Measuring diet cost at the individual level: a comparison of three methods.
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ABSTRACT:
SUBMITTER: Monsivais P
PROVIDER: S-EPMC3898278 | biostudies-other | 2013 Nov
REPOSITORIES: biostudies-other
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Project description:BackgroundBehavioural patterns are typically derived using unsupervised multivariate methods such as principal component analysis (PCA), latent profile analysis (LPA) and cluster analysis (CA). Comparability and congruence between the patterns derived from these methods has not been previously investigated, thus it's unclear whether patterns from studies using different methods are directly comparable. This study aimed to compare behavioural patterns derived across diet, physical activity, sedentary behaviour and sleep domains, using PCA, LPA and CA in a single dataset.MethodsParent-report and accelerometry data from the second wave (2011/12; child age 6-8y, n = 432) of the HAPPY cohort study (Melbourne, Australia) were used to derive behavioural patterns using PCA, LPA and CA. Standardized variables assessing diet (intake of fruit, vegetable, sweet, and savoury discretionary items), physical activity (moderate- to vigorous-intensity physical activity [MVPA] from accelerometry, organised sport duration and outdoor playtime from parent report), sedentary behaviour (sedentary time from accelerometry, screen time, videogames and quiet playtime from parent report) and sleep (daily sleep duration) were included in the analyses. For each method, commonly used criteria for pattern retention were applied.ResultsPCA produced four patterns whereas LPA and CA each generated three patterns. Despite the number and characterisation of the behavioural patterns derived being non-identical, each method identified a healthy, unhealthy and a mixed pattern. Three common underlying themes emerged across the methods for each type of pattern: (i) High fruit and vegetable intake and high outdoor play ("healthy"); (ii) poor diet (either low fruit and vegetable intake or high discretionary food intake) and high sedentary behaviour ("unhealthy"); and (iii) high MVPA, poor diet (as defined above) and low sedentary time ("mixed").ConclusionWithin this sample, despite differences in the number of patterns derived by each method, a good degree of concordance across pattern characteristics was seen between the methods. Differences between patterns could be attributable to the underpinning statistical technique of each method. Therefore, acknowledging the differences between the methods and ensuring thorough documentation of the pattern derivation analyses is essential to inform comparison of patterns derived through a range of approaches across studies.
| S-EPMC8315509 | biostudies-literature
Project description:There has been increasing interest in measuring under-five mortality as a health indicator and as a critical measure of human development. In countries with complete vital registration systems that capture all births and deaths, under-five mortality can be directly calculated. In the absence of a complete vital registration system, however, child mortality must be estimated using surveys that ask women to report the births and deaths of their children. Two survey methods exist for capturing this information: summary birth histories and complete birth histories. A summary birth history requires a minimum of only two questions: how many live births has each mother had and how many of them have survived. Indirect methods are then applied using the information from these two questions and the age of the mother to estimate under-five mortality going back in time prior to the survey. Estimates generated from complete birth histories are viewed as the most accurate when surveys are required to estimate under-five mortality, especially for the most recent time periods. However, it is much more costly and labor intensive to collect these detailed data, especially for the purpose of generating small area estimates. As a result, there is a demand for improvement of the methods employing summary birth history data to produce more accurate as well as subnational estimates of child mortality.We used data from 166 Demographic and Health Surveys (DHS) to develop new empirically based methods of estimating under-five mortality using children ever born and children dead data. We then validated them using both in- and out-of-sample analyses. We developed a range of methods on the basis of three dimensions of the problem: (1) approximating the average length of exposure to mortality from a mother's set of children using either maternal age or time since first birth; (2) using cohort and period measures of the fraction of children ever born that are dead; and (3) capturing country and regional variation in the age pattern of fertility and mortality. We focused on improving estimates in the most recent time periods prior to a survey where the traditional indirect methods fail. In addition, all of our methods incorporated uncertainty. Validated against under-five estimates generated from complete birth histories, our methods outperformed the standard indirect method by an average of 43.7% (95% confidence interval [CI] 41.2-45.2). In the 5 y prior to the survey, the new methods resulted in a 53.3% (95% CI 51.3-55.2) improvement. To illustrate the value of this method for local area estimation, we applied our new methods to an analysis of summary birth histories in the 1990, 2000, and 2005 Mexican censuses, generating subnational estimates of under-five mortality for each of 233 jurisdictions.The new methods significantly improve the estimation of under-five mortality using summary birth history data. In areas without vital registration data, summary birth histories can provide accurate estimates of child mortality. Because only two questions are required of a female respondent to generate these data, they can easily be included in existing survey programs as well as routine censuses of the population. With the wider application of these methods to census data, countries now have the means to generate estimates for subnational areas and population subgroups, important for measuring and addressing health inequalities and developing local policy to improve child survival. Please see later in the article for the Editors' Summary.
| S-EPMC2854123 | biostudies-literature
Project description:Anesthesiologists need tools to accurately track postoperative outcomes. The accuracy of patient report in identifying a wide variety of postoperative complications after diverse surgical procedures has not previously been investigated.In this cohort study, 1,578 adult surgical patients completed a survey at least 30 days after their procedure asking if they had experienced any of 18 complications while in the hospital after surgery. Patient responses were compared to the results of an automated electronic chart review and (for a random subset of 750 patients) to a manual chart review. Results from automated chart review were also compared to those from manual chart review. Forty-two randomly selected patients were contacted by telephone to explore reasons for discrepancies between patient report and manual chart review.Comparisons between patient report, automated chart review, and manual chart review demonstrated poor-to-moderate positive agreement (range, 0 to 58%) and excellent negative agreement (range, 82 to 100%). Discordance between patient report and manual chart review was frequently explicable by patients reporting events that happened outside the time period of interest.Patient report can provide information about subjective experiences or events that happen after hospital discharge, but often yields different results from chart review for specific in-hospital complications. Effective in-hospital communication with patients and thoughtful survey design may increase the quality of patient-reported complication data.
| S-EPMC5083125 | biostudies-literature
Project description:Our study explored the application of methods to generalize randomized controlled trial results to a target population without individual-level data. We compared 4 methods using aggregate data for the target population to generalize results from the international trial, Justification for the Use of Statins in Prevention: an Intervention Trial Evaluating Rosuvastatin (JUPITER), to a target population of trial-eligible patients in the UK Clinical Practice Research Datalink (CPRD). The gold-standard method used individual data from both the trial and CPRD to predict probabilities of being sampled in the trial and to reweight trial participants to reflect CPRD patient characteristics. Methods 1 and 2 used weighting methods based on simulated individual data or the method of moments, respectively. Method 3 weighted the trial's subgroup-specific treatment effects to match the distribution of an effect modifier in CPRD. Method 4 calculated the expected absolute benefits in CPRD assuming homogeneous relative treatment effect. Methods based on aggregate data for the target population generally yielded results between the trial and gold-standard estimates. Methods 1 and 2 yielded estimates closest to the gold-standard estimates when continuous effect modifiers were represented as categorical variables. Although individual data or data on joint distributions remains the best approach to generalize trial results, these methods using aggregate data might be useful tools for timely assessment of randomized trial generalizability.
| S-EPMC6357813 | biostudies-literature
Project description:Snake venom is an extremely interesting natural mixture of proteins and peptides, characterized by both high diversity and high pharmacological potential. Much attention has been paid to the study of venom composition of different species and also detailed analysis of the properties of individual components. Since proteins and peptides are the active ingredients in venom, rapidly developing proteomic techniques are used to analyze them. During such analyses, one of the routine operations is to measure the protein concentration in the sample. The aim of this study was to compare five methods used to measure protein content in venoms of two snake species: the Viperids representative, Agkistrodon contortrix, and the Elapids representative, Naja ashei. The study showed that for A. contortrix venom, the concentration of venom protein measured by four methods is very similar and only the NanoDrop method clearly stands out from the rest. However, in the case of N. ashei venom, each technique yields significantly different results. We hope that this report will help to draw attention to the problem of measuring protein concentration, especially in such a complex mixture as animal venoms.
| S-EPMC7142616 | biostudies-literature
Project description:An intermediate response measure that accurately predicts efficacy in a new setting at the individual level could be used both for prediction and personalized medical decisions. In this article, we define a predictive individual-level general surrogate (PIGS), which is an individual-level intermediate response that can be used to accurately predict individual efficacy in a new setting. While methods for evaluating trial-level general surrogates, which are predictors of trial-level efficacy, have been developed previously, few, if any, methods have been developed to evaluate individual-level general surrogates, and no methods have formalized the use of cross-validation to quantify the expected prediction error. Our proposed method uses existing methods of individual-level surrogate evaluation within a given clinical trial setting in combination with cross-validation over a set of clinical trials to evaluate surrogate quality and to estimate the absolute prediction error that is expected in a new trial setting when using a PIGS. Simulations show that our method performs well across a variety of scenarios. We use our method to evaluate and to compare candidate individual-level general surrogates over a set of multi-national trials of a pentavalent rotavirus vaccine.
| S-EPMC5991262 | biostudies-literature
Project description:OBJECTIVE:The Social Cognition Psychometric Evaluation (SCOPE) study supported the utility and practicality of the Hinting task as a measure of social cognition/mentalizing in clinical trials, specifically with the SCOPE authors' stringent scoring system. However, it remains unclear whether the SCOPE scoring system is necessary for the task to be judged as psychometrically sound. METHOD:Independent raters rescored data from the three phases of SCOPE using the Hinting task's original scoring criteria. Psychometric properties of the task when scored with the original criteria versus more stringent SCOPE criteria were compared in a large sample of individuals with chronic schizophrenia (n = 397) and matched controls (n = 300) as well as a smaller sample of individuals with early psychosis (n = 38) and controls (n = 39). RESULTS:In both samples, SCOPE criteria resulted in lowered average scores and reduced ceiling effects. Further, revised scoring resulted in strengthened relationships between the hinting task and outcome measures in the chronic sample, and better differentiated early psychosis patients from controls. Conversely, test-retest reliability and internal consistency estimates were not improved using revised scoring and remained suboptimal, particularly for healthy controls. CONCLUSION:Overall, SCOPE scoring criteria improved some psychometric properties and clinical utility, suggesting that these criteria should be considered for implementation.
| S-EPMC7301277 | biostudies-literature
Project description:Assessment of management-induced changes in soil quality is important to sustaining high crop yield. A large diversity of cultivated soils necessitate identification development of an appropriate soil quality index (SQI) based on relative soil properties and crop yield. Whereas numerous attempts have been made to estimate SQI for major soils across the World, there is no standard method established and thus, a strong need exists for developing a user-friendly and credible SQI through comparison of various available methods. Therefore, the objective of this article is to compare three widely used methods to estimate SQI using the data collected from 72 soil samples from three on-farm study sites in Ohio. Additionally, challenge lies in establishing a correlation between crop yield versus SQI calculated either depth wise or in combination of soil layers as standard methodology is not yet available and was not given much attention to date. Predominant soils of the study included one organic (Mc), and two mineral (CrB, Ko) soils. Three methods used to estimate SQI were: (i) simple additive SQI (SQI-1), (ii) weighted additive SQI (SQI-2), and (iii) statistically modeled SQI (SQI-3) based on principal component analysis (PCA). The SQI varied between treatments and soil types and ranged between 0-0.9 (1 being the maximum SQI). In general, SQIs did not significantly differ at depths under any method suggesting that soil quality did not significantly differ for different depths at the studied sites. Additionally, data indicate that SQI-3 was most strongly correlated with crop yield, the correlation coefficient ranged between 0.74-0.78. All three SQIs were significantly correlated (r = 0.92-0.97) to each other and with crop yield (r = 0.65-0.79). Separate analyses by crop variety revealed that correlation was low indicating that some key aspects of soil quality related to crop response are important requirements for estimating SQI.
| S-EPMC4141864 | biostudies-literature
Project description:The global covid-19 pandemic puts great pressure on medical resources worldwide and leads healthcare professionals to question which individuals are in imminent need of care. With appropriate data of each patient, hospitals can heuristically predict whether or not a patient requires immediate care. We adopted a deep learning model to predict fatality of individuals tested positive given the patient's underlying health conditions, age, sex, and other factors. As the allocation of resources toward a vulnerable patient could mean the difference between life and death, a fatality prediction model serves as a valuable tool to healthcare workers in prioritizing resources and hospital space. The models adopted were evaluated and refined using the metrics of accuracy, specificity, and sensitivity. After data preprocessing and training, our model is able to predict whether a covid-19 confirmed patient is likely to be dead or not, given their information and disposition. The metrics between the different models are compared. Results indicate that the deep learning model outperforms other machine learning models to solve this rare event prediction problem.
| S-EPMC7556112 | biostudies-literature
Project description:BackgroundTo compare three molecular methods, PCR-RFLP for internal transcribed spacer, PCR sequencing and high resolution melting analysis shown reliable sensitivity and specificity for detecting Leishmania tropica as a model for cutaneous leishmaniasis (CL) as the perspective overview for scientific and economic approaches.MethodsThis study was carried out between 2015 and 2016 in Leishmaniasis Research Center in Kerman University of Medical Sciences, Kerman, Iran. The positives smears (n=50) were obtained from patients referred from the health clinics in a major anthroponotic CL (ACL) focus, southeastern Iran. Only smear preparations with the same grade were selected according to the method described by the WHO for future PCR assays.ResultsAll three molecular methods had capability to identify positive samples at species level with the same specificity and sensitivity. However, these techniques were different in simplicity, consuming time, and cost effectiveness. Although additional enzymatic process in PCR-RFLP provided good resolution to find Leishmania species but this would cause time and cost increases.ConclusionHRM (high resolution melting) is a relatively new technique that allows direct characterization of PCR amplicons in a closed system with more simplicity, cost effectiveness and time-consuming compared with other PCR-based assays for epidemiological or clinical identification purposes.
| S-EPMC5527031 | biostudies-literature