Final Project Assignment#1: Project & Data Description

final_Project_assignment_1

final_project_data_description

Project & Data Description

Author

Lauren Zichittella

Published

March 31, 2023

Assignment 1: Proposed project

library(tidyverse)
library(ggplot2)
library(readxl)
knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)

Data Description

The Vaccine Adverse Event Reporting System (VAERS) was created by the Food and Drug Administration and Centers for Disease Control as an early warning system for identifying safety concerns in licensed vaccines. It accepts and analyzes reports of potential adverse events occurring after vaccination. These reports can be submitted by anyone; doctors, vaccine administering sites, and the general public.

VAERS data is partitioned into three tables specific to the type of information captured: 1) Demographics & limited comorbidities of person receiving dose; dates of exposure, symptom onset, report; type of adverse event and severity 2) Details on the vaccine exposure including: name, type, manufacturer, dose, route, and site 3) MedRa coded symptoms associated with vaccinated events

Proposal for assignment

Rather than utilize this data to evaluate safety signals, I’d like to characterize the population submitting reports to this system over time, specifically in the period prior to and post pandemic. The goal is to compare these results to actual rates of vaccination to identify outliers, or conditions where the rate of reporting does not correlate with the rate of vaccination.

Plan of attack: 1) Limit population to adults receiving flu vaccine and years 2018 - 2022 2) Characterize each year of data 3) Explore trends over this time period 4) Overlap trends with vaccine coverage estimates for same time period and demographic (sources TBD but I think geography will be most interesting)

library(readr)


# Temp import 
X2017VAERSDATA <- read_csv("posts/laurenzichittella_finalprojectdata/2017VAERSDATA.csv")

Error: 'posts/laurenzichittella_finalprojectdata/2017VAERSDATA.csv' does not exist in current working directory ('C:/Users/laure/Desktop/RProjects/601_Spring_2023/posts').

X2017VAERSVAX <- read_csv("posts/laurenzichittella_finalprojectdata/2017VAERSVAX.csv")

Error: 'posts/laurenzichittella_finalprojectdata/2017VAERSVAX.csv' does not exist in current working directory ('C:/Users/laure/Desktop/RProjects/601_Spring_2023/posts').

# Read in VAERS VAX, filter to retain flu vax only
# Read in VAERS data, filter to retain adults with flu vax only (join)

--- title: "Final Project Assignment#1: Project & Data Description" author: "Lauren Zichittella" description: "Project & Data Description" date: "03/31/2023" format: html: df-print: paged toc: true code-copy: true code-tools: true css: styles.css categories: - final_Project_assignment_1 - final_project_data_description editor_options: chunk_output_type: console --- ## Assignment 1: Proposed project ```{r} #| label: setup #| warning: false #| message: false library(tidyverse) library(ggplot2) library(readxl) knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE) ``` ### Data Description The Vaccine Adverse Event Reporting System (VAERS) was created by the Food and Drug Administration and Centers for Disease Control as an early warning system for identifying safety concerns in licensed vaccines. It accepts and analyzes reports of potential adverse events occurring after vaccination. These reports can be submitted by anyone; doctors, vaccine administering sites, and the general public. VAERS data is partitioned into three tables specific to the type of information captured: 1) Demographics & limited comorbidities of person receiving dose; dates of exposure, symptom onset, report; type of adverse event and severity 2) Details on the vaccine exposure including: name, type, manufacturer, dose, route, and site 3) MedRa coded symptoms associated with vaccinated events ### Proposal for assignment Rather than utilize this data to evaluate safety signals, I'd like to characterize the population submitting reports to this system over time, specifically in the period prior to and post pandemic. The goal is to compare these results to actual rates of vaccination to identify outliers, or conditions where the rate of reporting does not correlate with the rate of vaccination. Plan of attack: 1) Limit population to adults receiving flu vaccine and years 2018 - 2022 2) Characterize each year of data 3) Explore trends over this time period 4) Overlap trends with vaccine coverage estimates for same time period and demographic (sources TBD but I think geography will be most interesting) ```{r} library(readr) # Temp import X2017VAERSDATA <- read_csv("posts/laurenzichittella_finalprojectdata/2017VAERSDATA.csv") X2017VAERSVAX <- read_csv("posts/laurenzichittella_finalprojectdata/2017VAERSVAX.csv") # Read in VAERS VAX, filter to retain flu vax only # Read in VAERS data, filter to retain adults with flu vax only (join) ```