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Modeling and calibration for exposure to time-varying, modifiable risk factors: the example of smoking behavior in India.

ABSTRACT: Risk factors increase the incidence and severity of chronic disease. To examine future trends and develop policies addressing chronic diseases, it is important to capture the relationship between exposure and disease development, which is challenging given limited data.To develop parsimonious risk factor models embeddable in chronic disease models, which are useful when longitudinal data are unavailable.The model structures encode relevant features of risk factors (e.g., time-varying, modifiable) and can be embedded in chronic disease models. Calibration captures time-varying exposures for the risk factor models using available cross-sectional data. We illustrate feasibility with the policy-relevant example of smoking in India.The model is calibrated to the prevalence of male smoking in 12 Indian regions estimated from the 2009-2010 Indian Global Adult Tobacco Survey. Nelder-Mead searches (250,000 starting locations) identify distributions of starting, quitting, and restarting rates that minimize the difference between modeled and observed age-specific prevalence. We compare modeled life expectancies to estimates in the absence of time-varying risk exposures and consider gains from hypothetical smoking cessation programs delivered for 1 to 30 years.Calibration achieves concordance between modeled and observed outcomes. Probabilities of starting to smoke rise and fall with age, while quitting and restarting probabilities fall with age. Accounting for time-varying smoking exposures is important, as not doing so produces smaller estimates of life expectancy losses. Estimated impacts of smoking cessation programs delivered for different periods depend on the fact that people who have been induced to abstain from smoking longer are less likely to restart.The approach described is feasible for important risk factors for numerous chronic diseases. Incorporating exposure-change rates can improve modeled estimates of chronic disease outcomes and of the long-term effects of interventions targeting risk factors.

SUBMITTER: Goldhaber-Fiebert JD

PROVIDER: S-EPMC4115057 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature

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Modeling and calibration for exposure to time-varying, modifiable risk factors: the example of smoking behavior in India.

Goldhaber-Fiebert Jeremy D JD Brandeau Margaret L ML

Medical decision making : an international journal of the Society for Medical Decision Making 20140129 2

<h4>Background</h4>Risk factors increase the incidence and severity of chronic disease. To examine future trends and develop policies addressing chronic diseases, it is important to capture the relationship between exposure and disease development, which is challenging given limited data.<h4>Objective</h4>To develop parsimonious risk factor models embeddable in chronic disease models, which are useful when longitudinal data are unavailable.<h4>Design</h4>The model structures encode relevant feat ...[more]

PMID: 24477078

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Project description:PurposeIn studies of effects of time-varying drug exposures, adequate adjustment for time-varying covariates is often necessary to properly control for confounding. However, the granularity of the available covariate data may not be sufficiently fine, for example when covariates are measured for participants only when their exposure levels change.MethodsTo illustrate the impact of choices regarding the frequency of measuring time-varying covariates, we simulated data for a large target trial and for large observational studies, varying in covariate measurement design. Covariates were measured never, on a fixed-interval basis, or each time the exposure level switched. For the analysis, it was assumed that covariates remain constant in periods of no measurement. Cumulative survival probabilities for continuous exposure and non-exposure were estimated using inverse probability weighting to adjust for time-varying confounding, with special emphasis on the difference between 5-year event risks.ResultsWith monthly covariate measurements, estimates based on observational data coincided with trial-based estimates, with 5-year risk differences being zero. Without measurement of baseline or post-baseline covariates, this risk difference was estimated to be 49% based on the available observational data. With measurements on a fixed-interval basis only, 5-year risk differences deviated from the null, to 29% for 6-monthly measurements, and with magnitude increasing up to 35% as the interval length increased. Risk difference estimates diverged from the null to as low as -18% when covariates were measured depending on exposure level switching.ConclusionOur simulations highlight the need for careful consideration of time-varying covariates in designing studies on time-varying exposures. We caution against implementing designs with long intervals between measurements. The maximum length required will depend on the rates at which treatments and covariates change, with higher rates requiring shorter measurement intervals.

Project description:Polymer flooding is one of the most incipient chemical-based enhanced oil recovery process that utilizes the injection of polymer solutions into oil reservoirs. The presence of a polymer in water increases the viscosity of the injected fluid, which upon injection reduces the water-to-oil mobility ratio and the permeability of the porous media, thereby improving oil recovery. The objective of this work is to investigate strategies that would help increase oil recovery. For that purpose, we have studied the effect of injection pressure and increasing polymer concentration on flooding performance. This work emphasizes on the development of a detailed mathematical model describing fluid saturations, pressure, and polymer concentration during the injection experiments and predicts oil recovery. The mathematical model developed for simulations is a black oil model consisting of a two-phase flow (aqueous and oleic) of polymeric solutions in one-dimensional porous media as a function of time and z-coordinate. The mathematical model consisting of heterogeneous, nonlinear, and simultaneous partial differential equations efficiently describes the physical process and consists of various parameters and variables that are involved in our lab-scale process to quantify and analyze them. A dimensionless numerical solution is achieved using the finite difference method. We implement the second-order high-accuracy central and backward finite-divided-difference formula along the z-direction that results in the discretization of the partial differential equations into ordinary differential equations with time as an independent variable. The input parameters such as porosity, permeability, saturation, and pore volume obtained from experimental data by polymer flooding are used in the simulation of the developed mathematical model. The model-predicted and commercial reservoir (CMG)-simulated oil production is in good agreement with experimental oil recoveries with a root-mean-square error (RMSE) in the range of 1.5-2.5 at a maximum constant pressure of 3.44 MPa as well as with temporal variation of the injection pressure between 2.41 and 3.44 MPa.

Dataset Information

Modeling and calibration for exposure to time-varying, modifiable risk factors: the example of smoking behavior in India.

Publications

Modeling and calibration for exposure to time-varying, modifiable risk factors: the example of smoking behavior in India.

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