The effects of cold region meteorology and specific environment on the number of hospital admissions for chronic kidney disease: An investigate with a distributed lag nonlinear model

Abstract

Objective: To explore the effects of daily mean temperature (°C), average daily air pressure (hPa), humidity (%), wind speed (m/s), particulate matter (PM) 2.5 (μg/m³) and PM10 (μg/m³) on the admission rate of chronic kidney disease (CKD) patients admitted to the Second Affiliated Hospital of Harbin Medical University in Harbin and to identify the indexes and lag days that impose the most critical influence. Methods: The R language Distributed Lag Nonlinear Model (DLNM), Excel, and SPSS were used to analyze the disease and meteorological data of Harbin from 01 January 2010 to 31 December 2019 according to the inclusion and exclusion criteria. Results: Meteorological factors and air pollution influence the number of hospitalizations of CKD to vary degrees in cold regions, and differ in persistence or delay. Non-optimal temperature increases the risk of admission of CKD, high temperature increases the risk of obstructive kidney disease, and low temperature increases the risk of other major types of chronic kidney disease. The greater the temperature difference is, the higher its contribution is to the risk. The non-optimal wind speed and non-optimal atmospheric pressure are associated with increased hospital admissions. PM2.5 concentrations above 40 μg/m³ have a negative impact on the results. Conclusion: Cold region meteorology and specific environment do have an impact on the number of hospital admissions for chronic kidney disease, and we can apply DLMN to describe the analysis.

Keywords

chronic kidney disease / distributed hysteresis nonlinear model / number of hospital admissions / meteorological factors / air pollution

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