Understanding the economic burden of nonsevere nocturnal hypoglycemic events: impact on work productivity, disease management, and resource utilization.

OBJECTIVE: Nonsevere hypoglycemic events are common and may occur in one-third of persons with diabetes as often as several times a week. This study's objective was to examine the economic burden of nonsevere nocturnal hypoglycemic events (NSNHEs). METHODS: A 20-minute Web-based survey, with items derived from the literature, expert input, and patient interviews, assessing the impact of NSNHEs was administered in nine countries to 18 years and older patients with self-reported diabetes having an NSNHE in the past month. RESULTS: A total of 20,212 persons were screened, with 2,108 respondents meeting criteria and included in the analysis sample. The cost of lost work productivity per NSNHE was estimated to be between $10.21 (Germany) and $28.13 (the United Kingdom), representing 3.3 to 7.5 hours of lost work time per event. A reduction in work productivity (presenteeism) was also reported. Compared with respondents' usual blood sugar monitoring practice, on average, 3.6 ± 6.6 extra tests were conducted in the week following the event at a cost of approximately $87.1 per year. Additional costs were also incurred for doctor visits as well as medical care required because of falls or injuries incurred during the NSNHE for an annual cost of $2,111.3 per person per year. When taking into consideration the multiple impacts of NSNHEs for the total sample and the frequency that these events occur, the resulting total annual economic burden was $288,000 or $127 per person per event. CONCLUSIONS: NSNHEs have serious consequences for patients. Greater attention to treatments that reduce NSNHEs can have a major impact on reducing the economic burden of diabetes.

Non-severe nocturnal hypoglycemic events: experience and impacts on patient functioning and well-being.

PURPOSE: Non-severe nocturnal hypoglycemic events (NSNHEs) are hypoglycemic events that occur during sleep but do not require medical assistance from another individual. This study was conducted to better understand the NSNHEs as patients actually experience them in their daily life, and how they impacted functioning and well-being. METHODS: Nine focus groups were held in four countries with diabetics (Type 1 and Type 2) who had experienced an NSNHE within the previous month: France (2 groups); Germany (2 groups); United Kingdom (2 groups); and United States (3 groups). These groups were audio-taped, translated to English where applicable, and analyzed thematically. RESULTS: Seventy-eight people with diabetes participated in the focus groups: 41 (53 %) were female and 37 (47 %) were male; 24 (31 %) had Type 1 diabetes, and 54 (69 %) had Type 2 diabetes. Participant reports were grouped into several major themes: next day effects, symptoms, sleep impacts, social impacts, corrective action, practical management, feelings about NSNHEs, and work impacts. CONCLUSIONS: People with both Type 1 and Type 2 diabetes experience NSNHEs. The range of impact on these patients is wide, from very mild to severe with a majority of participants experiencing strong impacts that limit their daily functioning. This finding suggests that NSNHEs are more impactful than previously believed.

Cost-effectiveness of switching patients with type 2 diabetes from insulin glargine to insulin detemir in Chinese setting: a health economic model based on the PREDICTIVE study.

OBJECTIVES: To evaluate the long-term cost-effectiveness of switching from insulin glargine (IGla) to insulin detemir (IDet) in patients with type 2 diabetes in the setting of Chinese secondary and tertiary hospitals. METHODS: A published and validated computer simulation model of diabetes (the Center for Outcomes Research model) was used to make the long-term (30 years) projection of health economic outcomes. Patient demographic information and clinical end points were derived from a subgroup analysis of the Predictable Results and Experience in Diabetes through Intensification and Control to Target: an International Variability Evaluation (PREDICTIVE) study. Baseline risk factors and racial characteristic data were obtained from Chinese cohort studies. The diabetes management and complications costs were obtained from Chinese published data and adjusted to 2010 values by using the Chinese consumer price index. An annual discounting rate of 3% was used for both health and cost outcomes, and one-way sensitivities analysis was performed, which illustrated that the results were robust. RESULTS: Conversion to IDet from IGla was projected to improve patient life expectancy by 0.06 year and 0.48 quality-adjusted life-years. Drug costs and management costs of diabetes mellitus were increased by US$368 (US$17,466 vs. US$17,097) and US$31 (US$5464 vs. US$5433), respectively. However, the costs of complications, including cerebrovascular disease, renal complications, ulcer/amputation/neuropathy, eye complications, and hypoglycemia events, were reduced by US$819 (US$21,294 vs. US$22,114), resulting in a total direct medical cost saving of US$420 when converting to IDet. CONCLUSIONS: Conversion to IDet from an IGla regimen improved life expectancy and was a cost-saving treatment approach in a Chinese setting.

The impact of non-severe hypoglycemic events on work productivity and diabetes management.

OBJECTIVES: Hypoglycemia is a common complication of treatment with certain diabetes drugs. Non-severe hypoglycemic events (NSHEs) occur more frequently than severe events and account for the majority of total events. The objective of this multi-country study was to identify how NSHEs in a working population affect productivity, costs, and self-management behaviors. METHODS: A 20-minute survey assessing the impact of NSHEs was administered via the Internet to individuals (≥ 18 years of age) with self-reported diabetes in the United States, United Kingdom, Germany, and France. The analysis sample consisted of all respondents who reported an NSHE in the past month. Topics included: reasons for, duration of, and impact of NSHE(s) on productivity and diabetes self-management. RESULTS: A total of 1404 respondents were included in this analysis. Lost productivity was estimated to range from $15.26 to $93.47 (USD) per NSHE, representing 8.3 to 15.9 hours of lost work time per month. Among individuals reporting an NSHE at work (n = 972), 18.3% missed work for an average of 9.9 hours (SD 8.4). Among respondents experiencing an NSHE outside working hours (including nocturnal), 22.7% arrived late for work or missed a full day. Productivity loss was highest for NSHEs occurring during sleep, with an average of 14.7 (SD 11.6) working hours lost. In the week following the NSHE, respondents required an average of 5.6 extra blood glucose test strips. Among respondents using insulin, 25% decreased their insulin dose following the NSHE. CONCLUSIONS: NSHEs are associated with substantial economic consequences for employers and patients. Greater attention to treatments that reduce NSHEs could have a major, positive impact on lost work productivity and overall diabetes management.

Examining the ability to detect change using the TRIM-Diabetes and TRIM-Diabetes Device measures.

PURPOSE: Responsiveness is defined as the ability of an instrument to accurately detect change when it has occurred and is an essential psychometric property of a patient-reported outcomes (PRO) measure to understand and interpret study findings. This study examined the responsiveness of 2 Treatment Related Impact Measures (TRIMs): The TRIM-Diabetes (TRIM-D) and TRIM-Diabetes Device (TRIM-DD) as well as confirmed their measurement models in a randomized controlled trial (RCT) design. METHODS: The data were collected in a multi-center, randomized, open-label (2 × 12 week), cross-over study of two prefilled pens in subjects with type 1 or type 2 diabetes, age 18 or older. Internal and external responsiveness were examined. To confirm the measurement model identified in the previous study, the Bentler comparative fit index (CFI) and internal consistency for the RCT sample scores were examined and compared. RESULTS: Based on a priori criteria, tests of responsiveness were confirmed with patients having significant improvements over time ranging from 2.7 (Psychological Health) to 11.1 (Treatment Burden) (P < 0.01) (effect sizes ranging from 0.2 to 0.8). The previous measurement model factor structure was confirmed (CFI ranging from 0.8 to 1.0), and internal consistency of the TRIMs was similar to the developmental findings. CONCLUSIONS: The total score as well as all domain scores of the TRIMs was significantly responsive over time, thus acceptable internal and external responsiveness of TRIM-D and TRIM-DD are concluded. To date, all validation evidence supports the use of these two measures in future clinical trials.

Measuring the health status burden in hemodialysis patients using the SF-36® health survey.

PURPOSE: The SF-36, a generic measure of 8 domains of health-related quality of life (HRQOL), has been widely used to examine HRQOL of end-stage renal disease (ESRD) patients undergoing hemodialysis (HD). The current study synthesizes existing literature to examine which SF-36 domains capture the largest burden in this patient population. METHODS: A literature search of published studies that presented descriptive statistics for baseline SF-36 scale scores from HD patients was conducted. Disease burden was estimated by comparing HD patients' SF-36 scores to those from either a control group or a general population normative sample taken from the same country. For each study, Cohen d effect sizes for between-sample differences were calculated for each scale. RESULTS: Twenty-six articles that matched set criteria were identified. Estimation of differences between HD patients and comparison groups showed that the SF-36 physical functioning scale yielded the largest weighted mean effect size across studies (d = 1.46), followed by the general health (d = 1.29) and role physical (d = 1.21) scales. CONCLUSIONS: Among the eight domains of the SF-36, physical functioning, general health, and role physical scales best captured disease burden for HD patients. The disease burden negatively impacts physical HRQOL more strongly than mental HRQOL.

Patient treatment satisfaction after switching to NovoMix® 30 (BIAsp 30) in the IMPROVE™ study: an analysis of the influence of prior and current treatment factors.

PURPOSE: Understanding treatment satisfaction (TS) for diabetes is increasingly important as treatment options increase. This study examines treatment satisfaction with NovoMix® 30 in an observational study in patients with type 2 diabetes. METHODS: The DiabMedSat assesses Overall, Treatment Burden, Symptom and Efficacy Treatment Satisfaction. The impact of type of pretreatment variables on TS was examined by ANOVA at baseline and week 26. Satisfaction at week 26 was examined by t-test and effect size. Linear regression models examined impact of prior treatment factors (age, gender, duration of diabetes, type of prior treatment and diabetes-related comorbidities) and current treatment factors (weight gain, hypoglycemic events, reaching therapeutic goal) on TS. RESULTS: The data set comprised 17,488 persons. Prior treatment with insulin had a more positive impact on baseline satisfaction. At week 26, there were no differences between type of prior treatment groups in Overall, Symptoms and Burden TS. Current treatment with NovoMix 30 significantly improved TS. Regression analyses examining the combined effect of pretreatment factors and current treatment factors found that all factors except for age-impacted TS although the domains impacted varied. CONCLUSIONS: Patients treated with NovoMix 30 reported improved treatment satisfaction, and the improvement is considered clinically meaningful to patients.

Understanding and assessing the impact of treatment in diabetes: the Treatment-Related Impact Measures for Diabetes and Devices (TRIM-Diabetes and TRIM-Diabetes Device).

PURPOSE: Diabetes is a debilitating illness requiring lifelong management. Treatments can be varied in terms of mode of administration as well as type of agent. Unfortunately, most patient reported outcome measures currently available to assess the impact of treatment are specific to diabetes type, treatment modality or delivery systems and are designed to be either a HRQoL or treatment satisfaction measure. To address these gaps, the Treatment Related Impact Measure-Diabetes and Device measures were developed. This paper presents the item development and validation of the TRIM Diabetes/Device. METHODS: Patient interviews were conducted to collect the patient perspective and ensure high content validity. Interviews were hand coded and qualitatively analyzed to identify common themes. A conceptual model of the impact of diabetes medication was developed and preliminary items for the TRIM-Diabetes/Device were generated and cognitively debriefed. Validation data was collected via an on-line survey and analyzed according to an a priori statistical analysis plan to validate the overall score as well as each domain. Item level criteria were used to reduce the preliminary item pool. Next, factor analysis to identify structural domains was performed. Reliability and validity testing was then performed. RESULTS: One hundred and five patients were interviewed in focus groups, individual interviews and for cognitive debriefing. Five hundred seven patients participated in the validation study. Factor analysis identified seven domains: Treatment Burden, Daily Life; Diabetes Management; Psychological Health; Compliance and Device Function and Bother. Internal consistency reliability coefficients of the TRIM-Diabetes/Device ranged from 0.80 and 0.94. Test-retest reliability of the TRIM-Diabetes/Device ranged from 0.71 to 0.89. All convergent and known groups validity hypotheses were met for the TRIM-Diabetes/Device total scores and sub-scales. CONCLUSION: Validation is an ongoing and iterative process. These findings are the first step in that process and have shown that both the TRIM-Diabetes and the TRIM-Diabetes Device have acceptable psychometric properties. Future research is needed to continue the validation process and examine responsiveness and the validity of the TRIM-Diabetes/Device in a clinical trial population.

Qualitative research and content validity: developing best practices based on science and experience.

PURPOSE: Establishing content validity for both new and existing patient-reported outcome (PRO) measures is central to a scientifically sound instrument development process. Methodological and logistical issues present a challenge in regard to determining the best practices for establishing content validity. METHODS: This paper provides an overview of the current state of knowledge regarding qualitative research to establish content validity based on the scientific methodological literature and authors' experience. RESULTS: Conceptual issues and frameworks for qualitative interview research, developing the interview discussion guide, reaching saturation, analysis of data, developing a theoretical model, item generation and cognitive debriefing are presented. Suggestions are offered for dealing with logistical issues regarding facilitator qualifications, ethics approval, sample recruitment, group logistics, taping and transcribing interviews, honoraria and documenting content validity. CONCLUSIONS: It is hoped this paper will stimulate further discussion regarding best practices for establishing content validity so that, as the PRO field moves forward, qualitative research can be evaluated for quality and acceptability according to scientifically established principles.

Psychological insulin resistance: patient beliefs and implications for diabetes management.

PURPOSE: To define and understand patient psychological insulin resistance (PIR) and its impact on diabetes management. METHODS: Systematic literature review of peer-refereed journals using the MEDLINE database, including all articles in English from 1985 to 2007. The population included patients with type 1 and type 2 diabetes, insulin naïve, and those currently using insulin. A total of 116 articles were reviewed. RESULTS: PIR is impacted by patients' beliefs and knowledge about diabetes and insulin, negative self-perceptions and attitudinal barriers, the fear of side effects and complications from insulin use, as well as lifestyle adaptations, restrictions required by insulin use, and social stigma. These etiological influences, both independently and in combination, constitute a patient's PIR and may result in the reluctance of patients to both initiate and intensify treatment, leading to delayed treatment initiation and compromised glucose control. CONCLUSIONS: PIR is complex and multifaceted. It plays an important, although often ignored, role in diabetes management. Assisting health care professionals in better understanding PIR from the patient's perspective should result in improved treatment outcomes. By tailoring treatments to patients' PIR, clinicians may be better able to help their patients begin insulin treatment sooner and improve compliance, thus facilitating target glycemic control.

Utility values for symptomatic non-severe hypoglycaemia elicited from persons with and without diabetes in Canada and the United Kingdom.

OBJECTIVE: To elicit societal and patient utilities associated with diabetic symptomatic non-severe hypoglycaemia for three health states: 1) rare (quarterly), 2) intermittent (monthly), 3) and frequent (weekly) hypoglycaemia episodes. METHODS: Using validated health states, time trade-off utilities were elicited from 51 Canadian respondents with diabetes, and 79 respondents in Canada and 75 respondents in the United Kingdom (UK) without diabetes. RESULTS AND DISCUSSION: Each hypoglycaemic episode was associated with a reduction in utility and persons with diabetes consistently reported slightly higher utility values than respondents without diabetes. The utility for diabetes without hypoglycaemia ranged from 0.88 to 0.97, the mean utility for rare hypoglycaemic events (quarterly) ranged between 0.85 and 0.94. The utility for the intermittent state (monthly) ranged from 0.77 to 0.90 and from 0.66 to 0.0.83 for the frequent state (weekly). Differences were observed between respondents without diabetes in Canada and the UK. Using a multivariate linear OLS regression, the estimated utilities associated with a single hypoglycaemic event were -0.0033 and -0.0032 for respondents with diabetes and without diabetes, respectively. CONCLUSION: Among respondents with and without diabetes, there was a demonstrable utility loss associated with hypoglycaemia. Considering a utility loss of 0.03 as a minimum clinically important difference for persons with diabetes, the evidence from this study indicates that as low as ten symptomatic non-severe hypoglycaemic episodes per year may be of clinical importance and that the importance increases with frequency of episodes. Integrating directly elicited utility values such as those reported here will improve the quality and applicability of economic evaluations of diabetes treatment.

A critical review of the literature on fear of hypoglycemia in diabetes: Implications for diabetes management and patient education.

OBJECTIVE: In many individuals with diabetes, the unpleasant symptoms and negative consequences associated with hypoglycemia may result in significant anxiety or even a fear of hypoglycemia (FoH). This fear may have significant clinical implications for diabetes management. The aim of this review is to integrate existing research on FoH (its measurement, predictors, correlates, impact and treatment) and discuss its implications for diabetes management and patient education. METHODS: A literature search was conducted using Medline and Embase. The search was limited to journal articles published in English from 1985 to 2007 inclusive. Three hundred and one abstracts were reviewed and 273 were rejected on the basis of non-relevance. In addition to the 28 papers included, six additional papers were identified by further searches and were added to this review. RESULTS: FoH appears to be a widespread phenomenon. It is measured primarily through the use of a specific scale, the Hypoglycemic Fear Survey (HFS). There are a number of factors that relate to whether an individual is likely to develop FoH including whether there is a history of hypoglycemia in an individual, length of time since first insulin treatment, and a higher level of variability in blood glucose level. FoH has been linked to both state and trait anxiety although the relationship is complex. CONCLUSIONS: There is evidence that FoH may have a significant negative impact on diabetes management, metabolic control and subsequent health outcomes. There is evidence that blood glucose (BG) awareness training and CBT can reduce levels of fear and improve disease management. More research is needed on how FoH arises and the individual variables which predict its development. In addition, well designed research is required to better understand the behavioral and medical impact of FoH, and interventions to reduce it. PRACTICE IMPLICATIONS: There is some evidence to suggest that interventions including BG awareness training and cognitive behavioral therapy can reduce levels of fear and improve disease management. While many aspects of FoH require further well-designed research, it is evident that this phenomenon can have a major impact on diabetes management and needs to be specifically addressed in patient education programs.

A Markov modelled pharmacoeconomic analysis of bimatoprost 0.03% in the treatment of glaucoma as an alternative to filtration surgery in Italy.

OBJECTIVE: Glaucoma is generally managed by decreasing the intraocular pressure (IOP) to a level believed to prevent further damage to the optic disc and loss of visual field. This may be achieved medically or surgically. The objective of this pharmacoeconomic analysis was to investigate the 4-year costs of bimatoprost 0.03% (Lumigan) eye drops as an alternative to filtration surgery (FS) for glaucoma patients on maximum tolerable medical therapy (MTMT). RESEARCH DESIGN AND METHOD: A Markov model was designed using effectiveness and resource use data from a randomized clinical trial and expert statements (Delphi panel). The RCT covered 83 patients on MTMT. The Model compared bimatoprost with FS. In the bimatoprost model arm patients began treatment with bimatoprost. If target IOP (-20%) was not reached using medical therapy the patient proceeded with FS. In the FS model arm, FS was performed after the first ophthalmologist visit. Unit costs were obtained from an Italian chart and tariffs review (healthcare sector perspective). RESULTS: The RCT showed that 74.7% of the patients delayed the need for FS by 3 months. The Markov model forecasted that 64.2% of the patients could delay the need for FS by 1 year, and forecasted 34.0% could avoid FS after 4 years. The 4-year cost per patient in the bimatoprost and FS arms was E3438 and E4194, respectively (incremental costs of E755). The major cost drivers for the bimatoprost arm were patients who needed combination therapy or FS if the target IOP was not reached. In the FS arm, the major cost drives were the initial surgery costs and pressure-lowering medications used as add-on therapy after FS. CONCLUSIONS: The analysis shows that in a 4-year perspective bimatoprost is cheaper compared to FS. In addition, the postponement of FS associated with bimatoprost may have important implications for waiting list planning.

Insulin degludec versus insulin glargine in type 1 and type 2 diabetes mellitus: a meta-analysis of endpoints in phase 3a trials.

INTRODUCTION: Insulin degludec (degludec) is a basal insulin with an ultra-long, stable action profile and reduced pharmacodynamic variability. Seven phase 3a trials compared degludec with insulin glargine (glargine). Patient-level meta-analyses were performed to obtain a comprehensive overview of differences between the insulin preparations, possible because consistent outcome definitions were utilized. METHODS: Three categories of trials were analyzed: basal-bolus-treated type 1 diabetes mellitus (T1DMB/B), insulin-naïve type 2 diabetes mellitus (T2DMinsulin-naïve), and basal-bolus-treated T2DM (T2DMB/B). Regression models were adjusted for baseline characteristics. Endpoints analyzed were glycosylated hemoglobin (HbA1c), fasting plasma glucose (FPG), insulin dose and hypoglycemic rates analyzed in mutually exclusive groups: non-severe nocturnal, non-severe daytime, and severe. RESULTS: As with previous treat-to-target trials, reductions in HbA1c were similar between degludec and glargine. Reductions in FPG were significantly greater with degludec in T1DMB/B and T2DMinsulin-naïve. Total daily insulin dose was significantly lower with degludec in T1DMB/B and T2DMinsulin-naïve. Estimated hypoglycemia rate ratios for degludec/glargine were as follows for T1DMB/B, T2DMinsulin-naïve and T2DMB/B, respectively: non-severe nocturnal 0.83, 0.64, 0.75 (all P < 0.05); non-severe daytime 1.14 [not significant (ns)], 0.89 (ns), and 0.83 (P < 0.05). Rate ratios for severe events were 1.12 (ns) (T1DMB/B); 0.14 (P < 0.05) (T2DMinsulin-naïve); and not analyzed (T2DMB/B) due to too few events. CONCLUSIONS: Compared with glargine, degludec is associated with equivalent HbA1c control and significantly lower nocturnal hypoglycemia rates. In T1DMB/B and T2DMinsulin-naïve, degludec is also associated with significantly greater reductions in FPG and lower total doses of insulin versus glargine.

Impact of nocturnal hypoglycemic events on diabetes management, sleep quality, and next-day function: results from a four-country survey.

OBJECTIVES: Non-severe nocturnal hypoglycemic events (NSNHEs) may have a major impact on patients. The objective was to determine how NSNHEs affect diabetes management, sleep quality, functioning, and to assess if these impacts differ by diabetes type or country. METHODS: An internet survey to adults with diabetes in the US, UK, Germany, and France. RESULTS: Of 6756 screened respondents, 1086 reported an NSNHE in the past month. For this last event, respondents with type 2 required significantly more time than type 1 to recognize and respond to the event (1.5 vs 1.1 hours), 25.7% (T1) and 18.5% (T2) decreased their normal insulin dose due to their most recent NSNHE. All respondents were likely to take 1-2 additional self-monitored blood glucose measurements on the day following. NSNHEs were associated with a high proportion of respondents contacting a healthcare professional (18.6% T1, 27.8% T2) reporting they could not return to sleep at night (13.3% T1, 13.4% T2), and tiredness on the day following the event (71.2% for both). Of the respondents working for pay, 18.4% T1 and 28.1% T2 reported being absent from work due to the NSNHE, and a substantial proportion of respondents (8.7% T1, 14.4% T2) also reported missing a meeting or work appointment or not finishing a task on time. Compared with other countries, respondents from France may experience a more substantial impact on diabetes management and daily functioning following an NSNHE. Potential limitations in this study include recall and selection bias; however, these biases are not believed to have impacted findings in any meaningful way. CONCLUSIONS: NSNHEs are associated with a substantial impact on diabetes management, sleep quality, and next-day functioning.

The impact of non-severe hypoglycemic events on daytime function and diabetes management among adults with type 1 and type 2 diabetes.

OBJECTIVES: To describe daytime non-severe hypoglycemic events (NSHEs), assess their impact on patient functioning and diabetes self-management, and examine if these impacts differ by diabetes type or country. METHODS: Internet survey to adults with diabetes in the US, UK, Germany, and France. RESULTS: Of 6756 screened respondents, 2439 reported a daytime NSHE in the past month. NSHEs occurred while active (e.g., running errands) (45.1%), 29.6% while not active (e.g., watching TV), and 23.8% at work. On average, it took half a day to respond and recover from NSHE. Respondents monitored their glucose 5.7 extra times on average over the following week. On the day of event, type 1 respondents tested significantly more often than type 2 (p<0.05). Type 2 were less likely to confirm NSHE with glucose test (p<0.001). Following NSHE, 12.6% of respondents reduced total insulin by an average of 7.6 units (SD=8.3). Total units and days with reduced dosing was significantly less, whilst number of additional glucose tests and time to recover was significantly longer if NSHE occurred at work (p<0.001). Type 1 decreased insulin doses more often (p<0.001); however, type 2 decreased a greater number of units (p<0.01). Compared with other countries, US respondents were more likely to eat a light or full meal and respondents in France took significantly longer than all other countries to recognize (p<0.05), respond to (p<0.001), and recover from (p<0.001) NSHE, used significantly more monitoring tests the day of (p<0.05) and over the subsequent week (p<0.001), and decreased their normal insulin dose more (p<0.001). Limitations of the study include potential recall bias and selection bias. CONCLUSIONS: NSHEs are associated with a significant impact on patient functioning and diabetes management.

Cost of self-monitoring of blood glucose in the United States among patients on an insulin regimen for diabetes.

BACKGROUND: People with diabetes are at an increased risk of developing numerous complications, resulting in increased health care expenditures, economic burden, and higher mortality. For patients using an insulin pump or multiple insulin injections, self-monitoring of blood glucose (SMBG) is recognized as a core component of effective diabetes self-management. However, little is known about the real-world frequency and true costs associated with SMBG as a percentage of an insulin regimen in the United States. OBJECTIVE: To evaluate SMBG frequency, SMBG-related costs (including blood glucose test strips and testing supplies), and insulin therapy costs among insulin-dependent patients with diabetes and at least 1 pharmacy claim for blood glucose testing strips during a 12-month follow-up period. METHODS: A retrospective database analysis was conducted using the IMS LifeLink Health Plan Claims database to capture the frequency and costs associated with SMBG in relation to a specific insulin regimen, and SMBG expenditure compared with other treatment costs. The study employed a retrospective cohort analysis of patients with 2 or more claims for insulin between January 1, 2007, and June 30, 2009, with the first such claim representing the index date. All patients were required to have 6 months of pre-index continuous enrollment (pre-index period) and 12 months of post-index continuous enrollment (follow-up period). Patients were also required to have a diagnosis of diabetes in the pre-index period and to have no gaps of more than 90 days between consecutive insulin claims during the 360-day follow-up period. Patients without at least 1 pharmacy claim for blood glucose testing strips during the 12-month follow-up period and patients with pharmacy claims with extreme values (greater than 1,500 strips) were excluded. Depending on the insulin types used within the 30 days immediately following their index date, patients were subcategorized into 1 of 4 insulin regimen groups (basal, bolus, premixed, or basal-bolus). Patients' frequency of blood glucose testing was measured throughout their 12-month post-index follow-up period through analysis of clinical codes found on pharmacy claims. Quantity supplied fields on pharmacy claims were used to calculate total tests utilized over the follow-up period (e.g., 50 test strips dispensed=50 tests assumed). Insulin-related costs were also evaluated for the 12-month follow-up period. RESULTS: Among an initial sample of 373,946 patients with at least 2 claims for insulin between January 1, 2007, and June 30, 2009, 45,555 patients (12.2%) formed the final overall cohort who met the inclusion and exclusion criteria. SMBG-related pharmacy costs accounted for 27% of the insulin-and SMBG-related treatment costs for insulin users with an average $772 per patient in prescription testing strips and supplies versus $2,078 for insulin prescriptions and supplies. With an overall mean utilization for pharmacy-based SMBG testing of 764.3 strips per year, the average cost per testing strip was $0.98. Annual SMBG costs were 24.5% of total insulin and SMBG-related pharmacy costs for the basal insulin group compared with 35.8% for bolus, 21.0% for premixed, and 26.4% for basal-bolus. CONCLUSION: For insulin users with at least 1 pharmacy claim for glucose test strips, SMBG-related costs accounted for about one-fourth of total insulin and SMBG-related pharmacy costs.

Cost of Self-Monitoring of Blood Glucose in Canada among Patients on an Insulin Regimen for Diabetes.

INTRODUCTION: People with diabetes are at a higher risk of developing a variety of medical conditions relative to those without diabetes, resulting in increased healthcare costs. Self-monitoring of blood glucose (SMBG) is accepted as a recommended element of effective diabetes self-management. However, little is known about the real-world frequency and actual expenditures associated with SMBG, as well as the impact of SMBG costs relative to the cost of diabetes treatments. The primary objective is to evaluate the real-world utilization and costs of SMBG tests in Canada among insulin-treated diabetes patients during a 12-month follow-up period. METHODS: A retrospective cohort study was conducted using the IMS Brogan Inc. Drug Plan database from July 1, 2006 through June 30, 2010. Total costs during the 12-month follow-up period were assessed, focusing on blood glucose (BG) testing strip costs, insulin therapy costs, and costs associated with oral antidiabetics medications. All prevalent patients with two or more prescriptions for insulin between January 1, 2007 and December 31, 2009 were initially included in the analysis, the first prescription serving as their index date. Depending on the insulin type(s) used, patients were subcategorized into one of four insulin regimen groups (basal, bolus, premix, or basal-bolus). RESULTS: Among an initial sample of patients with two or more claims for insulin between January 1, 2007 and December 31, 2009, 142,551 met the aforementioned inclusion and exclusion criteria. An overall mean utilization of pharmacy-based blood glucose testing of approximately 1,094 strips per person per year was observed, with an average cost per testing strip of Canadian $0.79. SMBG treatment costs for insulin users ($860), specifically those associated with prescription testing strips, totaled 41.6% of the average annual pharmacy costs of diabetes-related prescriptions ($2,068). CONCLUSION: This study shows that SMBG accounts for approximately 40% of the total diabetes-related pharmacy costs for insulin users.

Hypoglycemia: an overview of fear of hypoglycemia, quality-of-life, and impact on costs.

BACKGROUND: The clinical goal in the treatment of diabetes is to achieve good glycemic control. Tight glycemic control achieved with intensive glucose lowering treatment reduces the risk of long-term micro- and macro-vascular complications of diabetes, resulting in an improvement in quality-of-life for the patient and decreased healthcare costs. The positive impact of good glycemic control is, however, counterbalanced by the negative impact of an increased incidence of hypoglycemia. METHODS: A search of PubMed was conducted to identify published literature on the impact of hypoglycemia, both on patient quality-of-life and associated costs to the healthcare system and society. RESULTS: In people with type 1 or type 2 diabetes, hypoglycemia is associated with a reduction in quality-of-life, increased fear and anxiety, reduced productivity, and increased healthcare costs. Fear of hypoglycemia may promote compensatory behaviors in order to avoid hypoglycemia, such as decreased insulin doses, resulting in poor glycemic control and an increased risk of serious health consequences. Every non-severe event may be associated with a utility loss in the range of 0.0033-0.0052 over 1 year, further contributing to the negative impact. LIMITATIONS: This review is intended to provide an overview of hypoglycemia in diabetes and its impact on patients and society, and consequently it is not a comprehensive evaluation of all studies reporting hypoglycemic episodes. CONCLUSION: To provide the best possible care for patients and a cost-effective treatment strategy for healthcare decision-makers, a treatment that provides good glycemic control with a limited risk of hypoglycemia would be a welcome addition to diabetes management options.

Healthcare costs of fast-acting insulin analogues versus short-acting human insulin for Danish patients with type 2 diabetes on a basal-bolus regimen.

AIMS: Fast-acting insulin analogues (FAIAs) reduce hypoglycaemia and improve administration flexibility compared with short-acting human insulin (SHI). This analysis examines whether these benefits translate into cost offsets when comparing the total treatment costs for FAIA versus SHI used as basal-bolus therapy for treating type 2 diabetes (T2D). METHODS: Registry data covering the Danish population including demographic variables, prescription, hospital and primary care data formed the basis for analysis. To capture patients on basal-bolus therapy only, inclusion criteria were ≥2 prescriptions of either long-acting insulin analogues (LAIAs) or neutral protamine Hagedorn (NPH) insulin (basal component), and ≥2 prescriptions for either an FAIA or SHI (bolus component) during the inclusion period (1 January-31 December 2005). Patients using LAIAs (n = 521) or NPH (n = 2695) were analysed separately. Within each basal cohort, patients using FAIAs or SHI were matched regarding observable variables using propensity scores. Healthcare costs were analysed for a follow-up period (maximum 2 years post-inclusion). RESULTS: Within each cohort, matching produced groups with similar observed covariates. Overall direct healthcare costs in the LAIA cohort were €4183 and €5289 for FAIA and SHI, respectively. In the NPH cohort, costs were €4940 and €4699 for FAIA and SHI, respectively. For both basal cohorts, cost differences between FAIA and SHI were not statistically significant. LIMITATIONS: As the propensity score model cannot account for unobserved variables, conclusions of causality cannot be made. Moreover, exclusion of indirect costs and application of hospital contact charges accrued in the discharge year only may result in an underestimation of overall healthcare costs. CONCLUSION: Using matched cohorts, treating patients with T2D using basal-bolus regimens containing FAIAs was no more costly to the Danish healthcare system than regimens using SHI. FAIAs provide a flexible administration and optimal glucose control for a similar cost.