TABLE 2.
Multivariable analysis of demographics, known risk factors for kidney disease, and exposure frequencies between cases and controls.
| Mean ± SD or N (%) |
N 997 |
Odds ratio* (95%CI) |
p-Values* |
|---|---|---|---|
| Demographics and known risk factors for kidney diseasea | |||
| Age | 997 | 1.071 (1.055,1.088) | <0.0001 |
| Gender | 0.0042 | ||
| Male | 848 | 4.002 (1.548,10.344) | |
| Female | 149 | (Reference) | |
| Systolic blood pressure>140 |
0.7639 | ||
| Yes | 45 | 0.863 (0.329,2.262) | |
| No | 947 | (Reference) | |
| Diastolic blood pressure>90 |
0.4519 | ||
| Yes | 36 | 1.496 (0.524,4.277) | |
| No | 956 | (Reference) | |
| Self-reported history of diabetes |
0.5005 | ||
| Yes | 30 | 1.367 (0.551,3.392) | |
| No | 967 | (Reference) | |
| Family history of end-stage kidney disease |
0.0540 | ||
| Yes | 89 | 1.814 (0.990,3.324) | |
| No | 908 | (Reference) | |
| Body mass index (BMI kg/m2, continuous) |
997 | 0.944 (0.9,0.991) | 0.0196 |
| Occupational history/exposuresb | |||
| Agricultural field work | <0.0001 | ||
| Yes | 506 | 2.483 (1.587,3.886) | |
| No | 491 | (Reference) | |
| Agricultural non-field work |
0.6511 | ||
| Yes | 409 | 0.908 (0.598,1.379) | |
| No | 588 | (Reference) | |
| Sugar mill work | 0.0003 | ||
| Yes | 307 | 2.334 (1.478,3.685) | |
| No | 689 | (Reference) | |
| Work with rice | <0.0001 | ||
| Yes | 101 | 3.144 (1.847,5.353) | |
| No | 896 | (Reference) | |
| Work with corn | 0.0272 | ||
| Yes | 345 | 1.602 (1.054,2.435) | |
| No | 652 | (Reference) | |
| Work with banana | 0.0009 | ||
| Yes | 48 | 3.185 (1.605,6.323) | |
| No | 949 | (Reference) | |
| Work with sesame seed | 0.7636 | ||
| Yes | 140 | 0.919 (0.531,1.592) | |
| No | 857 | (Reference) | |
| Work with cotton | 0.9882 | ||
| Yes | 146 | 0.996 (0.599,1.656) | |
| No | 851 | (Reference) | |
| Work with beans | 0.1748 | ||
| Yes | 88 | 1.518 (0.831,2.773) | |
| No | 909 | (Reference) | |
| Work with “other crops” |
0.0854 | ||
| Yes | 68 | 1.786 (0.922,3.460) | |
| No | 929 | (Reference) | |
| Work with cattle | 0.5025 | ||
| Yes | 72 | 1.253 (0.648,2.425) | |
| No | 925 | (Reference) | |
| Work with or exposure to pesticides |
0.1441 | ||
| Yes | 363 | 1.375 (0.897,2.108) | |
| No | 634 | (Reference) | |
| Non-occupational exposuresb | |||
| Beer consumption | 0.8936 | ||
| Yes | 633 | 0.967 (0.589,1.586) | |
| No | 364 | (Reference) | |
| Lija consumption | 0.0023 | ||
| Yes | 452 | 2.102 (1.305,3.386) | |
| No | 545 | (Reference) | |
| Water consumption groups (liters per day) |
|||
| 1 | 124 | (Reference) | |
| 2 | 257 | 1.603 (0.662,3.881) | 0.2956 |
| 3 | 196 | 1.905 (0.781,4.649) | 0.1568 |
| 4 | 221 | 2.141 (0.911,5.031) | 0.0806 |
| 5 | 179 | 3.588 (1.52,8.467) | 0.0035 |
a
Logistic model: eGFR = Age + Gender + SBP > 140 + DBP > 90 + Diabetes + Family history of ESKD + BMI (continuous). eGFR was dichotomous variable ≤60/>60 mL/min/ 1.73 m2.
b
Logistic model: eGFR = Age + Gender + SBP > 140 + DBP > 90 + Diabetes + Family history of ESKD + BMI (continuous) + each exposure. eGFR was dichotomous variable ≤60/>60 mL/ min/1.73 m2.
*
Odds ratios and p-values were calculated using logistic regression models.