Heat exposure and chronic kidney disease: a temporal link in a Taiwanese agricultural county

Chronic Kidney Disease Among Agricultural Workers in Taiwan: A Nationwide Population-Based Study

Introduction

Chronic kidney disease (CKD) in agricultural communities is a significant public health issue. We aimed to investigate the epidemiology of CKD among Taiwanese farmers and its association with outdoor heat exposure.

Methods

A nested case-control study was conducted on participants in the National Adult Health Examination (NAHE) from 2012 to 2018. The farming occupation was identified through National Health Insurance data. The primary outcomes of interest were the development of CKD, defined as a decreased estimated glomerular filtration rate (eGFR) with diagnosis by physicians, and CKD of undetermined etiology (CKDu), defined as CKD excluding common traditional etiologies. We calculated the county-wide average ambient temperature from a climate reanalysis dataset (ERA5-Land). All CKD cases were matched 1:2 to non-CKD participants by age and biological sex. We estimated the odds ratios (ORs) of CKD and CKDu for farmers and changes in mean ambient temperature (°C) before the examination.

Results

We identified 844,412 farmers and 3,750,273 nonfarmers. Among 24.9% of farmers and 7.4% of nonfarmers with reduced kidney function, only 1 in 7 received a diagnosis of CKD. The farming occupation was independently predictive of CKDu (OR = 1.09, 95% confidence interval [CI] = 1.001-1.18) but not CKD. Increased ambient temperature (°C) was associated with a higher risk of CKD (OR = 1.023, 95% CI = 1.017-1.029), with particularly strong associations observed among middle-aged participants and diabetics.

Conclusions

Taiwanese farmers might have a higher risk of developing CKDu. Outdoor heat exposure is associated with the development of CKD, and middle-aged participants and those with diabetes are more vulnerable than the general population.

Exploring the antecedents of AI adoption for effective HRM practices in the Indian pharmaceutical sector

Purpose: The aim of this research is to investigate the factors that facilitate the adoption of artificial intelligence (AI) in order to establish effective human resource management (HRM) practices within the Indian pharmaceutical sector. Design/methodology/approach: A model explaining the antecedents of AI adoption for building effective HRM practices in the Indian pharmaceutical sector is proposed in this study. The proposed model is based on task-technology fit theory. To test the model, a two-step procedure, known as partial least squares structural equational modeling (PLS-SEM), was used. To collect data, 160 HRM employees from pharmacy firms from pan India were approached. Only senior and specialized HRM positions were sought. Findings: An examination of the relevant literature reveals factors such as how prepared an organization is, how people perceive the benefits, and how technological readiness influences AI adoption. As a result, HR systems may become more efficient. The PLS-SEM data support all the mediation hypothesized by proving both full and partial mediation, demonstrating the accuracy of the proposed model. Originality: There has been little prior research on the topic; this study adds a great deal to our understanding of what motivates human resource departments to adopt AI in the pharmaceutical companies of India. Furthermore, AI-related recommendations are made available to HRM based on the results of a statistical analysis.