flumodelr: An R Package for Estimating Attributable Influenza Morbidity and Mortality

<img src="man/figures/flumodelr_cdc122ex.jpeg" width=750 height=400 align="center"/>

Overview

The following software is a collection of R functions and datasets to examine attributable influenza morbidity and mortality. The software draws from multiple peer-reviewed studies, as well as unpublished SAS and R code developed by other researchers (see acknowledgements).

Installation

# install from CRAN: (Not implemented yet)
install.packages("flumodelr")

# Or the the development version from GitHub:
# install.packages("devtools")
devtools::install_github("kmcconeghy/flumodelr")

This package relies heavily on the suite of packages and programming termed "Tidyverse", e.g. "dplyr", "lubridate", that must be installed for proper functioning.

Usage

The primary goal of this package is to provide a toolkit for researchers who wish to estimate the attributable proportion of an outcome due to influenza morbidity and mortality. Conceptually this is achieved through estimating a baseline rate of the outcome in the absence of influenza, then using a measure of influenza endemicity to identify the marginal rate due to influenza morbidity. The particular measure of influenza endemicity and outcome being modelled is specified by the user.

Simple Example

library(flumodelr, quietly = T, warn.conflicts = F)
library(tidyverse, quietly = T, warn.conflicts = F)

Example data

fludta <- fludta
fludta

Fit Traditional Serfling Model

flu_fit <- fluserf(fludta, outc = perc_fludeaths, time = yrweek_dt)

Assumes epidemic influenza period of Sept. - May. Fits a cyclical trend line, y0, along with an threshold limit for epidemicity, y0_ul.

flu_fit %>% select (year, week, perc_fludeaths, y0, y0_ul)

Influenza Time-Series Plot

An convenience function for plotting the above fit lines.

fluplot(flu_fit, xvar=yrweek_dt, perc_fludeaths, y0, y0_ul,
                    ylab="% Deaths due to P&I", 
        title="Serfling Model")

Vignettes

Several companion documents have been written to assist interested readers with the background and use of the package. We suggest you start here: Introduction to flumodelr or if the package is installed:

vignette("01-Introduction", "flumodelr")

Latest Version Updates

This package is in the early stages of development. Many features may change in a short amount of time.

Future Goals for flumodelr

The basic functions of the package have been tested and validated against a published research study. The current goal is focused on streamlining package functions and preparing a manuscript on comparison of methods for publication.

Important References

1. Serfling RE. Methods for current statistical analysis of excess pneumonia-influenza deaths. Public Health Rep. 1963 Jun; 78(6): 494 - 506. Pubmed Link

2. Thompson WW1, Weintraub E, Dhankhar P, Cheng PY, Brammer L, Meltzer MI, Bresee JS, Shay DK. Estimates of US influenza-associated deaths made using four different methods. Influenza Other Respir Viruses. 2009 Jan;3(1):37-49. Pubmed Link

Authors

Kevin W. McConeghy, Pharm.D, M.S., Brown University School of Public Health, Dept. Health Services Research
Rob van Aalst, Sanofi Pasteur
Andrew Zullo, Pharm.D., MSc, Ph.D., Brown University School of Public Health, Dept. Health Services Research
Nina Joyce, Ph.D., Brown University School of Public Health, Dept. Health Services Research

Acknowledgements

The programming was derived with guidance and example code from the following individuals.

• Yinong Young-Xu and Ellyn Russo. White River Junction VA Medical Center, VT. Department of Veteran Affairs.

• Baltazar Nunes. Researcher, Epidemiological Research Unit (Head), Departament of Epidemiology.