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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
doi: 10.2478/fzm-2023-0009
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Abstract:
Objective To explore the effects of daily mean temperature (℃), 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. -
Figure 1. Line chart of monthly mean meteorological elements in Harbin from 2010 to 2020
(A) Monthly average temperature Line Chart: The lowest monthly average temperature shows a fluctuating upward trend; (B) Monthly average humidity line chart: Since 2019, the snowfall in winter has decreased significantly, and the precipitation in summer has increased significantly with overall increasing precipitation. (C) Monthly mean atmospheric pressure broken line chart: the high-level atmospheric pressure slightly decreased, whereas the low-level atmospheric pressure slightly increased, and the overall change of atmospheric pressure was significantly related to the season, showing a trend of low in summer and high in winter. (D) Monthly average wind speed broken line chart: from January 2013 to May 2017, the monthly average wind speed reached 4.61m /s, then gradually decreased in 2019.
Figure 2. Annual cold wave days in Harbin from 2010 to 2020 with a maximum of 133 days and a minimum of 82 days
Figure 3. Changes of PM2.5 and PM10 concentrations in Harbin from 2013 to 2020. The overall trend was downward, and the concentration was higher in winter
Figure 4. Composition of age and sex of chronic kidney disease (CKD) patients admitted to the Second Hospital of Harbin Medical University for ten years
Figure 5. The time distribution of patients with chronic kidney disease (CKD) admitted to the Second Hospital of Harbin Medical University in Harbin from 2010 to 2020
(A) The etiological diagnosis of CKD patients admitted to the Second Hospital of Medical University in Harbin. The top ranked etiological diagnosis in the past ten years: glomerular disease (18%), obstructive nephropathy (15%), diabetes nephropathy (5%), hypertensive nephropathy (3%), and chronic kidney disease (51%) with different CKD stages (no cause specified); (B) Seasonal changes of etiological diagnosis of patients with chronic kidney disease admitted to the Second Hospital of Medical University in Harbin. Diabetes nephropathy and glomerular disease occur most frequently in spring, obstructive nephropathy in summer, and hypertensive nephropathy and chronic kidney disease in autumn
Figure 6. Stacking chart of daily and monthly changes of the number of chronic kidney disease (CKD) patients admitted to the Second Hospital of Harbin Medical University in Harbin. The maximum number of inpatients was 31, dated on 09 October 2017, followed by 28 on 08 October 2019. This pattern might be related to outpatient service after the National Day holiday. The number of people has fluctuated and increased in the first five years and remained relatively stable at a high level in the last five years, which is presumably related to the adjustment of local reimbursement policies. The number of hospital admissions each year basically presents a "M" type change with the month, and the rising trend occurs in winter and spring seasons (February to April) and in autumn and winter seasons (October to December), both of which belong to the seasonal change period
Figure 7. Impact of ten-year daily average temperature and hysteresis on chronic kidney disease (CKD) in Harbin
Figure 8. Impact of ten-year daily temperature difference and hysteresis on chronic kidney disease (CKD) in Harbin
Figure 9. Impact of 10-year daily wind speed and lag on chronic kidney disease (CKD) in Harbin
Figure 10. Impact of ten-year daily pressure and hysteresis on chronic kidney disease (CKD) in Harbin
Table 1. Effect value of extremely low daily average temperature on the number of inpatients with different lag days in each subgroup
Number -29.4℃ RR (95%CI) lag0 lag10 lag20 lag30 Gender Male 10 531 1.05[0.92-1.19] 1.00[0.93-1.07] 0.97[0.91-1.04] 1.19[1.05-1.34] Female 8 124 1.00[0.88-1.14] 1.04[0.97-1.12] 1.01[0.94-1.08] 1.17[1.02-1.33] Age < 65 13 396 1.02[0.90-1.15] 1.00[0.94-1.07] 0.96[0.90-1.02] 1.21[1.08-1.36] ≥ 65 5 259 1.05[0.90-1.22] 1.06[0.97-1.15] 1.06[0.98-1.15] 1.09[0.90-1.27] Main diagnosis of etiology Obstructive nephropathy 2 725 1.02[0.83-1.26] 0.97[0.26-1.09] 0.89[0.80-1.00] 1.43[1.17-1.76] Diabetes nephropathy 966 1.13[0.83-1.54] 1.09[0.92-1.30] 0.97[0.83-1.14] 0.92[0.69-1.22] Hypertension nephropathy 457 1.01[0.65-1.55] 1.28[1.01-1.62] 1.09[0.87-1.36] 1.68[1.10-2.56] Glomerular nephropathy 3 397 1.07[0.89-1.28] 1.03[0.93-1.14] 0.99[0.90-1.09] 1.26[1.05-1.50] CKD of unknown etiology 9 593 1.0[0.91-1.16] 1.02[0.96-1.10] 1.01[0.95-1.08] 1.12[0.99-1.25] 
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