Bond, R.

Brennan, A.

Cooke, D.

Heller, S.

Kruger, J.

Thokala, P.

Elsevier - Publisher Connector 

Classification Codes), and Disease Count (identified using ICD9 codes). Baseline
model included age, gender and race. Weighted linear regression analyses were
used to risk-adjust PCS and MCS. Model performance was evaluated using adjus-
ted-R squares. RESULTS: According to the MEPS, there were 20.9 million diabetic
patients (unweighted sample 2155) in 2008 with mean age 59.4 (14.2) years. The
mean ( SD) PCS andMCS scores for diabetes patients were 40.51 (0.37) and 48.94
(0.34), respectively. The baseline model (age, gender and race) performance was:
(PCS: R20.0920; MCS: 0.0207). The HRQL-CI explained most of the variation in the
PCS scores (R20.3197). The disease count (R20.2704) and Elixhauser (R20.2679)
explained similar amount of variation in PCS. Unlike PCS, Elixhauser (R20.1865)
performed best in explaining variance in MCS, followed by HRQL-CI (R20.1493)
and Disease count (R20.0905). D’Hoore Index did not perform well in both PCS
(R20.1829) and MCS (R20.0516). CONCLUSIONS: Recently developed HRQL-CI
performed better than other comorbidity measures for risk adjusting PCS; how-
ever, Elixhauser comorbidity measures performed better than HRQL-CI for MCS.
PDB110
ADAPTING AND VALIDATING DIABETES SIMULATION MODELS ACROSS
SETTINGS: ACCOUNTING FOR MORTALITY DIFFERENCES USING
ADMINISTRATIVE DATA FROM AUSTRALIA
Hayes AJ1, Clarke PM1, Davis W2
1The University of Sydney, Sydney, NSW, Australia, 2The University of Western Australia,
Fremantle, Australia
OBJECTIVES: To develop age and sex-specific risk equations for predicting mortal-
ity following major complications of diabetes, using a large linked administrative
dataset fromWestern Australia (WA), (n13884 patients) and to incorporate these
into the UKPDS Outcomes Model. To compare the original and adapted models in
predictions of survival and life expectancy followingmyocardial infarction, stroke,
heart failure, amputation and renal failure, and incremental benefits associated
with changes in common risk factors. METHODS: We estimated a multivariate
logistic regression model for the probability of death in the year of a complication,
and a multivariate semi-parametric survival model (Gompertz) for years beyond
the year of the complication. Covariates in the models included the type of com-
plication, comorbidities, sex, type 1 diabetes and age. Using representative input
data and clinical risk factors for Australian patients we ran Monte Carlo simula-
tions of the original and adapted models. Parameter uncertainty was evaluated
using 1000 bootstrapped coefficients of all model risk equations. RESULTS: Simu-
lated survival using Australian mortality equations fell inside the 95% confidence
interval of observed survival, whilst survival using UK mortality equations fell
outside of the interval. The two versions of the model generated differences in life
expectancy following specific events; for example life expectancy of a 74 year old
followingmyocardial infarction was 2.74 (95% CI 2.07-3.42) years for UK versus 4.33
(3.85-4.72) years for WA. However there was little impact of using alternative mor-
tality equations on incremental QALYs gained as a result of reducing Hba1c or
systolic blood pressure, or on aggregate outcomes of life expectancy for a cohort
initially free of complications. CONCLUSIONS: Mortality following major compli-
cations varies across diabetic populations and this can impact on estimates of life
expectancy, but it appears to have less impact on incremental benefits of interven-
tions that are commonly used in pharmacoeconomic analyses.
PDB111
ACCOUNTING FOR PSYCHOLOGICAL DETERMINANTS OF TREATMENT
RESPONSE IN HEALTH ECONOMIC SIMULATION MODELS OF BEHAVIOURAL
INTERVENTIONS: A CASE STUDY IN TYPE 1 DIABETES
Kruger J1, Brennan A1, Thokala P1, Cooke D2, Bond R3, Heller S1
1University of Sheffield, Sheffield, UK, 2University College London, London, UK, 3University of
Sussex, Falmer, UK
OBJECTIVES: Health economic modelling has paid limited attention to incorporat-
ing the effects patients’ psychological characteristics can have on the effectiveness
of a treatment. The objective of this study was to test the feasibility of incorporat-
ing psychological prediction models of treatment response within an economic
model of a diabetes structured education programme: Dose Adjustment For Nor-
mal Eating (DAFNE). METHODS: Data from the National Institute for Health Re-
search DAFNE Research Programme were used to support all analyses. Three re-
gression models were used to investigate the relationships between patients’
baseline psychological characteristics and their 12-month HbA1c response to
DAFNE. The regression models were integrated with a patient-level simulation
model of type 1 diabetes to evaluate the cost-effectiveness of two new policies
(providing DAFNE only to predicted responders and offering a follow-up interven-
tion to predicted non-responders) compared with current practice. The model es-
timated costs and quality-adjusted life-years over a 50-year timehorizon fromaUK
National Health Service perspective. Deterministic sensitivity analyses were
conducted. RESULTS: Psychological predictors of treatment response were suc-
cessfully integrated with the health economic simulation model and allowed new
treatment policies to be evaluated. The results suggest that providing DAFNE only
to predicted responders is dominated by current practice (incremental costs
ranged from£297 to £616 and incremental QALYs from–0.112 to –0.209). This result
was insensitive to the psychological prediction model used and to the majority of
sensitivity analysis assumptions tested. The results suggest that providing a fol-
low-up intervention to predicted non-responders dominates current practice. This
result was sensitive to model assumptions. CONCLUSIONS: By collecting data on
psychological variables for a subgroup of patients before an intervention, we can
construct predictive models of treatment response to behavioural interventions
and incorporate these into health economic simulationmodels to investigatemore
complex treatment policies. Further research using this methodology is indicated.
INDIVIDUAL’S HEALTH – Clinical Outcomes Studies
PIH1
THE CLINICAL CONSEQUENCES OF EXPOSURE TO CLINICALLY IMPORTANT
DRUG-DRUG INTERACTIONS
Malone DC, Hines LE, Brown S
University of Arizona, Tucson, AZ, USA
OBJECTIVES: To evaluate the clinical and economic consequences of exposure to
potential drug-drug interactions (PDDIs).METHODS: This study was a case-control
study conducted usingMarketScan data from 2004 to 2008. A total of 20 PDDIs were
evaluated. Eligible persons were required to have a new object drug (medication
affected by the interaction claim. For each person the presence of a PDDI (case) was
ascertained using first and last dates of object drug usage overlapped with those
dates for the precipitant drug (medicaition causing the interation). The follow-up
period was 30 days for all PDDIs and 60 days for amiodarone. For each PDDI, cases
were matched one-to-one to controls using unconditional logistic regression with
three variables: age, gender and Charlson score. Matches occurred where the dif-
ference in propensity scores between a case and control was between 0.0001 and
0.01. Medical outcomes were PDDI-pair specific based on a priori defined health
outcomes (using ICD9 codes) occurring during the follow-up period. Logistic and
GLM regression models were constructed to evaluate the presence of negative
medical outcomes and costs, respectively. RESULTS: The total number of case/
control pairs per PDDI of interest ranged from 570 for digoxin/azole antifungals to
128,423 for warfarin/statins. For negative health events, non-significant findings
occurred for only three PDDIs. Among the 17 PDDIs with significant findings, odds
ratios for negative outcomes ranged from 1.67 (95%CI:1.39-2.02) for warfarin/fi-
brates to 12.50 (95%CI:2.96-52.77) for digoxin/macrolide antibiotics. With respect to
total health care costs, higher expenditures occurred among persons with a PDDI
than controls for all 20 PDDIs examined (p 0.001). Cost differences ranged from
$554 for warfin/fibrates to $8814 for digoxin/azole antifungals. CONCLUSIONS: Per-
sons exposed to PDDIs were more likely to experience negative health outcomes
and have higher costs as compared to propensity-score matched controls. PDDIs
are associated with significant morbidity and expense.
PIH2
RELATIONSHIP BETWEEN MUSCLE MASS AND MUSCLE STRENGTH IN THE
ELDERLY POPULATION IN THE UNITED STATES
Chen L, Nelson DR, Zhao Y, Cui Z, Johnston JA
Eli Lilly and Company, Inc., Indianapolis, IN, USA
OBJECTIVES: To characterize the relationship between muscle mass and muscle
strength in the US elderly population, and examine potential sources of population
heterogeneity.METHODS: This study included individuals aged 50 and above from
the National Health and Nutrition Examination Survey (NHANES) 1999-2004 data-
bases. Distributions of muscle mass measured via the height-adjusted appendic-
ular skeletonmusclemass (aASM, in kg/m2), andmuscle strength via the isokinetic
quadriceps strength (IQS, inNewtons)were examined, stratified by age and gender.
The relationship between muscle mass (aASM) and muscle strength (IQS) was
summarized using the partial correlation coefficient adjusting for age and gender.
The effects of individual comorbid medical conditions and body mass index (BMI)
on the relationship between muscle mass and muscle strength were assessed us-
ing a series of multivariable regression models, using survey strata and weighting,
with aASM, age, gender, and each variable of interest (e.g., diabetes) predicting
muscle strength; and with an interaction between aASM and the variable of inter-
est included to assess for effect modification. RESULTS: The study included 5139
individuals with a mean age of 66.2 years and 50.2% female. Mean (SE) aASM was
7.2 (0.03) and declined with age, from 7.6 (0.05) for 50-54 year olds to 6.4 (0.04) for
those 80 and older. Mean (SE) IQSwas 362.3 (2.8), declining from 426.6 (6.7) for 50-54
year olds to 241.6 (6.5) for those 80 and older. aASM and IQS were positively corre-
lated (partial correlation coefficient0.365, p0.0001). Interactions between ASM
and several factors (e.g., diabetes, arthritis and BMI) were statistically significant
(P0.05). CONCLUSIONS: Among individuals aged 50 and above in the United
States,musclemass andmuscle strength are positively correlated. BMI and certain
comorbid medical conditions (e.g., diabetes and arthritis) appear to modify the
effect of muscle mass on muscle strength.
PIH3
USE OF MIXED TREATMENT COMPARISON METHODS IN ESTIMATING
EFFICACY OF TREATMENTS FOR HEAVY MENSTRUAL BLEEDING
Hoaglin D1, Filonenko A2, Wasiak R3
1Independent Consultant, Sudbury, MA, USA, 2Bayer Pharma AG, Berlin, Germany, 3United
BioSource Corporation, London, UK
OBJECTIVES: For use in a cost-effectiveness model, synthesize available data to
derive estimates of efficacy for several classes of treatments for heavy menstrual
bleeding (HMB).METHODS: A systematic review identified randomized controlled
trials that reported data onmenstrual blood loss (MBL) at baseline and one ormore
follow-up times. The primary measure of efficacy was the proportion of women
who achieved MBL80mL per cycle (month), as measured by the alkaline hematin
method. Complicating the analysis, some trials reported various summary statis-
tics for MBL, and others used scores from pictorial blood-loss assessment charts
(PBAC). Estimation of the primarymeasure from those diverse data took advantage
of the approximately lognormal distributions of MBL and PBAC scores. Also, re-
ported follow-up times varied substantially. Estimates of efficacy by treatment
class and time were obtained from a Bayesian mixed treatment comparison (MTC)
model. The model also included effects for treatment class, study, and the combi-
nation of treatment class and study and an adjustment for baseline mean MBL.
RESULTS: The evidence network comprised eight treatment classes and 34 ran-
A191V A L U E I N H E A L T H 1 5 ( 2 0 1 2 ) A 1 – A 2 5 6


PDB111 Accounting for Psychological Determinants of Treatment Response in Health Economic Simulation Models of Behavioural Interventions: A Case Study in Type 1 Diabetes 

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PDB111 Accounting for Psychological Determinants of Treatment Response in Health Economic Simulation Models of Behavioural Interventions: A Case Study in Type 1 Diabetes

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