Measuring Health and Diseases: Diabetes - Essay Example

Measuring Health and Diseases: Diabetes Diabetes is a condition that can no longer be ignored, as its prevalence has dramatically increase over thepast two decades and is expected to continue rising, particularly in developing countries. The disease Diabetes affects over 250 million people worldwide, with over 53 million people in Europe and 23 million the United States affected (Diabetes Atlas, Anon. 2009). Diabetes is actually not a single disease, but instead it represents a group of heterogeneous conditions that are distinguished by hyperglycemia and glucose intolerance that may be caused in defects in insulin production, action, or a combination of both factors. The condition has varying levels of severity and can lead to a number of serious complications, including premature death, if not controlled properly (CDC 2008). There are three common types of diabetes, though 1%-5% of all cases are outside of these categories. Type I diabetes is characterized by onset of diabetes as a child or young adult, and regular insulin injections are necessary for patient survival. Type II diabetes occurs as adults gradually lose the ability to produce sufficient insulin in the pancreas, and it accounts for 90%-95% of diabetes cases in adults. Gestational diabetes is a glucose intolerance that develops during pregnancy. Frequent measurements of blood glucose and measurement of glycohemoglobin (A1c) are the most common methods of diabetic testing (Norman 2010). Diabetes is an important topic for research and review because of its large impact on global populations. Journal Articles Reviewed in this Paper 1. “Measuring Diabetes Self-Care: A psychometric analysis of the Self-Care Inventory-Revised with Adults’’ (Weinger et al. 2005) 2. “Identification of Distinct Self-Management Styles of Adolescents With Type 1 Diabetes” (Schneider et al. 2007) 3. “Diabetes Self Management Profile for Flexible Insulin Regimens: Cross-Sectional and Longitudinal Analysis of Psychometric Properties in a Pediatric Sample” (Wysocki et al. 2005) 4. “Initiation of Insulin Therapy in Elderly Patients Taking Oral Antidiabetes Drugs” (Pérez et al. 2009) Review of “Measuring Diabetes Self-Care: A psychometric analysis of the Self-Care Inventory-revised with adults’’ (Weinger et al., 2005) Diabetes is a unique condition because it is chronic, often lasting the entire adult life of the patient, and treatment requires a variety of changes in a patient’s lifestyle as well as completion of prescribed medications, such as insulin injections, by the patient in a largely unsupervised manner. Normally, the patient’s progress is measured by regular interviews and patients are often instructed to keep a daily diary (Burant 2004, p.71-73). Because of this independent activity on the part of the patient, it is important to monitor not only the effectiveness of treatments in a clinical environment, such as those completed in clinical studies or research settings, but also to monitor the effectiveness of diabetes treatment considering patient adherence to their physician’s recommendations, which is why the study “Measuring Diabetes Self-Care: A psychometric analysis of the Self-Care Inventory-revised with adults” is particularly important in diabetes research. This article was located by searching the terms “Measuring Diabetes” from the Google.com search homepage, and was listed among the first page of results. In this study, a measure of the amount of adult diabetes patients adhering to self-care recommendations recommended by their physicians is gauged by self-report of the patients through three studies. This is achieved by the use of several different tests applicable to Type I, Type II or a mix of the two types of patients. Participants were given The Summary of Diabetes Self-Care Activities (ADSCA), which is a twenty-five item self-report that measured the frequency that diabetic tasks were performed over the preceding seven days. The survey included a variety of activities such as foot care, smoking, medications, glucose monitoring, diet, and exercise. Additionally, they also completed a Benefits/Barrier Scale test to assess the cause of poor adherence, the PAID 20-item test for diabetes related emotional issues, the CIDS 21-item test of patient self-efficacy and confidence in administration of their physician’s recommendations, the SCL-90 90-item questionnaire to assess overall psychiatric symptomatology, the RSE Scale a 10-item assessment of patient self-worth, and the HBA1C that determines glycemic control for the past 2-3 months. The use of these seven test in conjunction with one another makes the data more relevant by allowing sufficient data for the isolation of not only numbers but also causes of good and poor self-adherence, making this data applicable in the treatment of diabetes because of its ability to shed light on trends in patient self-adherence that may be useful to physicians and clinicians. The author explains that the tests listed above, were chosen based on the recommendation of a panel of multidisciplinary experts. The data results in a set of discrete variables, or variable with concrete values that are analyzed by a variety of statistical tests, from resulting tests that are then analyzed using SAS version 8.02 at the SAS Institute in Cary, NC (Yang 2007). The author presented the data in two clearly organized tables showing the SCI-R rotated factor and estimated correlation factor by survey participant characteristic. Addition of two to three meaningful tables could improve the usability of this report (Wilson 2004). The data in this report suggests that adults have varying levels of adherence to their diabetic treatment plans that may affect the progress of their disease and overall health. In the order given in the report the statistical tests were: Test Purpose Exploratory principal component factor analysis To examine the pattern of loadings between the tests for evidence of a large general factor that would support use of a total score Forced factor analysis for three–and two–orthogonal factor solutions using Varimax To identify meaningful common factor between tests Cronbachs α-coefficient To assess the internal consistency of the SCI-R and SDSCA data Paired t tests, effect size, and Guyatts statistic To examine responsiveness to important clinical change “Identification of Distinct Self-Management Styles of Adolescents With Type 1 Diabetes” (Schneider et al. 2005) Type I diabetes was originally called juvenile-onset diabetes, though it may occur in any age group with children and adolescents being the most commonly affected (Eckman 2010). Because many of the treatments for diabetes are self-monitored and self-administered, treating pediatric patients becomes a unique challenge of collaboration between physicians, patients, and parents, which is why research such as this article remains significant. This article was found by reading a number of pertinent review articles on DiabetesJournal.org, the website of the American Diabetes Association, and was pulled as a related article from the review of Johnson’s paper entitled “Methodological issues in diabetes research. Measuring adherence” (1992). The goal of this research was to establish a profile-based understanding for how adolescents managed their diabetes. Participants in the survey were youths between the ages of ten and sixteen diagnosed with Type I diabetes. Each participant and their parents were administered a form of the Diabetes Self-Management Profile (DSMP) interview-based exam that had been modified so that young children would be able to understand the wording. This test grades on five areas of diabetic management: insulin administration, meal planning, self-care adjustments, blood glucose testing, and exercise. It averages these subsets to give a composite score between 0.00 and 1.00, a continuous or non-discrete variable. This test is highly effective, but it also more strongly correlates with the HBA1C than some others, which must be considered when analyzing data (Harris et al. 2000). This study may not take into account bias of the test, and the paper gives little information as to the exact nature of modifications made to the test itself, which may represent some bias in the data. The author offers no rational for choosing this test to measure youth Type I diabetes over others available. Researchers completed cluster analyses separately for the parent and youth report forms to categorize patients based on their score patterns in different areas of diabetes management. The results are presented in two tables and a graph figure that displays the clusters of the youth self-management scores. The data in this report suggests that adolescents with diabetes fall into several major profile groups based on how they handle their condition. In the order given in the report the statistical tests were: Test Purpose Means of Subscales and Intercorrelations Weak to moderate magnitudes of correlation suggest that a profile-based system is appropriate to classify youth adherence Cluster Analysis of Parent Scores Identify profiles and gauge correlation with child scores Cluster Analysis of Child Scores Identify profiles and gauge correlation with parent scores Review of “Diabetes Self Management Profile for Flexible Insulin Regimens: Cross-Sectional and Longitudinal Analysis of Psychometric Properties in a Pediatric Sample” (Wysocki et al. 2005) Typifying modern regimes of treatment for diabetics has been a field of study that has attracted a good deal of contemporary interest. Numerous test have been developed, and their ability to measure the effectiveness of diabetic treatment has been analyzed by a variety of methods. This paper significant because it contributes to overall understanding of the psychometric properties of one commonly used test, the Diabetes Self-Management Profile (DSMP) interview-based exam, particularly in pediatric patients. This article was found by searching for related articles to the above paper by Schneider et al. (2005). The goal of this research was to use work is establishing if flexible insulin regimens play a role in adolescent adherence to diabetic treatments to further validate the DSMP-F interview delivered by telephone. It explores the effectiveness of the interview method when delivered potentially by other means, such as questionnaire, hand-held device, or interactive telephone survey. The author collects the scores of each participants DSMP-F by telephone and assesses the effectiveness of the test when delivered in this method, explaining that this method was used to validate the correlation of the scores and ensure that the test is sound. The data was collected and the mean ± 1 standard deviation of raw scores for each DSMP-F and total obtained from 161 parents and 117 youths ≥11 years of age were reported in a single table. The report goes on to explain that the results were analyzed against the HBA1C test, showing some consistency in results and little correlation with change over the six month period. The results are simple to interpret when presented graphically as a table, but could be improved by comparing with other standards or tests instead of just one. The data in this report suggests that the DSMP-F coorelates with other standard measurements for diabetes and is thus an effective test. In the order given in the report the statistical tests were: Test Purpose Mean Standard Deviation for Raw Scores Show results of raw data Internal Consistency (Cronbach’s alpha coefficient) Show that the DSMP-F total score does not vary against other standards Review of “Initiation of Insulin Therapy in Elderly Patients Taking Oral Antidiabetes Drugs” (Pérez et al. 2009) Type II diabetes is a progressive condition that continues throughout the patient’s life and must be both monitored and treated as it progresses. Its onset may occur at any time during the patient’s life and so instances of geriatric onset do occur. Oral antidiabetes drugs are also called oral hypoglycemic agents or oral antihyperglycemic agents (Rendell 2004). They may be used in conjunction with diet and lifestyle changes to help prevent the onset of Type II diabetes in adulthood, though lifestyle modification should be the first preventative strategy (Gagnon 2006). This study sought to estimate the number of patients starting insulin therapy that had previously be prescribed antidiabetic drugs. All participants were elderly patients older than 66 years of age at the studies onset. Using the Quebec Health Insurance Board, a population-base inception cohort study was done to show hospital admissions of 69,674 people and whether they started an insulin prescription after being prescribed antidiabetic drugs. The author does not discuss why this method was used, though large sample size was a benefit of using this method of data collection. The author does address some bias that may exist by excluding those not covered by insurance from the study. This method is effective because of both the large sample size and targeted demographic given by using the insurance database. The author presents the statistical results in the form of several figures. The first is a flow chart that shows the initial patients and outcomes in a logical manner, though it does not indicate the time of onset, which would be helpful to those looking to apply the data. The next figure is a graph of the probability versus time of an elderly patient initiating insulin treatment after starting oral antidiabetic medication, which serves to fill in information that the first figure neglected in an easy-to-follow manner that clearly shows a linear direct relationship between the variables. The author also gives two tables listing characteristics and factors of the participants, allowing other researchers to be aware of the data’s demographic and adjust their application accordingly. The data in this report suggests that as time passes, oral antidiabetics are more likely to fail in geriatric patients. In the order given in the report the statistical tests were: Test Purpose 2-D x/y data plot Show relationship between time and insulin treatment onset in elderly patients Each of these research-based articles shows that statistical analysis can be used to make inferences about the results of diabetic treatment studies. Studies like this are more useful to other researchers when presented in a clear and logical manner that is easy to understand and addresses major characteristics or limitations of the methods used. Diabetes treatments can be improved by using statistical applications to understand many aspects of the self-treatment of the conditions in diverse patient groups and tailor clinical treatments to the patient. References Burant, Charles, 2004, Medical Management of Type Two Diabetes, 5th Edition, American Diabetes Association, Alexandria, VA. Eckman, A 2010, Type One Diabetes, MedlinePlus, U.S. National Library of Medicine, NIH National Institute of Health, Baltimore, MD, accessed 18 August 2010, < http://www.nlm.nih.gov/medlineplus/ency/article/000305.htm>. Centers for Disease Control and Prevention (CDC) 2008, National diabetes fact sheet: general information and national estimates on diabetes in the United States, 2007, U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, Atlanta, GA, accessed 18 August 2010, . Diabetes Atlas 2009, International Diabetes Federation, 3rd Edition, Brussels, Belgium, accessed 18 August 2010, . Gagnon, L 2006, DREAM: Rosiglitazone Effective in Preventing Diabetes, Medscape Medical News,accessed 18 August 2010, . Harris, M A, Wysocki, T, Sadler, M, Wilkinson, K, Harvery, L M, Buckloh, L M, Mauras, N, and White, N H 2000, Validation of a structured interview for the assessment of diabetes Self-Management, European Association for the Study of Diabetes Annual Meeting 1999: Complication of Diabetes, vol. 23, no 9, pp. 1423-1428. Johnson, SB 1992, Methodological issues in diabetes research. Measuring adherence, Diabetes Care, vol. 15, no 11. pp. 1658-1667. Norman, J 2010, Assessing How Diabetes is Controlled, Endocrine Web, accessed 18 August 2010, < http://www.endocrineweb.com/diabetes/control.html>. Pérez, N, Moisan, J, Sirois, C, Poirier, P, Grégoire, J 2009, Initiation of insulin therapy in elderly patients taking oral antidiabetes drugs, Canadian Medical Association Journal, vol. 180, no. 13, pp. 1310-1315. Rendell, Marc 2004, Advances in Diabetes for the Millennium: Drug Therapy of Type 2 Diabetes MedGenMed, vol. 6, no. 3, pp. 9. Schneider, S, Iannotti, R, Nansel, T, Haynie, D,Simons-Morton,B, Sobel, D, Zeitzoff, L, Clark, L, Plotnick, L 2007,Identification of Distinct Self-Management Styles of Adolescents With Type 1 Diabetes, Diabetes Care, vol. 30, no. 5, pp.1107-1112. Weinger, K, Butler, H, Welch, G, and La Greca, A 2005, Measuring Diabetes Self-Care: A psychometric analysis of the Self-Care Inventory-revised with adults, Diabetes Care, vol. 28, no. 6, pp. 1346–1352. Wilson, TW and Linden, A 2004,Letter: Measuring Diabetes Management, Health Affairs, vol. 23, no. 6, pp. 277-278 Wysocki, T, Xing, D,Fiallo-Scharer, R, Doyle, E, Block, J, Tsalikian, E, Beck, R, Ruedy, K, Kollman, C, Harris, M, and Tamborlane, W 2005, Diabetes Self Management Profile for Flexible Insulin Regimens: Cross-Sectional and Longitudinal Analysis of Psychometric Properties in a Pediatric Sample, Diabetes Care, vol. 28, no. 8, pp. 2034–2035. 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