DIP: Natural History Model for Major Depression with Incidence and Prevalence

BackgroundMajor depression is a treatable disease, and untreated depression can lead to serious health complications. Therefore, prevention, early identification, and treatment efforts are essential. Natural history models can be utilized to make informed decisions about interventions and treatments of major depression.MethodsWe propose a natural history model of major depression. We use steady-state analysis to study the discrete-time Markov chain model. For this purpose, we solved differential equations and tested the parameter and transition probabilities empirically.ResultsWe showed that bias in parameters might collectively cause a significant mismatch in a model. If incidence is correct, then lifetime prevalence is 33.2% for females and 20.5% for males, which is higher than reported values. If prevalence is correct, then incidence is .0008 for females and .00065 for males, which is lower than reported values. The model can achieve feasibility if incidence is at low levels and recall bias of the lifetime prevalence is quantified to be 31.9% for females and 16.3% for males.LimitationsModel is limited to major depression, and patients who have other types of depression are assumed healthy. We assume that transition probabilities (except incidence rates) are correct.ConclusionWe constructed a preliminary model for the natural history of major depression. We determine the lifetime prevalence are underestimated. We conclude that the average incidence rates may be underestimated for males. Our findings mathematically prove the arguments around the potential discordance between reported incidence and lifetime prevalence rates.

Pathways to a cancer-free future: a protocol for modelled evaluations to minimise the future burden of colorectal cancer in Australia

IntroductionWith almost 50% of cases preventable and the Australian National Bowel Cancer Screening Program in place, colorectal cancer (CRC) is a prime candidate for investment to reduce the cancer burden. The challenge is determining effective ways to reduce morbidity and mortality and their implementation through policy and practice. Pathways-Bowel is a multistage programme that aims to identify best-value investment in CRC control by integrating expert and end-user engagement; relevant evidence; modelled interventions to guide future investment; and policy-driven implementation of interventions using evidence-based methods.Methods and analysisPathways-Bowel is an iterative work programme incorporating a calibrated and validated CRC natural history model for Australia (Policy1-Bowel) and assessing the health and cost outcomes and resource use of targeted interventions. Experts help identify and prioritise modelled evaluations of changing trends and interventions and critically assess results to advise on their real-world applicability. Where appropriate the results are used to support public policy change and make the case for optimal investment in specific CRC control interventions. Fourteen high-priority evaluations have been modelled or planned, including evaluations of CRC outcomes from the changing prevalence of modifiable exposures, including smoking and body fatness; potential benefits of daily aspirin intake as chemoprevention; increasing CRC incidence in people aged <50 years; increasing screening participation in the general and Aboriginal and Torres Strait Islander populations; alternative screening technologies and modalities; and changes to follow-up surveillance protocols. Pathways-Bowel is a unique, comprehensive approach to evaluating CRC control; no prior body of work has assessed the relative benefits of a variety of interventions across CRC development and progression to produce a list of best-value investments.Ethics and disseminationEthics approval was not required as human participants were not involved. Findings are reported in a series of papers in peer-reviewed journals and presented at fora to engage the community and policymakers.

A natural history model for planning prostate cancer testing: calibration and validation using Swedish registry data

Recent prostate cancer screening trials have given conflicting results and it is unclear how to reduce prostate cancer mortality while minimising overdiagnosis and overtreatment. Prostate cancer testing is a partially observable process, and planning for testing requires either extrapolation from randomised controlled trials or, more flexibly, modelling of the cancer natural history.An existing US prostate cancer natural history model (Gulati et al, Biostatistics 2010;11:707-719) did not model for differences in survival between Gleason 6 and 7 cancers and predicted too few Gleason 7 cancers for contemporary Sweden. We re-implemented and re-calibrated the US model to Sweden. We extended the model to more finely describe the disease states, their time to biopsy-detectable cancer and prostate cancer survival. We first calibrated the model to the incidence rate ratio observed in the European Randomised Study of Screening for Prostate Cancer (ERSPC) together with age-specific cancer staging observed in the Stockholm PSA (prostate-specific antigen) and Biopsy Register; we then calibrated age-specific survival by disease states under contemporary testing and treatment using the Swedish National Prostate Cancer Register.After calibration, we were able to closely match observed prostate cancer incidence trends in Sweden. Assuming that patients detected at an earlier stage by screening receive a commensurate survival improvement, we find that the calibrated model replicates the observed mortality reduction in a simulation of ERSPC.Using the resulting model, we predicted incidence and mortality following the introduction of regular testing. Compared with a model of the current testing pattern, organised 8 yearly testing for men aged 55–69 years was predicted to reduce prostate cancer incidence by 0.11% with no increase in the mortality rate. The model is open source and suitable for planning for effective prostate cancer screening into the future.Author summaryA naïve perspective is that cancer screening is simple: people are screened, some cancers are detected early, and cancer mortality rates decline. However, the mathematics for screening becomes difficult quickly, it is hard to infer causation from observational data, and even large randomised screening studies provide limited evidence. Simulations are therefore important for planning cancer screening.We found an older US prostate cancer natural history model to be poorly suited for contemporary Sweden. We therefore re-implemented and re-calibrated the US model using data from Swedish registries.Our revised model, the Stockholm “Prostata” model, provides predictions similar to those observed in the detailed Swedish registers on prostate cancer incidence and mortality. By modelling the mechanisms of the screening effect, we can predict the benefits and harms under a range of screening interventions.

Predicting the effectiveness of the Finnish population-based colorectal cancer screening programme

Objective Because colorectal cancer (CRC) has a long natural history, estimating the effectiveness of CRC screening programmes requires long-term follow-up. As an alternative, we here demonstrate the use of a temporal multi-state natural history model to predict the effectiveness of CRC screening. Methods In the Finnish population-based biennial CRC screening programme using faecal occult blood tests (FOBT), which was conducted in a randomised health services study, we estimated the pre-clinical incidence, the mean sojourn time (MST), and the sensitivity of FOBT using a Markov model to analyse data from 2004 to 2007. These estimates were applied to predict, through simulation, the effects of five rounds of screening on the relative rate of reducing advanced CRC with 6 years of follow-up, and on the reduction in mortality with 10 years of follow-up, in a cohort of 500,000 subjects aged 60 to 69. Results For localised and non-localised CRC, respectively, the MST was 2.06 and 1.36 years and the sensitivity estimates were 65.12% and 73.70%. The predicted relative risk of non-localised CRC and death from CRC in the screened compared with the control population was 0.86 (95% CI: 0.79–0.98) and 0.91 (95% CI: 0.85–1.02), respectively. Conclusion Based on the preliminary results of the Finnish CRC screening programme, our model predicted a 9% reduction in CRC mortality and a 14% reduction in advanced CRC.