Search

Background. As the complexity of microsimulation models increases, concerns about model transparency are heightened. Methods. The authors conducted model “experiments” to explore the impact of variations in “deep” model parameters using 3 colorectal cancer (CRC) models. All natural history models were calibrated to match observed data on adenoma prevalence and cancer incidence but varied in their underlying specification of the adenocarcinoma process. The authors projected CRC incidence among individuals with an underlying adenoma or preclinical cancer v. those without any underlying condition and examined the impact of removing adenomas. They calculated the percentage of simulated CRC cases arising from adenomas that developed within 10 or 20 years prior to cancer diagnosis and estimated dwell time—defined as the time from the development of an adenoma to symptom-detected cancer in the absence of screening among individuals with a CRC diagnosis. Results. The 20-year CRC incidence among 55-year-old individuals with an adenoma or preclinical cancer was 7 to 75 times greater than in the condition-free group. The removal of all adenomas among the subgroup with an underlying adenoma or cancer resulted in a reduction of 30% to 89% in cumulative incidence. Among CRCs diagnosed at age 65 years, the proportion arising from adenomas formed within 10 years ranged between 4% and 67%. The mean dwell time varied from 10.6 to 25.8 years. Conclusions. Models that all match observed data on adenoma prevalence and cancer incidence can produce quite different dwell times and very different answers with respect to the effectiveness of interventions. When conducting applied analyses to inform policy, using multiple models provides a sensitivity analysis on key (unobserved) “deep” model parameters and can provide guidance about specific areas in need of additional research and validation.

Background. Microsimulation models are important decision support tools for screening. However, their complexity makes them difficult to understand and limits realization of their full potential. Therefore, it is important to develop documentation that clarifies their structure and assumptions. The authors demonstrate this problem and explore a solution for natural history using 3 independently developed colorectal cancer screening models. Methods. The authors first project effectiveness and cost-effectiveness of colonoscopy screening for the 3 models (CRC-SPIN, SimCRC, and MISCAN). Next, they provide a conventional presentation of each model, including information on structure and parameter values. Finally, they report the simulated reduction in clinical cancer incidence following a one-time complete removal of adenomas and preclinical cancers for each model. They call this new measure the maximum clinical incidence reduction (MCLIR). Results. Projected effectiveness varies widely across models. For example, estimated mortality reduction for colonoscopy screening every 10 years from age 50 to 80 years, with surveillance in adenoma patients, ranges from 65% to 92%. Given only conventional information, it is difficult to explain these differences, largely because differences in structure make parameter values incomparable. In contrast, the MCLIR clearly shows the impact of model differences on the key feature of natural history, the dwell time of preclinical disease. Dwell times vary from 8 to 25 years across models and help explain differences in projected screening effectiveness. Conclusions. The authors propose a new measure, the MCLIR, which summarizes the implications for predicted screening effectiveness of differences in natural history assumptions. Including the MCLIR in the standard description of a screening model would improve the transparency of these models.

Background: The Colorectal Cancer Simulated Population model for Incidence and Natural history (CRC-SPIN) is a new microsimulation model for the natural history of colorectal cancer that can be used for comparative effectiveness studies of colorectal cancer screening modalities. Methods: CRC-SPIN simulates individual event histories associated with colorectal cancer, based on the adenoma-carcinoma sequence: adenoma initiation and growth, development of preclinical invasive colorectal cancer, development of clinically detectable colorectal cancer, death from colorectal cancer, and death from other causes. We present the CRC-SPIN structure and parameters, data used for model calibration, and model validation. We also provide basic model outputs to further describe CRC-SPIN, including annual transition probabilities between various disease states and dwell times. We conclude with a simple application that predicts the impact of a one-time colonoscopy at age 50 on the incidence of colorectal cancer assuming three different operating characteristics for colonoscopy. Results: CRC-SPIN provides good prediction of both the calibration and the validation data. Using CRC-SPIN, we predict that a one-time colonoscopy greatly reduces colorectal cancer incidence over the subsequent 35 years. Conclusions: CRC-SPIN is a valuable new tool for combining expert opinion with observational and experimental results to predict the comparative effectiveness of alternative colorectal cancer screening modalities. Impact: Microsimulation models such as CRC-SPIN can serve as a bridge between screening and treatment studies and health policy decisions by predicting the comparative effectiveness of different interventions. As such, it is critical to publish model descriptions that provide insight into underlying assumptions along with validation studies showing model performance. Cancer Epidemiol Biomarkers Prev; 19(8); 1992–2002. ©2010 AACR.

Background The Centers for Medicare and Medicaid Services (CMS) considered whether to reimburse computed tomographic colonography (CTC) for colorectal cancer screening of Medicare enrollees. To help inform its decision, we evaluated the reimbursement rate at which CTC screening could be cost-effective compared with the colorectal cancer screening tests that are currently reimbursed by CMS and are included in most colorectal cancer screening guidelines, namely annual fecal occult blood test (FOBT), flexible sigmoidoscopy every 5 years, flexible sigmoidoscopy every 5 years in conjunction with annual FOBT, and colonoscopy every 10 years. Methods We used three independently developed microsimulation models to assess the health outcomes and costs associated with CTC screening and with currently reimbursed colorectal cancer screening tests among the averagerisk Medicare population. We assumed that CTC was performed every 5 years (using test characteristics from either a Department of Defense CTC study or the National CTC Trial) and that individuals with findings of 6 mm or larger were referred to colonoscopy. We computed incremental cost-effectiveness ratios for the currently reimbursed screening tests and calculated the maximum cost per scan (ie, the threshold cost) for the CTC strategy to lie on the efficient frontier. Sensitivity analyses were performed on key parameters and assumptions. Results Assuming perfect adherence with all tests, the undiscounted number life-years gained from CTC screening ranged from 143 to 178 per 1000 65-year-olds, which was slightly less than the number of life-years gained from 10-yearly colonoscopy (152–185 per 1000 65-year-olds) and comparable to that from 5-yearly sigmoidoscopy with annual FOBT (149–177 per 1000 65-year-olds). If CTC screening was reimbursed at $488 per scan (slightly less than the reimbursement for a colonoscopy without polypectomy), it would be the most costly strategy. CTC screening could be cost-effective at $108–$205 per scan, depending on the microsimulation model used. Sensitivity analyses showed that if relative adherence to CTC screening was 25% higher than adherence to other tests, it could be cost-effective if reimbursed at $488 per scan. Conclusions CTC could be a cost-effective option for colorectal cancer screening among Medicare enrollees if the reimbursement rate per scan is substantially less than that for colonoscopy or if a large proportion of otherwise unscreened persons were to undergo screening by CTC.

Microsimulation models that describe disease processes synthesize information from multiple sources and can be used to estimate the effects of screening and treatment on cancer incidence and mortality at a population level. These models are characterized by simulation of individual event histories for an idealized population of interest. Microsimulation models are complex and invariably include parameters that are not well informed by existing data. Therefore, a key component of model development is the choice of parameter values. Microsimulation model parameter values are selected to reproduce expected or known results though the process of model calibration. Calibration may be done by perturbing model parameters one at a time or by using a search algorithm. As an alternative, we propose a Bayesian method to calibrate microsimulation models that uses Markov chain Monte Carlo. We show that this approach converges to the target distribution and use a simulation study to demonstrate its finite-sample performance. Although computationally intensive, this approach has several advantages over previously proposed methods, including the use of statistical criteria to select parameter values, simultaneous calibration of multiple parameters to multiple data sources, incorporation of information via prior distributions, description of parameter identifiability, and the ability to obtain interval estimates of model parameters. We develop a microsimulation model for colorectal cancer and use our proposed method to calibrate model parameters. The microsimulation model provides a good fit to the calibration data. We find evidence that some parameters are identified primarily through prior distributions. Our results underscore the need to incorporate multiple sources of variability (i.e., due to calibration data, unknown parameters, and estimated parameters and predicted values) when calibrating and applying microsimulation models.

This study compared the time patterns of suicide attempts among outpatients starting depression treatment with medication or psychotherapy.Outpatient claims from a prepaid health plan were used to identify new episodes of depression treatment beginning with an antidepressant prescription in primary care (N=70,368), an antidepressant prescription from a psychiatrist (N=7,297), or an initial psychotherapy visit (N=54,123). Outpatient and inpatient claims were used to identify suicide attempts or possible suicide attempts during the 90 days before and 180 days after the start of treatment.Overall incidence of suicide attempt was highest among patients receiving antidepressant prescriptions from psychiatrists (1,124 per 100,000), lower among those starting psychotherapy (778 per 100,000), and lowest among those receiving antidepressant prescriptions in primary care (301 per 100,000). The pattern of attempts over time was the same in all three groups: highest in the month before starting treatment, next highest in the month after starting treatment, and declining thereafter. Results were unchanged after eliminating patients receiving overlapping treatment with medication and psychotherapy. Overall incidence of suicide attempt was higher in adolescents and young adults, but the time pattern was the same across all three treatments.The pattern of suicide attempts before and after starting antidepressant treatment is not specific to medication. Differences between treatments and changes over time probably reflect referral patterns and the expected improvement in suicidal ideation after the start of treatment.

Objectives: To evaluate demographic and clinical predictors of suicide attempt and suicide death in a population-based sample of people treated for bipolar disorder (BD). Methods: Computerized records were used to identify 32,360 individuals treated for BD at two large prepaid health plans. Suicide attempts were identified using computerized records of outpatient visit diagnoses and hospital discharge diagnoses. Suicide deaths were identified using state death certificate data. Results: Overall event rates were 1.06 per 1,000 person-years for suicide death, 5.6 per 1,000 person-years for suicide attempt leading to hospitalization, and 13.9 per 1,000 person-years for suicide attempt not leading to hospitalization. Men had a significantly lower rate of suicide attempt [hazard ratio (HR) 0.68, 95% confidence interval (CI) 0.56-0.83] but a higher rate of suicide death (HR 2.70, 95% CI 1.69-4.31). Suicide attempts were significantly more frequent among younger patients, but suicide deaths did not vary significantly by age. Substance use comorbidity was significantly related to risk of suicide attempt (HR 2.53, 95% CI 2.07-3.09) but not to risk of suicide death (HR 1.02, 95% CI 0.54-1.93). Comorbid anxiety disorder was associated with significantly higher risk of both suicide attempt (HR 1.40, 95% CI 1.14-1.72) and suicide death (HR 1.81, 95% CI 1.09-2.99). Conclusions: Among people treated for BD, risk of suicide death is significantly related to male sex and comorbid anxiety disorder. The predictors of suicide death differ markedly from predictors of suicide attempt. Language: en

Administrative data were used to examine early dropout among 16,451 health plan members calling to request psychotherapy for depression. Compared to members referred to group-model therapists, those referred to network-model therapists were more likely to drop out before the initial visit (OR 2.33, 95% CI 2.17 – 2.50) but less likely to drop out after the first visit (OR 0.45, 95% CI 0.43 – 0.48). These differences were unaffected by adjustment for neighborhood income and educational attainment, antidepressant use, or generosity of insurance coverage. Efforts to increase the effectiveness of psychotherapy may required different strategies in group- and network-model practice.

A pilot study evaluated whether the addition of online coaching from a peer specialist increased participation in an online program featuring educational and interactive modules to promote self-management of bipolar disorder.A total of 118 participants with bipolar disorder recruited from online and in-person support groups and clinical settings were enrolled in MyRecoveryPlan. Half the group was randomly assigned to receive online coaching support from a certified peer specialist.Participants offered online peer coaching were more likely to return to the program after registration (71% versus 44%, p=.005) and to continue using the program after three weeks (38% versus 9%, p.001). This pattern was consistent for all program elements.The addition of online coaching from a peer specialist significantly increased engagement and retention in the program, but further research is needed to evaluate the program's effects on clinical and recovery outcomes.

Quality of antidepressant treatment remains disturbingly poor. Rates of medication adherence and follow-up contact are especially low in primary care, where most depression treatment begins. Telephone care management programs can address these gaps, but reliance on live contact makes such programs less available, less timely, and more expensive.Evaluate the feasibility, acceptability, and effectiveness of a depression care management program delivered by online messaging through an electronic medical record.Randomized controlled trial comparing usual primary care treatment to primary care supported by online care managementNine primary care clinics of an integrated health system in Washington stateTwo hundred and eight patients starting antidepressant treatment for depression.Three online care management contacts with a trained psychiatric nurse. Each contact included a structured assessment (severity of depression, medication adherence, side effects), algorithm-based feedback to the patient and treating physician, and as-needed facilitation of follow-up care. All communication occurred through secure, asynchronous messages within an electronic medical record.An online survey approximately five months after randomization assessed the primary outcome (depression severity according to the Symptom Checklist scale) and satisfaction with care, a secondary outcome. Additional secondary outcomes (antidepressant adherence and use of health services) were assessed using computerized medical records.Patients offered the program had higher rates of antidepressant adherence (81% continued treatment more than 3 months vs. 61%, p = 0.001), lower Symptom Checklist depression scores after 5 months (0.95 vs. 1.17, p = 0.043), and greater satisfaction with depression treatment (53% "very satisfied" vs. 33%, p = 0.004).The trial was conducted in one integrated health care system with a single care management nurse. Results apply only to patients using online messaging.Our findings suggest that organized follow-up care for depression can be delivered effectively and efficiently through online messaging.

In March 2004 the U.S. Food and Drug Administration (FDA) warned physicians and patients regarding increased risk of suicide with 10 newer antidepressant drugs. Available data leave considerable uncertainty regarding actual risk of suicide attempt and death by suicide during antidepressant treatment. The authors used population-based data to evaluate the risk of suicide death and serious suicide attempt in relation to initiation of antidepressant treatment.Computerized health plan records were used to identify 65,103 patients with 82,285 episodes of antidepressant treatment between Jan. 1, 1992, and June 30, 2003. Death by suicide was identified by using state and national death certificate data. Serious suicide attempt (suicide attempt leading to hospitalization) was identified by using hospital discharge data.In the 6 months after the index prescription of antidepressant treatment, 31 suicide deaths (40 per 100,000 treatment episodes) and 76 serious suicide attempts (93 per 100,000) were identified in the study group. The risk of suicide attempt was 314 per 100,000 in children and adolescents, compared to 78 per 100,000 in adults. The risk of death by suicide was not significantly higher in the month after starting medication than in subsequent months. The risk of suicide attempt was highest in the month before starting antidepressant treatment and declined progressively after starting medication. When the 10 newer antidepressants included in the FDA advisory were compared to older drugs, an increase in risk after starting treatment was seen only for the older drugs.The risk of suicide during acute-phase antidepressant treatment is approximately one in 3,000 treatment episodes, and risk of serious suicide attempt is approximately one in 1,000. Available data do not indicate a significant increase in risk of suicide or serious suicide attempt after starting treatment with newer antidepressant drugs.

OBJECTIVE: To compare the predictive accuracy of two validated indices, one that uses self-reported variables and a second that uses variables derived from administrative data sources, to predict future hospitalization. To compare the predictive accuracy of these same two indices for predicting future functional decline. DESIGN: A longitudinal cohort study with 4 years of follow-up. SETTING: A large staff model HMO in western Washington State. PARTICIPANTS: HMO Enrollees 65 years and older (n = 2174) selected at random to participate in a health promotion trial and who completed a baseline questionnaire. MEASUREMENT: Predicted probabilities from the two indices were determined for study participants for each of two outcomes: hospitalization two or more times in 4 years and functional decline in 4 years, measured by Restricted Activity Days. The two indices included similar demographic characteristics, diagnoses, and utilization predictors. The probabilities from each index were entered into a Receiver Operating Characteristic (ROC) curve program to obtain the Area Under the Curve (AUC) for comparison of predictive accuracy. RESULTS: For hospitalization, the AUC of the self-report and administrative indices were .696 and .694, respectively (difference between curves, P = .828). For functional decline, the AUC of the two indices were .714 and .691, respectively (difference between curves, P = .144). CONCLUSIONS: Compared with a self-report index, the administrative index affords wider population coverage, freedom from nonresponse bias, lower cost, and similar predictive accuracy. A screening strategy utilizing administrative data sources may thus prove more valuable for identifying high risk older health plan enrollees for population-based interventions designed to improve their health status.

Background Microsimulation models are an important tool for estimating the comparative effectiveness of interventions through prediction of individual-level disease outcomes for a hypothetical population. To estimate the effectiveness of interventions targeted toward high risk groups, the mechanism by which risk factors influence the natural history of disease must be specified. We propose a method for evaluating these risk factor assumptions as part of model-building. Methods We used simulation studies to examine the impact of risk factor assumptions on the relative rate (RR) of colorectal cancer (CRC) incidence and mortality for a cohort with a risk factor compared to a cohort without the risk factor using an extension of the CRC-SPIN model for colorectal cancer. We also compared the impact of changing age at initiation of screening colonoscopy for different risk mechanisms. Results Across CRC-specific risk factor mechanisms, the RR of CRC incidence and mortality decreased (towards one) with increasing age. The rate of change in RRs across age groups depended on both the risk factor mechanism and the strength of the risk factor effect. Increased non-CRC mortality attenuated the effect of CRC-specific risk factors on the RR of CRC when both were present. For each risk factor mechanism, earlier initiation of screening resulted in more life years gained, though the magnitude of life years gained varied across risk mechanisms. Conclusions Simulation studies can provide insight into both the effect of risk factor assumptions on model predictions and the type of data needed to calibrate risk factor models.