Development of a life expectancy table for individuals with type 1 diabetes.

AIMS/HYPOTHESIS: Tables reporting life expectancies by common risk factors are available for individuals with type 2 diabetes; however, there is currently no published equivalent for individuals with type 1 diabetes. We aimed to develop a life expectancy table using a recently published simulation model for individuals with type 1 diabetes. METHODS: The simulation model was developed using data from a real-world population of patients with type 1 diabetes selected from the Swedish National Diabetes Register. The following six important risk factors were included in the life table: sex; age; current smoking status; BMI; eGFR; and HbA1c. For each of 1024 cells in the life expectancy table, a synthetic cohort containing 1000 individuals was created, with other risk factors assigned values representative of the real-world population. The simulations were executed for all synthetic cohorts and life expectancy for each cell was calculated as mean survival time of the individuals in the respective cohort. RESULTS: There was a substantial variation in life expectancy across patients with different risk factor levels. Life expectancy of 20-year-old men varied from 29.3 years to 50.6 years, constituting a gap of 21.3 years between those with worst and best risk factor levels. In 20-year-old women, this gap was 18.9 years (life expectancy range 35.0-53.9 years). The variation in life expectancy was a function of the combination of risk factor values, with HbA1c and eGFR consistently showing a negative and positive correlation, respectively, with life expectancy at any level combination of other risk factors. Individuals with the lowest level (20 kg/m2) and highest level of BMI (35 kg/m2) had a lower life expectancy compared with those with a BMI of 25 kg/m2. Non-smokers and women had a higher life expectancy than smokers and men, respectively, with the difference in life expectancy ranging from 0.4 years to 2.7 years between non-smokers and smokers, and from 1.9 years to 5.9 years between women and men, depending on levels of other risk factors. CONCLUSIONS/INTERPRETATION: The life expectancy table generated in this study shows a substantial variation in life expectancy across individuals with different modifiable risk factors. The table allows for rapid communications of risk in an easily understood format between healthcare professionals, health economists, researchers, policy makers and patients. Particularly, it supports clinicians in their discussion with patients about the benefits of improving risk factors.

Impact of Comorbid Conditions on Healthcare Expenditure and Work-related Outcomes in Patients With Rheumatoid Arthritis.

OBJECTIVE: To evaluate the effect of comorbid conditions on direct healthcare expenditure and work-related outcomes in patients with rheumatoid arthritis (RA). METHODS: This is a retrospective analysis of the Medical Expenditure Panel Survey from 2006 to 2015 in 4967 adults with RA in the United States. Generalized linear models were used for healthcare expenditure and income, logistic models for employment status, and zero-inflated negative binomial models for absenteeism. Thirteen comorbid conditions were included as potential predictors of direct cost- and work-related outcomes. The models were adjusted for sociodemographic factors including sex, age, region, marital status, race/ethnicity, income, education, and smoking status. RESULTS: Patients with RA with heart failure (HF) had the highest incremental annual healthcare expenditure (US$8205, 95% CI $3683-$12,726) compared to those without the condition. Many comorbid conditions including hypertension (HTN), diabetes, depression, chronic obstructive pulmonary disease, cancer, stroke, and HF reduced the chance of patients with RA aged between 18-64 years being employed. Absenteeism of employed patients with RA was significantly affected by HTN, depression, disorders of the eye and adnexa, or stroke. On average, RA patients with HF earned US$15,833 (95% CI $4435-$27,231) per year less than RA patients without HF. CONCLUSION: Comorbid conditions in patients with RA were associated with higher annual healthcare expenditure, lower likelihood of employment, higher rates of absenteeism, and lower income. Despite its low prevalence, HF was associated with the highest incremental healthcare expenditure and the lowest likelihood of being employed compared to other common comorbid conditions.

Association of technologically assisted integrated care with clinical outcomes in type 2 diabetes in Hong Kong using the prospective JADE Program: A retrospective cohort analysis.

BACKGROUND: Diabetes outcomes are influenced by host factors, settings, and care processes. We examined the association of data-driven integrated care assisted by information and communications technology (ICT) with clinical outcomes in type 2 diabetes in public and private healthcare settings. METHODS AND FINDINGS: The web-based Joint Asia Diabetes Evaluation (JADE) platform provides a protocol to guide data collection for issuing a personalized JADE report including risk categories (1-4, low-high), 5-year probabilities of cardiovascular-renal events, and trends and targets of 4 risk factors with tailored decision support. The JADE program is a prospective cohort study implemented in a naturalistic environment where patients underwent nurse-led structured evaluation (blood/urine/eye/feet) in public and private outpatient clinics and diabetes centers in Hong Kong. We retrospectively analyzed the data of 16,624 Han Chinese patients with type 2 diabetes who were enrolled in 2007-2015. In the public setting, the non-JADE group (n = 3,587) underwent structured evaluation for risk factors and complications only, while the JADE (n = 9,601) group received a JADE report with group empowerment by nurses. In a community-based, nurse-led, university-affiliated diabetes center (UDC), the JADE-Personalized (JADE-P) group (n = 3,436) received a JADE report, personalized empowerment, and annual telephone reminder for reevaluation and engagement. The primary composite outcome was time to the first occurrence of cardiovascular-renal diseases, all-site cancer, and/or death, based on hospitalization data censored on 30 June 2017. During 94,311 person-years of follow-up in 2007-2017, 7,779 primary events occurred. Compared with the JADE group (136.22 cases per 1,000 patient-years [95% CI 132.35-140.18]), the non-JADE group had higher (145.32 [95% CI 138.68-152.20]; P = 0.020) while the JADE-P group had lower event rates (70.94 [95% CI 67.12-74.91]; P < 0.001). The adjusted hazard ratios (aHRs) for the primary composite outcome were 1.22 (95% CI 1.15-1.30) and 0.70 (95% CI 0.66-0.75), respectively, independent of risk profiles, education levels, drug usage, self-care, and comorbidities at baseline. We reported consistent results in propensity-score-matched analyses and after accounting for loss to follow-up. Potential limitations include its nonrandomized design that precludes causal inference, residual confounding, and participation bias. CONCLUSIONS: ICT-assisted integrated care was associated with a reduction in clinical events, including death in type 2 diabetes in public and private healthcare settings.

Born equal? The distribution of government Medicare spending for children.

Providing equitable care is an objective of many national healthcare systems. Using the birth cohort of the nationally representative Longitudinal Study of Australian Children linked with the Medicare Benefits Scheme billing data who were recruited in 2004 at ages 0-1 years and assessed biennially for six waves, we assessed the distribution of out-of-hospital government Medicare spending by household income. 4853 children followed over 11 years were included in the study. Distributions of major spending components including general practitioner and specialist care were assessed using concentration indices. Trends in the inequalities as children grow were investigated. The results showed that after controlling for health care needs, total government Medicare spending over 0-11 years of age favoured the rich (concentration index 0.041). The Medicare spending for general practitioner care was equal (concentration index 0.005) while for specialist care and diagnostics and imaging were 'pro-rich' (concentration index 0.108 and 0.088 respectively). Children from poorer families were most disadvantaged when aged 0-1 years in specialist spending, and the disparity lessened as children approached adolescence. Our findings suggest that income-related inequalities exist in government Medicare spending particularly in the first few years of life. As early years of life are a critical window in childhood development and building block for future health, the results warrant further investigation and attention from policy makers.

Cost-effectiveness of gastric band surgery for overweight but not obese adults with type 2 diabetes in the U.S.

AIM: To determine the cost-effectiveness of gastric band surgery in overweight but not obese people who receive standard diabetes care. METHOD: A microsimulation model (United Kingdom Prospective Diabetes Study outcomes model) was used to project diabetes outcomes and costs from a two-year Australian randomized trial of gastric band (GB) surgery in overweight but not obese people (BMI 25 to 30kg/m2) on to a comparable population of U.S. adults from the National Health and Nutrition Examination Survey (N=254). Estimates of cost-effectiveness were calculated based on the incremental cost-effectiveness ratios (ICERs) for different treatment scenarios. Costs were inflated to 2015 U.S. dollar values and an ICER of less than $50,000 per QALY gained was considered cost-effective. RESULTS: The incremental cost-effectiveness ratio for GB surgery at two years exceeded $90,000 per quality-adjusted life year gained but decreased to $52,000, $29,000 and $22,000 when the health benefits of surgery were assumed to endure for 5, 10 and 15 years respectively. The cost-effectiveness of GB surgery was sensitive to utility gained from weight loss and, to a lesser degree, the costs of GB surgery. However, the cost-effectiveness of GB surgery was affected minimally by improvements in HbA1c, systolic blood pressure and cholesterol. CONCLUSIONS: GB surgery for overweight but not obese people with T2D appears to be cost-effective in the U.S. setting if weight loss endures for more than five years. Health utility gained from weight loss is a critical input to cost-effectiveness estimates and therefore should be routinely measured in populations undergoing bariatric surgery.

Predicting the Long-Term Gains in Health-Related Quality of Life After Total Knee Arthroplasty.

BACKGROUND: We investigated the predictors of long-term gains in quality-adjusted life years (QALYs) from total knee arthroplasty (TKA) and the patient attributes that predicted cost-effective TKA. METHODS: Data on TKA patients (n = 570) from 2006 to 2007 were extracted from a single-institution registry. QALY gains over 7 years post surgery were calculated from health-related quality of life (HrQoL) scores measured preoperatively and annually postoperatively using the short-form health survey (SF-12) instrument. Multivariate linear regression analysis investigated the predictors of QALY gain from TKA from a broad range of preoperative patient characteristics and was used to predict QALY gains for each individual. Patients were grouped into deciles according to their predicted QALY gain, and the cost-effectiveness of each decile was plotted on the cost-effectiveness plane. Patient attribute differences between deciles were decomposed. RESULTS: After exclusions and dropout, data were available for 488 patients. The average estimated QALY gain over 7 years was 0.77 (95% confidence interval [CI] 0.70-0.83). Predictors significantly associated with smaller QALY gains were comorbidities (Charlson comorbidity index 3+ coefficient -0.54 CI -0.15 to -0.92), the absence of severe osteoarthritis in the ipsilateral knee (-0.51 CI -0.16 to -0.85), preoperative HrQoL (standardized coefficient -0.34 CI -0.26 to -0.43), the requirement for an interpreter (-0.24 CI -0.05 to -0.44), and age (-0.01 CI -0.01 to -0.02). The largest difference between cost-effective and non-cost-effective deciles was relatively high preoperative HrQoL in the non-cost-effective decile. CONCLUSION: TKA is likely to be cost-effective for most patients except those with unusually high preoperative HrQoL or a lack of severe osteoarthritis. The poorer outcomes for those requiring an interpreter requires further research.

Using Patient-Reported Outcomes for Economic Evaluation: Getting the Timing Right.

BACKGROUND: Patient-reported outcome measures (PROMs) are becoming increasingly popular in orthopedic surgery. Preoperative and postoperative follow-up often elicit PROMs in the form of generic quality-of-life instruments (e.g., Short Form health survey SF-12 [SF-12]) that can be used in economic evaluation to estimate quality-adjusted life-years (QALYs). However, the timing of postoperative measurement is still under debate. OBJECTIVES: To explore the timing of postoperative PROMs collection and the implications for bias in QALY estimation for economic evaluation. METHODS: We compared the accuracy of QALY estimation on the basis of utilities derived from the SF-12 at one of 6 weeks, 3 months, 6 months, and 12 months after total knee arthroplasty, under different methods of interpolation between points. Five years of follow-up data were extracted from the St. Vincent's Melbourne Arthroplasty Outcomes (SMART) registry (n = 484). The SMART registry collects follow-up PROMs annually and obtained more frequent outcomes on subset of patients (n = 133). RESULTS: Postoperative PROM collection at 6 weeks, 6 months, or 12 months biased the estimation of QALY gain from total knee arthroplasty by -41% (95% confidence interval [CI] -59% to -22%), 18% (95% CI 4%-32%), and -8% (95% CI -18% to -2%), respectively. This bias was minimized by collecting PROMs at 3 months postoperatively (6% error; 95% CI -9% to 21%). CONCLUSIONS: The timing of PROM collection and the interpolation assumptions between measurements can bias economic evaluation. In the case of total knee arthroplasty, we recommend a postoperative measurement at 3 months with linear interpolation between preoperative and postoperative measures. The design of economic evaluations should consider timing and interpolation issues.

Temporal validation of the UKPDS outcomes model using 10-year posttrial monitoring data.

OBJECTIVE: To evaluate the accuracy of the UK Prospective Diabetes Study Outcomes Model (UKPDS-OM) in predicting clinical outcomes during the UKPDS posttrial monitoring (PTM) period. RESEARCH DESIGN AND METHODS: At trial end in 1997, the 4,031 surviving UKPDS patients, of the 5,102 originally enrolled in the study, returned to their usual care providers, with no attempts made to maintain them in their randomized therapy groups. PTM risk factor data were collected for 5 years and clinical outcome data for 10 years. The UKPDS-OM was used firstly to forecast likely progression of HbA1c, systolic blood pressure, total-to-HDL cholesterol ratio, and smoking status and secondly to estimate the likely first occurrence of seven major diabetes-related complications or death from any cause. Model predictions were compared against observed PTM data for risk factor time paths and survival probabilities for major diabetes complications. RESULTS: UKPDS-OM-forecasted risk factor time paths were similar to those observed for HbA1c (up to 3 years) and total-to-HDL cholesterol ratio but underestimated for systolic blood pressure and smoking status. Predicted 10-year event probabilities were similar to those observed for blindness, ischemic heart disease, myocardial infarction, and renal failure but were higher for heart failure and death from any cause and lower for stroke and amputation. CONCLUSIONS: The UKPDS-OM has good predictive accuracy for two of four risk factor time paths and for 10-year clinical outcome probabilities with the exception of stroke, amputation, heart failure, and death from any cause. An updated version of the model incorporating PTM data is being developed.

Measuring achievement: changes in risk factors for cardiovascular disease in Australia.

A possible measure for evaluating health system performance is the achievement index, which can be calculated using prevalence and distribution of a health measure across different socioeconomic groups. This study extends this approach by examining how achievement can be represented on a two-dimensional plane with the x-axis being the difference in mean ill-health and the y-axis being the difference in an absolute measure of inequality based on the generalised concentration index. The achievement plane is an easily understandable visual aid which provides a method of tracking changes in health and inequality over time, as well as uncertainty around these measures. We also demonstrate how comparisons over time and at different levels of inequality aversion can be undertaken using measures of net achievement. To illustrate the use of the achievement plane, we compared changes in prevalence of various cardiovascular risk factors and absolute inequality in the distribution of these factors, using data from four successive Australian National Health Surveys conducted between 1989 and 2005. While self-reported rates of smoking and high cholesterol have been declining, inequalities have been rising as the greatest reductions in these risk factors have been among higher income groups. Conversely for risk factors where the prevalence has been increasing, health inequalities are either not changing (i.e. diabetes and obesity), or diminishing over time (overweight/obese). All of these changes can be summarized using an achievement plane and graphs of net achievement to examine changes in prevalence and distribution of these risk factors over time.

Can self-rated health scores be used for risk prediction in patients with type 2 diabetes?

OBJECTIVE: To investigate whether self-rated health profiles compiled using the EuroQol group's visual analog scale (EQ VAS) are independent predictors of vascular events and major complications in people with type 2 diabetes after controlling for standard clinical risk factors. RESEARCH DESIGN AND METHODS: The study is based on 7,348 individuals with a mean follow-up of 2.4 years after completing the EQ-5D questionnaire. We used Cox proportional hazards modeling to estimate hazard ratios associated with EQ VAS scores after controlling for baseline covariates: age, sex, smoking status, diabetes duration, A1C, systolic blood pressure, BMI, plasma lipids, and prior clinical history. RESULTS: A 10-point higher EQ VAS score was associated with a 6% (95% CI 1-11) lower risk of vascular events and a 22% (95% CI 15-28) lower risk of diabetes complications. CONCLUSIONS: Self-rated health profiles compiled using the EQ VAS provide valuable information on patient risk in addition to that determined from clinical risk factors alone.

Assessing the impact of visual acuity on quality of life in individuals with type 2 diabetes using the short form-36.

OBJECTIVE: We sought to ascertain quality-of-life measures and utility values associated with visual acuity in type 2 diabetes. RESEARCH DESIGN AND METHODS: The Medical Outcome Study Short Form with 36 items (SF-36) was administered to 4,051 individuals with type 2 diabetes who were enrolled in the Lipids in Diabetes Study, and their best attainable vision was determined using an Early Treatment of Diabetic Retinopathy Study chart, expressed as a LogMAR score. Eight domain scores and a utility value representing an overall quality-of-life score were calculated using predefined algorithms. The associations between quality of life measured and best-eye visual acuity were assessed graphically and by regression analysis. RESULTS: All eight SF-36 domain scores were negatively associated with reduced visual acuity. The impact of lower levels of visual acuity ranged from a decline of 1.3 units for a 0.1-LogMAR increase for physical functioning and 0.6 units in mental health. Regression analysis indicated a negative association (P < 0.001) between utility and reduced visual acuity after controlling for sex, BMI, smoking status, and history of diabetes complications. Patients whose LogMAR scores equated to legally blind had, on average, 0.054 (95% CI 0.034-0.074) lower utility compared with patients with normal visual acuity. CONCLUSIONS: Reduced visual acuity is negatively associated with quality of life. The utility scores estimated here should inform studies quantifying the burden of diabetes and those evaluating potential therapies for treating or preventing diabetic eye diseases.

Self-reported health: reliability and consequences for health inequality measurement.

Self-reported health (SRH) is one of the most frequently employed measures for assessing income-related health inequalities between counties. A previous study has shown that 28% of respondents changed their assessment of their health status when asked a SRH question on two occasions in the same survey (first as part of self-completed questionnaire and then in a personal interview). This study re-examines this issue using another survey where SRH was again asked twice of respondents, but this time the personal interview was first and self-completion second. We find the same variation in responses, but the predominant direction is away from the 'extreme' categories 'Excellent' and 'Poor' which is the opposite direction to the previous study. We therefore conclude that the most likely explanation is a mode of administration effect that makes people less likely to choose the extreme categories in a self-completion questionnaire, but not a personal interview. However, this effect has a relatively minor impact on measures of inequality. This is due to a large proportion of the movement (i.e. movement to the middle) not being related to income and hence does not systematically impact on the cumulative distribution of health across this measure of socio-economic status.

Estimating utility values for health states of overweight and obese individuals using the SF-36.

OBJECTIVE: To use health-related quality-of-life (HRQoL) data from the Australian 1995 National Health Survey to estimate the impact of obesity (as measured by body mass index or BMI) on utility and quality-adjusted life expectancy (QALE). METHOD: SF-36 responses from 12,661 individuals in the general population were transformed into utility values using the SF-6D algorithm developed by Brazier and colleagues. Separate regression analyses for males and females were used to examine the impact of BMI and five obesity-related medical conditions (diabetes, coronary heart disease, depression, musculoskeletal disorders, and cancer) on utility. The utility estimates were used to provide indicative estimates of the decrease in QALE associated with being overweight or obese. RESULTS: There was a statistically significant negative relationship between BMI and utility for males and females. For males (females), the marginal effect of a one-unit increase in BMI was associated with a -0.0024 (-0.0034) decrement in utility. Based on these estimates, a non-smoking male (female) aged 40 years who is obese can expect 7.2 (8.7) years less of QALE over their remaining lifetime. CONCLUSIONS: Results suggest that BMI is negatively associated with utility. Evaluation of policies designed to prevent or treat obesity should capture HRQoL as an outcome.

Testing the convergent validity of the contingent valuation and travel cost methods in valuing the benefits of health care.

In this study, the convergent validity of the contingent valuation method (CVM) and travel cost method (TCM) is tested by comparing estimates of the willingness to pay (WTP) for improving access to mammographic screening in rural areas of Australia. It is based on a telephone survey of 458 women in 19 towns, in which they were asked about their recent screening behaviour and their WTP to have a mobile screening unit visit their nearest town. After eliminating missing data and other non-usable responses the contingent valuation experiment and travel cost model were based on information from 372 and 319 women, respectively. Estimates of the maximum WTP for the use of mobile screening units were derived using both methods and compared. The highest mean WTP estimated using the TCM was $83.10 (95% C.I. $99.06-$68.53), which is significantly less than the estimate of $148.09 ($131.13-$166.60) using the CVM. This could be due to the CVM estimates also reflecting non-use values such as altruism, or a range of potential biases that are known to affect both methods. Further tests of validity are required in order to gain a greater understanding of the relationship between these two methods of estimating WTP.