Secondary Sponsor Quotes

Most of the crime-ridden minority neighborhoods in New York City, especially areas like East New York, where many of the characters in Eric Garner’s story grew up, had been artificially created by a series of criminal real estate scams. One of the most infamous had involved a company called the Eastern Service Corporation, which in the sixties ran a huge predatory lending operation all over the city, but particularly in Brooklyn. Scam artists like ESC would first clear white residents out of certain neighborhoods with scare campaigns. They’d slip leaflets through mail slots warning of an incoming black plague, with messages like, “Don’t wait until it’s too late!” Investors would then come in and buy their houses at depressed rates. Once this “blockbusting” technique cleared the properties, a company like ESC would bring in a new set of homeowners, often minorities, and often with bad credit and shaky job profiles. They bribed officials in the FHA to approve mortgages for anyone and everyone. Appraisals would be inflated. Loans would be approved for repairs, but repairs would never be done. The typical target homeowner in the con was a black family moving to New York to escape racism in the South. The family would be shown a house in a place like East New York that in reality was only worth about $15,000. But the appraisal would be faked and a loan would be approved for $17,000. The family would move in and instantly find themselves in a house worth $2,000 less than its purchase price, and maybe with faulty toilets, lighting, heat, and (ironically) broken windows besides. Meanwhile, the government-backed loan created by a lender like Eastern Service by then had been sold off to some sucker on the secondary market: a savings bank, a pension fund, or perhaps to Fannie Mae, the government-sponsored mortgage corporation. Before long, the family would default and be foreclosed upon. Investors would swoop in and buy the property at a distressed price one more time. Next, the one-family home would be converted into a three- or four-family rental property, which would of course quickly fall into even greater disrepair. This process created ghettos almost instantly. Racial blockbusting is how East New York went from 90 percent white in 1960 to 80 percent black and Hispanic in 1966.

Fiscal Numbers (the latter uniquely identifies a particular hospitalization for patients who might have been admitted multiple times), which allowed us to merge information from many different hospital sources. The data were finally organized into a comprehensive relational database. More information on database merger, in particular, how database integrity was ensured, is available at the MIMIC-II web site [1]. The database user guide is also online [2]. An additional task was to convert the patient waveform data from Philips’ proprietary format into an open-source format. With assistance from the medical equipment vendor, the waveforms, trends, and alarms were translated into WFDB, an open data format that is used for publicly available databases on the National Institutes of Health-sponsored PhysioNet web site [3]. All data that were integrated into the MIMIC-II database were de-identified in compliance with Health Insurance Portability and Accountability Act standards to facilitate public access to MIMIC-II. Deletion of protected health information from structured data sources was straightforward (e.g., database fields that provide the patient name, date of birth, etc.). We also removed protected health information from the discharge summaries, diagnostic reports, and the approximately 700,000 free-text nursing and respiratory notes in MIMIC-II using an automated algorithm that has been shown to have superior performance in comparison to clinicians in detecting protected health information [4]. This algorithm accommodates the broad spectrum of writing styles in our data set, including personal variations in syntax, abbreviations, and spelling. We have posted the algorithm in open-source form as a general tool to be used by others for de-identification of free-text notes [5].