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

Individual predisposition and the intricate interplay between systemic biomarkers and periodontal risk in a general population

BACKGROUND

Increasing age is associated with systemic diseases as well as with periodontal diseases. We wondered whether a biological age score constructed exclusively from systemic biomarkers would reflect periodontal risk factors at baseline and tooth loss as well as periodontal outcome during 10 years follow-up.

METHODS

From the Study of Health in Pomerania (SHIP) 2256 participants (1072 male, 1184 female) were studied for the relationship of the systemic biomarkers glycated hemoglobin (HbA1c), low density lipoprotein cholesterol (LDL), fibrinogen, white blood cell count, blood pressure, and waist circumference to their age. Construction of a biological age (BA) score allowed its comparison with the participants' actual chronological age (CA) and their predisposition to periodontal disease.

RESULTS

Though nearly identical in CA, participants appearing younger than their true age had a significantly reduced burden of periodontal risk factors. If BA > CA, then risk factors were more frequent including smoking, oral hygiene, dental visits, education, and income. After 10 years, in participants with identical CA, tooth loss followed their BA calculated at baseline, that is, with BA > CA fewer teeth were preserved. Similarly, periodontal measures varied according to BA; sex differences were obvious. Most significant were BA-related differences in inflammatory and anthropometry parameters.

CONCLUSIONS

The results support the assumption that risk profiles aggregated in BA constitute a characteristic susceptibility pattern unique to each individual, common to both systemic and periodontal diseases. Although BA was constructed exclusively from systemic measures at baseline, BA reflects the oral conditions at follow-up.

An Overview of R in Health Decision Sciences

As the complexity of health decision science applications increases, high-level programming languages are increasingly adopted for statistical analyses and numerical computations. These programming languages facilitate sophisticated modeling, model documentation, and analysis reproducibility. Among the high-level programming languages, the statistical programming framework R is gaining increased recognition. R is freely available, cross-platform compatible, and open source. A large community of users who have generated an extensive collection of well-documented packages and functions supports it. These functions facilitate applications of health decision science methodology as well as the visualization and communication of results. Although R’s popularity is increasing among health decision scientists, methodological extensions of R in the field of decision analysis remain isolated. The purpose of this article is to provide an overview of existing R functionality that is applicable to the various stages of decision analysis, including model design, input parameter estimation, and analysis of model outputs.

Some Health States Are Better Than Others: Using Health State Rank Order to Improve Probabilistic Analyses

BACKGROUND

Probabilistic sensitivity analyses (PSA) may lead policy makers to take nonoptimal actions due to misestimates of decision uncertainty caused by ignoring correlations. We developed a method to establish joint uncertainty distributions of quality-of-life (QoL) weights exploiting ordinal preferences over health states.

METHODS

Our method takes as inputs independent, univariate marginal distributions for each QoL weight and a preference ordering. It establishes a correlation matrix between QoL weights intended to preserve the ordering. It samples QoL weight values from their distributions, ordering them with the correlation matrix. It calculates the proportion of samples violating the ordering, iteratively adjusting the correlation matrix until this proportion is below an arbitrarily small threshold. We compare our method with the uncorrelated method and other methods for preserving rank ordering in terms of violation proportions and fidelity to the specified marginal distributions along with PSA and expected value of partial perfect information (EVPPI) estimates, using 2 models: 1) a decision tree with 2 decision alternatives and 2) a chronic hepatitis C virus (HCV) Markov model with 3 alternatives.

RESULTS

All methods make tradeoffs between violating preference orderings and altering marginal distributions. For both models, our method simultaneously performed best, with largest performance advantages when distributions reflected wider uncertainty. For PSA, larger changes to the marginal distributions induced by existing methods resulted in differing conclusions about which strategy was most likely optimal. For EVPPI, both preference order violations and altered marginal distributions caused existing methods to misestimate the maximum value of seeking additional information, sometimes concluding that there was no value.

CONCLUSIONS

Analysts can characterize the joint uncertainty in QoL weights to improve PSA and value-of-information estimates using Open Source implementations of our method.

Quantifying demographic and socioeconomic transitions for computational epidemiology: an open-source modeling approach applied to India

BACKGROUND

Demographic and socioeconomic changes such as increasing urbanization, migration, and female education shape population health in many low- and middle-income countries. These changes are rarely reflected in computational epidemiological models, which are commonly used to understand population health trends and evaluate policy interventions. Our goal was to create a "backbone" simulation modeling approach to allow computational epidemiologists to explicitly reflect changing demographic and socioeconomic conditions in population health models.

METHODS

We developed, evaluated, and "open-sourced" a generalized approach to incorporate longitudinal, commonly available demographic and socioeconomic data into epidemiological simulations, illustrating the feasibility and utility of our approach with data from India. We constructed a series of nested microsimulations of increasing complexity, calibrating each model to longitudinal sociodemographic and vital registration data. We then selected the model that was most consistent with the data (i.e., greater accuracy) while containing the fewest parameters (i.e., greater parsimony). We validated the selected model against additional data sources not used for calibration.

RESULTS

We found that standard computational epidemiology models that do not incorporate demographic and socioeconomic trends quickly diverged from past mortality and population size estimates, while our approach remained consistent with observed data over decadal time courses. Our approach additionally enabled the examination of complex relations between demographic, socioeconomic and health parameters, such as the relationship between changes in educational attainment or urbanization and changes in fertility, mortality, and migration rates.

CONCLUSIONS

Incorporating demographic and socioeconomic trends in computational epidemiology is feasible through the "open source" approach, and could critically alter population health projections and model-based evaluations of health policy interventions in unintuitive ways.