16.2.14 Other Variables

Variables not specified in the Publication Scoring Criteria can be released only after an additional, case-by-case review by the department’s Statistical De-Identification Expert or Statistical De-Identification Supervisor Expert. We suggest that this review considers population size-based scores if population size of the characteristics of a variable is present, or otherwise scores based on number of groups or categories.

1. Other Variable Scores Based on Population Size

Table 30: Generalized Scoring Criteria based on Statewide Population

Population Size	Score
Population >4,000,000	+1
Population 300,001 – 4,000,000	+2
Population 100,001 – 300,000	+3
Population 20,001 – 100,000	+5
Population ≤20,000	+7

The above table shows scoring criteria for population size in general and can be used to score veteran status and educational attainment. Examples follow below of how these variables can be considered by using data at the Census American Community Survey.

Veteran Status

For the California statewide population estimate of 1,467,025 listed below for the adult civilian veteran population, a score of +2 would be assigned from Table 2 based on Statewide Population (Population 300,001 – 4,000,000).

Table 31: Veteran Status recorded from the 2021 American Community Survey

Veteran Status for Population 18 years and over	California Estimate	Margin of Error
Civilian veterans	1,467,026	±9,913

Note that even though there is an implicit age associated with veteran status, this information is incorporated into the population estimate used for the score. Therefore, an additional score modifier for the age should not be applied.

Other aspects of the subpopulation should be considered when assessing risk. For example, more than 90 percent of the veteran population is male.[43] Therefore, reporting of veterans by sex would have more risk than the scores for sex based on the statewide population due to the small number of female veterans.

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Educational Attainment

Similarly, the population estimates in the table below can be used to score Education Attainment Status by using Table 2.

Table 32: Educational Attainment Status recorded from the 2021 American Community Survey

Educational Attainment for Population 25 years and over	California Estimate	Margin of Error
Less than 9th grade, no high school diploma	2,342,364	±15,809
9th to 12th grade, no high school diploma	1,893,671	±12,037
High school graduate (includes equivalency)	5,477,154	±28,244
Some college, no degree	5,496,578	±16,961
Associate's degree	2,135,865	±13,333
Bachelor's degree	5,855,383	±22,797
Graduate or professional degree	3,596,055	±25,828
High school graduate or higher	22,561,035	±20,560
Bachelor's degree or higher	9,451,438	±40,058

For example, the smallest California population with a recorded educational attainment level is 2,135,865 for an Associate degree. Thus, a score of +2 (Population 300,001 – 4,000,000) from the Table 2 would be assigned. Whereas the score would be +1 (Population >4,000,000) if educational attainment is combined from the table into two categories as follows:

• No college (sum of rows 1-3): 9,713,189 (2,342,364 + 1,893,671 + 5,477,154)

• At least some college (sum of rows 4-7): 17,083,881 (5,496,578 + 2,135,865 + 5,855,383 + 3,596,055)

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2. Other Variable Scores Based on Number of Groups or Categories

Table 33: Other Variable Scoring Based on Number of Groups or Categories

Number of Groups or Categories	Score
<5 groups or categories	+3
5-9 groups	+5
10+ groups	+7

If population size is not available for a variable, then score the variable based on number of groups or categories as well as the characteristics of the variables.

For example, legal class groupings associated with a patient’s commitment to the state hospital system are an example of groups or categories. At the highest level, patients can be categorized into two groups: forensic commitment or civil commitments. At a more granular level, patients can be categorized into specific legal classes such defendants found incompetent to stand trial, parolees diagnosed with mental health disorders, individuals found to be not guilty by reason of insanity, mentally ill prisoners transferred from prison to a state hospital for mental health care, individuals committed to the state hospital system as sexually violent predators, or patients civilly committed to a state hospital as described in the Lanterman-Petris-Short Act.

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Table 34: Example Scoring for Legal Class Groupings

Groups or Categories	Score
2 Groups Forensic commitments Civil commitments	+3
6 Groups Incompetent to stand trial Offenders with a mental health disorder Not guilty by reason of insanity Mentally ill prisoners Sexually violent predators Lanterman-Petris-Short Act commitments	+5

Some additional examples can be found in “Data with more Specificity” Section 5.61. Along with number of groups or categories, consider the specificity of the groups or categories, that is, whether the variable represents an aggregation (e.g., Diagnosis Related Groups) or a specific item (e.g., ICD-10 Code).

Also consider the availability of the variable to the public when also associated with other information, particularly the availability of variables that are personal characteristics.

How publicly identifiable the trait in question is should also be considered. For example, unreconstructed cleft palate is a physically identifiable trait. Dyslexia is a condition that, while not physically identifiable, may be something an individual mentions they have and should also be considered a publicly identifiable trait.