TABLE 2.
Adjusted Prevalence Ratio of Diabetes Specific vs. No Diabetes-Specific Screeningi |
Adjusted Prevalence Ratio of Any Diabetes Screening vs. Noneii |
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---|---|---|---|---|---|---|
Characteristic | PR (95% CI) | P-pwise | P | PR (95% CI) | P-pwise | P |
Sex(a) | . | <0.00005 | . | <0.00005 | ||
Female | 1.15 (1.10–1.20) | <.00005 | . | 1.08 (1.06, 1.09) | <.00005 | . |
Male | Ref | . | . | Ref | . | . |
Race/ethnicity(a) | . | <0.0001 | . | <0.00005 | ||
Asian | 1.02 (0.94, 1.11) | 0.56 | . | 0.96 (0.91, 1.02) | 0.20 | . |
Black | 1.00 (0.92, 1.09) | 0.99 | . | 0.92 (0.90, 0.94) | <.00005 | . |
Hispanic | 1.12 (1.01, 1.24) | 0.03 | . | 1.00 (0.97, 1.04) | 0.96 | . |
Other | 1.05 (1.00, 1.09) | 0.04 | . | 0.98 (0.95, 1.02) | 0.32 | . |
White | Ref | . | . | Ref | . | . |
Age, years§(a) | . | <0.00005 | . | <0.00005 | ||
18–27 | Ref | . | . | Ref | . | . |
28–47 | 1.23 (1.17, 1.30) | <.00005 | . | 1.09 (1.07, 1.12) | <.00005 | . |
48–67 | 1.43 (1.31, 1.55) | <.00005 | . | 1.17 (1.14, 1.21) | <.00005 | . |
68+ | 0.93 (0.72, 1.21) | 0.62 | . | 0.93 (0.82, 1.06) | 0.27 | . |
Evidence of primary care outpatient healthcare utilization(b) |
. | <0.00005 | . | <0.00005 | ||
Yes | 1.80 (1.62, 2.00) | <.00005 | . | 1.48 (1.36–1.61) | <.00005 | . |
No | Ref | . | . | Ref | . | . |
Each adjusted model depends upon the specific variable and their position along with directed acyclic graph (DAG or causal graph). We created a DAG to identify confounders and mediators of the predictors of interest (available upon request).
These age categories were those provided by the California Department of Health Care Services to the study investigators
Diabetes-specific screening (N=15,315) vs. no diabetes-specific screening [includes both non-specific or no screening] (N=35,600).
Any diabetes screening [includes both diabetes-specific screening and non-specific diabetes screening] (N=35,083) vs. no screening (N=15,832)
Controlling for 3 main demographic variables (sex, race/ethnicity, age) and county type (rural/urban), unless it is the predictor variable of interest.
Controlling for main demographic variables, county type, psychiatric diagnosis, comorbid substance abuse, and comorbid metabolic disorders.