Characterization of CKD illness representation profiles using patient-level factors.

BACKGROUND: Illness perceptions are the unique perspective individuals have on their illness, based on their context and experiences, and are associated with patient outcomes including coping and adherence. The purpose of this study was to explore characteristics that may be driving membership in illness perceptions cluster groups for adults with chronic kidney disease (CKD). METHODS: This study was conducted within the multicenter longitudinal Chronic Renal Insufficiency Cohort (CRIC) Study. Cross-sectional data were collected and combined with CRIC data. Illness perceptions were measured using the Revised Illness Perception Questionnaire. Clustering analysis was conducted in R, and bivariate analysis including linear regression was performed in STATA 16. RESULTS: The sample (n = 197) had a mean age of 68, was 52% women, 53% non-White, and mean estimated glomerular filtration rate (eGFR) 56 ml/min/1.73 m2. Three cluster groups were identified, labeled as "Disengaged" (n = 20), "Well-Resourced" (n = 108), and "Distressed" (n = 69). The "Disengaged" group was characterized by low CKD knowledge, many recent hospitalization days, and the lowest perceived CKD burden. The "Well-Resourced" group was characterized by the highest levels of education, CKD knowledge, optimism, and medication adherence. The "Distressed" group was characterized by the highest levels of depression scores, comorbidity burden, CKD burden, CKD symptoms, and lowest optimism. Group membership significantly predicted the number of hospitalization days in adjusted analyses. CONCLUSIONS: Illness perceptions groups are associated with number of hospitalization days but are independent of many patient characteristics. Illness perceptions data could be used to tailor care for specific patients at risk for poor health outcomes.

Assessment of the vitamin D status and its determinants in young healthy students from Palestine.

The global prevalence of vitamin D deficiency is high. Poor vitamin D status, especially in women, has been reported in several countries in the Middle East despite adequate year-round sunlight for vitamin D synthesis. However, data on vitamin D status in Palestine are scarce. The aim of this cross-sectional study was to evaluate vitamin D status based on serum concentrations of 25-hydroxycholecalciferol [25-(OH)D] among young healthy Palestinian students (18-27 years) and to assess associations between 25-(OH)D concentrations and several predictors. The mean 25-(OH)D concentration of women (n 151) was 27⋅2 ± 14⋅5 nmol/l, with the majority having insufficient (31⋅1 %) or deficient (<60 %) 25-(OH)D status. Only 7 % of women achieved sufficient or optimal 25-(OH)D status. In contrast, men (n 52) had a mean 25-(OH)D concentration of 58⋅3 ± 14⋅5 nmol/l, with none classified as deficient, and most obtaining sufficient (55⋅8 %) or even optimal 25-(OH)D status (11⋅5 %). Among women, 98 % wore a hijab and 74 % regularly used sunscreen. Daily dietary vitamin D intake (3-d 24-h recalls) was 45⋅1 ± 36⋅1 IU in the total group (no sex differences). After adjustment, multiple linear regression models showed significant associations between 25-(OH)D concentrations and the use of supplements (B = 0⋅069; P = 0⋅020) and dietary vitamin D (B = 0⋅001; P = 0⋅028). In gender-stratified analysis, the association between supplement use and 25-(OH)D concentrations was significant in women (B = 0⋅076; P = 0⋅040). The vitamin D status of women in the present cohort is critical and appears to be mainly due to wearing a hijab, regular use of sunscreen and low dietary vitamin D intake. The vitamin D status of the women should be improved by taking vitamin D containing supplements or fortified foods.

Implementing and evaluating a Gaussian mixture framework for identifying gene function from TnSeq data.

The rapid acceleration of microbial genome sequencing increases opportunities to understand bacterial gene function. Unfortunately, only a small proportion of genes have been studied. Recently, TnSeq has been proposed as a cost-effective, highly reliable approach to predict gene functions as a response to changes in a cell's fitness before-after genomic changes. However, major questions remain about how to best determine whether an observed quantitative change in fitness represents a meaningful change. To address the limitation, we develop a Gaussian mixture model framework for classifying gene function from TnSeq experiments. In order to implement the mixture model, we present the Expectation-Maximization algorithm and a hierarchical Bayesian model sampled using Stan's Hamiltonian Monte-Carlo sampler. We compare these implementations against the frequentist method used in current TnSeq literature. From simulations and real data produced by E.coli TnSeq experiments, we show that the Bayesian implementation of the Gaussian mixture framework provides the most consistent classification results.

The cost-effectiveness of reclassification sampling for prevalence estimation.

BACKGROUND: Typically, a two-phase (double) sampling strategy is employed when classifications are subject to error and there is a gold standard (perfect) classifier available. Two-phase sampling involves classifying the entire sample with an imperfect classifier, and a subset of the sample with the gold-standard. METHODOLOGY/PRINCIPAL FINDINGS: In this paper we consider an alternative strategy termed reclassification sampling, which involves classifying individuals using the imperfect classifier more than one time. Estimates of sensitivity, specificity and prevalence are provided for reclassification sampling, when either one or two binary classifications of each individual using the imperfect classifier are available. Robustness of estimates and design decisions to model assumptions are considered. Software is provided to compute estimates and provide advice on the optimal sampling strategy. CONCLUSIONS/SIGNIFICANCE: Reclassification sampling is shown to be cost-effective (lower standard error of estimates for the same cost) for estimating prevalence as compared to two-phase sampling in many practical situations.

Incorporating duplicate genotype data into linear trend tests of genetic association: methods and cost-effectiveness.

The genome-wide association (GWA) study is an increasingly popular way to attempt to identify the causal variants in human disease. Duplicate genotyping (or re-genotyping) a portion of the samples in a GWA study is common, though it is typical for these data to be ignored in subsequent tests of genetic association. We demonstrate a method for including duplicate genotype data in linear trend tests of genetic association which yields increased power. We also consider the cost-effectiveness of collecting duplicate genotype data and find that when the relative cost of genotyping to phenotyping and sample acquisition costs is less than or equal to the genotyping error rate it is more powerful to duplicate genotype the entire sample instead of spending the same money to increase the sample size. Duplicate genotyping is particularly cost-effective when SNP minor allele frequencies are low. Practical advice for the implementation of duplicate genotyping is provided. Free software is provided to compute asymptotic and permutation based tests of association using duplicate genotype data as well as to aid in the duplicate genotyping design decision.

The cost effectiveness of duplicate genotyping for testing genetic association.

We consider a modification to the traditional genome wide association (GWA) study design: duplicate genotyping. Duplicate genotyping (re-genotyping some of the samples) has long been suggested for quality control reasons; however, it has not been evaluated for its statistical cost-effectiveness. We demonstrate that when genotyping error rates are at least m%, duplicate genotyping provides a cost-effective (more statistical power for the same price) design alternative when relative genotype to phenotype/sample acquisition costs are no more than m%. In addition to cost and error rate, duplicate genotyping is most cost-effective for SNPs with low minor allele frequency. In general, relative genotype to phenotype/sample acquisition costs will be low when following up a limited number of SNPs in the second stage of a two-stage GWA study design, and, thus, duplicate genotyping may be useful in these situations. In cases where many SNPs are being followed up at the second stage, duplicate genotyping only low-quality SNPs with low minor allele frequency may be cost-effective. We also find that in almost all cases where duplicate genotyping is cost-effective, the most cost-effective design strategy involves duplicate genotyping all samples. Free software is provided which evaluates the cost-effectiveness of duplicate genotyping based on user inputs.