A Model of Individual BMI Trajectories

A risk factor model of body mass index (BMI) is an important building block of health simulations aimed at estimating government policy effects with regard to overweight and obesity. We created a model that generates representative population level distributions and that also mimics realistic BMI trajectories at an individual level so that policies aimed at individuals can be simulated. The model is constructed by combining several datasets. First, the population level distribution is extracted from a large, cross-sectional dataset. The trend in this distribution is estimated from historical data. In addition, longitudinal data are used to model how individuals move along typical trajectories over time. The model faithfully describes the population level distribution of BMI, stratified by sex, level of education and age. It is able to generate life course trajectories for individuals which seem plausible, but it does not capture extreme fluctuations, such as rapid weight loss.

Assessment of harms, benefits, and cost-effectiveness of prostate cancer screening: A micro-simulation study of 230 scenarios

Background

Prostate cancer screening incurs a high risk of overdiagnosis and overtreatment. An organized and age‐targeted screening strategy may reduce the associated harms while retaining or enhancing the benefits.

Methods

Using a micro‐simulation analysis (MISCAN) model, we assessed the harms, benefits, and cost‐effectiveness of 230 prostate‐specific antigen (PSA) screening strategies in a Dutch population. Screening strategies were varied by screening start age (50, 51, 52, 53, 54, and 55), stop age (51‐69), and intervals (1, 2, 3, 4, 8, and single test). Costs and effects of each screening strategy were compared with a no‐screening scenario.

Results

The most optimum strategy would be screening with 3‐year intervals at ages 55–64 resulting in an incremental cost‐effectiveness ratio (ICER) of €19 733 per QALY. This strategy predicted a 27% prostate cancer mortality reduction and 28 life years gained (LYG) per 1000 men; 36% of screen‐detected men were overdiagnosed. Sensitivity analyses did not substantially alter the optimal screening strategy.

Conclusions

PSA screening beyond age 64 is not cost‐effective and associated with a higher risk of overdiagnosis. Similarly, starting screening before age 55 is not a favored strategy based on our cost‐effectiveness analysis.

Modelling the impact of maternal HIV on uninfected children: correcting current estimates

A mathematical model, populated primarily with data from South Africa, was developed to model the numbers of children affected by maternal HIV, and the number who will experience long-term negative developmental consequences. A micro-simulation model generated two scenarios. The first simulated a cohort of women whose HIV status mimicked that of a target population, and mother-child dyads by way of age- and disease-specific fertility rates. Factors defining risk were used to characterize the simulated environment. The second scenario simulated mother-child dyads without maternal HIV. In the first scenario an estimated 26% of children are orphaned, compared to 10% in the absence of HIV. And a further 19% of children whose mother is alive when they turn 18 are affected by maternal HIV. School drop-out among all children increased by 4 percentage points because of maternal HIV, similarly population level estimates of abuse and negative mental health outcomes are elevated. Relative to HIV unaffected children, HIV affected have elevated risk of poor outcomes, however not all will suffer long-term negative consequences. Interventions to protect children should target the proportion of children at risk, while interventions to mitigate harm should target the smaller proportion of children who experience long-term negative outcomes..

A New Model for Evaluation of Interventions to Prevent Obesity in Early Childhood

Background: Childhood obesity is a serious public health issue. In Australia, 1 in 4 children is already affected by overweight or obesity at the time of school entry. Governments around the world have recognized this problem through investment in the prevention of pediatric obesity, yet few interventions in early childhood have been subjected to economic evaluation. Information on cost-effectiveness is vital to decisions about program implementation. A challenge in evaluating preventive interventions in early childhood is to capture long-term costs and outcomes beyond the duration of an intervention, as the benefits of early obesity prevention will not be realized until some years into the future. However, decisions need to be made in the present, and modeling is one way to inform such decisions. Objective: To describe the conceptual structure of a new health economic model (the Early Prevention of Obesity in CHildhood (EPOCH) model) for evaluating childhood obesity interventions; and to validate the epidemiologic predictions. Methods and Results: We use an individual-level (micro-simulation) method to model BMI trajectories and the progression of obesity from early childhood to adolescence. The equations predicting individual BMI change underpinning our model were derived from data from the population-representative study, the Longitudinal Study of Australian Children (LSAC). Our approach is novel because it will account for costs and benefits accrued throughout childhood and adolescence. As a first step to validate the epidemiological predictions of the model, we used input data representing over 250,000 children aged 4/5 years, and simulated BMI and obesity trajectories until adolescence. Simulated mean BMI and obesity prevalence for boys and girls were verified by nationally-representative data on children at 14/15 years of age. Discussion: The EPOCH model is epidemiologically sound in its prediction of both BMI trajectories and prevalence of obesity for boys and girls. Future developments of the model will include socio-economic position and will incorporate the impacts of obesity on healthcare costs. The EPOCH model will help answer: when is it best to intervene in childhood; what are the most cost-effective approaches and which population groups will benefit most from interventions.

A simulation study of the role of cohort forces in mortality patterns

This study uses the micro-simulation method to investigate the role of cohort forces in age-dependent mortality pattern. We test the micro mechanisms for cohort evolution and mortality selection, and how these two biological and demographic forces may interact with epidemiologic transition to shape the cohort age-dependence of mortality pattern in both early- and later-transition countries. We show that cohort evolution is due to the declining rate of mortality acceleration at the individual level, which is associated with lower initial mortality rates but not smaller variance of frailty distribution in later birth cohorts. The steeper slope of mortality acceleration at the population level among later birth cohorts is due to mortality selection mechanism associated with smaller variance of frailty distribution but not lower initial mortality rates. These two forces jointly shape the non-crossover cohort age-dependence of mortality pattern regardless of the differential mechanisms of epidemiologic transition in early- and later-transition countries.

Chapter 5: Actual and counterfactual smoking prevalence rates in the U.S. population via microsimulation

The smoking history generator (SHG) developed by the National Cancer Institute simulates individual life/smoking histories that serve as inputs for the Cancer Intervention and Surveillance Modeling Network (CISNET) lung cancer models. In this chapter, we review the SHG inputs, describe its outputs, and outline the methodology behind it. As an example, we use the SHG to simulate individual life histories for individuals born between 1890 and 1984 for each of the CISNET smoking scenarios and use those simulated histories to compute the corresponding smoking prevalence over the period 1975-2000.