Healthcare expenditure on Indigenous and non-Indigenous Australians at high risk of cardiovascular disease.

BACKGROUND: In spite of bearing a heavier burden of death, disease and disability, there is mixed evidence as to whether Indigenous Australians utilise more or less healthcare services than other Australians given their elevated risk level. This study analyses the Medicare expenditure and its predictors in a cohort of Indigenous and non-Indigenous Australians at high risk of cardiovascular disease. METHODS: The healthcare expenditure of participants of the Kanyini Guidelines Adherence with the Polypill (GAP) pragmatic randomised controlled trial was modelled using linear regression methods. 535 adult (48% Indigenous) participants at high risk of cardiovascular disease (CVD) were recruited through 33 primary healthcare services (including 12 Aboriginal Medical Services) across Australia. RESULTS: There was no significant difference in the expenditure of Indigenous and non-Indigenous participants in non-remote areas following adjustment for individual characteristics. Indigenous individuals living in remote areas had lower MBS expenditure ($932 per year P < 0.001) than other individuals. MBS expenditure was found to increase with being aged over 65 years ($128, p = 0.013), being female ($472, p = 0.003), lower baseline reported quality of life ($102 per 0.1 decrement of utility p = 0.004) and a history of diabetes ($324, p = 0.001), gout ($631, p = 0.022), chronic obstructive pulmonary disease ($469, p = 0.019) and established CVD whether receiving guideline-recommended treatment prior to the trial ($452, p = 0.005) or not ($483, p = 0.04). When controlling for all other characteristics, morbidly obese patients had lower MBS expenditure than other individuals (-$887, p = 0.002). CONCLUSION: The findings suggest that for the majority of participants, once individuals are engaged with a primary care provider, factors other than whether they are Indigenous determine the level of Medicare expenditure for each person. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN 126080005833347.

Recent trends in life expectancy for people with type 1 diabetes in Sweden.

AIMS/HYPOTHESIS: People with type 1 diabetes have reduced life expectancy (LE) compared with the general population. Our aim is to quantify mortality changes from 2002 to 2011 in people with type 1 diabetes in Sweden. METHODS: This study uses health records from the Swedish National Diabetes Register (NDR) linked with death records. Abridged period life tables for those with type 1 diabetes aged 20 years and older were derived for 2002-06 and 2007-11 using Chiang's method. Cox proportional hazard models were used to assess trends in overall and cause-specific mortality. RESULTS: There were 27,841 persons aged 20 years and older identified in the NDR as living with type 1 diabetes between 2002 and 2011, contributing 194,685 person-years of follow-up and 2,018 deaths. For men with type 1 diabetes, the remaining LE at age 20 increased significantly from 47.7 (95% CI 46.6, 48.9) in 2002-06 to 49.7 years (95% CI 48.9, 50.6) in 2007-11. For women with type 1 diabetes there was no significant change, with an LE at age 20 of 51.7 years (95% CI 50.3, 53.2) in 2002-06 and 51.9 years (95% CI 50.9, 52.9) in 2007-11. Cardiovascular mortality significantly reduced, with a per year HR of 0.947 (95% CI 0.917, 0.978) for men and 0.952 (95% CI 0.916, 0.989) for women. CONCLUSIONS/INTERPRETATION: From 2002-06 to 2007-11 the LE at age 20 of Swedes with type 1 diabetes increased by approximately 2 years for men but minimally for women. These recent gains have been driven by reduced cardiovascular mortality.

Change in bias in self-reported body mass index in Australia between 1995 and 2008 and the evaluation of correction equations.

BACKGROUND: Many studies have documented the bias in body mass index (BMI) determined from self-reported data on height and weight, but few have examined the change in bias over time. METHODS: Using data from large, nationally-representative population health surveys, we examined change in bias in height and weight reporting among Australian adults between 1995 and 2008. Our study dataset included 9,635 men and women in 1995 and 9,141 in 2007-2008. We investigated the determinants of the bias and derived correction equations using 2007-2008 data, which can be applied when only self-reported anthropometric data are available. RESULTS: In 1995, self-reported BMI (derived from height and weight) was 1.2 units (men) and 1.4 units (women) lower than measured BMI. In 2007-2008, there was still underreporting, but the amount had declined to 0.6 units (men) and 0.7 units (women) below measured BMI. The major determinants of reporting error in 2007-2008 were age, sex, measured BMI, and education of the respondent. Correction equations for height and weight derived from 2007-2008 data and applied to self-reported data were able to adjust for the bias and were accurate across all age and sex strata. CONCLUSIONS: The diminishing reporting bias in BMI in Australia means that correction equations derived from 2007-2008 data may not be transferable to earlier self-reported data. Second, predictions of future overweight and obesity in Australia based on trends in self-reported information are likely to be inaccurate, as the change in reporting bias will affect the apparent increase in self-reported obesity prevalence.

A meta-analysis of health state valuations for people with diabetes: explaining the variation across methods and implications for economic evaluation.

PURPOSE: To review published studies on the effect of diabetes and its complications on utility scores to establish whether there is systematic variation across studies and to examine the implications for the estimation of quality-adjusted life years (QALYs). METHODS: A systematic review was performed using studies reporting QALY measures elicited from people with diabetes including those with a history of complications. Meta-analysis was used to obtain the average utility, and meta-regression was employed to examine the impact of study characteristics and elicitation methods on these values. The effect of different utility scores on QALYs was examined using diabetes simulation models. RESULTS: In the meta-analysis based on 45 studies reporting 66 values, the average utility score was 0.76 (95% CI 0.75-0.77). A meta-regression showed significant variation due to age, method of elicitation and the proportion of males. The average utility score for individual complications ranged from 0.48 (95% CI 0.25, 0.71) for chronic renal disease to 0.75 (95% CI 0.73, 0.78) for myocardial infarction, and these differences produced meaningful changes in simulated QALYs. There was significant heterogeneity between studies. CONCLUSIONS: We provide summary utility scores for diabetes and its major complications that could help inform economic evaluation and policy analysis.

A Patient-Level Model to Estimate Lifetime Health Outcomes of Patients With Type 1 Diabetes.

OBJECTIVE: To develop a patient-level simulation model for predicting lifetime health outcomes of patients with type 1 diabetes and as a tool for economic evaluation of type 1 diabetes treatment based on data from a large, longitudinal cohort. RESEARCH DESIGN AND METHODS: Data for model development were obtained from the Swedish National Diabetes Register. We derived parametric proportional hazards models predicting the absolute risk of diabetes complications and death based on a wide range of clinical variables and history of complications. We used linear regression models to predict risk factor progression. Internal validation was performed, estimates of life expectancies for different age-sex strata were computed, and the impact of key risk factors on life expectancy was assessed. RESULTS: The study population consisted of 27,841 patients with type 1 diabetes with a mean duration of follow-up of 7 years. Internal validation showed good agreement between the predicted and observed cumulative incidence of death and 10 complications. Simulated life expectancy was ∼13 years lower than that of the sex- and age-matched general population, and patients with type 1 diabetes could expect to live with one or more complications for ∼40% of their remaining life. Sensitivity analysis showed the importance of preventing renal dysfunction, hypoglycemia, and hyperglycemia as well as lowering HbA1c in reducing the risk of complications and death. CONCLUSIONS: Our model was able to simulate risk factor progression and event histories that closely match the observed outcomes and to project events occurring over patients' lifetimes. The model can serve as a tool to estimate the impact of changing clinical risk factors on health outcomes to inform economic evaluations of interventions in type 1 diabetes.

Medication non-adherence in a cohort of chronically ill Australians: A case of missed opportunities.

BACKGROUND AND OBJECTIVES: This study investigated the effect of management - including home medicines reviews and chronic disease management plans funded through the Medicare Benefits Schedule - on self-reported medication non-adherence. METHOD: An observational cohort study including 244 individuals with an exacerbation of chronic illness enrolled into the Care Navigation randomised controlled trial of integrated care. Non-adherence was measured using the Morisky-Greene-Levine self-reported adherence tool. RESULTS: The cohort comprised an equal number of older men and women with, on average, three chronic diseases, receiving between five and 10 unique medications each month and visiting a general practitioner (GP) more than 50 times in the year prior to completing the questionnaire. Forty per cent reported non-adherence, which was greater in males (relative risk [RR]: 1.73; 95% confidence interval [CI]: 1.25, 2.54) and in patients reporting a recent fall (RR 1.40; 95% CI: 1.02, 1.89). GP-initiated chronic disease management programs did not influence adherence. DISCUSSION: Despite almost weekly contact with GPs, two in five patients were non-adherent. Better methods of encouraging adherence are needed.

Comparison of the HUI3 and the EQ-5D-3L in a nursing home setting.

BACKGROUND: Accurately assessing changes in the quality of life of older people living permanently in nursing homes is important. The multi-attribute utility instrument most commonly used and recommended to assess health-related quality of life in the nursing home population is the three-level EuroQol EQ-5D-3L. To date, there have been no studies using the Health Utilities Index Mark III (HUI3). The purpose of this study was to compare the level of agreement and sensitivity to change of the EQ-5D-3L and HUI3 in a nursing home population. METHODS: EQ-5D-3L and HUI3 scores were measured as part of a cluster randomised controlled trial of nurse led care coordination in a nursing home population in Perth, Western Australia at baseline and 6-month follow up. RESULTS: Both measures were completed for 199 residents at baseline and 177 at 6-month follow-up. Mean baseline utility scores for EQ-5D-3L (0.45; 95% CI 0.41-0.49) and HUI3 (0.15; 95% CI 0.10-0.20) were significantly different (Wilcoxon signed rank test, p<0.01) and agreement was poor to moderate between absolute scores from each instrument (intra-class correlation coefficient = 0.63). The EQ-5D-3L appeared more sensitive to change over the 6-month period. CONCLUSION: Our findings show that the EQ-5D-3L and HUI3 estimate different utility scores among nursing home residents. These differences should be taken into account, particularly when considering the implications of the cost-effectiveness of particular interventions and we conclude that the HUI3 is no better suited to measuring health-related quality of life in a nursing home population when compared to the EQ-5D-3L.

Optimal strategies for monitoring lipid levels in patients at risk or with cardiovascular disease: a systematic review with statistical and cost-effectiveness modelling.

BACKGROUND: Various lipid measurements in monitoring/screening programmes can be used, alone or in cardiovascular risk scores, to guide treatment for prevention of cardiovascular disease (CVD). Because some changes in lipids are due to variability rather than true change, the value of lipid-monitoring strategies needs evaluation. OBJECTIVE: To determine clinical value and cost-effectiveness of different monitoring intervals and different lipid measures for primary and secondary prevention of CVD. DATA SOURCES: We searched databases and clinical trials registers from 2007 (including the Cochrane Central Register of Controlled Trials, MEDLINE, EMBASE, the Clinical Trials Register, the Current Controlled Trials register, and the Cumulative Index to Nursing and Allied Health Literature) to update and extend previous systematic reviews. Patient-level data from the Clinical Practice Research Datalink and St Luke's Hospital, Japan, were used in statistical modelling. Utilities and health-care costs were drawn from the literature. METHODS: In two meta-analyses, we used prospective studies to examine associations of lipids with CVD and mortality, and randomised controlled trials to estimate lipid-lowering effects of atorvastatin doses. Patient-level data were used to estimate progression and variability of lipid measurements over time, and hence to model lipid-monitoring strategies. Results are expressed as rates of true-/false-positive and true-/false-negative tests for high lipid or high CVD risk. We estimated incremental costs per quality-adjusted life-year. RESULTS: A total of 115 publications reported strength of association between different lipid measures and CVD events in 138 data sets. The summary adjusted hazard ratio per standard deviation of total cholesterol (TC) to high-density lipoprotein (HDL) cholesterol ratio was 1.25 (95% confidence interval 1.15 to 1.35) for CVD in a primary prevention population but heterogeneity was high (I(2) = 98%); similar results were observed for non-HDL cholesterol, apolipoprotein B and other ratio measures. Associations were smaller for other single lipid measures. Across 10 trials, low-dose atorvastatin (10 and 20 mg) effects ranged from a TC reduction of 0.92 mmol/l to 2.07 mmol/l, and low-density lipoprotein reduction of between 0.88 mmol/l and 1.86 mmol/l. Effects of 40 mg and 80 mg were reported by one trial each. For primary prevention, over a 3-year period, we estimate annual monitoring would unnecessarily treat 9 per 1000 more men (28 vs. 19 per 1000) and 5 per 1000 more women (17 vs. 12 per 1000) than monitoring every 3 years. However, annual monitoring would also undertreat 9 per 1000 fewer men (7 vs. 16 per 1000) and 4 per 1000 fewer women (7 vs. 11 per 1000) than monitoring at 3-year intervals. For secondary prevention, over a 3-year period, annual monitoring would increase unnecessary treatment changes by 66 per 1000 men and 31 per 1000 women, and decrease undertreatment by 29 per 1000 men and 28 per 1000 men, compared with monitoring every 3 years. In cost-effectiveness, strategies with increased screening/monitoring dominate. Exploratory analyses found that any unknown harms of statins would need utility decrements as large as 0.08 (men) to 0.11 (women) per statin user to reverse this finding in primary prevention. LIMITATION: Heterogeneity in meta-analyses. CONCLUSIONS: While acknowledging known and potential unknown harms of statins, we find that more frequent monitoring strategies are cost-effective compared with others. Regular lipid monitoring in those with and without CVD is likely to be beneficial to patients and to the health service. Future research should include trials of the benefits and harms of atorvastatin 40 and 80 mg, large-scale surveillance of statin safety, and investigation of the effect of monitoring on medication adherence. STUDY REGISTRATION: This study is registered as PROSPERO CRD42013003727. FUNDING: The National Institute for Health Research Health Technology Assessment programme.

Economic evaluation of "healthy beginnings" an early childhood intervention to prevent obesity.

OBJECTIVE: To determine the costs and cost-effectiveness of an early childhood home visiting program delivered to families in socio-economically disadvantaged areas of Sydney, Australia during 2007-2010. METHODS: Economic evaluation of a randomized controlled trial, the healthy beginnings (HB) trial, from the perspective of the health funder. Intervention resources were determined from local health district records in 2012 $AUD. Health-care resource utilization was determined through patient-level data linkage. RESULTS: The cost of HB intervention in the clinical trial over 2 years was $1309 per child (2012 $AUD). The incremental cost-effectiveness ratio was $4230 per unit BMI avoided and $631 per 0.1 reduction in BMI z-score. It was estimated that the program could be delivered in practice for $709 per child; with incremental cost-effectiveness ratios of $2697 per unit BMI avoided and $376 per 0.1 reduction in BMI z-score. CONCLUSIONS: We present the first economic evaluation of an effective obesity prevention initiative in early childhood. HB is a moderately priced intervention with demonstrated effectiveness that offers similar or better value for money than existing obesity prevention or treatment interventions targeted at older children.

A meta-analysis of the relative risk of mortality for type 1 diabetes patients compared to the general population: exploring temporal changes in relative mortality.

AIMS: Type 1 diabetes has been associated with an elevated relative risk (RR) of mortality compared to the general population. To review published studies on the RR of mortality of Type 1 diabetes patients compared to the general population, we conducted a meta-analysis and examined the temporal changes in the RR of mortality over time. METHODS: Systematic review of studies reporting RR of mortality for Type 1 diabetes compared to the general population. We conducted meta-analyses using a DerSimonian and Laird random effects model to obtain the average effect and the distribution of RR estimates. Sub-group meta-analyses and multivariate meta-regression analysis was performed to examine heterogeneity. Summary RR with 95% CIs was calculated using a random-effects model. RESULTS: 26 studies with a total of 88 subpopulations were included in the meta-analysis and overall RR of mortality was 3.82 (95% CI 3.41, 3.4.29) compared to the general population. Observations using data prior to 1971 had a much larger estimated RR (5.80 (95% CI 4.20, 8.01)) when compared to: data between; 1971 and 1980 (5.06 (95% CI 3.44, 7.45)); 1981-90 (3.59 (95% CI 3.15, 4.09)); and those after 1990 (3.11 (95% CI 2.47, 3.91)); suggesting mortality of Type 1 diabetes patients when compared to the general population have been improving over time. Similarly, females (4.54 (95% CI 3.79-5.45)) had a larger RR estimate when compared to males (3.25 (95% CI 2.82-3.73) and the meta-regression found evidence for temporal trends and sex (p<0.01) accounting for heterogeneity between studies. CONCLUSIONS: Type 1 diabetes patients' mortality has declined at a faster rate than the general population. However, the largest relative improvements have occurred prior to 1990. Emphasis on intensive blood glucose control alongside blood pressure control and statin therapy may translate into further reductions in mortality in coming years.

Severe hypoglycemia and mortality after cardiovascular events for type 1 diabetic patients in Sweden.

OBJECTIVE: To examine whether previous severe hypoglycemic events were associated with the risk of all-cause mortality after major cardiovascular events (myocardial infarction [MI] or stroke) in patients with type 1 diabetes. RESEARCH DESIGN AND METHODS: This study is based on data from the Swedish National Diabetes Register linked to patient-level hospital records, prescription data, and death records. We selected patients with type 1 diabetes who visited a clinic during 2002-2010 and experienced a major cardiovascular complication after their clinic visit. We estimated a two-part model for all-cause mortality after a major cardiovascular event: logistic regression for death within the first month and a Cox proportional hazards model conditional on 1-month survival. At age 60 years, 5-year cumulative mortality risk was estimated from the models for patients with and without prior diabetes complications. RESULTS: A total of 1,839 patients experienced major cardiovascular events, of whom 403 had previously experienced severe hypoglycemic events and 703 died within our study period. A prior hypoglycemic event was associated with a significant increase in mortality after a cardiovascular event, with hazard ratios estimated at 1.79 (95% CI 1.37-2.35) within the first month and 1.25 (95% CI 1.02-1.53) after 1 month. Patients with prior hypoglycemia had an estimated 5-year cumulative mortality risk of 52.4% (95% CI 45.3-59.5) and 39.8% (95% CI 33.4-46.3) for MI and stroke, respectively. CONCLUSIONS: We have found evidence that patients with type 1 diabetes in Sweden with prior severe hypoglycemic events have increased risk of mortality after a cardiovascular event.

Optimal strategies for identifying kidney disease in diabetes: properties of screening tests, progression of renal dysfunction and impact of treatment - systematic review and modelling of progression and cost-effectiveness.

BACKGROUND: Annual screening for adults with type 2 diabetes to detect the early onset of kidney disease is widely recommended, but the recommendations are based on a limited methodological approach. In addition, there are continuing uncertainties about underlying rates of progression of the condition and the benefits of treatments with angiotensin-converting enzyme inhibitors and angiotensin receptor blockers. OBJECTIVES: We aimed to estimate the clinical value and cost-effectiveness of different screening intervals to diagnose early diabetic kidney disease. DATA SOURCES: We used the following databases for the literature review (searched January 2005 to August 2010): MEDLINE, EMBASE and the Cochrane Database of Systematic Reviews. Individual patient data were obtained from the Oxford Regional Prospective Diabetes Study and the Collaborative Atorvastatin Diabetes Study. METHODS: Data from systematically identified randomised trials reporting the impact on renal outcomes of angiotensin-converting enzyme inhibitors and angiotensin 2 receptor blockers for type 1 and type 2 diabetes patients with normoalbuminuria and microalbuminuria were pooled to derive estimates of effect. Individual patient data for type 1 and type 2 diabetes patients were used to obtain parameters describing progression and variability of measurement over time for the albumin-to-creatinine ratio (ACR) and estimated glomerular filtration rate. Based on accepted diagnostic thresholds, we modelled whether these tests accurately identified patients who were developing early diabetic kidney disease and required intensification of treatment. Cost-effectiveness analyses were carried out using simulation outcome models to estimate the incremental costs per quality-adjusted life-year (QALY) for different screening intervals. RESULTS: In total, 49 trials (n = 34,082 patients) were eligible for inclusion in the systematic review. For type 1 diabetes, pooled estimates of urinary albumin excretion (UAE) for treated patients with microalbuminuria were on average 67% [95% confidence interval (CI) 54% to 77%] lower at the end of the trial than for untreated patients. There was no significant treatment effect for patients with normoalbuminuria (p interaction = 0.006). For treated patients with type 2 diabetes and normoalbuminuria or microalbuminuria, UAE was lower by, on average, 21% (95% CI 97% to 32%) or 27% (95% CI 15% to 38%), respectively. The proportion (95% CI) of men and women with type 1 diabetes screened annually for microalbuminuria over 6 years and inaccurately identified as having microalbuminuria would be 48% (43% to 53%) and 55% (48% to 61%), respectively. The corresponding proportions for type 2 diabetes are 36% (32% to 42%) and 48% (41% to 55%). Decreasing the screening interval to 3-yearly would reduce this for men with type 1 diabetes to 38% (33% to 44%), with an increase in those not identified over 6 years from 1.5% (95% CI 1% to 2%) to 4% (95% CI 3% to 5%). For type 1 diabetes, incremental cost per QALY [standard deviation (SD)] of a 5-yearly compared with a 4-yearly screening interval was £3612 (£6586), increasing to £9601 (£34,112) for annual compared with 2-yearly screening. The probability that the intervention is cost saving is around 25%, and it has around an 80% chance of being below a cost-effectiveness threshold of £30,000. For type 2 diabetes, incremental cost per QALY (SD) of a yearly compared with a 2-yearly screening interval was £606 (£1782). The intervention is almost certainly below a cost-effectiveness threshold of £5000. CONCLUSIONS: These results support current UK guidance, which recommends annual screening with ACR to identify early kidney disease in patients with diabetes, despite a high false-positive rate leading to, at worst, unnecessary or, at best, early therapeutic intervention. For type 1 diabetes, screening costs for annual compared with 2-yearly screening are well within the bounds of accepted cost-effectiveness. Annual screening is even more cost-effective in type 2 diabetes than in type 1 diabetes. Identification of alternative markers for developing diabetic nephropathy may improve targeting of treatment for those at high risk. FUNDING: The National Institute for Health Research Health Technology Assessment programme.

Simulating lifetime outcomes associated with complications for people with type 1 diabetes.

OBJECTIVES: The aim of this study was to develop a discrete-time simulation model for people with type 1 diabetes mellitus, to estimate and compare mean life expectancy and quality-adjusted life-years (QALYs) over a lifetime between intensive and conventional blood glucose treatment groups. METHODS: We synthesized evidence on type 1 diabetes patients using several published sources. The simulation model was based on 13 equations to estimate risks of events and mortality. Cardiovascular disease (CVD) risk was obtained from results of the DCCT (diabetes control and complications trial). Mortality post-CVD event was based on a study using linked administrative data on people with diabetes from Western Australia. Information on incidence of renal disease and the progression to CVD was obtained from studies in Finland and Italy. Lower-extremity amputation (LEA) risk was based on the type 1 diabetes Swedish inpatient registry, and the risk of blindness was obtained from results of a German-based study. Where diabetes-specific data were unavailable, information from other populations was used. We examine the degree and source of parameter uncertainty and illustrate an application of the model in estimating lifetime outcomes of using intensive and conventional treatments for blood glucose control. RESULTS: From 15 years of age, male and female patients had an estimated life expectancy of 47.2 (95 % CI 35.2-59.2) and 52.7 (95 % CI 41.7-63.6) years in the intensive treatment group. The model produced estimates of the lifetime benefits of intensive treatment for blood glucose from the DCCT of 4.0 (95 % CI 1.2-6.8) QALYs for women and 4.6 (95 % CI 2.7-6.9) QALYs for men. Absolute risk per 1,000 person-years for fatal CVD events was simulated to be 1.37 and 2.51 in intensive and conventional treatment groups, respectively. CONCLUSIONS: The model incorporates diabetic complications risk data from a type 1 diabetes population and synthesizes other type 1-specific data to estimate long-term outcomes of CVD, end-stage renal disease, LEA and risk of blindness, along with life expectancy and QALYs. External validation was carried out using life expectancy and absolute risk for fatal CVD events. Because of the flexible and transparent nature of the model, it has many potential future applications.