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At least 73 records · Page 4Linked to original sources
PubMed1995

Delivery of hepatitis B immunisations in a selection of computerised general practices.

AIM: To assess the value of computerised general practices in providing information concerning the delivery of hepatitis B immunisation. METHODS: Hepatitis B immunisation data from August 1990 to June 1991 were collected from 27 general practices participating in a sentinel network. RESULTS: The study identified significant limitations in the use of data from computerised general practices for estimating hepatitis B immunisation coverage. While an accurate coverage figure could not be estimated, the results did suggest that hepatitis B coverage for three doses was at least 59.5% and that its use was very similar to the triple vaccine and measles/MMR for the third dose. Hepatitis B immunisation delivery outside the desirable time periods was common at 44%, suggesting a fairly disrupted immunisation schedule for many children. CONCLUSIONS: The relatively infrequent delivery of hepatitis B vaccine at the same time as other vaccinations may reflect provider concern about administering multiple injections at the same visit. Further improvement in the collection of data by computerised practices is necessary before the full value of this data source can be realised. Improvements in reminder/recall systems would improve the efficiency with which hepatitis B immunisation is delivered.

Child, Preschool↗
PubMed1994

Evaluating the accuracy of transcribed computer-stored immunization data.

OBJECTIVE: To evaluate the accuracy of immunization records transcribed into a computer-based immunization tracking system and to assess factors that contribute to inaccurate or incomplete immunization record keeping. DESIGN: Computer-stored immunization records were analyzed for 2098 children up to 2 years of age at the time of their most recent well-child visit to the UCLA Children's Health Center over a 12-month period. For children whose immunizations were not up to date, the computer-stored records were analyzed for sources of inaccuracy by comparison with the handwritten records from which the computer-stored data were transcribed. RESULTS: An underimmunization rate of 22.5% (472 of 2098) was observed based on analysis of the computer-stored records. Comparison of the computer-stored and handwritten records revealed an overall transcription error rate of at least 10.2%. In addition, 38.4% of these apparently underimmunized children had received unrecorded immunizations from providers outside UCLA. When transcription errors were corrected and other available sources of immunization data were taken into account, the estimated rate of underimmunization decreased from 22.5% to 10.9%. CONCLUSION: Unavoidable inaccuracies can diminish the utility of the data recorded in an immunization tracking system. Some inaccuracies are related to the process of transcription, but failures to record and communicate immunization data consistently also contribute to the inaccuracy of computer-stored immunization records.

Child Health Services↗
PubMedDate not supplied

Characteristics of infants who undergo neonatal autopsy.

To identify factors associated with the performance of a neonatal autopsy, we surveyed autopsy practice patterns in a tertiary care neonatal intensive care unit for 1 year. After 56 neonatal deaths, 33 (59%) autopsies were performed. We used multivariable analysis to compare the clinical and demographic characteristics of infants who had an autopsy with those who did not. We found two variables to be negatively correlated with having an autopsy performed: birth asphyxia (p < 0.05) and Medicaid coverage (p < 0.05). Early neonatal death (< or = 2 days of age) was not correlated with lack of an autopsy. Pulmonary hypoplasia occurred more often in the group having autopsy. However, only birth asphyxia was significantly correlated with lack of an autopsy after adjusting for other variables in the analysis. The primary cause of death was not associated with performance of an autopsy. We found no significant association between consent for autopsy and the characteristics of the physician requesting it. These data suggest that the infant's socioeconomic status and diagnosis may influence parental consent for autopsy. There may be other characteristics of physicians who request it, parents who consent, or other socioeconomic factors influencing consent for the neonatal autopsy.

Adult↗
PubMed1997

[Structure and negative sequelae of medical errors in care for angina pectoris].

12 cardiologists trained as experts in assessment of medical care quality (MCQ) made a computer-assisted expertise of the care rendered to 110 anginal patients. Of these 68 patients had angina of effort (AE) and 42 had unstable angina (UA). This made up 10% of annual number of anginal patients treated in the cardiological clinic in 1996. Medical errors were of the same type in both the groups. Inadequate collection of information, erroneous diagnosis, treatment, continuity occurred in 50, 30, 15 and 5% of all the errors, respectively. Negative effects of the errors were mild (less seriously suffered AE patients), but led to waste of health service resources.

Angina Pectoris↗
PubMedDate not supplied

Sadistic statistics.

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Data Interpretation, Statistical↗
PubMed1991

Improper statistics.

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Data Interpretation, Statistical↗
PubMedDate not supplied

Using statistics.

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Data Interpretation, Statistical↗
PubMed1989

The box plot: a simple visual method to interpret data.

Exploratory data analysis involves the use of statistical techniques to identify patterns that may be hidden in a group of numbers. One of these techniques is the "box plot," which is used to visually summarize and compare groups of data. The box plot uses the median, the approximate quartiles, and the lowest and highest data points to convey the level, spread, and symmetry of a distribution of data values. It can also be easily refined to identify outlier data values and can be easily constructed by hand. We apply box plots to tabular data from two recently published articles to show how readers can use box plots to improve the interpretation of data in complex tables. The box plot, like other visual methods, is more than a substitute for a table: It is a tool that can improve our reasoning about quantitative information. We recommend that the box plot be used more frequently.

Alcohol Drinking↗
PubMed1994

The statistical analysis of single-subject data: a comparative examination.

BACKGROUND AND PURPOSE: The purposes of this study were to examine whether the use of three different statistical methods for analyzing single-subject data led to similar results and to identify components of graphed data that influence agreement (or disagreement) among the statistical procedures. METHODS: Forty-two graphs containing single-subject data were examined. Twenty-one were AB charts of hypothetical data. The other 21 graphs appeared in Journal of Applied Behavioral Analysis, Physical Therapy, Journal of the Association for Persons With Severe Handicaps, and Journal of Behavior Therapy and Experimental Psychiatry. Three different statistical tests--the C statistic, the two-standard deviation band method, and the split-middle method of trend estimation--were used to analyze the 42 graphs. RESULTS: A relatively low degree of agreement (38%) was found among the three statistical tests. The highest rate of agreement for any two statistical procedures (71%) was found for the two-standard deviation band method and the C statistic. A logistic regression analysis revealed that overlap in single-subject graphed data was the best predictor of disagreement among the three statistical tests (beta = .49, P < .03). CONCLUSION AND DISCUSSION: The results indicate that interpretation of data from single-subject research designs is directly influenced by the method of data analysis selected. Variation exists across both visual and statistical methods of data reduction. The advantages and disadvantages of statistical and visual analysis are described.

Data Interpretation, Statistical↗
PubMed1991

Interpretation of interaction in factorial analysis of variance design.

The validity of statistical conclusions in medical research depends on proper analysis and interpretation of collected data. One potential area of invalidity is the inappropriate post hoc analysis of statistically significant interactions in the analysis of variance of factorial designs. This paper examines the statistical explanations included in 83 studies published in three leading medical journals where the findings indicated significant interaction effects. Only 24 per cent of the reported statistically significant interactions had an accompanying correct interpretation. The most common form of misinterpretation involved a comparison of individual cell means within a row or column of one factor used in the design. This interpretation did not conform to the factorial ANOVA model with interaction. This misinterpretation occurs when the correct omnibus test of a hypothesis is followed by an incorrect post hoc analysis and/or an inaccurate assessment of the original statistical result.

Analysis of Variance↗
PubMed1980

Interpretation of research data: selected statistical procedures.

Selected statistical procedures used in the analysis of research data are presented. The relationship of significance testing to research hypotheses is explained in terms of tests of differences and correlation. Also, the differences, assumptions, and advantages and disadvantages of parametric and nonparametric statistics are discussed. With regard to each statistic presented, emphasis is placed on the hypotheses that would be tested, the kinds of data for which the statistic is appropriate, the method of calculation, and how to test for "significance." The selected statistical procedures include the Student's t-test and chi square. An explanation of the concept of correlation is provided, and several correlation coefficients are discussed, including the Pearson r, Spearman rho, Kendall's tau, the point biserial, biserial, phi coefficient, and contingency coefficient. Pharmacists must know basic statistical procedures in order to be able to effectively interpret the results of published research or to appropriately analyze data that have been collected in their own research endeavors.

Methods↗
PubMed1996

Technology assessment in critical care: understanding statistical analyses used to assess agreement between methods of clinical measurement.

Many new measurement methods that employ various technologies to measure physiological parameters have been introduced into the field of critical care. Clinical assessment of these new methods occurs through the conduct of method-comparison studies in which the level of agreement between a new measurement method and a clinical standard method is determined. Clinicians and researchers are often faced with the complicated task of analyzing and interpreting the results of method-comparison studies. Use of correlation and linear regression techniques has been prevalent in method-comparison studies but has proven inappropriate and inadequate in determining how well methods compare. The purposes of this article are to briefly review the terms of accuracy, agreement, and the precision in context with method-comparison studies, and discuss inappropriate and appropriate statistical analyses and their interpretation. Appropriate data analysis of method-comparison studies will aid in determining not only whether new monitoring methods can be interchanged or used in place of existing methods, but whether new methods warrant further research of their effect on patient outcomes.

Critical Care↗