HFRS distribution in China, 2004–2018
From January 1, 2004 to December 31, 2018, 190 203 cases of HFRS were reported nationwide in China, with an average annual incidence rate of 0.950 per 100,000 people, with the highest incidence in 2004 (1.926 per 100,000) and the lowest in 2018 (0.86 per 100,000) (Fig. 1A), and the cases showed obvious seasonal fluctuations (Fig. 1B). HFRS cases existed every month and showed an obvious dual-season mode every year, with a spring peak from May to June and a winter peak from November to December. The highest number of cases were in May and November, with the composition ratios accounting of 9.51% and 17.06%, respectively (Fig. 1B).
The incidence and number of HFRS cases reported in China, 2004–2018. (A) Number of cases and incidence by year. Trend of the incidence rate of HFRS between 2004 and 2018 shown by the joinpoint regression (upper right corner). The red squares represent the observed crude incidence of HFRS and the lines represent the slope of the annual percentage change (APC). (B) The pink line represents the monthly incidence of HFRS. The bar chart shows the number of cases at peak and trough.
The incidence of HFRS in northern regions was higher than that in the south, especially in Heilongjiang, Liaoning, Jining, Shaanxi, Shandong and Hebei provinces. Relatively few cases existed in south China, which were mainly concentrated in Jiangxi, Zhejiang, Hunan and Fujian (Figs. S1 and S2). Spatial autocorrelation analysis indicated that HFRS cases were positively correlated (Moran’s I = 0.09, p < 0.1), see Fig. S3. The incidence of HFRS noticeably decreased, with − 24.97% APC (95% confidence interval − 33.2 to − 15.7%, P = 0.001) before 2008, then remained stable until 2018 (P = 0.314, 2008–2012; P = 0.315, 2012–2018) (Fig. 1A). Obviously, residents can become infected with HFRS throughout the year.
We observed HFRS infection in all age groups, and the patients were mainly male, with a male to female ratio of 3:1. The age of onset was mainly between 35 and 75 years, with the highest annual mean incidence in the 50–55 age group (1.785 per 100,000; Fig. S4). The majority of HFRS cases were agricultural workers (121,777 cases, 68.32%), followed by domestic workers, housekeepers, the unemployed (21,147 cases, 11.8%), and industrial workers (20,574 cases, 11.54%) (Fig. S5).
Meteorological factors distribution in China, 2004–2018
The average annual Tmean was 13.27 °C, precipitation was 70.51 mm, RH was 66.22%, SH was 175.43 h, and WS was 2.15 m/s (Table 1). We also compared the mean values of each meteorological factor over the four seasons. Meteorological conditions show distinct seasonal changes, with higher Tmean, SH, RH and precipitation in summer, and higher WS in spring (Fig. 2).
Boxplots of five meteorological variables and the number of HFRS cases in four seasons, 2004–2018 (n = 15*93 in each season). (A–F) Seasonal patterns of weather conditions. The Kruskal–Wallis test was used to compare the nonnormally distributed characteristics of five meteorological factors and HFRS incidence among the four seasons. Null hypothesis: the median values across the four seasons are equal. Alternative hypothesis: At least one of the median values of the four seasons is different from the others. Spring (March–May), summer (June–August), autumn (September–November) and winter (December–February).
Relationship between meteorological factors and HFRS, 2004–2018
Pearson’s correlation analysis revealed that the incidence of HFRS was correlated with meteorological factors: WS (r = 0.11***), SH (r = 0.04**), Tmean (r = − 0.19***), precipitation (r = − 0.1***), and RH (r = − 0.03*) (Fig. 3).
Pearson correlation coefficient between weather conditions and HFRS in China. *0.05 ≥ p > 0.01; **0.01 ≥ p > 0.001; ***≤ 0.001.
The DLNM model showed an association between HFRS and the five meteorological conditions, with a lag of 0–6 months. In the univariate models (Fig. 4), five meteorological conditions were associated with HFRS incidence, with relative risks (RR) ranging from 0.17 to 4.68 at Tmean, 0.76–1.19 for precipitation, 0.48–1.44 for RH, 0.20–3.73 for SH, and 0.08–2.20 for WS. The maximum RR values, including commensurable meteorological and lag time, for the five meteorological conditions were 4.68 (Tmean, − 23 °C, lag 6 months), 1.13 (precipitation, 0 mm, lag 3.6 months), 1.44 (RH, 89%, lag 6 months), 3.73 (SH, 325 h, lag 3.4 months), 2.20 (WS, 0.8 m/s, lag 3.8 months), respectively.
Contour plot of the exposure–response relationship between the incidence of HFRS and five meteorological conditions in the univariate model. The Y-axis represents the lag period from 0 to 6 months. The x-axis represents the range of observations for each variable. RR stands for relative risk, red stands for RR > 1, white stands for RR = 1, and blue stands for RR < 1.
In the multivariate models (Fig. 5), the RR values were 0.52–3.05 (Tmean), 0.87–1.22 (precipitation), 0.20–1.44 (RH), 0.39–1.86 (SH), 0.32–2.08 (WS), respectively. The maximum RR values, including commensurable meteorological and lag time, for the five meteorological conditions were 3.05 (Tmean, − 23 °C, lag 6 months), 1.22 (precipitation, 0 mm, lag 6 months), 1.44 (RH, 87%, lag 1.2 months), 1.86 (SH, 325 h, lag 3.4 months), 2.08 (WS, 0.8 m/s, lag 0 months), respectively.
Contour plot of the exposure–response relationship between the incidence of HFRS and five meteorological conditions in the multivariate model. The Y-axis represents the lag period from 0 to 6 months. The x-axis represents the range of observations for each variable. RR stands for relative risk, red stands for RR > 1, white stands for RR = 1, and blue stands for RR < 1.
Cumulative relative risks at 0–6 months lag
We found that the cumulative relative risk of meteorological factors (with 0–6 months lag) was associated with HFRS incidence. In the univariate models (Fig. 6), the meteorological conditions associated positively with HFRS risk are -22.2–14.8 °C (Tmean), 55.4–68.4% (RH) & 86.4–88.4% (RH), 18–50 mm (precipitation), 179.4–258.4 h (SH), 2.00–2.15 m/s (WS). In the multivariate models (Fig. 7), the meteorological conditions are -23–14.79 °C (Tmean), 69–89% (RH), 50–95 mm (precipitation), 179.4–278.4 h (SH), 1.70–2.00 m/s (WS).
Summary of cumulative exposure–response curves for HFRS incidence with a lag of 0–6 months for meteorological factors from 2004 to 2018 in the univariate model. The Y-axis represents the relative risk of each variable. The x-axis represents the range of observations for each variable. Red lines represent means estimated using the DLNM model, shaded areas represent 95% confidence intervals.
Summary of cumulative exposure–response curves for HFRS incidence with a lag of 0–6 months for meteorological factors from 2004 to 2018 in the multivariate model. The Y-axis represents the relative risk of each variable. The x-axis represents the range of observations for each variable. Red lines represent means estimated using the DLNM model, shaded areas represent 95% confidence intervals.
The lag relationship between meteorological factors and the incidence of HFRS
The results of DLNMs were shown in Fig. S6. In multivariate models, median values of Tmean, precipitation, RH, median SH and WS being used as references, the RR of HFRS incidence with lag 0–6 months were calculated with the 2.5th, 25th, 75th and 97.5th percentile of Tmean, precipitation, RH, SH and WS, respectively. The multivariate plots showed that RR was significantly high (RR > 1) from lag month 0 (RR = 1.31, 95% CI 1.06–1.62) to lag month 2.2 (RR = 1.11, 95% CI 1.01–1.22). Under the 25th Tmean, high RRs were observed from lag month 0 (RR = 1.13, 95% CI 1.03–1.24) to lag month 1.8 (RR = 1.09, 95% CI 1.03–1.16) under the extremely low Tmean (2.5th percentile). Under the 75th RH, the RRs were significantly high for a lag of 0.4 months (RR = 1.03, 95% CI 1.00–1.07) to 2.6 months (RR = 1.05, 95% CI 1.02–1.08). Furthermore, the RRs for lag month 0.4 (RR = 1.09, 95% CI 1.03–1.16) to month 2.4 (RR = 1.09, 95% CI 1.04–1.14) and lag month 5.6 (RR = 1.07, 95% CI 1.01–1.13) to lag month 6 (RR = 1.13, 95% CI 1.05–1.22) were significantly high under the 97.5th percentile of RH. Under the 75th SH, a lag of 1.6 months (RR = 1.03, 95% CI 1.01–1.06) to a lag of 5.2 months (RR = 1.02, 95% CI 1.01–1.04) RRs were high.
Figure S7 showed the lag-specific association between meteorological factors and HFRS incidence. Significant RRs were observed at lags of 3 and 6 months when Tmean was 15–27 °C and -23–15 °C, respectively. Precipitation of 0–15 and 55–90 mm resulted in significantly higher RR values after 6 months. When RH exceeded 82% after 6 months, the RR was significantly high. In addition, SH and WS with a lag of 3 months had high RRs at 180–325 h and 2.55–2.75 m/s, respectively.
Source: Ecology - nature.com
