Econstudentlog

A few diabetes papers of interest

i. Impact of Parental Socioeconomic Status on Excess Mortality in a Population-Based Cohort of Subjects With Childhood-Onset Type 1 Diabetes.

“Numerous reports have shown that individuals with lower SES during childhood have increased morbidity and all-cause mortality at all ages (10–14). Although recent epidemiological studies have shown that all-cause mortality in patients with T1D increases with lower SES in the individuals themselves (15,16), the association between parental SES and mortality among patients with childhood-onset T1D has not been reported to the best of our knowledge. Our hypothesis was that low parental SES additionally increases mortality in subjects with childhood-onset T1D. In this study, we used large population-based Swedish databases to 1) explore in a population-based study how parental SES affects mortality in a patient with childhood-onset T1D, 2) describe and compare how the effect differs among various age-at-death strata, and 3) assess whether the adult patient’s own SES affects mortality independently of parental SES.”

“The Swedish Childhood Diabetes Registry (SCDR) is a dynamic population-based cohort reporting incident cases of T1D since 1 July 1977, which to date has collected >16,000 prospective cases. […] All patients recorded in the SCDR from 1 January 1978 to 31 December 2008 were followed until death or 31 December 2010. The cohort was subjected to crude analyses and stratified analyses by age-at-death groups (0–17, 18–24, and ≥25 years). Time at risk was calculated from date of birth until death or 31 December 2010. Kaplan-Meier analyses and log-rank tests were performed to compare the effect of low maternal educational level, low paternal educational level, and family income support (any/none). Cox regression analyses were performed to estimate and compare the hazard ratios (HRs) for the socioeconomic variables and to adjust for the potential confounding variables age at onset and sex.”

“The study included 14,647 patients with childhood-onset T1D. A total of 238 deaths (male 154, female 84) occurred in 349,762 person-years at risk. The majority of mortalities occurred among the oldest age-group (≥25 years of age), and most of the deceased subjects had onset of T1D at the ages of 10–14.99 years […]. Mean follow-up was 23.9 years and maximum 46.5 years. The overall standardized mortality ratio up to the age of 47 years was 2.3 (95% CI 1.35–3.63); for females, it was 2.6 (1.28–4.66) and for males, 2.1 (1.27–3.49). […] Analyses on the effect of low maternal educational level showed an increased mortality for male patients (HR 1.43 [95% CI 1.01–2.04], P = 0.048) and a nonsignificant increased mortality for female patients (1.21 [0.722–2.018], P = 0.472). Paternal educational level had no significant effect on mortality […] Having parents who ever received income support was associated with an increased risk of death in both males (HR 1.89 [95% CI 1.36–2.64], P < 0.001) and females (2.30 [1.43–3.67], P = 0.001) […] Excluding the 10% of patients with the highest accumulated income support to parents during follow-up showed that having parents who ever received income support still was a risk factor for mortality.”

“A Cox model including maternal educational level together with parental income support, adjusting for age at onset and sex, showed that having parents who received income support was associated with a doubled mortality risk (HR 1.96 [95% CI 1.49–2.58], P < 0.001) […] In a Cox model including the adult patient’s own SES, having parents who received income support was still an independent risk factor in the younger age-at-death group (18–24 years). Among those who died at age ≥25 years of age, the patient’s own SES was a stronger predictor for mortality (HR 2.46 [95% CI 1.54–3.93], P < 0.001)”

“Despite a well-developed health-care system in Sweden, overall mortality up to the age of 47 years is doubled in both males and females with childhood-onset T1D. These results are in accordance with previous Swedish studies and reports from other comparable countries […] Previous studies indicated that low SES during childhood is associated with low glycemic control and diabetes-related morbidity in patients with T1D (8,9), and the current study implies that mortality in adulthood is also affected by parental SES. […] The findings, when stratified by age-at-death group, show that adult patients’ own need of income support independently predicted mortality in those who died at ≥25 years of age, whereas among those who died in the younger age-group (18–24 years), parental requirement of income support was still a strong independent risk factor. None of the present SES measures seem to predict mortality in the ages 0–17 years perhaps due to low numbers and, thus, power.”

ii. Exercise Training Improves but Does Not Normalize Left Ventricular Systolic and Diastolic Function in Adolescents With Type 1 Diabetes.

“Adults and adolescents with type 1 diabetes have reduced exercise capacity (810), which increases their risk for cardiovascular morbidity and mortality (11). The causes for this reduced exercise capacity are unclear. However, recent studies have shown that adolescents with type 1 diabetes have lower stroke volume during exercise, which has been attributed to alterations in left ventricular function (9,10). Reduced left ventricular compliance resulting in an inability to fill the left ventricle appropriately during exercise has been shown to contribute to the lower stroke volume during exercise in both adults and adolescents with type 1 diabetes (12).

Exercise training is recommended as part of the management of type 1 diabetes. However, the effects of exercise training on left ventricular function at rest and during exercise in adolescents with type 1 diabetes have not been investigated. In particular, it is unclear whether exercise training improves cardiac hemodynamics during exercise in adolescents with diabetes. Therefore, we aimed to assess left ventricular volumes at rest and during exercise in a group of adolescents with type 1 diabetes compared with adolescents without diabetes before and after a 20-week exercise-training program. We hypothesized that exercise training would improve exercise capacity and exercise stroke volume in adolescents with diabetes.”

RESEARCH DESIGN AND METHODS Fifty-three adolescents with type 1 diabetes (aged 15.6 years) were divided into two groups: exercise training (n = 38) and nontraining (n = 15). Twenty-two healthy adolescents without diabetes (aged 16.7 years) were included and, with the 38 participants with type 1 diabetes, participated in a 20-week exercise-training intervention. Assessments included VO2max and body composition. Left ventricular parameters were obtained at rest and during acute exercise using MRI.

RESULTS Exercise training improved aerobic capacity (10%) and stroke volume (6%) in both trained groups, but the increase in the group with type 1 diabetes remained lower than trained control subjects. […]

CONCLUSIONS These data demonstrate that in adolescents, the impairment in left ventricular function seen with type 1 diabetes can be improved, although not normalized, with regular intense physical activity. Importantly, diastolic dysfunction, a common mechanism causing heart failure in older subjects with diabetes, appears to be partially reversible in this age group.”

“This study confirms that aerobic capacity is reduced in [diabetic] adolescents and that this, at least in part, can be attributed to impaired left ventricular function and a blunted cardiac response to exercise (9). Importantly, although an aerobic exercise-training program improved the aerobic capacity and cardiac function in adolescents with type 1 diabetes, it did not normalize them to the levels seen in the training group without diabetes. Both left ventricular filling and contractility improved after exercise training in adolescents with diabetes, suggesting that aerobic fitness may prevent or delay the well-described impairment in left ventricular function in diabetes (9,10).

The increase in peak aerobic capacity (∼12%) seen in this study was consistent with previous exercise interventions in adults and adolescents with diabetes (14). However, the baseline peak aerobic capacity was lower in the participants with diabetes and improved with training to a level similar to the baseline observed in the participants without diabetes; therefore, trained adolescents with diabetes remained less fit than equally trained adolescents without diabetes. This suggests there are persistent differences in the cardiovascular function in adolescents with diabetes that are not overcome by exercise training.”

“Although regular exercise potentially could improve HbA1c, the majority of studies have failed to show this (3134). Exercise training improved aerobic capacity in this study without affecting glucose control in the participants with diabetes, suggesting that the effects of glycemic status and exercise training may work independently to improve aerobic capacity.”

….

iii. Change in Medical Spending Attributable to Diabetes: National Data From 1987 to 2011.

“Diabetes care has changed substantially in the past 2 decades. We examined the change in medical spending and use related to diabetes between 1987 and 2011. […] Using the 1987 National Medical Expenditure Survey and the Medical Expenditure Panel Surveys in 2000–2001 and 2010–2011, we compared per person medical expenditures and uses among adults ≥18 years of age with or without diabetes at the three time points. Types of medical services included inpatient care, emergency room (ER) visits, outpatient visits, prescription drugs, and others. We also examined the changes in unit cost, defined by the expenditure per encounter for medical services.”

RESULTS The excess medical spending attributed to diabetes was $2,588 (95% CI, $2,265 to $3,104), $4,205 ($3,746 to $4,920), and $5,378 ($5,129 to $5,688) per person, respectively, in 1987, 2000–2001, and 2010–2011. Of the $2,790 increase, prescription medication accounted for 55%; inpatient visits accounted for 24%; outpatient visits accounted for 15%; and ER visits and other medical spending accounted for 6%. The growth in prescription medication spending was due to the increase in both the volume of use and unit cost, whereas the increase in outpatient expenditure was almost entirely driven by more visits. In contrast, the increase in inpatient and ER expenditures was caused by the rise of unit costs. […] The increase was observed across all components of medical spending, with the greatest absolute increase in the spending on prescription medications ($1,528 increase), followed by inpatient visits ($680 increase) and outpatient visits ($430 increase). The absolute change in the spending on ER and other medical services use was relatively small. In relative terms, the spending on ER visits grew more than five times, faster than that of prescription medication and other medical components. […] Among the total annual diabetes-attributable medical spending, the spending on inpatient and outpatient visits dropped from 40% and 23% to 31% and 19%, respectively, between 1987 and 2011, whereas spending on prescription medication increased from 27% to 41%.”

“The unit costs rose universally in all five measures of medical care in adults with and without diabetes. For each hospital admission, diabetes patients spent significantly more than persons without diabetes. The gap increased from $1,028 to $1,605 per hospital admission between 1987 and 2001, and dropped slightly to $1,360 per hospital admission in 2011. Diabetes patients also had higher spending per ER visit and per purchase of prescription medications.”

“From 1999 to 2011, national data suggest that growth in the use and price of prescription medications in the general population is 2.6% and 3.6% per year, respectively; and the growth has decelerated in recent years (22). Our analysis suggests that the growth rates in the use and prices of prescription medications for diabetes patients are considerably higher. The higher rate of growth is likely, in part, due to the growing emphasis on achieving glycemic targets, the use of newer medications, and the use of multidrug treatment strategies in modern diabetes care practice (23,24). In addition, the growth of medication spending is fueled by the rising prices per drug, particularly the drugs that are newly introduced in the market. For example, the prices for newer drug classes such as glitazones, dipeptidyl peptidase-4 inhibitors, and incretins have been 8 to 10 times those of sulfonylureas and 5 to 7 times those of metformin (9).”

“Between 1987 and 2011, medical spending increased both in persons with and in persons without diabetes; and the increase was substantially greater among persons with diabetes. As a result, the medical spending associated with diabetes nearly doubled. The growth was primarily driven by the spending in prescription medications. Further studies are needed to assess the cost-effectiveness of increased spending on drugs.”

iv. Determinants of Adherence to Diabetes Medications: Findings From a Large Pharmacy Claims Database.

“Adults with type 2 diabetes are often prescribed multiple medications to treat hyperglycemia, diabetes-associated conditions such as hypertension and dyslipidemia, and other comorbidities. Medication adherence is an important determinant of outcomes in patients with chronic diseases. For those with diabetes, adherence to medications is associated with better control of intermediate risk factors (14), lower odds of hospitalization (3,57), lower health care costs (5,79), and lower mortality (3,7). Estimates of rates of adherence to diabetes medications vary widely depending on the population studied and how adherence is defined. One review found that adherence to oral antidiabetic agents ranged from 36 to 93% across studies and that adherence to insulin was ∼63% (10).”

“Using a large pharmacy claims database, we assessed determinants of adherence to oral antidiabetic medications in >200,000 U.S. adults with type 2 diabetes. […] We selected a cohort of members treated for diabetes with noninsulin medications (oral agents or GLP-1 agonists) in the second half of 2010 who had continuous prescription benefits eligibility through 2011. Each patient was followed for 12 months from their index diabetes claim date identified during the 6-month targeting period. From each patient’s prescription history, we collected the date the prescription was filled, how many days the supply would last, the National Drug Code number, and the drug name. […] Given the difficulty in assessing insulin adherence with measures such as medication possession ratio (MPR), we excluded patients using insulin when defining the cohort.”

“We looked at a wide range of variables […] Predictor variables were defined a priori and grouped into three categories: 1) patient factors including age, sex, education, income, region, past exposure to therapy (new to diabetes therapy vs. continuing therapy), and concurrent chronic conditions; 2) prescription factors including refill channel (retail vs. mail order), total pill burden per day, and out of pocket costs; and 3) prescriber factors including age, sex, and specialty. […] Our primary outcome of interest was adherence to noninsulin antidiabetic medications. To assess adherence, we calculated an MPR for each patient. The ratio captures how often patients refill their medications and is a standard metric that is consistent with the National Quality Forum’s measure of adherence to medications for chronic conditions. MPR was defined as the proportion of days a patient had a supply of medication during a calendar year or equivalent period. We considered patients to be adherent if their MPR was 0.8 or higher, implying that they had their medication supplies for at least 80% of the days. An MPR of 0.8 or above is a well-recognized index of adherence (11,12). Studies have suggested that patients with chronic diseases need to achieve at least 80% adherence to derive the full benefits of their medications (13). […] [W]e [also] determined whether a patient was persistent, that is whether they had not discontinued or had at least a 45-day gap in their targeted therapy.”

“Previous exposure to diabetes therapy had a significant impact on adherence. Patients new to therapy were 61% less likely to be adherent to their diabetes medication. There was also a clear age effect. Patients 25–44 years of age were 49% less likely to be adherent when compared with patients 45–64 years of age. Patients aged 65–74 years were 27% more likely to be adherent, and those aged 75 years and above were 41% more likely to be adherent when compared with the 45–64 year age-group. Men were significantly more likely to be adherent than women […I dislike the use of the word ‘significant’ in such contexts; there is a difference in the level of adherence, but it is not large in absolute terms; the male vs female OR is 1.14 (CI 1.12-1.16) – US]. Education level and household income were both associated with adherence. The higher the estimated academic achievement, the more likely the patient was to be adherent. Patients completing graduate school were 41% more likely to be adherent when compared with patients with a high school equivalent education. Patients with an annual income >$60,000 were also more likely to be adherent when compared with patients with a household income <$30,000.”

“The largest effect size was observed for patients obtaining their prescription antidiabetic medications by mail. Patients using the mail channel were more than twice as likely to be adherent to their antidiabetic medications when compared with patients filling their prescriptions at retail pharmacies. Total daily pill burden was positively associated with antidiabetic medication adherence. For each additional pill a patient took per day, adherence to antidiabetic medications increased by 22%. Patient out-of-pocket costs were negatively associated with adherence. For each additional $15 in out-of-pocket costs per month, diabetes medication adherence decreased by 11%. […] We found few meaningful differences in patient adherence according to prescriber factors.”

“In our study, characteristics that suggest a “healthier” patient (being younger, new to diabetes therapy, and taking few other medications) were all associated with lower odds of adherence to antidiabetic medications. This suggests that acceptance of a chronic illness diagnosis and the potential consequences may be an important, but perhaps overlooked, determinant of medication-taking behavior. […] Our findings regarding income and costs are important reminders that prescribers should consider the impact of medication costs on patients with diabetes. Out-of-pocket costs are an important determinant of adherence to statins (26) and a self-reported cause of underuse of medications in one in seven insured patients with diabetes (27). Lower income has previously been shown to be associated with poor adherence to diabetes medications (15) and a self-reported cause of cost-related medication underuse (27).”

v. The Effect of Alcohol Consumption on Insulin Sensitivity and Glycemic Status: A Systematic Review and Meta-analysis of Intervention Studies.

“Moderate alcohol consumption, compared with abstaining and heavy drinking, is related to a reduced risk of type 2 diabetes (1,2). Although the risk is reduced with moderate alcohol consumption in both men and women, the association may differ for men and women. In a meta-analysis, consumption of 24 g alcohol/day reduced the risk of type 2 diabetes by 40% among women, whereas consumption of 22 g alcohol/day reduced the risk by 13% among men (1).

The association of alcohol consumption with type 2 diabetes may be explained by increased insulin sensitivity, anti-inflammatory effects, or effects of adiponectin (3). Several intervention studies have examined the effect of moderate alcohol consumption on these potential underlying pathways. A meta-analysis of intervention studies by Brien et al. (4) showed that alcohol consumption significantly increased adiponectin levels but did not affect inflammatory factors. Unfortunately, the effect of alcohol consumption on insulin sensitivity has not been summarized quantitatively. A review of cross-sectional studies by Hulthe and Fagerberg (5) suggested a positive association between moderate alcohol consumption and insulin sensitivity, although the three intervention studies included in their review did not show an effect (68). Several other intervention studies also reported inconsistent results (9,10). Consequently, consensus is lacking about the effect of moderate alcohol consumption on insulin sensitivity. Therefore, we aimed to conduct a systematic review and meta-analysis of intervention studies investigating the effect of alcohol consumption on insulin sensitivity and other relevant glycemic measures.”

“22 articles met criteria for inclusion in the qualitative synthesis. […] Of the 22 studies, 15 used a crossover design and 7 a parallel design. The intervention duration of the studies ranged from 2 to 12 weeks […] Of the 22 studies, 2 were excluded from the meta-analysis because they did not include an alcohol-free control group (14,19), and 4 were excluded because they did not have a randomized design […] Overall, 14 studies were included in the meta-analysis”

“A random-effects model was used because heterogeneity was present (P < 0.01, I2 = 91%). […] For HbA1c, a random-effects model was used because the I2 statistic indicated evidence for some heterogeneity (I2 = 30%).” [Cough, you’re not supposed to make these decisions that way, coughUS. This is not the first time I’ve seen this approach applied, and I don’t like it; it’s bad practice to allow the results of (frequently under-powered) heterogeneity tests to influence model selection decisions. As Bohrenstein and Hedges point out in their book, “A report should state the computational model used in the analysis and explain why this model was selected. A common mistake is to use the fixed-effect model on the basis that there is no evidence of heterogeneity. As [already] explained […], the decision to use one model or the other should depend on the nature of the studies, and not on the significance of this test”]

“This meta-analysis shows that moderate alcohol consumption did not affect estimates of insulin sensitivity or fasting glucose levels, but it decreased fasting insulin concentrations and HbA1c. Sex-stratified analysis suggested that moderate alcohol consumption may improve insulin sensitivity and decrease fasting insulin concentrations in women but not in men. The meta-regression suggested no influence of dosage and duration on the results. However, the number of studies may have been too low to detect influences by dosage and duration. […] The primary finding that alcohol consumption does not influence insulin sensitivity concords with the intervention studies included in the review of Hulthe and Fagerberg (5). This is in contrast with observational studies suggesting a significant association between moderate alcohol consumption and improved insulin sensitivity (34,35). […] We observed lower levels of HbA1c in subjects consuming moderate amounts of alcohol compared with abstainers. This has also been shown in several observational studies (39,43,44). Alcohol may decrease HbA1c by suppressing the acute rise in blood glucose after a meal and increasing the early insulin response (45). This would result in lower glucose concentrations over time and, thus, lower HbA1c concentrations. Unfortunately, the underlying mechanism of glycemic control by alcohol is not clearly understood.”

vi. Predictors of Lower-Extremity Amputation in Patients With an Infected Diabetic Foot Ulcer.

“Infection is a frequent complication of diabetic foot ulcers, with up to 58% of ulcers being infected at initial presentation at a diabetic foot clinic, increasing to 82% in patients hospitalized for a diabetic foot ulcer (1). These diabetic foot infections (DFIs) are associated with poor clinical outcomes for the patient and high costs for both the patient and the health care system (2). Patients with a DFI have a 50-fold increased risk of hospitalization and 150-fold increased risk of lower-extremity amputation compared with patients with diabetes and no foot infection (3). Among patients with a DFI, ∼5% will undergo a major amputation and 20–30% a minor amputation, with the presence of peripheral arterial disease (PAD) greatly increasing amputation risk (46).”

“As infection of a diabetic foot wound heralds a poor outcome, early diagnosis and treatment are important. Unfortunately, systemic signs of inflammation such as fever and leukocytosis are often absent even with a serious foot infection (10,11). As local signs and symptoms of infection are also often diminished, because of concomitant peripheral neuropathy and ischemia (12), diagnosing and defining resolution of infection can be difficult.”

“The system developed by the International Working Group on the Diabetic Foot (IWGDF) and the Infectious Diseases Society of America (IDSA) provides criteria for the diagnosis of infection of ulcers and classifies it into three categories: mild, moderate, or severe. The system was validated in three relatively small cohorts of patients […] The European Study Group on Diabetes and the Lower Extremity (Eurodiale) prospectively studied a large cohort of patients with a diabetic foot ulcer (17), enabling us to determine the prognostic value of the IWGDF system for clinically relevant lower-extremity amputations. […] We prospectively studied 575 patients with an infected diabetic foot ulcer presenting to 1 of 14 diabetic foot clinics in 10 European countries. […] Among these patients, 159 (28%) underwent an amputation. […] Patients were followed monthly until healing of the foot ulcer(s), major amputation, or death — up to a maximum of 1 year.”

“One hundred and ninety-nine patients had a grade 2 (mild) infection, 338 a grade 3 (moderate), and 38 a grade 4 (severe). Amputations were performed on 159 (28%) patients (126 minor and 33 major) within the year of follow-up; 103 patients (18%) underwent amputations proximal to and including the hallux. […] The independent predictors of any amputation were as follows: periwound edema, HR 2.01 (95% CI 1.33–3.03); foul smell, HR 1.74 (1.17–2.57); purulent and nonpurulent exudate, HR 1.67 (1.17–2.37) and 1.49 (1.02–2.18), respectively; deep ulcer, HR 3.49 (1.84–6.60); positive probe-to-bone test, HR 6.78 (3.79–12.15); pretibial edema, HR 1.53 (1.02–2.31); fever, HR 2.00 (1.15–3.48); elevated CRP levels but less than three times the upper limit of normal, HR 2.74 (1.40–5.34); and elevated CRP levels more than three times the upper limit, HR 3.84 (2.07–7.12). […] In comparison with mild infection, the presence of a moderate infection increased the hazard for any amputation by a factor of 2.15 (95% CI 1.25–3.71) and 3.01 (1.51–6.01) for amputations excluding the lesser toes. For severe infection, the hazard for any amputation increased by a factor of 4.12 (1.99–8.51) and for amputations excluding the lesser toes by a factor of 5.40 (2.20–13.26). Larger ulcer size and presence of PAD were also independent predictors of both any amputation and amputations excluding the lesser toes, with HRs between 1.81 and 3 (and 95% CIs between 1.05 and 6.6).”

“Previously published studies that have aimed to identify independent risk factors for lower-extremity amputation in patients with a DFI have noted an association with older age (5,22), the presence of fever (5), elevated acute-phase reactants (5,22,23), higher HbA1c levels (24), and renal insufficiency (5,22).”

“The new risk scores we developed for any amputation, and amputations excluding the lesser toes had higher prognostic capability, based on the area under the ROC curve (0.80 and 0.78, respectively), than the IWGDF system (0.67) […] which is currently the only one in use for infected diabetic foot ulcers. […] these Eurodiale scores were developed based on the available data of our cohort, and they will need to be validated in other populations before any firm conclusions can be drawn. The advantage of these newly developed scores is that they are easier for clinicians to perform […] These newly developed risk scores can be readily used in daily clinical practice without the necessity of obtaining additional laboratory testing.”

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September 12, 2017 - Posted by | Cardiology, Diabetes, Economics, Epidemiology, Health Economics, Infectious disease, Medicine, Microbiology, Statistics

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