuronide (BPS-G) were assessed in fasting spot urine samples collected during the health examinations at baseline and 3 y later. Associations with incident diabetes were examined using Prentice-weighted Cox regression models adjusted for potential confounders.Results:A total of 201 incident cases of type 2 diabetes were diagnosed over the follow-up, including 30 in the subcohort. Compared with participants with the lowest average BPA exposure (below the first quartile), participants in the second, third, and fourth quartile groups of exposure had a near doubling of the risk of type 2 diabetes, with a hazard ratio 2.56 (95% CI: 1.16, 5.65), 2.35 (95% CI: 1.07, 5.15), and 1.56 (95% CI: 0.68, 3.55), respectively. The detection of BPS-G in urine at one or both time points was associated with incident diabetes, with an 2.81 (95% CI: 1.74, 4.53).Discussion:This study shows positive associations between exposure to BPA and BPS and the incidence of type 2 diabetes, independent of traditional diabetes risk factors. Our results should be confirmed by recent, population-based observational studies in different populations and settings. Overall, these findings raise concerns about using BPS as a BPA substitute. Further research on BPA analogs is warranted. https://doi.org/10.1289/EHP5159

Empirical Likelihood Approach for Aligning Information from Multiple Surveys

SummaryWhen two surveys carried out separately in the same population have common variables, it might be desirable to adjust each survey's weights so that they give equal estimates for the common variables. This problem has been studied extensively and has often been referred to as alignment or numerical consistency. We develop a design‐based empirical likelihood approach for alignment and estimation of complex parameters defined by estimating equations. We focus on a general case when a single set of adjusted weights, which can be applied to both common and non‐common variables, is produced for each survey. The main contribution of the paper is to show that the impirical log‐likelihood ratio statistic is pivotal in the presence of alignment constraints. This pivotal statistic can be used to test hypotheses and derive confidence regions. Hence, the empirical likelihood approach proposed for alignment possesses the self‐normalisation property, under a design‐based approach. The proposed approach accommodates large sampling fractions, stratification and population level auxiliary information. It is particularly well suited for inference about small domains, when data are skewed. It includes implicit adjustments when the samples considerably differ in size. The confidence regions are constructed without the need for variance estimates, joint‐inclusion probabilities, linearisation and re‐sampling.

Political Environment and Implementation of Public Private Partnership Infrastructure Development in Kenya

Public Private Partnership (PPP) is a long term agreement between the public and private sector where risks and rewards are shared in developing a public facility. Private sector play a monumental role in bridging public finance deficit on capital projects. In Kenya, the government has created an enabling environment for private sector participation in the country’s infrastructure development which is meant to spur economic growth. This study sought to establish the effect of political environment on the implementation of Public Private Partnership infrastructure development projects in Kenya. The cross-sectional descriptive survey research methodology was used in the study. The population of the study was sixtythree PPP projects being implemented in Kenya across different sectors. One project was used in pretesting of research instruments. Therefore, the actual population used in the study was sixty-two projects. The study adopted systematic sampling technique where the first n th element was randomly selected. A sample size of 31 was selected from the sampling frame using sampling fraction. Questionnaires were administered to procurement officers charged with implementation of PPPs in the sampled organizations. To ensure validity of the data, the research questionnairre was verified by experts made up of the research supervisors. The research instruments were pretested during pilot study. Qualitative data was analyzed and presented through descriptions while the quantitative data was analyzed using descriptive statistics, measures of central tendency, measures of dispersion and inferential statistics. Multiple regression analysis was used to determine the relationship between dependent and independent variables. The study found that most of the organizations were in the initial stages of implementation of PPPs. The majority of the respondents considered the political environment in Kenya unstable because of the disputed presidential elections which was happening at the time the data was collected. The study also found political environment had significant influence on the implementation of PPPs in Kenya. The respondents also identified corruption, poor project selection, lack of experts and cost over-runs as other factors that affected implementation of PPPs. The results of the study will inform policy makers in public entities, County and National Governments to put in place necessary mechanisms to manage politics and fight corruption to increase uptake of Public Private Partnership for infrastructure development in Kenya.