Abstract
Chronic kidney disease of unknown origin (CKDu) is an epidemic concentrated in agricultural communities in Central and South America, including young, male sugarcane harvesters. The purpose of this analysis is to understand early changes in kidney function among a cohort of first-time sugarcane harvesters and to determine risk factors for kidney function decline. Joint latent class mixed models were used to model sub-population kidney function trajectory over the course of 4 years (2012-2016). Probability weighted logistic regression was used to determine personal health, community, and individual behavior risk factors associated with sub-population assignment. Data analysis occurred in 2019. Of 181 new workers median age 19 years old (IQR: 4), 39 (22%) were identified as having non-stable kidney function with an annual age-adjusted decline of estimated glomerular filtration rate (eGFR) of -1.0 ml/min per 1.73 m2 (95% CI: -3.4, 1.3). Kidney function (OR: 0.96; 95% CI: 0.93, 0.98), mild hypertension (OR: 5.21; 95% CI: 2.14, 13.94), and having a local home of residence (OR: 7.12; 95% CI: 2.41, 26.02) prior to employment in sugarcane were associated with non-stable eGFR sub-population assignment. Mild hypertension may be an early indicator of the development of CKDu. A better understanding of preexisting risk factors is needed to determine why individuals are entering the workforce with reduced kidney function and elevated blood pressure and increased risk of renal function decline.
Highlights
Chronic kidney disease of unknown origin (CKDu) is an epidemic concentrated in agricultural communities in Central and South America, including young, male sugarcane harvesters
Of 181 new workers median age 19 years old (IQR: 4), 39 (22%) were identified as having non-stable kidney function with an annual age-adjusted decline of estimated glomerular filtration rate of -1.0 ml/min per 1.73 m2
Kidney function (OR: 0.96; 95% CI: 0.93, 0.98), mild hypertension (OR: 5.21; 95% CI: 2.14, 13.94), and having a local home of residence (OR: 7.12; 95% CI: 2.41, 26.02) prior to employment in sugarcane were associated with non-stable estimated glomerular filtration rate (eGFR) sub-population assignment
Summary
Joint latent class mixed models were used to model sub-population kidney function trajectory over the course of 4 years (2012–2016). Probability weighted logistic regression was used to determine personal health, community, and individual behavior risk factors associated with sub-population assignment. Because kidney function at time of hire is related to attrition from the workforce [24] we used joint latent class mixed models to model the shape of longitudinal kidney function change while simultaneously modeling loss to follow-up [25]. Time was treated as season (1–5 harvest seasons) in the linear mixed model. The longitudinal change in eGFR was modelled with a quadratic time trend with random terms for intercept and 2-degree polynomial time. Unconditional models were used to determine class-membership. Class-specific baseline risk functions were adjusted for continuous age. The R package “lcmm” was used [26]
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