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Research Article

Epidemiological Characteristics, Seasonal Dynamic Patterns, and Associations with Meteorological Factors of Rubella in Shaanxi Province, China, 2005–2018

Yu Ma Department of Epidemiology, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University, Xi’an, People’s Republic of China;
Shaanxi Provincial Center for Disease Control and Prevention, Xi’an, People’s Republic of China

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Kun Liu Department of Epidemiology, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University, Xi’an, People’s Republic of China;

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Weijun Hu Shaanxi Provincial Center for Disease Control and Prevention, Xi’an, People’s Republic of China

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Shuxuan Song Department of Epidemiology, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University, Xi’an, People’s Republic of China;

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Shaobai Zhang Shaanxi Provincial Center for Disease Control and Prevention, Xi’an, People’s Republic of China

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Zhongjun Shao Department of Epidemiology, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University, Xi’an, People’s Republic of China;

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DOI:
https://doi.org/10.4269/ajtmh.20-0585
Volume/Issue:
Volume 104: Issue 1
Page(s):
166–174
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ABSTRACT

Rubella occurs worldwide, causing approximately 100,000 cases annually of congenital rubella syndrome, leading to severe birth defects. Better targeting of public health interventions is needed to achieve rubella elimination goals. To that end, we measured the epidemiological characteristics and seasonal dynamic patterns of rubella and determined its association with meteorological factors in Shaanxi Province, China. Data on rubella cases in Shaanxi Province from 2005 to 2018 were obtained from the Chinese National Notifiable Disease Reporting System. The Morlet wavelet analysis was used to estimate temporal periodicity of rubella incidence. Mixed generalized additive models were used to measure associations between meteorological variables (temperature and relative humidity) and rubella incidence. A total of 17,185 rubella cases were reported in Shaanxi during the study period, for an annual incidence of 3.27 cases per 100,000 population. Interannual oscillations in rubella incidence of 0.8–1.4 years, 3.8–4.8 years, and 0.5 years were detected. Both temperature and relative humidity exhibited nonlinear associations with the incidence of rubella. The accumulative relative risk of transmission for the overall pooled estimates was maximized at a temperature of 0.23°C and relative humidity of 41.6%. This study found that seasonality and meteorological factors have impact on the transmission of rubella; public health interventions to eliminate rubella must consider periodic and seasonal fluctuations as well as meteorological factors.

INTRODUCTION

Rubella is among the most rapidly spreading infectious diseases in the world. 1 It is caused by the rubella virus, which is the sole member of the genus Rubivirus within the Togaviridae family of animal viruses. 2 The virus is spread mainly from person to person via respiratory transmission, most often through droplets or aerosol particles containing pathogens. The incubation period of rubella is between 14 and 23 days 3 ; most cases of infection lead to fever and rashes of short duration, and the most common complication is transient joint involvement such as polyarthralgia or arthritis. Infection in pregnant women, particularly during the first 16 weeks of pregnancy, can lead to stillbirth, preterm birth, or infants with a constellation of congenital malformations known as congenital rubella syndrome (CRS). 4

Benefitting from the vaccine of rubella, incidence of rubella has declined in many countries following implementation of vaccination strategies beginning in the 1970s 5 ; rubella remains an important and widespread pathogen and a public health concern worldwide. At the World Health Assembly in May 2012, all 194 member states endorsed the goal of eliminating rubella in five of the six WHO regions by 2020. By 2016, only the Americas had reached its elimination target, and the rubella virus is still circulating in the other five regions including the Western Pacific region (WPR), which includes China. 6 The incidence of rubella in WPR was 18.41 per million in 2019, which increased nearly eight times compared with 2017, which accounts for 90% of rubella cases worldwide, and most of the cases come from China and Japan. 7 Outbreaks have occurred in many countries, generating large numbers of CRS cases and placing a substantial burden on families and society. 8 Challenges to the goal of eliminating rubella include large nonimmunized populations, vaccine hesitancy in target populations, and weak healthcare services with low routine vaccination coverage. Based on the experience of rubella elimination in other countries, it appears likely that large-scale epidemics may come back at some point after implementing immunization strategies. In Japan, the rubella immunization program was launched in 1995, but a large-scale outbreak occurred between 2012 and 2014, leading to 45 CRS cases. 9

In China, rubella-containing vaccine (RCV) was introduced nationwide into the government’s Expanded Program on Immunization (EPI) in 2008. Children younger than 2 years are provided with two doses of RCV, including one dose of measles–rubella combined at age 8 months and one dose of measles–mumps–rubella combined vaccine at 18 months. 10 However, according to the WHO, restricting vaccination to children will lead the epidemic to shift toward older age-groups. 3 In some provinces in China, this phenomenon occurred after the rubella immunization program was implemented. 11,12 The highest incidence was found in those aged 15–19 years, whereas before the vaccination program, it had been in the 10- to 14-year age-group. If incidence is further shifted toward those older than 20 years, an increase in CRS will likely be seen. In addition, whereas overall incidence of China has decreased in recent years because of the immunization program, the accumulation of susceptible individuals has led to a rebound in the epidemic, and almost 30,000 cases occurred in 2019, according to the data from the National Notifiable Disease Reporting System (NNDRS). In that epidemic, cases were concentrated in the 15- to 19-year age-group, which led many students to leave school and to temporary school closures. In Shaanxi Province, according to a seroepidemiological investigation launched in 2017, only 73.4% of people aged 6–18 years had a positive rubella antibody (IgG), 13 and it is thus likely that large-scale epidemics will return at some point. 9

As a weather sensitivity disease, the meteorological factors are understood to play an important role in seasonal respiratory virus epidemics. 14 Previous literatures suggest that temperature is the most important meteorological factor affecting transmission of respiratory infectious diseases. 15–17 Some studies have also suggested that relative humidity is an important factor to be considered in the transmission of rubella. 14,18 Previous studies have found that morbidity from rubella displays clear seasonal variation. In most parts of the world, incidence is highest in spring, with large epidemics occurring every 3–8 years. 19 The timing of epidemics varies between the Northern and Southern Hemispheres, with peaks from March to June and from August to December, respectively. In China and Europe, high incidence is consistently seen from March to June, 18,20 whereas in South Africa and Peru, it is seen from May to October. 21,22 These seasonal patterns suggest an influence of climate factors on the rubella epidemiology. However, there have been few investigations of the relationships among seasonality, meteorological factors, and the dynamics of rubella, and studies to date have yielded inconsistent findings. 23,24 According to previous research, average temperature and relative humidity have substantial influence on the dynamics of rubella, but the specific relationship between meteorological factors and rubella incidence has not been determined. Previous studies have used seasonally forced “susceptible, exposed, infected and recovered,” “susceptible–exposed–infectious,” and “susceptible–infected–recovered” models to examine seasonal dynamics of rubella. 25–27 Although these models can ascertain the main seasonal features of observed time series for infectious diseases, they cannot be used for more detailed study of nonseasonal periodic characteristics of time series or to describe the relationships between meteorological factors and disease.

Considering these limitations, wavelet analysis has been widely used in recent years to measure periodicity of infectious disease time series. For example, multi-periodicity was detected using wavelet analysis for other infectious diseases, such as dengue and hantavirus. 28,29 Mixed generalized additive models (MGAMs) have also been extensively used to analyze the health effects of exposure and response to meteorological factors. 30,31

The study was conducted as a time series analysis of rubella dynamics using surveillance data for the years 2005−2018. We measured epidemiological characteristics and seasonal dynamic patterns of rubella as well as associations between meteorological factors and rubella incidence in Shaanxi Province, China, using the Morlet wavelet analysis and MGAM.

MATERIALS AND METHODS

Data for the study were obtained from Shaanxi Province, located in the northwestern part of China and covering a land area of 209,000 km2 (Supplemental Figure S1). The total population of Shaanxi is approximately 38 million, and the geography of the province spans three different terrains and climates, including mountains, plains, and plateaus. The overall annual average temperature is 13.0°C, and annual average precipitation is 576.9 mm. 32 Shaanxi Province was divided into three areas based on mean daily temperature and humidity as follows: northern Shaanxi, central Shaanxi plain, and southern Shaanxi.

Rubella case data.

Data on rubella cases were collected from the Chinese measles and rubella surveillance system, which is part of the NNDRS, which covers all types and all levels of medical institutions throughout China. The diagnosis of rubella cases is based on the diagnostic criteria GB17009-1997 (used from 1997 to 2007) and WS-297-2008 (used from 2008 to the present). We adopted the confirmed cases, including both clinically diagnosed cases and laboratory confirmed cases, from 2005 to 2018. The rubella dataset contains data on the age, gender, residential address, date of disease onset, date of diagnosis, and disease classification for all reported cases of rubella. Analyses for the present study included both clinically diagnosed cases and laboratory confirmed cases from 2005 to 2018. A case is confirmed based on standard diagnostic procedures set by the Chinese Ministry of Health (http://www.nhc.gov.cn/wjw/s9491/wsbz_2.shtml).

Climatic, geographical, and demographic data.

Daily meteorological data including temperature and humidity in each county of Shaanxi Province were taken from the China Meteorological Data Sharing Service System. Demographic data were obtained from the Shaanxi Statistical Yearbook. 32

Morlet wavelet analysis.

To estimate the temporal periodicity of rubella incidence, we used the Morlet wavelet analysis of time series. Wavelet analysis is an extension of Fourier analysis that is more efficient and yields more precise results for time–frequency, as well as requiring less code for multiple time scales. 33 The Morlet wavelet analysis is especially suitable for nonstationary signals such as infectious disease time series. 29,30 The complex Morlet wavelet w used in this study can be defined as
w = e 2 i π f t x − t 2 e − t 2 / 2 s 2 ,
where i is the imaginary operator ( i   =   − 1 ) , f is the frequency in Hz, and t is time in seconds. The time scale was set at one cycle, with minimum resolution set at four cycles. To optimize the calculation, the convolution of Equation 1 was performed in the Fourier domain. w 2 is defined as the wavelet power spectrum expressing the magnitude of the time series’ fluctuation in a given wavelet scale and time domain, as follows:
w 2 ( s ) = 1 N ∑ n = 0 N − 1 | w n ( s ) | 2  ,
Averaging the wavelet power spectrum over a given period yields the global wavelet spectrum, from which characteristics and intensity of periodic fluctuations can be clearly discerned. Wavelet analyses were performed using MATLAB (version R2009b, MathWorks.lnc, Natick, MA) toolbox.

Analysis of meteorological factors and incidence of rubella.

We used MGAM to describe the associations between meteorological variables and rubella incidence. MGAM adjusts for data autocorrelation by adding an autoregressive term as a random effect 34 . The model can also better control for autocorrelation of residuals, and the parameters and standard error estimates are more robust than in generalized additive models (GAM) or distributed lag non-linear models (DLNM) 34–36 . A spline function for time is applied to describe time trends of the independent variables (temperature and humidity in our study).

In our study data, there was a weak correlation from 0.093 to 0.149 between average temperature and average relative humidity based on Spearman rank correlation analysis. Accordingly, we considered both average temperature and average relative humidity as explanatory variables in the MGAM model. The MGAM generic model formula is described by Equation 3 below. The fixed effect is described by Equation 4, and the autoregressive random effect term, expressed as τit, is defined by Equation 5, where ln denotes the binomial link function and ln(E(y it )) is the count of daily cases during the study period.
l i t = n s ( tim e i , 9 * years ) + f ( tem p i t , 4 ) + f ( hum i i t , 3 ) ,
τ i t = ( ln ( y i , t − k ∗   ) − l i , t − k ) ,
ns ( tim e i , 9   years ) refers to using a natural cubic spline function with 9 degrees of freedom per year to control for seasonal and long-term trends. The time span of the data was from January 1, 2005 to December 31, 2018. f denotes smoothing functions realized by the natural cubic spline cross basis functions with uniform lag numbers. 36 Temp and humi represent the daily average temperature and relative humidity, which have four and three corresponding degrees of freedom, respectively. Finally, β ik is the coefficient of the autoregressive effect term. We specified a lag time of 2 weeks for both average temperature and average relative humidity. The order p of the autocorrelation term was set to 7. The degrees of freedom, order of the autocorrelation term, and lag time were determined by Akaike’s information criterion. The effects of meteorological factors on rubella incidence were quantified by estimating accumulative RRs.
Finally, a multivariate random variable meta-analysis model was used to estimate overall pooled effects. 37 The model is described by the following equation:
θ i ′  ∼  N k   ( θ ,  S i   +   ψ )
where θ is the coefficient of function f in Equation 3, θ i represents the valid parameter of area i, and θ i ′ is the value of θ i as estimated by Equation 4. θ i ′ conforms to a multivariate normal distribution   N k ( θ i ,  S i ) , where k represents the number of dimensions. Finally, ψ represents the variance–covariance matrix between the different areas.

Heterogeneity between the different geographical areas was measured using Cochran’s Q test and quantified by I 2. 38 Because the ranges of temperature and average humidity exhibited substantial overlap across geographical areas, these variables were analyzed on an absolute scale. Mixed generalized additive model analyses were conducted using the R package (v. 3.6.2, MathSoft Inc., Seattle, WA), 36 available on the R statistical platform. 39 Two-sided statistical tests were used, and the significance level was set at α = 0.05.

RESULTS

Descriptive statistics.

A total of 17,185 rubella cases were reported in Shaanxi Province during the study period, with an annual incidence of 3.27 cases per 100,000 population. The number of cases was notably higher during the pre-vaccination period before the peak of rubella in 2011 than during the postvaccination period (2012–2018), with a sharp increase in the number of rubella cases observed in 2011. Seasonal variation in rubella incidence was observed, with annual peaks in spring between April and June (Figure 1). The incidence was approximately 1.5 times as high in men compared with women (Table 1), and the areas of highest incidence within the province varied by year (Figure 2). The proportion of cases in children younger than 6 years declined steadily after 2014, with the exception of 2017, in which there were only 33 total cases. The median age among all cases from 2005 to 2011 was 10 years (interquartile range: 5.0–14.0 years), and for cases after 2012, it was 14 years (interquartile range: 10.0–17.0 years). Students made up the largest occupational group among cases, with the exception of 2017.

Figure 1.
Figure 1.

Weekly and annual incidence of rubella in Shaanxi Province, 2005–2018. This figure appears in color at www.ajtmh.org.

Citation: The American Journal of Tropical Medicine and Hygiene 104, 1; 10.4269/ajtmh.20-0585

Table 1

Epidemiological characteristics of rubella in Shaanxi Province, China, 2005–2018
Total cases Incidence (per 100,000) Gender ratio Age distribution (years), % Occupation, %
(Male:female) < 6 6–18 > 18 Peasant Student Scattered children Childcare Others
2005 478 1.31 1.34 44.8 46.9 8.4 5.2 46.2 30.8 13.6 4.2
2006 1841 4.95 1.30 34.2 57.2 8.6 2.9 60.5 17.4 17.7 1.5
2007 2,159 5.78 1.34 21.3 66.3 12.4 2.2 69.4 12 11.1 5.2
2008 1,595 4.29 1.45 26.3 63.3 10.4 2.3 68.3 14.1 11.3 4
2009 1,687 4.26 1.44 27.1 60.1 12.8 2.4 63.7 13.3 14.7 5.9
2010 1,270 3.38 1.43 38.5 47.1 14.4 3.5 47.7 17.5 25.1 6.1
2011 4,621 12.38 1.36 25 64.1 10.8 4.5 62.9 11.4 16.2 5
2012 1,138 2.97 1.38 31.4 50.8 17.8 10 50.4 20.1 14 5.5
2013 636 1.59 1.48 32.7 44.7 22.6 15.3 44.3 21.7 12.1 6.6
2014 268 0.45 1.31 40.7 35.1 24.3 15.3 35.8 32.1 10.5 6.3
2015 804 2.46 1.56 3 84.7 12.3 2.4 87.7 2.2 0.9 6.8
2016 591 1.72 1.34 0.7 88.2 11.2 2.2 90.2 0.5 0.2 6.9
2017 33 0.05 2.30 54.5 27.3 18.2 12.1 27.3 42.4 15.2 3
2018 64 0.15 1.56 12.5 43.3 43.8 6.3 73.4 10.9 3.1 6.3
Figure 2.
Figure 2.

Annual incidence of rubella in Shaanxi Province by county, 2005–2018. The map was created by Shuxuan Song in ArcGIS 10.1 Software, ESRI Inc., Redlands, CA (https://www.arcgis.com/index.html). This figure appears in color at www.ajtmh.org.

Citation: The American Journal of Tropical Medicine and Hygiene 104, 1; 10.4269/ajtmh.20-0585

Periodic changes in rubella incidence.

Weekly time series of rubella cases are shown in Figure 3A, and the interannual oscillations ranged from 0.5 to 4.8 years between 2005 and 2018 (Figure 3B). The time scales of 0.8–1.4 years, 3.8–4.8 years, and 0.5 years exhibited significant annual periodicity. The time scale of 0.8–1.4 years showed the highest global power, with the main impact observed from 2005 to 2012, mostly representing the pre-vaccination period. Time scales of 3.8–4.8 years and 0.5 years showed weaker global power, with impact ranges over the entire time series for the 3.8- to 4.8-year time scale and from 2010 to 2012 for the 0.5-year time scale (Figure 3C). The distribution of significant periodic signals at different time scales in the time domain exhibited local variation. Finally, the annual periodicity for the time series throughout the study period broadly followed a trend similar to that of rubella incidence.

Figure 3.
Figure 3.

Wavelet power spectrum of rubella time series in Shaanxi Province, 2005–2018. (A) Annual time series of Rubella cases normalized by SD. (B) Wavelet power spectra of the annual time series showing periodicity of the incidence of rubella. The period (vertical axis) corresponding to the yellow area represents the obvious period of the incidence of rubella, and the time (horizontal axis) corresponding to the yellow area represents years affected by the obvious period. The area inside the solid red line indicates a significant periodic oscillation according to the reliability test. The black arc is the cone of influence, which defines the effect of the boundary treatment. (C) Global wavelet power spectra. The blue solid line shows the average power at different time scales from 2005 to 2018, the red dashed line indicates the significance threshold, and the blue solid line above the red dashed line indicates that it has a significant period. This figure appears in color at www.ajtmh.org.

Citation: The American Journal of Tropical Medicine and Hygiene 104, 1; 10.4269/ajtmh.20-0585

Associations between meteorological factors and rubella incidence.

Although there were some differences in the magnitude of effect estimates between the three areas of Shaanxi Province, daily average temperature exhibited an overall nonlinear association with rubella in the pattern of an inverted V shape (Figure 4B). There was a positive correlation between temperature and RR of rubella for temperatures up to 0.23°C, at which the RR hit a peak of 1.22. Region-specific peaks were observed at −1.17°C, 3.85°C, and 7.42°C for the northern, central, and southern regions, respectively. However, the overall trends of the curves were consistent for the three regions. Based on the test of significance of residual heterogeneity, 68.2% of variation derived from between-region differences (Q = 25.18, P = 0.001) (Table 2).

Figure 4.
Figure 4.

Cumulative effects of meteorological factors on rubella in Shaanxi Province, 2005–2018. (A) Geographical division of Shaanxi Province (B and C) association between cumulative relative risks of rubella and temperature and relative humidity. Black bold lines represent pooled effects, and dashed lines represent area-specific estimates. Reference values were the medians (14.5°C for temperature and 67.0% for relative humidity). This figure appears in color at www.ajtmh.org.

Citation: The American Journal of Tropical Medicine and Hygiene 104, 1; 10.4269/ajtmh.20-0585

Table 2

Heterogeneity of meteorological variables on the incidence of rubella in geographical areas of Shaanxi Province, China
Variable Range Reference value Cochran Q test* I 2 Akaike information criterion Bayesian information criterion
Q Df P
Temperature (°C) (−12.74, 29.31) 13.49 25.18 8 0.001 68.20 35.18 41.97
Relative humidity (%) (25.99, 92.23) 67.79 17.90 10 0.057 44.10 30.29 44.45

All models were based on the absolute scale measurement of temperature and relative humidity with an intercept-only model being fitted.

Cochran Q test was used to test the significance of residual heterogeneity with the null hypothesis as no heterogeneity.

Relative humidity exhibited a nonlinear association with rubella incidence (intercept-only model) following an inverted W shape (Figure 4C). For the overall pooled estimated, there was a positive correlation between relative humidity and rubella when humidity was less than 41.6%. The correlation was then negative, except when relative humidity was between 68.8% and 71.0%, where the correlation was once again positive. Trends in the association between relative humidity and rubella were consistent overall across the three geographical regions, although the inflection points of the curves differed between regions. Based on the test of significance of residual heterogeneity, variation between the geographical regions (I 2 = 44.10) was marginal significant (Q = 17.90, P = 0.057) (Table 2).

DISCUSSION

Efforts to eliminate rubella have been stymied in recent years by several factors, especially in the Western Pacific and African regions. China is among the countries that have experienced frequent and widespread rubella outbreaks. Because of limited immunization rates among adults, rubella incidence is gradually shifting from younger age-groups to older individuals. There is a significant increase in rubella incidence among adults older than 20 years in Shaanxi. This trend is illustrated by findings from a nationwide study from 2005 to 2017. 40 This study found that high-risk rubella populations have shifted from children to middle school students and that the proportion of affected adults has gradually increased in China. The trend means that brings the potential for increased rates of CRS. One of the most likely reasons for this pattern is that rubella vaccination was implemented later in Shaanxi than in some of the more economically developed provinces, most of which have supplied rubella vaccine to women of childbearing age. The phenomenon of rising age among rubella cases has also occurred in Romania and Poland. Although an immunization program during childhood was implemented in these countries, large-scale outbreaks have continued to occur and have been mainly concentrated in the 10- to 19-year age-group. 41,42 Regarding such trends, the WHO has recommended that supplementary immunization efforts be implemented to increase immunity rates among adolescents. 43

Our study uses mathematical models to precisely measure periodicity of rubella incidence for the first time, thus providing a new perspective on the dynamics of this disease. By decomposing two-dimensional time series, the wavelet model used in this study represents an improvement over other models used in previous. 44 The data analyzed cover both pre-vaccination and postvaccination periods, thus enabling clear assessment of changes in rubella incidence and periodicity following implementation of vaccination strategies. In the postvaccination period, although the number of cases of rubella was greatly reduced, resulting in a decrease in the periodicity, especially the 0.5-year and 0.8- to 1.4-year cycles were almost disappeared, the inherent periodicity of the rubella epidemic would not change. We observed interannual oscillations of 0.5, 0.8–1.4, and 3.8–4.8 years in time series analysis. The 0.5-year cycle corresponds to the two peaks of winter and late spring each year. These peaks are weak and thus difficult to detect through epidemiological analysis, especially the winter peak. The estimated seasonal fluctuations found in our study are the same as those in the temperate or subtropical regions of North America, Europe, and South America, but differ from fluctuations found in near-equatorial regions such as central and northern Africa as well as Taiwan of China. 45 In subtropical or temperate regions, the morbidity rate is often highest in winter and spring. After the rubella vaccine was added to the Chinese EPI in 2008, the periodicity of both 0.5 and 0.8- to 1.4-year interannual oscillations was gradually weakened, although the 3.8- to 4.8-year oscillation was still evident. The 3.8- to 4.8-year pattern is a stable epidemic periodicity that has been observed in almost all geographical areas of Shaanxi Province, although it varies in other parts of China and in other countries. In this study, the 3.8- to 4.8-year pattern was stable and was not affected by vaccination. Many experts believe that this pattern results from a combination of climate and population factors, changes in overall population immunity, and virus mutation patterns.

MGAM were used to analyze the effect of climate on the incidence of rubella. To our best knowledge, our study is the first to quantitatively analyze the relationship between meteorological factors and rubella. We found nonlinear associations of both temperature and relative humidity with rubella incidence. These patterns have also been observed in previous studies of other infectious diseases transmitted through the respiratory tract including measles and mumps. 46,47 It was found that a minimum temperature and maximum relative humidity were associated with the highest risk of mumps. 46 The differing patterns observed for different infectious diseases may stem from differences in transmission characteristics of the viruses. The peak RR in the overall pooled estimates curve corresponded to an average temperature of about 0°C, which generally occurs in Shaanxi in winter. Similarly, a British study suggested that winter temperatures also influence the annual dynamics of measles. 17

The biological plausibility of a nonlinear relationship between meteorological variables and rubella incidence is supported by pathogenic laboratory evidence. Cold and dry conditions appear to encourage transmission of the rubella virus by increasing the survival of the virus in aerosols and increasing its volatility on aerosol surfaces. By contrast, hot, wet conditions appear to discourage aerosol transmission of rubella by reducing the quantity of the virus that is aerosolized and likely also by reducing the survival of the virus in aerosol. 40 This understanding of the dynamics of rubella transmission in relation to temperature and humidity is consistent with our findings. Shaanxi is located in a temperate zone, and its climate is cold and dry in winter and hot and rainy in summer. Incidence of rubella was in turn elevated in winter and spring and lower in summer and autumn.

Climate-related changes in rubella transmission rates are also understood to be driven by human behavior. 48 In the winter months, doors and windows are left closed to protect against the cold from outdoors. This can lead to increased indoor transmission of disease, especially in schools, where many students are in close contact and the virus can spread quickly among them. Behavioral factors help explain why winter outbreaks mainly occur in schools. During the spring and summer, interpersonal activity and contact tend to increase with warming temperatures, and the virus can spread more easily among the entire population, which can lead to widespread epidemics. This is borne out by the findings from our study, in which the RR of rubella was high in spring and early summer, with elevated risk generally lasting until the temperature rose to a level limiting outdoor activities. This high-risk period generally lasts for a substantial amount of time; thus, a large number of cases were accumulated. Finally, some studies have found that rubella incidence tends to peak during school periods, with lower transmission rates during the school summer and winter vacations. 49 However, we did not find strong evidence of this pattern in our analyses, mirroring results from a Japanese study. 9

In our study, the oscillation effects declined during some periods; however, the seasonal periodic characteristics remained stable. This suggests that vaccination most likely exhibits a vertical effect on the model of rubella dynamics, by which vaccination changes the amplitude of the periodic oscillations but would not change the size of the time scales. This conclusion is also supported by findings that vaccination exhibits a vertical perturbation effect by reducing growth in the rate of infected individuals as a proportion of the number of susceptible individuals. 17

Seasonality is a characteristic feature of many pathogens 50 and drives the timing of interventions. For vaccine-preventable infections such as rubella, interannual fluctuations in transmission can result in complex multi-annual rubella dynamics, 51 which in turn may affect the effectiveness of vaccination programs. 52 For many infectious diseases, seasonal patterns can be influenced by both environmental and behavioral factors. Thus, both types of factors must be quantified to determine optimal timing for interventions. The present study lays a foundation to predict future rubella epidemics and to strengthen public health measures through development of an early warning system.

Previous research has shown that temperature has a major effect on the occurrence of respiratory diseases, 16,17 and the dynamics of rubella are more sensitive to temperature than to relative humidity. In our study, although geographic variation in relative humidity failed to reach statistical significance, findings offer some evidence of variation in relative humidity across the three geographical areas of the province. Given the modest geographic variability across Shaanxi Province, it is likely that examinations of larger geographic areas across a wider range of latitude and longitude would show increased heterogeneity in relative humidity. 53

This study has a few strengths: this study uses wavelet analysis and MGAM to model epidemic characteristics of rubella in relation to meteorological factors across geographical regions with varying climatic characteristic. This modeling strategy is suitable for epidemiological research using data on infectious diseases from multiple regions. Our methods also address limitations of generalized additive and distributed lag non-linear models with respect to data autocorrelation and obtaining accurate and plausible weather estimation effects. 54,55

Several limitations to our study must be acknowledged. First, we analyzed temperature and relative humidity, but we did not consider other meteorological factors such as sunshine, rainfall, air pressure, and wind speed. Future studies incorporating these factors will yield a richer understanding of the relationship between meteorological conditions and transmission of rubella. Second, owing to limitations of the primary immunization program and monitoring system in China, it is difficult to obtain accurate data on population vaccination status. Future studies incorporating vaccination status from other data sources are thus warranted. Third, there are some residual confounding factors such as demographic, socioeconomic, behavioral, and physiological factors that may affect the associations between meteorological variables and rubella incidence. Although most of our findings are consistent with previous literature, caution should be exercised when generalizing these results to other parts of China with differing birth rates, vaccination rates, cultural customs, and economic development.

CONCLUSION

We observed periodic oscillations in the incidence of rubella across multiple time scales, as well as nonlinear associations of temperature and relative humidity with rubella incidence. Following implementation of a national childhood immunization strategy, adults had the highest risk of contracting rubella. To improve efficacy of public health interventions aimed at eliminating rubella, periodic, seasonal, and meteorological factors must be considered when designing and implementing these interventions. Complementary immunization strategies should also be implemented for vulnerable groups, especially adolescents and women of childbearing age. Further combination of meteorological factors with other influence factors would construct the rubella warning system.

Supplemental figure

ACKNOWLEDGMENTS

This work was supported by the National Natural Science Foundation of China (81803289), National Major Infectious Disease Control Project (2017ZX10104001), and the Shaanxi Medical Research Project (2006D096). We thank all medical staff and health practitioners who have contributed to the elimination of Rubella. We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript.

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    Jennifer KK , Kayla MM , Pastore R , Grabovac V , Takashima Y , Alexander JP Jr , Reef SE , Hagan JE , 2020. Progress toward rubella elimination–Western Pacific region, 2000–2019. MMWR Morb Mortal Wkly Rep 69: 745–750.

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    Su Q , Ma C , Wen N , Fan C , Yang H , Wang H , Yin Z , Feng Z , Hao L , Yang W , 2018. Epidemiological profile and progress toward rubella elimination in China. 10 years after nationwide introduction of rubella vaccine. Vaccine 36: 2079–2085.

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    Duke-Sylvester SM , Bolzoni L , Reall LA , 2011. Strong seasonality produces spatial asynchrony in the outbreak of infectious diseases. J R Soc Interf 8: 817–825.

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Author Notes

Address correspondence to Shaobai Zhang, Shaanxi Provincial Center for Disease Control and Prevention, No. 3 Jiandong Road, Beilin Distract, Xi’an 710054, People’s Republic of China, E-mail: maolyzhang@163.com or Zhongjun Shao, Department of Epidemiology, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University, No. 169 Changle West Road, Xincheng Distract, Xi’an 710032, China, E-mail: 13759981783@163.com.

Disclosure: The funders had no role in the study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding authors had full access to all of the data in this study and had final responsibility for the decision to submit for publication.

Authors’ addresses: Yu Ma, Kun Liu, Shuxuan Song, and Zhongjun Shao, Department of Epidemiology, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University, Xi’an, People’s Republic of China, E-mails: mayu.u@163.com, liukun5959@qq.com, 897437763@qq.com, and 13759981783@163.com. Weijun Hu and Shaobai Zhang, Shaanxi Provincial Center for Disease Control and Prevention, Xi’an, People’s Republic of China, E-mails: 270091746@qq.com and maolyzhang@163.com.

These authors contributed equally to this work.

Copyright:
© The American Society of Tropical Medicine and Hygiene 2021
Received:
03 Jun 2020
|
Accepted:
25 Aug 2020
|
Published Online:
23 Nov 2020
Cover The American Journal of Tropical Medicine and Hygiene
The American Journal of Tropical Medicine and Hygiene
Print ISSN:
0002-9637
Online ISSN:
1476-1645
  • Figure 1.
    View in gallery
    Figure 1.

    Weekly and annual incidence of rubella in Shaanxi Province, 2005–2018. This figure appears in color at www.ajtmh.org.

  • Figure 2.
    View in gallery
    Figure 2.

    Annual incidence of rubella in Shaanxi Province by county, 2005–2018. The map was created by Shuxuan Song in ArcGIS 10.1 Software, ESRI Inc., Redlands, CA (https://www.arcgis.com/index.html). This figure appears in color at www.ajtmh.org.

  • Figure 3.
    View in gallery
    Figure 3.

    Wavelet power spectrum of rubella time series in Shaanxi Province, 2005–2018. (A) Annual time series of Rubella cases normalized by SD. (B) Wavelet power spectra of the annual time series showing periodicity of the incidence of rubella. The period (vertical axis) corresponding to the yellow area represents the obvious period of the incidence of rubella, and the time (horizontal axis) corresponding to the yellow area represents years affected by the obvious period. The area inside the solid red line indicates a significant periodic oscillation according to the reliability test. The black arc is the cone of influence, which defines the effect of the boundary treatment. (C) Global wavelet power spectra. The blue solid line shows the average power at different time scales from 2005 to 2018, the red dashed line indicates the significance threshold, and the blue solid line above the red dashed line indicates that it has a significant period. This figure appears in color at www.ajtmh.org.

  • Figure 4.
    View in gallery
    Figure 4.

    Cumulative effects of meteorological factors on rubella in Shaanxi Province, 2005–2018. (A) Geographical division of Shaanxi Province (B and C) association between cumulative relative risks of rubella and temperature and relative humidity. Black bold lines represent pooled effects, and dashed lines represent area-specific estimates. Reference values were the medians (14.5°C for temperature and 67.0% for relative humidity). This figure appears in color at www.ajtmh.org.

Figure 1.

Weekly and annual incidence of rubella in Shaanxi Province, 2005–2018. This figure appears in color at www.ajtmh.org.
Figure 2.

Annual incidence of rubella in Shaanxi Province by county, 2005–2018. The map was created by Shuxuan Song in ArcGIS 10.1 Software, ESRI Inc., Redlands, CA (https://www.arcgis.com/index.html). This figure appears in color at www.ajtmh.org.
Figure 3.

Wavelet power spectrum of rubella time series in Shaanxi Province, 2005–2018. (A) Annual time series of Rubella cases normalized by SD. (B) Wavelet power spectra of the annual time series showing periodicity of the incidence of rubella. The period (vertical axis) corresponding to the yellow area represents the obvious period of the incidence of rubella, and the time (horizontal axis) corresponding to the yellow area represents years affected by the obvious period. The area inside the solid red line indicates a significant periodic oscillation according to the reliability test. The black arc is the cone of influence, which defines the effect of the boundary treatment. (C) Global wavelet power spectra. The blue solid line shows the average power at different time scales from 2005 to 2018, the red dashed line indicates the significance threshold, and the blue solid line above the red dashed line indicates that it has a significant period. This figure appears in color at www.ajtmh.org.
Figure 4.

Cumulative effects of meteorological factors on rubella in Shaanxi Province, 2005–2018. (A) Geographical division of Shaanxi Province (B and C) association between cumulative relative risks of rubella and temperature and relative humidity. Black bold lines represent pooled effects, and dashed lines represent area-specific estimates. Reference values were the medians (14.5°C for temperature and 67.0% for relative humidity). This figure appears in color at www.ajtmh.org.
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    Su Q , Ma C , Wen N , Fan C , Yang H , Wang H , Yin Z , Feng Z , Hao L , Yang W , 2018. Epidemiological profile and progress toward rubella elimination in China. 10 years after nationwide introduction of rubella vaccine. Vaccine 36: 2079–2085.

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    • Export Citation
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    Metcalf CJ , Cohen C , Lessler J , McAnerney JM , Ntshoe GM , Puren A , Klepac P , Tatem A , Grenfell BT , Bjørnstad ON , 2013. Implications of spatially heterogeneous vaccination coverage for the risk of congenital rubella syndrome in South Africa. J R Soc Interf 10: 20120756.

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    • Export Citation
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    Metcalf CJ , Munayco CV , Chowell G , Grenfell BT , Bjørnstad ON , 2011. Rubella metapopulation dynamics and importance of spatial coupling to the risk of congenital rubella syndrome in Peru. J R Soc Interf 8: 369–376.

    • Search Google Scholar
    • Export Citation
  • 23.

    Zhang X , Ma SL , Liu ZD , He J , 2019. Correlation analysis of rubella incidence and meteorological variables based on Chinese medicine theory of Yunqi. Chin J Integr Med 25: 911–916.

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    • Export Citation
  • 24.

    Ganna RC , Jessica EM , Bryan TG , 2013. Characterizing the dynamics of rubella relative to measles: the role of stochasticity. J R Soc Interf 10: 20130643.

    • Search Google Scholar
    • Export Citation
  • 25.

    Duke-Sylvester SM , Bolzoni L , Reall LA , 2011. Strong seasonality produces spatial asynchrony in the outbreak of infectious diseases. J R Soc Interf 8: 817–825.

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    • Export Citation
  • 26.

    Altizer S , Dobson A , Hosseini P , Hudson P , Pascual M , Rohani P , 2006. Seasonality and the dynamics of infectious diseases. Ecol Lett 9: 467–484.

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    Zhang Q , Chen Y , Fu Y , Liu T , Zhang Q , Guo P , Ma W , 2019. Epidemiology of dengue and the effect of seasonal climate variation on its dynamics: a spatio-temporal descriptive analysis in the Chao-Shan area on China’s southeastern coast. BMJ Open 9: e024197.

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    • Export Citation
  • 29.

    Tian H et al. 2017. Interannual cycles of Hantaan virus outbreaks at the human–animal interface in Central China are controlled by temperature and rainfall. Proc Natl Acad Sci U S A 114: 8041–8046.

    • Search Google Scholar
    • Export Citation
  • 30.

    Zhang XJ , Ma WP , Zhao NQ , Wang XL , 2016. Time series analysis of the association between ambient temperature and cerebrovascular morbidity in the elderly in Shanghai, China. Sci Rep 6: 19052.

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    • Export Citation
  • 31.

    Ma WP , Gu S , Wang Y , Wang Ar , Zhao Nq , Song Yy , 2014. The use of mixed generalized additive modeling to assess the effect of temperature on the usage of emergency electrocardiography examination among the elderly in Shanghai. PLoS One 9: e100284.

    • Search Google Scholar
    • Export Citation
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    Shaanxi Statistics Bureau , 2006–2019. Shaanxi Statistical Yearbook. Beijing, China: China Statistics Press.

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    Cohen MX , 2019. A better way to define and describe Morlet wavelets for time-frequency analysis. NeuroImage 199: 81–86.

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    • Export Citation
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    • Export Citation
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    Zuur AF , Leno EN , Walker NJ , Saveliev AA , Smith GM , 2009. Mixed Effects Models and Extensions in Ecology with R, Statistics for Biology and Health. Seattle, WA: Springer Science and Business Media.

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    • Export Citation
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    Paradowska-Stankiewicz I , Czarkowski MP , Derrough T , Stefanoff P , 2013. Ongoing outbreak of rubella among young male adults in Poland: increased risk of congenital rubella infections. Euro Surveil 18: 20485.

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    • Export Citation
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    • Export Citation
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    Cazelles B , Chavez M , Magny GC , Guégan JL , Hales S , 2007. Time-dependent spectra analysis of epidemiological time-series with wavelets. J R Soc Interf 4: 625–636.

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    • Export Citation
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    Liu SC , Wang JD , Lee CY , Chou MC , 1998. Seasonal variation of chickenpox, mumps and rubella in Taiwanese children estimated by pediatric clinics. J Microbiol Immunol Infect 31: 217–224.

    • Search Google Scholar
    • Export Citation
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    Yang Q , Yang Z , Ding H , Zhang X , Dong Z , Hu W , Liu X , Wang M , Hu G , Fu C , 2014. The relationship between meteorological factors and mumps incidence in Guangzhou, China, 2005–2012. Hum Vaccin Immunother 10: 2421–2432.

    • Search Google Scholar
    • Export Citation
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    Yang Q , Fu C , Dong Z , Hu W , Wang M , 2014. The effects of weather conditions on measles incidence in Guangzhou, Southern China. Hum Vaccin Immunother 10: 1104–1110.

    • Search Google Scholar
    • Export Citation
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    Wesolowski A , Metcalf CJ , Eagle N , Kombich J , Grenfell BT , Bjørnstad ON , Lessler J , Tatem AJ , Buckee CO , 2015. Quantifying seasonal population fluxes driving rubella transmission dynamics using mobile phone data. Proc Natl Acad Sci U S A 112: 11114–11119.

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