Table 2.

Matching keys used by 15 US states and two cities for the deterministic matching method^a

Key	Description
1	Full LAST NAME + first six letters of FIRST NAME + full DOB
2	First letter of LAST NAME + letters 3–10 of LAST NAME + letters 2–9 of FIRST NAME + full DOB
3	Letters 2–7 of LAST NAME + first six letters of FIRST NAME + full DOB
4	First two letters of LAST NAME + first three letters of FIRST NAME + full SSN + full DOB
5	Full LAST NAME + first three letters of FIRST NAME + full DOB
6	Letters 3–5 of LAST NAME + first three letters of FIRST NAME + full DOB
7	First four letters of LAST NAME + first four letters of FIRST NAME + full DOB
8b	First letter of LAST NAME + letters 3–10 of LAST NAME + letters 2–9 of FIRST NAME + month and year of DOB
9b	First letter of LAST NAME + letters 3–10 of LAST NAME + letters 2–9 of FIRST NAME + day and year of DOB
10b	Full SSN
11b	First five letters of LAST NAME + first four letters of FIRST NAME + month and year of DOB
12b	First letter of LAST NAME + letters 3–10 of LAST NAME + letters 2–9 of FIRST NAME + month and year of DOB, switching the first and last name in one dataset
13b	First letter of LAST NAME + letters 3–10 of LAST NAME + letters 2–9 of FIRST NAME + day and year of DOB, switching the first and last name in one dataset
14b	First five letters of LAST NAME + first four letters of FIRST NAME + month and year of DOB, switching the first and last name in one dataset

DOB, date of birth; HIV, human immunodeficiency virus; SSN, social security number.

^aAutomated SAS^® (SAS Institute, Inc., Cary, North Carolina, USA) program used to match records on 14 keys. Manual review was required only when multiple records from one dataset matched to a single record in the other dataset on the same lowest key value.

^b

If matched on this key, the following three additional criteria had to be met to be considered a match:

1. Value of sex had to be same in both datasets or the full date of birth and digits one through four and six through nine of the social security number had to be the same in both datasets.
2. First name in the HIV dataset was not among the 20 most common names in the HIV dataset for the jurisdiction.
3. Last name in the HIV dataset was not among the 20 most common names in the HIV dataset for the jurisdiction.