breaches: Cyber Security Breaches

Description

data.frame of cyber security breaches involving health care records of 500 or more humans reported to the U.S. Department of Health and Human Services (HHS) as of June 27, 2014.

Usage

data(breaches)

Arguments

Format

A data.frame with 1055 observations on the following 24 variables:

Number: integer record number in the HHS data base
Name_of_Covered_Entity: factor giving the name of the entity experiencing the breach
State: Factor giving the 2-letter code of the state where the breach occurred. This has 52 levels for the 50 states plus the District of Columbia (DC) and Puerto Rico (PR).
Business_Associate_Involved: Factor giving the name of a subcontractor (or blank) associated with the breach.
Individuals_Affected: integer number of humans whose records were compromised in the breach. This is 500 or greater; U.S. law requires reports of breaches involving 500 or more records but not of breaches involving fewer.
Date_of_Breach: character vector giving the date or date range of the breach. Recodes as Dates in breach_start and breach_end.
Type_of_Breach: factor with 29 levels giving the type of breach (e.g., "Theft" vs. "Unauthorized Access/Disclosure", etc.)
Location_of_Breached_Information: factor with 41 levels coding the location from which the breach occurred (e.g., "Paper", "Laptop", etc.)
Date_Posted_or_Updated: Date the information was posted to the HHS data base or last updated.
Summary: character vector of a summary of the incident.
breach_start: Date of the start of the incident = first date given in Date_of_Breach above.

Details

The data primarily consists of breaches that occurred from 2010 through early 2014 when the extract was taken. However, a few breaches are recorded including 1 from 1997, 8 from 2002-2007, 13 from 2008 and 56 from 2009. The numbers of breaches from 2010 - 2014 are 211, 229, 227, 254 and 56, respectively. (A chi-square test for equality of the counts from 2010 through 2013 is 4.11, which with 3 degrees of freedom has a significance probability of 0.25. Thus, even though the lowest number is the first and the largest count is the last, the apparent trend is not statistically significant under the usual assumption of independent Poisson trials.)

The following corrections were made to the file:

Number	Name of Covered Entity	Corrections
45	Wyoming Department of Health	Cause of breach was missing. Added "Unauthorized
	Access / Disclosure" per `smartbrief.com/03/29/10`	55	Reliant Rehabilitation Hospital North
Cause of breach was missing. Added "Unauthorized		Houston	Access / Disclosure" per Dissent. "Two Breaches
		Involving Unauthorized Access Lead to Notification."
	`www.phiprivacy.net/two-breaches-involving-unauthorized-access-lead-to-notification`; approximately 2010-04-20. This web page has since been removed, apparently without having been captured by archive.net.]	123	Aetna
Cause of breach was missing. Added Improper			disposal per `Aetna.com/news/newsReleases/2010/0630`
157	Mayo Clinic	Cause of breach was missing. Added Unauthorized
	Access/Disclosure per Anderson, Howard. "Mayo Fires
"Employees in 2 Incidents: Both Involved			Unauthorized Access to Records."
		Data Breach Today. N.p., 4 Oct. 2010	341
`Saint Barnabas MedicL Center`	Misspelled "Saint Barnabas Medical Center"	347	`Americar Health Medicare`
Misspelled "American Health Medicare"	484	`Lake Granbury Medicl Ceter`	Misspelled "Lake Granbury Medical Center"
782	See list of Practices under Item 9	Replaced name as "Cogent Healthcare, Inc." checked
	from XML and web documents	805	Dermatology Associates of Tallahassee
Had 00/00/0000 on breach date. This was crossed			check to determine that it was Sept 4, 2013 with 916 records
815	Santa Clara Valley Medical Center	Mistype breach year as 09/14/2913 corrected as 09/14/2013	961
`Valley View Hosptial Association`	Misspelled "Valley View Hospital Association"	1034	Bio-Reference Laboratories, Inc.
Date changed from 00/00/000 to 2/02/2014 as			subsequently determined.

Examples

Run this code

# NOT RUN {
data(breaches)
quantile(breaches$Individuals_Affected)
# confirm that the smallest number is 500 
# -- and the largest is 4.9e6
# ... and there are no NAs

dDays <- with(breaches, breach_end - breach_start)
quantile(dDays, na.rm=TRUE)
# confirm that breach_end is NA or is later than 
# breach_start 
# }