Volume 27, Number 10—October 2021
Research
Predictors of Test Positivity, Mortality, and Seropositivity during the Early Coronavirus Disease Epidemic, Orange County, California, USA
, Tim Bruckner, Verónica M. Vieira, Catalina Medina, Vladimir N. Minin, Philip L. Felgner, Alissa Dratch, Matthew Zahn, Scott M. Bartell, and Bernadette Boden-Albala
Table 2
Generalized additive logistic regression results for odds of testing positive for SARS-CoV-2, Orange County, California, USA, March–August 2020*
| Characteristic | No. (%) |
Adjusted odds ratio† (95% CI) | |
|---|---|---|---|
| SARS-CoV-2 positive | Total tests | ||
| Age group, y | |||
| 0–4 | 487 (1.3) | 4,835 (1.53) | Referent |
| 5–9 | 490 (1.31) | 3,855 (1.22) | 1.62 (1.41–1.86) |
| 10–14 | 855 (2.28) | 5,064 (1.6) | 2.26 (2.00–2.56) |
| 15–19 | 2,124 (5.66) | 13,814 (4.36) | 2.32 (2.08–2.58) |
| 20–24 | 4,646 (12.37) | 31,727 (10.02) | 2.04 (1.85–2.26) |
| 25–29 | 4,640 (12.36) | 34,695 (10.96) | 1.74 (1.57–1.93) |
| 30–34 | 3,791 (10.1) | 29,900 (9.44) | 1.62 (1.46–1.79) |
| 35–39 | 3,291 (8.77) | 25,776 (8.14) | 1.67 (1.5–1.85) |
| 40–49 | 5,950 (15.85) | 44,835 (14.16) | 1.75 (1.58–1.93) |
| 50–59 | 5,747 (15.31) | 48,502 (15.32) | 1.54 (1.39–1.71) |
| 60–69 | 3,045 (8.11) | 36,294 (11.46) | 1.04 (0.94–1.16) |
| 70–79 | 1,404 (3.74) | 22,190 (7.01) | 0.77 (0.69–0.86) |
| >80 |
1,076 (2.87) |
15,139 (4.78) |
0.80 (0.72–0.9) |
| Sex | |||
| F | 19,076 (50.81) | 173,723 (54.87) | Referent |
| M |
18,470 (49.19) |
142,903 (45.13) |
1.20 (1.18–1.23) |
| Race or ethnicity | |||
| White | 12,195 (32.48) | 63,050 (19.91) | Referent |
| Asian | 1,573 (4.19) | 13,858 (4.38) | 0.55 (0.52–0.58) |
| Black | 289 (0.77) | 2,058 (0.65) | 0.58 (0.51–0.65) |
| Hispanic | 3,473 (9.25) | 9,147 (2.89) | 1.68 (1.6–1.76) |
| Native American | 56 (0.15) | 314 (0.1) | 0.82 (0.62–1.09) |
| Pacific Islander | 127 (0.34) | 1,600 (0.51) | 0.35 (0.29–0.42) |
| Unknown |
19,833 (52.82) |
226,599 (71.57) |
0.32 (0.31–0.33) |
| % Persons with college degree in ZIP code | |||
| 1st quartile | 20,665 (55.04) | 120,279 (37.99) | Referent |
| 2nd quartile | 9,484 (25.26) | 87,802 (27.73) | 0.89 (0.77–1.03) |
| 3rd quartile | 4,560 (12.15) | 64,604 (20.4) | 0.70 (0.58–0.84) |
| 4th quartile |
2,837 (7.56) |
43,941 (13.88) |
0.68 (0.56–0.83) |
| % Persons with insurance in ZIP code | |||
| 1st quartile | 19,749 (52.6) | 111,798 (35.31) | Referent |
| 2nd quartile | 10,371 (27.62) | 93,431 (29.51) | 0.83 (0.73–0.95) |
| 3rd quartile | 3,824 (10.18) | 53,201 (16.8) | 0.67 (0.56–0.8) |
| 4th quartile |
3,602 (9.59) |
58,196 (18.38) |
0.58 (0.48–0.7) |
| Population density, × 1,000 persons/km2‡ | 0.97 (0.9–1.04) | ||
| House crowding | 1.03 (1.02–1.04) | ||
*Excludes the coefficients for ZIP code–level median income and time because of interaction between median income and time. A random intercept was included for ZIP code. The period covered in this analysis is March 1–August 16, 2020. SARS-CoV-2, severe acute respiratory syndrome coronavirus 2. †Adjusted for all covariates listed plus ZIP code estimated median income and time of test in days. Model intercept represents odds of a White female in the 0–4-y age group in a ZIP code in the first quartile of college degree and insured with the average population density. The odds of this person testing positive for SARS-CoV-2 is estimated to be 0.19 (95% CI 0.16–0.22). ‡Estimated percentage of population density in a person’s ZIP code.