All study procedures were approved by the University of California, Davis Institutional Review Board. AKI during the work shift, defined by a specified increase in serum creatinine from the preshift to the post shift measure, was the primary outcome of interest. Using the recommended definition and stages of injury from the Kidney Disease: Improving Global Outcomes group,AKI was defined as an increase of the post shift serum creatinine by ≥0.3 mg/dL or ≥1.5 times the preshift creatinine. AKI staging was based on the following: stage 1 ; stage 2 and stage 3 . Variables thought to be associated with AKI were selected a priori based on a review of the literature and feasibility of collecting data in the field. Demographic variables included sex , age and level of education . Clinical data were also obtained. Estimated glomerular filtration rate was calculated using preshift serum creatinine based on the Chronic Kidney Disease Epidemiology Collaboration equation and categorised as ≥90 mL/min/1.732, ≥60 to <90 mL/min/1.732 or <60 mL/min/1.732 . Body mass index was calculated from preshift height and weight measurements , and classified based on WHO recommendations as normal weight , overweight or obese . Diabetes status was defined by standard categories of HbA1c:no diabetes , prediabetes or diabetes . Seated blood pressure was categorised as recommended by the Joint Commission:normal blood pressure , prehypertension , or hypertension .Variables related to occupation included years in agricultural work , how the worker was paid or salary, and the farm task they engaged in most during that day.Descriptive statistics were calculated for the outcome and potential risk factors,hydroponic growing in pooled models and stratified by sex. Differences between males and females were tested using χ2 tests.
We determined AKI classification using the three stages of AKI. We then estimated the association of categories of per cent change in body mass and heat strain with AKI categories. In regression models, we dichotomised AKI as ‘any’ versus ‘none’. Logistic regression models assessed the associations between AKI and predictor variables. Because of concerns that sex might modify the effects of interest, we fit models stratified by sex in addition to pooled models. To aid in interpretation of the main effects in models which included interaction terms, we centred the PSI at a selected reference point and subtracted 4 from each value of PSI. We defined heat strain as a continuous variable based on the PSI. Our first model estimated the age-adjusted association of body mass change and heat strain on AKI. The second model added physiological or traditional risk factors, including weight class, diabetes status, hypertension status, and personal or family history of kidney disease. The final model added occupational risk factors, including years in farm work, payment method and farm task. We tested for effect modification of significant predictors . The Akaike and Bayes information criteria are model goodness of fit measures that incorporate a complexity penalty in favour of parsimonious models, with smaller values indicating better model fit. We report these measures for all models and used them to select the final model, with preference going to Akaike in case of conflicting results. Investigator beliefs about possible causal linkages were encoded using directed acyclic graphs, to develop a parsimonious set of candidate covariates . Covariates were also screened for variation. Those without sufficient variation were dropped . For some categorical variables, levels were combined to increase the precision of estimates involving these factors. For example, diabetes was collapsed to HbA1c<5.7 vs ≥5.7. Farm task was also collapsed to picking versus other. Participants with missing values for critical variables were dropped . A total of 300 participants were enrolled in the study. Five participants were missing either preshift or postshift creatinine measures, and an additional 12 were missing variables required for calculating heat strain, and the final analyses therefore included 283 participants . Our sample consisted of 182 males and 101 females, and the majority of the participans were from Mexico . The mean age was 38.6 years . Most participants were overweight or obese and were prehypertensive or hypertensive . Most had eGFR≥90 mL/min/ 1.732 , were not patients with diabetes or prediabetes , and had no personal or family history of kidney disease .
The mean maximum temperature was 38.0°C , and the mean maximum heart rate was 130 bpm . Statistically significant differences between the two sexes are shown in table 1. Overall, men reported higher levels of education than women. Men also had higher rates of prehypertension or hypertension than women . Men reported more years working in agriculture . There were also differences in the payment method, with 30.8% of men being paid by the piece versus 19.8% of women . Using changes in serum creatinine from preshift measures to post shift measures, 31 participants met KDIGO criteria for stage 1 AKI . An additional four participants met criteria for stage 2 AKI. Stratified by sex, 22 males and 13 females met criteria for AKI. Among men, 10 experienced heat strain, as estimated by PSI≥7.5, and 3 of those met criteria for AKI. No women experienced heat strain. Additionally, most of the sample experienced loss of body mass during the shift, but most did not lose more than recommended by OSHA. For example, 118 of men and 53 of women lost <1.5% body mass. However, five of the men and two of the women who lost ≥1.5% body mass met criteria for AKI. There were no statistically significant unadjusted associations of AKI with either heat strain or the change in body mass classification. Table 3 shows the results of logistic regression models. In the age-adjusted and sex-adjusted model, heat strain was associated with 1.29 adjusted odds of AKI . In this model, stratified by sex, the association among men was significant , but not among women. In the model adding physiological characteristics, the association of heat strain on AKI was virtually unchanged . Obesity was significantly associated with AKI . In the stratified model, men who were overweight or obese had reduced odds of AKI . There were no statistically significant associations among females. The addition of occupational characteristics in the final model did not appreciably change previous associations. However, piece rate work was associated with 4.24 adjusted odds of AKI . In stratified models, this association was not significant among men. Among females, the adjusted odds of AKI rose to 102.81 . Additionally, there was a statistically significant association of years in agricultural work to AKI among females . We modelled BMI as a continuous variable and found an inverse linear association with AKI: for every one-point increase in BMI, the odds of AKI went down 0.09 .
In subsequent pooled models, we added the interaction terms. None were statistically associated with AKI, nor did the additions appreciably change the estimations. Additionally, the model-fit statistics did not improve with the addition of the interaction terms . Incident AKI occurred in 35 participants in our sample of 283 California agricultural workers in the summer of 2014, in 22 of the 182 males and 13 of 101 females . In males, heat strain as estimated by PSI was associated with increased odds of incident AKI. In females, heat strain measurement was not associated with AKI, but occupational factors such as years in agricultural work and being paid by the piece were associated with AKI. While manifested differently between the two sexes, these findings together suggest that incident AKI is an occupational risk factor of agricultural work. The association of heat strain and AKI is not surprising, given that occupational heat strain has been associated with increased risk of renal insufficiency.In particular,hydroponic dutch buckets heat strain has been named as one of the potential risk factors for the development of a chronic kidney disease identified among agricultural workers in Central America, India and Sri Lanka.Moreover, heat exposure has been linked to AKI in other studies of otherwise healthy individuals such as athletes and military recruits.Given the high ambient temperatures in the Central Valley and the strenuous nature of agricultural work, our estimates of heat strain using the PSI were surprisingly lower than we expected, particularly among women, none of whom experienced heat strain. Other researchers have found agricultural work to be associated with high levels of heat strain both in the USA and in other countries.The Central Valley has low levels of humidity, which may allow workers to maintain cooler body temperatures than in other agricultural areas with high humidity. In addition, California is progressive in its prevention of heat related illness through regulations of the state Occupational Safety and Health Administration . Under Cal-OSHA requirements, farmers are required to provide heat illness prevention training for workers and offer regular breaks to cool off and rehydrate . Despite research that suggests workers do not remember the information provided in the trainings and do not take recommended breaks,443 our estimates of heat strain indicate that workers in our sample do not experience high levels of heat strain. This may be related to the different farm tasks performed, suggesting that not all farm work is strenuous. For example, the majority of the women were involved in packing or weeding, which is less strenuous than picking. However, the finding that heat strain in males was statistically associated with incident AKI suggests that agricultural workers who do experience high levels of heat strain are at risk of adverse renal effects.
Fortunately, current research into simple interventions, such as the use of backpack water reservoirs and the enforcement of rest periods during agricultural work, has had an effect on symptoms of heat strain and may provide a potential means of protecting the kidneys. The finding that women are affected differently by occupational exposures than men may be expected due to known gender differences in agricultural work. However, we were surprised to see that the occupational risk factors of years in agricultural work and payment method were associated with AKI in women in our sample. Studies of women’s experiences in agricultural work have documented the risk of sexual harassment or assault, which often occurs around bathroom facilities.Women may tend to limit drinking or eating during their work shift to reduce their need to use these facilities, or may delay trips to the bathroom during the work day out of fear for their safety. In a study of women in India who experienced heat stress at work, researchers found that delayed urination among women was associated with increased risk of urinary tract infection and AKI.The association of years of agricultural work and AKI among women in our sample could suggest that chronic delayed urination may increase the risk of AKI.Our finding that workers paid by the piece had higher odds of AKI requires further investigation, particularly because our sample size yielded imprecise estimates, and wide CIs may be attributed to the low numbers of women who experienced AKI and were paid by the piece . Piece rate work incentivises the worker to work harder and to take fewer breaks by financially rewarding higher productivity.Women who are paid by the piece may have an extra incentive to not visit the bathroom during the work shift, and piece rate work is associated with other poor health outcomes, including higher rates of accidents,musculoskeletal injury and risk-taking among workers.The independent association of piece rate work on AKI among women in our models suggests that piece rate work is a marker of conditions potentially damaging to kidney function, and that this mechanism is separate from heat strain or hydration status. Alternatively, piece rate work may be a better measure of the factors suspected to be involved in the development of AKI, which could explain the associations found here. In either case, modifications to the pay structure may help prevent AKI in agricultural workers. While not a risk factor for AKI in our estimations, the majority of workers in our sample experienced volume depletion after an agricultural work shift as measured by change in body mass. Other estimates of fluid intake among agricultural workers suggest that the amount of water workers drink is not sufficient to replace fluids and electrolytes lost during the work shift,as many do not believe they are at risk for injury and do not adequately rehydrate.