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Rafael Meza

Publications and source records attributed to Rafael Meza.

5 recordsLinked to original sources

Patterns of Medical Care Cost by Service Type Associated with Lung Cancer Screening

Introduction: Lung cancer screening (LCS) increases early-stage cancer detection which may reduce cancer treatment costs. Little is known about how receipt of LCS affects healthcare costs in real-world clinical settings. Methods: This retrospective study analyzed utilization and cost data from the Population-based Research to Optimize the Screening Process Lung Consortium. We included individuals who met age and smoking LCS eligibility criteria and were engaged within four healthcare systems between February 5, 2015, and December 31, 2021. Generalized linear models estimated healthcare costs from the payer perspective during 12-months prior and 12-months post baseline LCS. We compared these costs to eligible individuals who did not receive LCS. Sensitivity analyses expanded our sample to age-eligible individuals with any smoking history noted in the electronic health record. Secondary analyses examined costs among a sample diagnosed with lung cancer. We reported mean predicted costs with average values for all other explanatory variables. Results: We identified 10,049 eligible individuals who received baseline LCS and 15,233 who did not receive baseline LCS. Receipt of baseline LCS was associated with additional costs of $3,698 compared to individuals not receiving LCS. Secondary analyses showed suggestive evidence that LCS prior to cancer diagnosis decreased healthcare costs compared to cancer diagnosed without screening. Conclusion: These findings suggest LCS increases healthcare costs in the year following screening. However, LCS also improves early-stage cancer detection and may reduce treatment costs following diagnosis. These results can inform future simulation models to guide LCS recommendations, and aid health policy decision makers on resource allocation.

econ.GN

Nonparametric Estimation of the Potential Impact Fraction and Population Attributable Fraction with Individual-Level and Aggregated Data

The estimation of the potential impact fraction (including the population attributable fraction) with continuous exposure data frequently relies on strong distributional assumptions. However, these assumptions are often violated if the underlying exposure distribution is unknown or if the same distribution is assumed across time or space. Nonparametric methods to estimate the potential impact fraction are available for cohort data, but no alternatives exist for cross-sectional data. In this article, we discuss the impact of distributional assumptions in the estimation of the population impact fraction, showing that under an infinite set of possibilities, distributional violations lead to biased estimates. We propose nonparametric methods to estimate the potential impact fraction for aggregated (mean and standard deviation) or individual data (e.g. observations from a cross-sectional population survey), and develop simulation scenarios to compare their performance against standard parametric procedures. We illustrate our methodology on an application of sugar-sweetened beverage consumption on incidence of type 2 diabetes. We also present an R package pifpaf to implement these methods.

stat.ME

Forecasting and Uncertainty in Modeling the 2014-2015 Ebola Epidemic in West Africa

The Ebola epidemic in West Africa is the largest ever recorded, with over 27,000 cases and 11,000 deaths as of June 2015. The public health response was challenged by difficulties with disease surveillance, which impacted subsequent analysis and decision-making regarding optimal interventions. We developed a stage-structured model of Ebola virus disease (EVD). A key feature of the model is that it includes a generalized correction term accounting for factors such as the fraction of cases reported and fraction of the population at risk (e.g. due to contact patterns, interventions, spatiotemporal spread, pre-existing immunity, asymptomatic cases, etc.). We generated a range of short-term forecasts for Guinea, Liberia, and Sierra Leone, which we then validated using subsequent data. We also used the model to examine the uncertainty in the relative contributions to transmission by the different stages of infection (early, late, and funeral). We found that a wide range of forecasted trajectories fit approximately equally well to the early data. However, by including the correction factor term the best-fit models correctly forecasted EVD activity for all three countries, both individually and for all countries combined. In particular, the model correctly forecasted the slow-down in Liberia, as well as the continued exponential growth in Sierra Leone through November 2014. Parameter unidentifiability issues hindered estimation of the relative contributions of each stage of transmission from incidence and deaths data alone, which poses a challenge in determining optimal intervention strategies, and underscores the need for additional data collection. Even with these limited data, however, it is still possible to accurately capture and predict the epidemic dynamics by using a simplified correction term that approximately accounts for the complex underlying factors driving disease spread.

q-bio.PE

Early Real-time Estimation of Infectious Disease Reproduction Number

When an infectious disease strikes a population, the number of newly reported cases is often the only available information that one can obtain during early stages of the outbreak. An important goal of early outbreak analysis is to obtain a reliable estimate for the basic reproduction number, $R_{0}$, from the limited information available. We present a novel method that enables us to make a reliable real-time estimate of the reproduction number at a much earlier stage compared to other available methods. Our method takes into account the possibility that a disease has a wide distribution of infectious period and that the degree distribution of the contact network is heterogeneous. We validate our analytical framework with numerical simulations.

q-bio.QM

Epidemics with general generation interval distributions

We study the spread of susceptible-infected-recovered (SIR) infectious diseases where an individual's infectiousness and probability of recovery depend on his/her "age" of infection. We focus first on early outbreak stages when stochastic effects dominate and show that epidemics tend to happen faster than deterministic calculations predict. If an outbreak is sufficiently large, stochastic effects are negligible and we modify the standard ordinary differential equation (ODE) model to accommodate age-of-infection effects. We avoid the use of partial differential equations which typically appear in related models. We introduce a "memoryless" ODE system which approximates the true solutions. Finally, we analyze the transition from the stochastic to the deterministic phase.

q-bio.PE