Diabetes

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Diabetes

  1. 1. ACE Prevention Briefing Paper no 3, St Cttee meeting March 2008 Interventions in pre-diabetes to prevent the onset of diabetes Researcher: Melanie Bertram Summary Introduction Diabetes was responsible for 9.6% of DALYs and 12.6% of total deaths in 2003, representing a large potential for health and expenditure savings through disease prevention. Pre-diabetic people are those with impaired fasting glucose or impaired glucose tolerance who are not yet diabetic. The aim of this study is to evaluate the cost-effectiveness of a screening program for pre-diabetes followed by treatment with pharmaceutical interventions (acarbose, metformin, orlistat, rosiglitazone) or lifestyle interventions (diet, exercise, diet plus exercise). Methods We used an epidemiological modelling approach to approximate the experience of pre-diabetic individuals in the Australian population, following their progression through diabetes, cardiovascular disease and renal failure. The model compares costs and healthy life years lived for each intervention compared to a “do nothing” scenario, which is representative of current practice. It is assumed that the effect of a lifestyle change will decay by 10% per year, whilst the effect of a pharmaceutical intervention remains constant throughout use. Results Three of the studied interventions have a mid-point cost-effectiveness ratio (CER) less than $50,000/DALY, below which an intervention is generally considered cost- effective. These include: diet plus exercise $36,000/DALY, exercise alone $47,000/DALY and metformin $47,000/DALY. The chance of the CER falling below $50,000/DALY is 97% for diet plus exercise, and 64% for both exercise and metformin. The addition of metformin as an incremental intervention to diet plus exercise was not cost-effective Conclusions Screening for pre-diabetes followed by a diet plus exercise is cost-effective and should be incorporated into current practice if workforce capacity can be increased to support the intervention. Further work involves evaluating a screening program targeted only at those with increased BMI and evaluating interventions in the Indigenous population. 1
  2. 2. ACE Prevention Briefing Paper no 3, St Cttee meeting March 2008 Interventions in pre-diabetes to prevent the onset of diabetes Researcher: Melanie Bertram 1. Background In the 1996 Burden of Disease Study, obesity accounted for 4.2% of all DALYs. In the 2003 update, it was responsible for 9.6% of DALY’s and 12.6% of total deaths (Begg, Vos et al. 2007)This increase is mostly due to the increase in the population distribution of BMI, but also partly due to the change in methodology, i.e. measuring BMI as a continuous rather than categorical variable. The majority of the burden caused by increased body weight is due to type 2 diabetes mellitus, which in 2003 was the 3rd highest contributor to the total burden, accounting for 5% of total DALYs (Begg, Vos et al. 2007). Incorporating the attributable CVD burden into the analysis would potentially put diabetes as the second leading cause of DALYs in Australia, responsible for 8.3% of the total burden. The AIHW estimated health expenditure of approximately $1.2 billion on overweight and obesity in 2003 (AIHW 2003). Health expenditure on diabetes was $835 million, or 1.7% of total, although this excludes the attributable CVD costs. This is projected to increase by 400% between 2003 and 2033 (Begg S 2007).The DiabCo$t study calculated societal costs of $2.2 billion per annum for diabetes (Colagiuri S 2003). A study undertaken by Access Economics for Diabetes Australia on the Economic Costs of Obesity estimated financial costs due to Diabetes attributable to obesity of $1.1 billion in 2005 (Access Economics Australia. 2006). Pre-diabetic people are those with impaired fasting glucose or impaired glucose tolerance who are not yet considered diabetic. There is evidence that it is possible to prevent the onset of diabetes by targeting interventions at people with pre-diabetes. A recent meta-analysis of all the available interventions shows efficacious results for a range of pharmaceutical and lifestyle interventions (Gillies, Abrams et al. 2007). Although these interventions show impressive results for the reduction of diabetes incidence, the challenge lies in identifying people at this stage. Currently, there is no systematic screening in place in Australia to identify people with pre-diabetes. The aim of this study is to evaluate the cost-effectiveness of a screening program for pre- diabetes followed by treatment with pharmaceutical interventions (Acarbose, Metformin, Orlistat or Rosiglitazone) and Lifestyle interventions (Diet, Exercise or Diet and Exercise combined). 2
  3. 3. ACE Prevention Briefing Paper no 3, St Cttee meeting March 2008 Interventions in pre-diabetes to prevent the onset of diabetes Researcher: Melanie Bertram 2. Modelling The methods for the CVD and Diabetes micro simulation model were developed in conjunction with the ACE-Prevention Economic Protocol. It is a discrete-time micro- simulation model that estimates the health impact of preventing diabetes amongst those with pre-diabetes. Micro simulation is a way of modelling disease whereby simulation is at the level of the individual; it creates individual life histories for members of a population, and then allows them to progress through various health states over their lifetime. Micro simulation is more flexible than Markov modelling, as an individual can have any number of health conditions at a given time, without explicit options for co-morbidity being defined. We used a discrete time period of 1 year for each cycle, rather than modelling continuous risks of health event. Transitions were explicitly modelled for four health states: (i) Glucose tolerance; (ii) Ischaemic heart disease; (iii) Stroke; and (iv) Renal failure in diabetes. Within the glucose tolerance state an individual may have normal glucose tolerance, be pre- diabetic or diabetic. The other health states were either present or absent. Individuals co-exist across these four main health states, but within each health state individuals can only exist in one of the sub-states at any given time. For example, an individual can have either impaired glucose tolerance or diabetes, but not both at the same time. In successive time periods the individual can move between these states. Figure 1 describes the sub-states and possible transitions for each of the four classes of health states. For each year lived in a health state an individual accrues an annual cost of disease treatment. These costs are “cost-offsets” and are used to calculate potential cost savings due to disease prevention. One thousand individual life-histories for each age and sex group were created. Random number draws determine whether an individual experiences an incident event, remits or dies. A random number between 0 and 1 was drawn for each potential transition. If the number fell within the incidence, remission or mortality range (i.e. between 0 and the transition probability), the individual would transit, otherwise he would remain in the current health state. Transitions could occur between multiple states in any given year – i.e. an IHD and a diabetic renal failure event may occur in the same year. 3
  4. 4. ACE Prevention Briefing Paper no 3, St Cttee meeting March 2008 Interventions in pre-diabetes to prevent the onset of diabetes Researcher: Melanie Bertram Glucose Tolerance Background Mortality Incidence Normal Pre- Incidence Diabetes Case Fatality Dead Glucose diabetes Tolerance Remission Case Fatality Ischemic heart disease/ Stroke/ Diabetic Renal Failure Background Mortality Incidence Case Fatality Disease Diseased Dead Free Figure 1: Diabetes and CVD microsimulation model 3. Intervention Parameters Briefly, a screening program was modelled based upon the National Health and Medical Research Council guidelines for screening of diabetes, but in this case applied to the population free of diabetes to identify pre-diabetes Figure 2 (Colagiuri, Hussain et al. 2004). People presenting at a GP with a risk factor for type 2 diabetes either 1)age > 55 or 2) age >45 plus high BMI, family history of type 2 diabetes or hypertension or 3) people from “high risk” groups are opportunistically screened for type 2 diabetes and pre-diabetes. Screening program costs are summarized in Table 1. Assumptions regarding participation of GP’s are based on the practice incentive programs for diabetes. These participation rates are achieved by offering financial incentives to GP’s for identifying diabetic patients. Whilst it is likely that without these incentives participation rates will be lower, the cost-effectiveness ratio will not be altered. The number of people seeing a GP every year is based on the report General Practice in Australia 2004. The remaining screening data applies screening guidelines for diabetes to the AusDiab population (Colagiuri, Hussain et al. 2004). 4
  5. 5. ACE Prevention Briefing Paper no 3, St Cttee meeting March 2008 Interventions in pre-diabetes to prevent the onset of diabetes Researcher: Melanie Bertram Opportunistic assessment for risk factors -58% of Step 1 GPs. Step 2 If =>1 risk factor measure FPG* – 53.3% of those presenting to GP FPG =>7.0 FPG < 5.5 Diabetes FPG 5.5-6.9 Diabetes Diabetes Likely Step 3 Unlikely 48.6% Uncertain 47.7% 3.7% Perform OGTT** Step 4 Retest in 3 years Confirm Diagnosis 1 Figure 2: Diabetes Screening Flowchart High participation rates are assumed, as the intervention is modelled in its steady state and optimal use. However, lower participation would initially be expected. Lowering participation rates, whilst reducing the total population health gain, will not reduce the cost-effectiveness ratio of the intervention. Table 1: Screening Program Costs SCREENING PROGRAM SUMMARY Government cost per patient screened $ 63 Patient cost per patient screened $ 11 Patient time cost per patient screened $ 60 Patient travel cost per patient screened $ 10 The efficacy of each of the interventions has been calculated using meta-analysis of the literature. A systematic review by Gillies et al (Gillies, Abrams et al. 2007) identified all identified all interventions for preventing the onset of diabetes and included a meta-analysis of pharmaceutical and lifestyle interventions. All studies identified in this paper were collected to enable separate meta-analyses of each of the individual pharmaceuticals. 1* FPG = Fasting Plasma Glucose Test ** OGTT = Oral Glucose Tolerance Test 5
  6. 6. ACE Prevention Briefing Paper no 3, St Cttee meeting March 2008 Interventions in pre-diabetes to prevent the onset of diabetes Researcher: Melanie Bertram The efficacy of each intervention is measured as a RR reduction in incidence of diabetes. Rosiglitazone and Acarbose also provide independent outcomes for CVD incidence which have been incorporated into the analysis. The RRs (Table 2) are calculated on an intention to treat basis. Using this value, along with the published follow up rate, we are able to calculate the RR in those who participate. This RR is applied to those who are treated in the model. An adherence rate of 50-70% (using a uniform distribution), consistent with ACE-Prevention assumptions, is applied to the pre-diabetics identified in the model. The efficacy of diet and exercise interventions is assumed to decay over time at a rate of 10% per year (Lindstrom, Ilanne-Parikka et al. 2006), whilst the pharmaceutical interventions are anticipated to have the same efficacy throughout use. Table 2: Intervention Efficacy (Source: Gillies, Abrams et al. 2007) Intervention RR Lower CI Upper CI Acarbose 0.602 0.353 1.028 Metformin 0.679 0.571 0.808 Orlistat 0.437 0.278 0.689 Rosiglitazone 0.38 0.33 0.44 Diet 0.667 0.486 0.915 Exercise 0.488 0.321 0.741 Diet + Exercise 0.486 0.402 0.587 Costs for each intervention are calculated on a yearly basis, including costs of seeing a GP (MBS), medication (PBS), monitoring costs (MBS), other health professionals (DVA) and time and travel costs as per ACE-Prevention economic protocol. The number of health practitioner visits and monitoring tests for each intervention are outlined in table 3. Summary costs are provided in table 4. Table 3: Average Yearly Resource Use Intervention GP visits Monitoring Dietician Exercise tests Physiologist Acarbose 2.4 1 - - Metformin 2.4 1 - - 6
  7. 7. ACE Prevention Briefing Paper no 3, St Cttee meeting March 2008 Interventions in pre-diabetes to prevent the onset of diabetes Researcher: Melanie Bertram Orlistat 4.3 1 - - Rosiglitazone 2.6 1 - - Diet 9 in year 1; 4 subsequent 2 1 years - Exercise 9 in year 1; 4 subsequent 4 1 - years Diet + Exercise 9 in year 1; 9 in year 1; 4 subsequent 4 subsequent 4 1 years years Table 4: Annual Intervention Costs Intervention Government Patient Government Time & plus patient Travel Acarbose $291 $248 $539 $72 Metformin $200 $58 $258 $74 Orlistat $320 $1,290 $1,610 $127 Rosiglitazone $312 $931 $1,243 $78 Diet $118 $103 $221 $442 Exercise $172 $164 $336 $548 Diet + Exercise $259 $91 $350 $688 Uncertainty was included around all parameters for which we were unsure of the true value. Aside from the intervention efficacy parameters already mentioned, Table 5 provides a summary of the uncertainty distributions included in the model. Table 5: Model Uncertainty parameters Parameter Distribution Mean SE(1) Source RR stroke in CHD Males Lognormal 1.32 1.22 Busselton (Knuiman and Females Lognormal 1.88 0.56 Vu 1996) RR CHD in stroke Males Lognormal 2.64 0.17 Busselton (Knuiman and Females Lognormal 2.85 0.09 Vu 1996) RR IHD in diabetes Males Lognormal 1.41 1.11 Framingham Females Lognormal 1.75 1.14 (D'Agostino, Russell et al. 2000) RR stroke in diabetes Males Lognormal 1.34 1.20 Framingham 7
  8. 8. ACE Prevention Briefing Paper no 3, St Cttee meeting March 2008 Interventions in pre-diabetes to prevent the onset of diabetes Researcher: Melanie Bertram Females Lognormal 1.72 1.24 (D'Agostino, Russell et al. 2000) 28-day CFR - ischaemic stroke Normal 0.12 0.02 NEMESIS (Thrift, Dewey et al. 2001; Dewey, Sturm et al. 2003) 28-day CFR – haemorrhagic stroke Normal 0.46 0.07 NEMESIS (Thrift, Dewey et al. 2001; Dewey, Sturm et al. 2003) RR – Relative risk; CFR – Case fatality rate (1) SE – standard error; for relative risks it is the standard error of the natural log of the relative risk 4. Results The cost of screening per identified patient is substantial in comparison to the annual cost of intervention (Table 6). Table 6: Cost of Screening per identified patient SCREENING PROGRAM SUMMARY Government cost per patient identified $ 176 Patient cost per patient identified $ 31 Patient time cost per patient identified $ 166 Patient travel cost per patient identified $ 29 The median cost-effectiveness ratio for each intervention is shown in Table 7. The uncertainty surrounding these values is shown in Figure 3, whereby each dot represents one iteration of the multivariate sensitivity analysis. Whilst some of the median CERs fall below the $50,000/DALY threshold, the uncertainty surrounding these results indicates there is a chance they are not cost effective. Table 7: Median cost-effectiveness ratios Intervention Cost-Effectiveness Ratio probability that CER < $50,000 Acarbose $57,000/DALY averted 40% Metformin $47,000/DALY averted 64% Orlistat $116,000/DALY averted <1% Rosiglitazone Dominated Diet $87,000/DALY averted 10% Exercise $47,000/DALY averted 64% 8
  9. 9. ACE Prevention Briefing Paper no 3, St Cttee meeting March 2008 Interventions in pre-diabetes to prevent the onset of diabetes Researcher: Melanie Bertram Diet + Exercise $36,000/DALY averted 97% The most cost-effective intervention is the Diet plus Exercise intervention. Total costs and effects for the Diet & Exercise intervention are shown in Table 8. An acceptability curve, Figure 3, showing the cost-effectiveness ratio on the x-axis and the probability that the CER falls below the given value on the y-axis indicates that there is a 97% chance that the cost-effectiveness ratio lies below $50,000/DALY. The addition of metformin to the diet plus exercise intervention was evaluated (Figure 5) however this is not cost-effective, with an incremental cost-effectiveness ratio of $89,800/DALY. Table 8: Total Costs and Benefits for Diet & Exercise intervention DALY Government Patient Costs Cost Offsets Costs ($ million) ($ million) ($ million) Median 107478 $ 3,869 $ 1,383 $ 1,332 Lower Uncertainty Interval 72026 $ 3,303 $ 1,126 $ 819 (2.5%) Upper Uncertainty Interval 140626 $ 4,612 $ 1,720 $ 1,980 (97.5%) 9
  10. 10. ACE Prevention Briefing Paper no 3, St Cttee meeting March 2008 Interventions in pre-diabetes to prevent the onset of diabetes Researcher: Melanie Bertram 30000 25000 20000 Lifestyle Total Cost ($ million) Exercise 15000 Diet Acarbose Metformin 10000 Orlistat Rosiglitazone 5000 $50,000/DALY 0 -800000 -600000 -400000 -200000 0 200000 400000 600000 800000 Total DALY averted Figure 3: Cost v DALY for pre-diabetes interventions 1 0.9 Probability intervention is cost-effective 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 10000 20000 30000 40000 50000 60000 Cost-Effectiveness Ratio Figure 4: Acceptability curve for Diet + Exercise Intervention 10
  11. 11. ACE Prevention Briefing Paper no 3, St Cttee meeting March 2008 Interventions in pre-diabetes to prevent the onset of diabetes Researcher: Melanie Bertram $7,000 Diet & Exercise $6,000 plus metformin $5,000 Total Cost ($million) $4,000 Diet&Exercise $3,000 $2,000 $1,000 $- 0 20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 Total DALY averted Figure 5: Pathway analysis 11
  12. 12. ACE Prevention Briefing Paper no 3, St Cttee meeting March 2008 Interventions in pre-diabetes to prevent the onset of diabetes Researcher: Melanie Bertram 5. Discussion The diet plus exercise intervention has a 97% chance of falling below the $50,000/DALY willingness-to-pay threshold. Both the exercise and metformin interventions have a 64% of this. It is not cost-effective to add metformin as a second intervention to those already receiving the diet plus exercise intervention. Increased BMI is responsible for a total of 7.5% of the avoidable disease burden. The model explicitly accounts for diabetes and CVD, which are responsible for 84% of the burden due to increased BMI. Given that a diet plus exercise intervention is likely to have an effect on BMI, there is potential for marginally higher health gains, however these are unlikely to substantially alter the outcomes. A more targeted screening program, using high BMI as a risk factor rather than age> 55, may be more cost- effective and is therefore the next stage in this analysis. Any modelling is necessarily a simplification of reality. Using microsimulation modelling allows individual level variation, so has the potential to more accurately reflect the variations in a population. However, it is also more data intensive, requiring additional data and this adds uncertainty. A discrete, yearly time period has been used, rather than continuous time, which is often used in microsimulation. As we have accurate yearly transitions, and are looking at chronic, not acute, disease, this is not likely to have a great effect on outcomes. We make the assumption that people must pass through the pre-diabetic state in order to be an incident case of diabetes. We believe this to be a logical progression. We have endeavoured to use the best available data, from published studies that have been well received. The representativeness of the data for the Indigenous population could be questioned; however future work involves contextualising the model to the Indigenous population. Overall we believe the model to be an accurate representation of the experience of this population in Australia, however validity testing is ongoing. 12
  13. 13. ACE Prevention Briefing Paper no 3, St Cttee meeting March 2008 Interventions in pre-diabetes to prevent the onset of diabetes Researcher: Melanie Bertram The use of dieticians and exercise physiologists by pre-diabetics is currently not covered in the MBS. In order for this intervention to be successful, a government subsidy would be needed, and demand for both these professions would be increased. Current workforce capacity is unlikely to be able to meet this increased demand, so more training places would be required, and this would depend on the teaching capacity in universities. Given that an increased efficacy is seen with the combination of diet and exercise, a subsidy could be based on attendance at both professionals, joint practices of dieticians and exercise physiologists may be necessary to ensure that patients participate appropriately. General Practitioners are already trained to recognise people at risk of diabetes and therefore no extra training would be required in order to implement the screening program. The pharmaceutical interventions, aside from Orlistat which is not cost-effective, are already listed on the PBS for alternative uses, implying stakeholder acceptability. The biggest issue with acceptability will be in engaging GP’s to participate in the screening program. Currently, the modelled participation rates are optimistic. However changing these rates will not change the cost-effectiveness ratio, only the total costs and total benefits, by reducing the number of people identified and participating. Rosiglitazone has potential adverse effects on the number of CVD events that occur in people using it. These outweigh the gain from diabetes prevention, thus Rosiglitazone is not recommended for use in this population. Acarbose is not well tolerated in a large percentage of people, thus would not be recommended as the first line pharmaceutical intervention but given metformin is more cost-effective acarbose would not be first line treatment. 6. Conclusions Lifestyle modification in people with pre-diabetes is cost-effective and should be recommended if workforce capacity can be increased in order to appropriately deliver the intervention. Identification of pre-diabetic individuals is the key to a successful intervention program, and further work regarding targeted screening programs is required. 13
  14. 14. ACE Prevention Briefing Paper no 3, St Cttee meeting March 2008 Interventions in pre-diabetes to prevent the onset of diabetes Researcher: Melanie Bertram References Access Economics Australia. (2006). The economic costs of obesity. A. Economics. Australian Institute of Health and Welfare. (2003). Health Expenditure Australia 2001-02. Canberra, AIHW. Begg, S., T. Vos, et al. (2007). The burden of disease and injury in Australia 2003. Canberra, Australian Institute of Health and Welfare. Begg S, V. T., Goss J, Mann N (2007). "An alternative approach to projecting health expenditure in Australia." Australian Health Review. Colagiuri S, C. R., Conway B, Grainger D, Davey P (2003). DiabCo$t Australia: Assessing the burden of Type 2 Diabetes in Australia. D. Australia. Canberra. Colagiuri, S., Z. Hussain, et al. (2004). "Screening for type 2 diabetes and impaired glucose metabolism: the Australian experience." Diabetes Care 27(2): 367-71. D'Agostino, R. B., M. W. Russell, et al. (2000). "Primary and subsequent coronary risk appraisal: new results from the Framingham study." Am Heart J 139(2 Pt 1): 272-81. Dewey, H. M., J. Sturm, et al. (2003). "Incidence and outcome of subtypes of ischaemic stroke: initial results from the north East melbourne stroke incidence study (NEMESIS)." Cerebrovasc Dis 15(1-2): 133-9. Gillies, C. L., K. R. Abrams, et al. (2007). "Pharmacological and lifestyle interventions to prevent or delay type 2 diabetes in people with impaired glucose tolerance: systematic review and meta-analysis." 334(7588): 299. Knuiman, M. W. and H. T. Vu (1996). "Risk factors for stroke mortality in men and women: The Busselton Study." J Cardiovasc Risk 3(5): 447-52. Lindstrom, J., P. Ilanne-Parikka, et al. (2006). "Sustained reduction in the incidence of type 2 diabetes by lifestyle intervention: follow-up of the Finnish Diabetes Prevention Study." 368(9548): 1673-9. Thrift, A. G., H. M. Dewey, et al. (2001). "Incidence of the major stroke subtypes: initial findings from the North East Melbourne stroke incidence study (NEMESIS)." Stroke 32(8): 1732-8. 14

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