A systematic review and meta-analysis of diabetes disease management programs

Context. Despite the rapid growth of disease management programs, there are still questions about their efficacy and effectiveness for improving patient outcomes and their ability to reduce costs associated with chronic disease. ^ Objective. To determine the effectiveness of disease management programs on improving the results of HbA1c tests, lipid profiles and systolic blood pressure (SBP) readings among diabetics. These three quantitative measures are widely accepted methods of determining the quality of a patient's diabetes management and the potential for future complications. ^ Data Sources. MEDLINE and CINAHL were searched from 1950 to June 2008 using MeSH terms designed to capture all relevant studies. Scopus pearling and hand searching were also done. Only English language articles were selected. ^ Study Selection. Titles and abstracts for the 2347 articles were screened against predetermined inclusion and exclusion criteria, yielding 217 articles for full screening. After full article screening, 29 studies were selected for inclusion in the review. ^ Data Extraction. From the selected studies, data extraction included sample size, mean change over baseline, and standard deviation for each control and experimental arm. ^ Results. The pooled results show a mean HbA1c reduction of 0.64%, 95% CI (-0.83 to -0.44), mean SBP reduction of 7.39 mmHg (95% CI to -11.58 to -3.2), mean total cholesterol reduction of 5.74 mg/dL (95% CI, -10.01 to -1.43), and mean LDL cholesterol reduction of 3.74 mg/dL (95% CI, -8.34 to 0.87). Results for HbA1c, SBP and total cholesterol were statistically significant, while the results for LDL cholesterol were not. ^ Conclusions. The findings suggest that disease management programs utilizing five hallmarks of care can be effective at improving intermediate outcomes among diabetics. However, given the significant heterogeneity present, there may be fundamental differences with respect to study-specific interventions and populations that render them inappropriate for meta-analysis. ^

AN ESTIMATION OF IN UTERO DRUG EXPOSURE BY DETERMINANTS OF FREQUENCY AND DISTRIBUTION OF MATERNAL DRUG CONSUMPTION

Data derived from 1,194 gravidas presenting at the observation unit of a city/county hospital between October 11, 1979 through December 7, 1979 were evaluated with respect to the proportion ingesting drugs during pregnancy. The mean age of the mother at the time of the interview was 22.0 years; 43.0 percent were Black; 34.0 percent Latin-American, 21.0 percent White and 2.0 percent other; mean gravida was 2.5 pregnancies; mean parity was 1.0; and mean number of previous abortions was 0.34. Completed interview data was available for 1,119 gravida, corresponding urinalyses for 997 subjects. Ninety and one-tenth percent (90.1 percent) of the subjects reported ingestion of one or more drug preparation(s) (prescription, OTC, or substances used for recreational purposes) during pregnancy with a range of 0 to 11 substances and a mean of 2.7. Dietary supplements (vitamins and minerals) were most frequently reported followed by non-narcotic analgesics. Seventy-six and one tenth percent (76.1 percent) of the population reported consumption of prescription medication, 42.5 percent reported consumption of over-the-counter medications, 45.7 percent reported consumption of a substance for recreational purposes and 4.3 percent reported illicit consumption of a substance. For selected substances, no measurable difference was found between obtaining the information from the interview method or from a urinalysis assay. ^

New methods for quantification and analysis of quantitative real-time polymerase chain reaction data

Quantitative real-time polymerase chain reaction (qPCR) is a sensitive gene quantitation method that has been widely used in the biological and biomedical fields. The currently used methods for PCR data analysis, including the threshold cycle (CT) method, linear and non-linear model fitting methods, all require subtracting background fluorescence. However, the removal of background fluorescence is usually inaccurate, and therefore can distort results. Here, we propose a new method, the taking-difference linear regression method, to overcome this limitation. Briefly, for each two consecutive PCR cycles, we subtracted the fluorescence in the former cycle from that in the later cycle, transforming the n cycle raw data into n-1 cycle data. Then linear regression was applied to the natural logarithm of the transformed data. Finally, amplification efficiencies and the initial DNA molecular numbers were calculated for each PCR run. To evaluate this new method, we compared it in terms of accuracy and precision with the original linear regression method with three background corrections, being the mean of cycles 1-3, the mean of cycles 3-7, and the minimum. Three criteria, including threshold identification, max R2, and max slope, were employed to search for target data points. Considering that PCR data are time series data, we also applied linear mixed models. Collectively, when the threshold identification criterion was applied and when the linear mixed model was adopted, the taking-difference linear regression method was superior as it gave an accurate estimation of initial DNA amount and a reasonable estimation of PCR amplification efficiencies. When the criteria of max R2 and max slope were used, the original linear regression method gave an accurate estimation of initial DNA amount. Overall, the taking-difference linear regression method avoids the error in subtracting an unknown background and thus it is theoretically more accurate and reliable. This method is easy to perform and the taking-difference strategy can be extended to all current methods for qPCR data analysis.^