Volume 29, Number 7—July 2023
Dispatch
Nonnegligible Seroprevalence and Predictors of Murine Typhus, Japan
Tetsuro Aita, Eiichiro Sando
, Shungo Katoh, Sugihiro Hamaguchi, Hiromi Fujita, and Noriaki Kurita
, Shungo Katoh, Sugihiro Hamaguchi, Hiromi Fujita, and Noriaki Kurita
Table 1
Characteristics and residential geographic features for participants in study of seroprevalence and predictors of murine typhus, by Rickettsia typhi IgG seropositivity status, Japan*
| Characteristic | R. typhi–positive, n = 269 | R. typhi–negative, n = 2,113 | Total, n = 2,382 |
|---|---|---|---|
| Sex | |||
| F | 123 (45.7) | 1,080 (51.1) | 1,203 (50.5) |
| M |
146 (54.3) |
1,033 (48.9) |
1,179 (49.5) |
| Age group, y | |||
| <40 | 2 (0.7) | 109 (5.2) | 111 (4.6) |
| 41–50 | 11 (4.1) | 269 (12.7) | 280 (11.8) |
| 51–60 | 15 (5.6) | 350 (16.6) | 365 (15.3) |
| 61–70 | 71 (26.4) | 695 (32.9) | 766 (32.2) |
| 71–80 | 113 (42) | 576 (27.2) | 689 (28.9) |
| >81 |
57 (21.2) |
114 (5.4) |
171 (7.2) |
| Site | |||
| Otaki | 180 (66.9) | 891 (42.2) | 1,071 (45) |
| Katsuura | 42 (15.6) | 250 (11.8) | 292 (12.2) |
| Kameda |
47 (17.5) |
972 (46) |
1,019 (42.8) |
| Medical history | |||
| None | 227 (84.4) | 1,787 (85) | 2,014 (84.9) |
| Scrub typhus | 8 (3.0) | 32 (1.5) | 40 (1.7) |
| Japanese spotted fever | 2 (0.7) | 0 | 2 (0.1) |
| Both | 0 | 1 (0.1) | 1 (0) |
| Unknown | 32 (11.9) | 283 (13.5) | 315 (13.3) |
| Missing |
0 |
10 |
10 |
| Environmental exposure history | |||
| Mountains | |||
| Yes | 70 (26) | 456 (21.6) | 526 (22.1) |
| No | 199 (74) | 1,657 (78.4) | 1,856 (77.9) |
| Agriculture | |||
| Yes | 135 (50.2) | 766 (36.3) | 901 (37.8) |
| No | 134 (49.8) | 1,347 (63.7) | 1,481 (62.2) |
| Bushes† | |||
| Yes | 141 (52.4) | 856 (40.5) | 997 (41.9) |
| No |
128 (47.6) |
1,257 (59.5) |
1,385 (58.1) |
| Environment surrounding the residence | |||
| Population density, persons/km2 (5th–95th percentile) | 244 (32–1,207) | 356 (45–3,354) | 306 (44–3,148) |
| Missing | 3 | 26 | 29 |
| Coasts, m2 (5th–95th percentile) | 0 (0–235,132) | 0 (0–232,102) | 0 (0–233,502) |
| Missing | 0 | 3 | 3 |
| Forests, m2 (5th–95th percentile) | 307,376 (76,276–664,633) | 269,342 (6,023–616,031) | 273,757 (7,231–621,401) |
| Missing | 0 | 3 | 3 |
| Farmland, m2 (5th–95th percentile) | 236,624 (14,833–458,114) | 233,350 (0–486,875) | 233,414 (0–483,051) |
| Missing | 0 | 3 | 3 |
| Rivers and lakes, m2 (5th–95th percentile) | 17,596 (0–101,103) | 13,917 (0–84,788) | 14,279 (0–86,124) |
| Missing | 0 | 3 | 3 |
| Wilderness, m2 (5th–95th percentile) | 0 (0–27,181) | 0 (0–25,095) | 0 (0–25,391) |
| Missing | 0 | 3 | 3 |
*Values are no. (%) except as indicated. Continuous and categorical variable data are presented as median (5th–95th percentile) and frequency (%). †Bushes refer to areas with small trees and weeds.
Page created: May 21, 2023
Page updated: June 20, 2023
Page reviewed: June 20, 2023
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