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PubMedDate not supplied

[Quality of data or quality of care? Comparison of diverse standarization methods by clinical severity, based on the discharge form, in the analysis of hospital mortality].

Using discharge abstract data, we analysed hospital mortality comparing four different methods of risk adjustment. All patients discharged from the S. Giovanni Battista (Molinette) hospital in Turin (Italy) between January 1996 and June 1999 (n = 169,746) were classified with All Patient Refined--Diagnosis Related Groups (APR-DRG). A first analysis evaluated the time trend of hospital mortality by semester. A second analysis compared hospital mortality during the last 12 months among eight units of internal medicine (n = 5592). All comparisons were made through logistic regression models. As the quality of discharge abstracts increased during time and showed variation among units with similar patients, all comparisons were repeated using four models, characterised by increasing predictivity and sensitivity to quality of data. In addition to crude comparisons (A), the other models included as risk factors: B) age and emergency admission; C) same as 'B' plus expected mortality by APR-DRG; D) same as 'B' plus expected mortality by APR-DRG and risk of death subclass. If no risk factors were considered (A), hospital mortality showed an increasing trend, with an odds ratio (OR) of 1.02 by semester, with a 95% confidence interval (CI) between 1.01 and 1.03. The association was weakened when age and mode of admission were taken into account (B) and disappeared when the APR-DRG expected mortality was also considered (C) (OR = 1.00; CI = 0.98-1.01). Finally, if the comparisons were adjusted also for the expected mortality by APR-DRG and risk of death subclass (D) a reversed trend appeared (OR = 0.95; CI = 0.94-0.97). The comparison among the units of internal medicine gave discordant results according to the method used to adjust for confounders. The most striking variations were detected for those units with the best and the worst clinical data. The unit with the poorer clinical data (average number of diagnoses per patient = 2.9) showed a crude OR of 1.38 (CI = 0.99-1.93) and an adjusted OR (D) of 1.71 (CI = 1.10-2.66); the unit with the best quality of data (average number of diagnoses per patient = 4.4) changed the OR from 1.55 (CI = 1.06-2.26) (A) to 0.66 (CI = 0.37-1.17) (D). In conclusion, these results confirm the high sensitivity of the APR-DRG classification to the quality of data and, more in general, suggest to be prudent when using powerful instruments like this to assess quality of care, especially if the quality of data among the units compared is less than optimal or not homogeneous.

Hospital Mortality↗
PubMed2006

Louisiana hospitals post price, quality data online.

Legislature calls for more transparency between hospitals, patients. Quality sections list the measure, condition, indicators, number of patients, and hospital score. Hospital quality managers play important role in creating site, content.

Centers for Medicare and Medicaid Services, U.S.↗
PubMed1998

Data decisiveness, data quality, and incongruence in phylogenetic analysis: an example from the monocotyledons using mitochondrial atp A sequences.

We examined three parallel data sets with respect to qualities relevant to phylogenetic analysis of 20 exemplar monocotyledons and related dicotyledons. The three data sets represent restriction-site variation in the inverted repeat region of the chloroplast genome, and nucleotide sequence variation in the chloroplast-encoded gene rbcL and in the mitochondrion-encoded gene atpA, the latter of which encodes the alpha-subunit of mitochondrial ATP synthase. The plant mitochondrial genome has been little used in plant systematics, in part because nucleotide sequence evolution in enzyme-encoding genes of this genome is relatively slow. The three data sets were examined in separate and combined analyses, with a focus on patterns of congruence, homoplasy, and data decisiveness. Data decisiveness (described by P. Goloboff) is a measure of robustness of support for most parsimonious trees by a data set in terms of the degree to which those trees are shorter than the average length of all possible trees. Because indecisive data sets require relatively fewer additional steps than decisive ones to be optimized on nonparsimonious trees, they will have a lesser tendency to be incongruent with other data sets. One consequence of this relationship between decisiveness and character incongruence is that if incongruence is used as a criterion of noncombinability, decisive data sets, which provide robust support for relationships, are more likely to be assessed as noncombinable with other data sets than are indecisive data sets, which provide weak support for relationships. For the sampling of taxa in this study, the atpA data set has about half as many cladistically informative nucleotides as the rbcL data set per site examined, and is less homoplastic and more decisive. The rbcL data set, which is the least decisive of the three, exhibits the lowest levels of character incongruence. Whatever the molecular evolutionary cause of this phenomenon, it seems likely that the poorer performance of rbcL than atpA, in terms of data decisiveness, is due to both its higher overall level of homoplasy and the fact that it is performing especially poorly at nonsynonymous sites.

Adenosine Triphosphatases↗
PubMed1996

Development and validation of diagnostic scores for atopic dermatitis incorporating criteria of data quality and practical usefulness.

No objective classification criteria for atopic dermatitis (AD) exist. Therefore the diagnosis is usually based on many variables including anamnestic, clinical, and laboratory findings. The aim of this study was to develop and validate a diagnostic score to standardize the diagnosis of atopic skin diathesis for clinical and epidemiological studies. In two separate studies, each consisting of cases and controls, 19 atopic binary features were examined by two independent experienced physicians and these features were classified as "objective," respectively, "subjective." On the basis of these criteria and two additional laboratory measures, we developed a score with high discriminative ability, using the logistic regression model and backward elimination. Ignoring "subjective" variables and the two laboratory measures, two additional models were built that had a worse fit in the original data, but still yielded high estimates of sensitivity (approximately 90%) and specificity (approximately 96%). Using the same data as for the model building, it is well known that these estimates are too optimistic. The validation study allows us to obtain unbiased estimates of sensitivity and specificity for the different scores and to investigate the influence of data quality-here given by the assessment of the reproducibility of the features (objective and subjective)-on the usefulness of diagnostic scores. The results of the validation study show that we developed simple and easy-to-use scores offering a base for a broad practical use in epidemiological and clinical research. In addition, we demonstrate that the criteria classified as "subjective" have no influence on the case-control status in the validation study.

Adolescent↗
PubMed1996

Standard reference materials and data quality assurance in the biomedical analysis of trace elements.

Accurate and precise analytical data of the concentrations of bio-analytes in bioclinical studies are of fundamental importance. Quality assurance procedures should always be performed to check the overall analytical work. This can be conveniently performed if appropriate standard reference materials with known concentrations of the analyte object of study are available. This paper underlines the key points related to the production and use of biological standard materials for trace element analysis. In particular, the present situation in the field of trace element determination in human biological fluids and the related problems are illustrated. The considerations given in this work may contribute to the preparation of the new biomarker standard materials.

Biomarkers↗
PubMed2006

Quantitative comparison of proteomic data quality between a 2D and 3D quadrupole ion trap.

A 2D ion trap has a greater ion trapping efficiency, greater ion capacity before observing space-charging effects, and a faster ion ejection rate than a traditional 3D ion trap mass spectrometer. These hardware improvements should result in a significant increase in protein identifications from complex mixtures analyzed using shotgun proteomics. In this study, we compare the quality and quantity of peptide identifications using data-dependent acquisition of tandem mass spectra of peptides between two commercially available ion trap mass spectrometers (an LTQ and an LCQ XP Max). We demonstrate that the increased trapping efficiency, increased ion capacity, and faster ion ejection rate of the LTQ results in greater than 5-fold more protein identifications, better identification of low-abundance proteins, and higher confidence protein identifications when compared with a LCQ XP Max.

Amino Acid Sequence↗
PubMedDate not supplied

Envirometrics. Part I: Modeling of water salinity and air quality data.

Envirometrics utilises advanced mathematical, statistical and information tools to extract information. Two typical environmental data sets are analysed using MVATOB (Multi Variate Analysis TOol Box). The first data set corresponds to the variable river salinity. Least median squares (LMS) detected the outliers whereas linear least squares (LLS) could not detect and remove the outliers. The second data set consists of daily readings of air quality values. Outliers are detected by LMS and unbiased regression coefficients are estimated by multi-linear regression (MLR). As explanatory variables are not independent, principal component regression (PCR) and partial least squares regression (PLSR) are used. Both examples demonstrate the superiority of LMS over LLS.

Air Pollutants↗
PubMed1994

The MOS 36-item Short-Form Health Survey (SF-36): III. Tests of data quality, scaling assumptions, and reliability across diverse patient groups.

The widespread use of standardized health surveys is predicated on the largely untested assumption that scales constructed from those surveys will satisfy minimum psychometric requirements across diverse population groups. Data from the Medical Outcomes Study (MOS) were used to evaluate data completeness and quality, test scaling assumptions, and estimate internal-consistency reliability for the eight scales constructed from the MOS SF-36 Health Survey. Analyses were conducted among 3,445 patients and were replicated across 24 subgroups differing in sociodemographic characteristics, diagnosis, and disease severity. For each scale, item-completion rates were high across all groups (88% to 95%), but tended to be somewhat lower among the elderly, those with less than a high school education, and those in poverty. On average, surveys were complete enough to compute scales scores for more than 96% of the sample. Across patient groups, all scales passed tests for item-internal consistency (97% passed) and item-discriminant validity (92% passed). Reliability coefficients ranged from a low of 0.65 to a high of 0.94 across scales (median = 0.85) and varied somewhat across patient subgroups. Floor effects were negligible except for the two role disability scales. Noteworthy ceiling effects were observed for both role disability scales and the social functioning scale. These findings support the use of the SF-36 survey across the diverse populations studied and identify population groups in which use of standardized health status measures may or may not be problematic.

Aged↗
PubMed2003

Air quality data from large cities.

This paper presents an assessment of the air quality for the principal cities in developed and developing countries. Part of the vast and widely dispersed information on air quality that is available at this time on the Internet was compiled, thus making possible a comprehensive evaluation of the tendencies that emerged at the end of the 20th century. Likewise, these values are compared to the air quality thresholds recommended by two international organizations: guideline levels of the World Health Organization (WHO) and limit values of the European Union (EU), in order to determine air quality concentration levels in large cities around the world. The current situation of air quality worldwide indicates that SO(2) maintains a downward tendency throughout the world, with the exception of some Central American and Asian cities. NO(2) maintains levels very close to the WHO guideline value around the world. For particulate matter, it is a major problem in almost all of Asia, exceeding 300 microg/m(3) in many cities. Ozone shows average values that exceed the selected guideline values in all of the analyses demonstrating that it is a global problem. In general, the worldwide trend is to a reduction in the concentrations of pollutants because of the increasingly strong restrictions which local governments and international organizations impose. However, in poor countries and those with low average incomes, concentrations of air pollutants remain high and the trend will be the elevation of their ground levels as they develop, making the problem even worse.

Air Pollutants↗
PubMed2025

Establishing a national pediatric stem cell transplantation registry in Iran addressing implementation and data quality challenges.

The Iranian Pediatric Hematopoietic Stem Cell Transplantation Registry (IPED-HSCT) was established to enhance data collection, improve patient outcomes, and support clinical research in pediatric hematopoietic stem cell transplantation. This study aimed to assess the feasibility and reliability of implementing a standardized registry in pediatric settings. A community-based participatory study was conducted across three pediatric HSCT centers in Iran. The registry development involved a multi-phase approach, including pilot testing and the implementation of a web-based system. Data were collected from fifty pediatric patients who underwent HSCT for both malignant and non-malignant conditions, with a focus on data completeness and user satisfaction. Statistical analyses were performed using IBM SPSS Statistics. The registry achieved a data completeness rate exceeding 90%, with a participant demographic of 31 males (62%) and 19 females (38%). Rigorous quality control measures and real-time validation rules were implemented, enhancing data reliability. User feedback indicated high satisfaction with the platform's design and training sessions. Challenges included variations in long-term follow-up data collection across centers. The IPED-HSCT Registry demonstrates that establishing a robust pediatric HSCT registry is feasible even in resource-limited settings. Its innovative features offer a scalable model for similar initiatives in developing countries. Future research should focus on ensuring long-term sustainability and fostering international collaborations to improve pediatric HSCT outcomes globally. not applicable.

Humans↗
PubMed2002

Impact of non-detects in water quality data on estimation of constituent mass loading.

Often, fractions of stormwater constituents are not detected above laboratory reporting limits and are reported as non-detect (ND), or censored data. Analysts and stormwater modelers represent these NDs in stormwater data sets using a variety of methods. Application of these different methods results in different estimates of constituent mean concentrations that will, in turn, affect mass loading computations. In this paper, different methods of data analysis were introduced to determine constituent mean concentrations from water quality datasets that include ND values. Depending on the number of NDs and the method of data analysis, differences ranging from 1 to 70 percent have been observed in mean values. Differences in mean values were, as shown by simulation, found to have significant impacts on estimations of constituent mass loading.

Environmental Monitoring↗