The selected model inputs and assumptions are described in Table 1. Clinical data required to inform the model structure and parameters were based on a combination of data from a year outcome study and findings from published academic literature [ 13 — 15 ]. Non-CVD-specific mortality was calculated using 5-year age-specific and sex-specific Chinese life tables [ 16 ]. Diabetes-risk, CVD risk, diabetes-specific mortality, and CVD-specific mortality were derived from the year follow-up study and converted into annual probabilities p using the following equation: where the P t is the cumulative risk over a period of years and t is the total number of years.
We validated the micro-simulation model, as has been done in previous studies [ 26 ]. Our study model was validated by comparing life expectancy generated by the mortality module and estimations from the Statistical Bureau of China for the same period S1 Table.
We carried out internal validation of the simulation model by testing its performance in replicating the incidence of CVD and mortality over 30 years of follow-up Table 2. We accounted for direct medical costs associated with avoided DM2, including the costs of clinic visits, medications, and inpatient hospital admissions, as well as cost savings due to avoided DM complications. The health economic parameters were collected from published academic literature. Annual treatment costs of DM2 and CVD were estimated based on data from a cross-sectional study of adults in China [ 20 ].
The costs of the implementation and regular screening visit were also calculated as part of total intervention costs, and were assigned only to the intervention subjects. The initial implementation costs came from the published literature from the DQDP. The screening costs for NPG participants include the cost of initial 2 h post-glucose screening test for each 3-month visit.
And costs of health management for IGT, DM2 and CDM patients include the screening costs for each 3-month visit, including the cost of the initial 2 h post-glucose screening test, confirmatory diagnosis from an oral glucose tolerance test in subjects who had a positive 2 h post-glucose test, and annual physical examination [ 12 ].
Additionally, we calculated the expenditures of community health workers implementing the health management for DM2 and CDM patients, given the consideration that providing these services would increase their workload. The unit labor cost was measured as actual expenditures based on the total costs and amounts of services [ 27 ].
The model used QALYs to quantify health outcomes. QALYs of individual subjects were calculated according to the time spent in different health states and the health utilities assigned to these states based on Chinese preferences Table 1.
For individuals with NPG, utilities value were determined as those of the general population at corresponding ages, and the time duration of utility was considered for the population according to the number of years with diabetes [ 21 ]. The utility value was adjusted as The mean decrement in this study was Given variation in the impact of diabetes on individuals of different ages, age-specific health utilities were calculated with a decrement rate of 0.
For individuals with NPG aged 50 years, the calculation of utilities considered only the impact of age. For example, the health utility of a subject with DM2 and CVD for 10 years at the age of 60 was calculated as 0. Cost-effectiveness analysis was performed using base-case and sensitivity analyses. In the base-case analyses, we estimated the costs and incremental costs per QALY gained over year and lifetime horizons after the intervention.
Furthermore, ICERs were calculated in terms of costs per QALY gained for intervention subjects compared to usual care in order to capture health improvement and cost differences. The population in each health state in the model was assigned a QALY weight and annual healthcare costs.
Half-cycle correction for both costs and health effects was applied to the model [ 29 ]. One-way and probabilistic sensitivity analyses were performed for the parameter uncertainties.
All key variations in the study may influence the performance of the intervention, costs, utilities, and discount rates were analyzed. Probabilistic sensitivity analyses were conducted using Monte Carlo simulations of iterations.
The initial intervention costs were fixed values, as data were calculated from pertinent literature. The cumulative incidence of DM2 and all-cause mortality over a year period were The mean delays to onset in the intervention group compared to the control group were 4. Thus, the model fit was good and simulation outcomes were reliable.
Health outcomes in the intervention and control groups are described in Table 2. Over a year period, the delayed onset of DM2 led to improvements of 1. Over a lifetime horizon, the intervention was associated with an increase in average overall survival of 3.
The lifetime incremental cost-effectiveness scatters plot was shown in Fig 2. QALY, quality-adjusted life-year. The results of deterministic sensitivity analyses over lifetime are presented in Fig 3 and Table 3. Key factors impacting ICER calculations in one-way sensitivity analyses included the hazard ratio of a CVD event, discount rate, annual treatment costs of diabetes, the utility of IGT, and the incremental screening costs Table 3.
The intervention strategy was cost-saving when these key parameters varied within the range tested. The discount rate had the largest impact on total health effectiveness, followed by utility of IGT, and hazard ratio of CVD event. The discount rate also made the largest contribution to the total costs per patient, followed by treatment costs of diabetes and CVD, and hazard ratio of DM2 onset.
When the hazard ratio of DM2 onset of the intervention group was 0. For same intervention carried out in a population with normal postprandial glucose, the incremental QALY and ICER of the intervention strategy are estimated at 1. To our knowledge, this is the first economic evaluation of a large community-based clinical trial of a lifestyle intervention strategy for diabetes prevention in China.
In addition to the main year follow-up data, all other data included into the study model were obtained from Chinese data sources. Our analyses are thus preferable to the other models based on health utilities and transition probabilities from more developed countries. By including information on costs related to health management, our study will enable policymakers to make better-informed decisions for future diabetes prevention.
Earlier detection and treatment of diabetes is associated with delay and prevention of complications [ 33 , 34 ]. Our study found a lifetime improvement of 1. In comparison to the year time horizon, there was no notable extra improvement in life expectancy.
This may be due to a rapid increase in mortality among people aged 75 years and older, as participants were aged 45 years on average at the time of the intervention, and the overall life expectancy in China in was Furthermore, as reported in follow-up studies from the DQDP, the widening difference in cardiovascular mortality between intervention and control participants became statistically significant only 23 years following the intervention [ 15 ].
Thus, our findings may be explained in part by the use of year follow-up data to simulate lifetime outcomes. Findings from the current study regarding improvements in quality-adjusted life-years are consistent with those of a systematic review, finding that lifestyle interventions were associated with a median increase of 0.
Our results show the DQDP to be better than similar diabetes prevention programs in Sweden increase of 0. S increased 0. These differing findings may stem from the simulation of effects of the intervention over only 10 years in these two studies, as well as from higher life expectancy and higher utility weights in the Sweden and U.
As reported in previous study, health utility differences between countries remain substantial [ 37 ]. Health utilities used in previous cost-effectiveness studies on lifestyle interventions for diabetes prevention were based on preferences from industrialized countries, whereas the utility values in our study were derived from Chinese data.
Compared to developed countries, China tends to assign higher utilities to the same health state and more QALYs for the same life expectancy.
Such differences in utility ratings can have substantial impacts on estimation of QALYs [ 25 ]. Enter information unique to your population into this calculator to see potential cost savings for your population. Two of the primary factors leading to differences in conclusions generated by these two tools are the estimates of 1 progression from prediabetes to type 2 diabetes; and 2 the reduction in risk provided by lifestyle intervention.
Both tools allow for some customization. Skip directly to site content Skip directly to page options Skip directly to A-Z link. National Diabetes Prevention Program. Section Navigation. Facebook Twitter LinkedIn Syndicate.
Learn more about the modernization effort. Hide glossary Glossary Study record managers: refer to the Data Element Definitions if submitting registration or results information. Search for terms. Save this study. Warning You have reached the maximum number of saved studies Human Coach-Based Diabetes Prevention Programs The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.
Federal Government. Know the risks and potential benefits of clinical studies and talk to your health care provider before participating. Read our disclaimer for details. Last Update Posted : October 18, See Contacts and Locations. Study Description. The purpose of this research study is to compare the effectiveness of a fully automated digital diabetes prevention program to standard of care human coach-based diabetes prevention programs for promoting clinically meaningful lifestyle changes to reduce the risk of type 2 diabetes in adults with prediabetes.
Detailed Description:. Arms and Interventions. The Sweetch app is a hyper-personalized mobile digital coach that provides users with tailored recommendations to promote healthy lifestyle behaviors minutes per week of physical activity, weight reduction, and healthy eating habits to reduce the risk of type 2 diabetes. Participants will attend a total of 16 weekly sessions during months 1 to 6 and 6 sessions during months 7 to Outcome Measures. At least 0.
Secondary Outcome Measures : Cost-effectiveness as assessed by the Markov model [ Time Frame: 12 months ] The investigators will compare the cost-effectiveness of the two interventions based on lifetime horizon by constructing a Markov model with model parameters populated from the trial results as well as other published literature.
The model will estimate the incremental cost-effectiveness ratio between the two interventions. Change in HbA1C percentage from baseline to 6 months and 12 months. Percentage weight percent change from baseline to 6 and 12 months. Absolute weight change kilograms from baseline to 6 and 12 months. Change in physical activity measure metabolic equivalent task MET -hours per week of physical activity assessed using blinded Actigraphy monthly serial consecutive 7-days wear period from baseline to 6 months and 12 months.
Change in physical activity measures average number of steps per day assessed using blinded Actigraphy monthly serial consecutive 7-days wear period from baseline to 6 months and 12 months.
To compare engagement with digital vs. Percentage engagement will be defined out of a total of 8 sessions in month Percentage engagement will be defined out of a total of 3 sessions in month To evaluate the correlation between self-reported PA data collected using different methods: Data collected and reported by hDPPs Self-reported PA data collected by study team obtained at 1-month intervals Objectively measured PA data Actigraphy obtained at 1-month intervals.
Eligibility Criteria. Inclusion Criteria: Provision of signed and dated informed consent form. Stated willingness to comply with all study procedures and availability for the duration of the study. Hemoglobin A1C 5.
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