aSchool of Public Health & Key Lab of Public Health Safety of the Ministry of Education & National Health Commission (NHC) Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China
bNational Center for Chronic Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 100050, China
cIntegrated Research on Disaster Risk (IRDR) International Center of Excellence on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health, Fudan University, Shanghai 200438, China
aSchool of Public Health & Key Lab of Public Health Safety of the Ministry of Education & National Health Commission (NHC) Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China
bNational Center for Chronic Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 100050, China
cIntegrated Research on Disaster Risk (IRDR) International Center of Excellence on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health, Fudan University, Shanghai 200438, China
Stock volatility constitutes an adverse psychological stressor, but few large-scale studies have focused on its impact on major adverse cardiovascular events (MACEs) and suicide. Here, we conducted an individual-level time-stratified case-crossover study to explore the association of daily stock volatility (daily returns and intra-daily oscillations for three kinds of stock indices) with MACEs and suicide among more than 12 million individual decedents from all counties in the mainland of China between 2013 and 2019. For daily stock returns, both stock increases and decreases were associated with increased mortality risks of all MACEs and suicide. There were consistent and positive associations between intra-daily stock oscillations and mortality due to MACEs and suicide. The excess mortality risks occurred at the current day (lag 0 d), persisted for two days, and were greatest for suicide and hemorrhagic stroke. Taking the present-day Shanghai and Shenzhen 300 Index as an example, a 1% decrease in daily returns was associated with 0.74%-1.04% and 1.77% increases in mortality risks of MACEs and suicide, respectively; the corresponding risk increments were 0.57%-0.85% and 0.92% for a 1% increase in daily returns and 0.67%-0.77% and 1.09% for a 1% increase in intra-daily stock oscillations. The excess risks were more pronounced among individuals aged 65-74 years, males, and those with lower education levels. Our findings revealed considerable health risks linked to sociopsychological stressors, which are helpful for the government and general public to mitigate the immediate cardiovascular and mental health risks associated with stock market volatility.
Ya Gao, Peng Yin, Haidong Kan, Renjie Chen, Maigeng Zhou.
Stock Volatility Increases the Mortality Risk of Major Adverse Cardiovascular Events and Suicide: A Case-Crossover Study of 12 Million Deaths.
Engineering, 2024, 42(11): 166-174 DOI:10.1016/j.eng.2024.05.010
Sociopsychological stressors, such as natural disasters [1], cancer diagnoses [2], sports events [3], unemployment [4], and economic decline [5], have been reported to increase the risk of a series of adverse health consequences [6], [7], [8]. Stock markets serve as essential channels for companies to raise capital and for investors to generate wealth. They are ubiquitous platforms that facilitate global economic expansion. The appeal of investing in these markets has grown recently due to technological advancements and easy access to trading platforms, attracting a diverse range of participants. Stock market volatility, epitomized by sudden and substantial price oscillations, engenders psychological anguish among investors. The attendant uncertainty, financial erosion, and apprehension of further descent collectively create tremendous pressure [9]. Such psychological ramifications have a profound influence on individuals’ cardiovascular and mental health, potentially leading to acute cardiovascular events as well as feelings of despair, unease and even thoughts of self-harm [10], [11], [12].
Previous studies have linked daily stock returns to major adverse cardiovascular events (MACEs) and suicide, the two most important fatal consequences [13], [14], [15]. For instance, Ma et al. [16] and Zhang et al. [17] reported U-shaped associations between daily stock returns and mortality from coronary heart disease and stroke, respectively, in Shanghai, China. According to Ref. [14], stock price decreases are associated with increased risks of hospitalization for attempted suicide. However, existing evidence is from ecological time-series studies that analyzed health data at the daily aggregate level, leading to difficulty in controlling for individual-level confounders [16], [18]. In addition, previous studies were mostly conducted in single cities or regions with limited sample sizes and generalizability of the results [17], [19]. Furthermore, all previous studies only evaluated the health consequences of daily stock returns, without separating the risks due to stock rises and falls, failing to consider the additional psychological shock from intra-daily stock oscillations that could vary in magnitude.
Stock markets in developing countries are deemed less mature than those in developed countries, partly due to the much greater proportion of individual investors rather than institutional investors. Consequently, investors in developing countries are thought to be more vulnerable to the psychosocial consequences associated with stock volatility. The potential public health implications of stock market volatility are substantial, as the health repercussions extend beyond individual investors to affect families, communities, and society at large. Recognizing and addressing the impact of psychosocial stress resulting from stock market volatility is crucial for developing effective interventions and support systems. Therefore, we conducted this individual-level, case-crossover study to investigate the impact of daily stock returns and intra-daily stock oscillations on mortality from MACEs and suicide using national death registry data from China. Stock returns refer to the percentage change in the closing price of stock indices on a trading day from the previous trading day, and oscillations represent the percentage change in stock indices within a single trading day. Potential effect modifications by gender, age, education level, season, and geographic region were also assessed.
2. Materials and methods
2.1. Study population and mortality data
We utilized the most nationally representative death registry, the China Cause of Death Reporting System (CDRS), which has been previously described [20]. According to the Chinese Center for Disease Control and Prevention, the CDRS collected death data for all 31 provincial regions in the mainland of China. This system covers 2 844 counties (equivalent to urban districts in the Chinese administrative system), accounting for all county-level units in the mainland of China. All deaths occurring in these counties were mandated to be registered in the CDRS with the decedents’ sociodemographic information and causes of death reported.
We extracted anonymous individual records for all decedents whose underlying cause of death was MACEs or suicide from the CDRS between January 2013 and December 2019. We strategically selected the period from January 2013 to December 2019 to leverage stabilized mortality data from the death registry starting in 2013 and to avoid any confounding effects of the coronavirus disease 2019 (COVID-19) pandemic. This seven-year timeframe also provides sufficient statistical power for examining stock volatility and ensures its relevance to typical economic conditions and impacts. The underlying cause of death refers to the disease or injury that initiated the train of morbid events leading directly to death, which was coded according to the International Classification of Diseases 10th Revision (ICD-10). Specifically, we included deaths from acute myocardial infarction (AMI; code I21), hemorrhagic stroke (codes I60-I62), ischemic stroke (code I63), and suicide (codes X60-X84, Y87.0) in this analysis. In addition, deaths from rheumatic heart disease (codes I00-I09) and myocardial disease (code I42) were extracted as negative controls for the analysis because of the lack of biological plausibility in the association with short-term stock market volatility. Information on the date of death, demographics (i.e., gender, age, and education), and residential address was also obtained. The study protocol was approved by the Institutional Review Board at the School of Public Health, Fudan University (IRB#2021-04-0889) with a waiver of informed consent.
2.2. Exposure data
We collected real-time index data for the Shanghai Stock Exchange Composite Index (Shanghai Index), Shenzhen Stock Exchange Composite Index (Shenzhen Index), and Shanghai and Shenzhen 300 Index (CSI 300) during the study period from the Wind website†(† https://www.wind.com.cn/.). The Shanghai Index is a capitalization-weighted index that tracks the daily price performance of all A-shares and B-shares listed on the Shanghai Stock Exchange. The Shenzhen Index is an index of 500 stocks that are traded on the Shenzhen Stock Exchange and is the main stock market index of the Shenzhen Stock Exchange. The CSI 300 is a free-float weighted index that consists of 300 A-share stocks listed on the Shanghai or Shenzhen Stock Exchanges. The calculation formulas for daily stock returns and intra-daily stock oscillations are as follows:
$\text{Daily stock returns}\ \left( \text{ } \% \text{ } \right)=\frac{\text{Closing price of today}\ -\ \text{Closing price of the previous trading day}}{\text{Closing price of the previous trading day}\ }$
$\text{Intra-daily stock oscillations ( } \% \text{ ) =}\ \frac{\text{Maximum price of today}\ -\ \text{Minimum price of today}}{\text{Opening price of today}\ }$
We removed extreme values of stock indices that exceeded three standard deviations from the means before the statistical analyses.
As air pollution and nonoptimal weather conditions are widely linked to the risk of MACEs and suicide [21], [22], we also collected the daily mean temperature, relative humidity, and air pollutant concentrations from the China Meteorological Data Service Centre and the China National Urban Air Quality Real-time Publishing Platform to allow for potential mutual adjustments.
2.3. Study design
Each death record was matched to the stock index by the date, and their associations were investigated by a time-stratified case-crossover approach. This design effectively controls for individual-level risk factors, such as demographic, socioeconomic, and behavioral factors, by self-matching [23], [24], [25], [26]. It also automatically removes temporal trends by selecting control groups within the same month. In our analysis, the case day was defined as the day of death. We then matched each case day with three or four control days in the same year, month, and day of the week to account for both long- and short-term variations and seasonality.
2.4. Statistical analysis
We applied conditional logistic regression models to analyze the mortality risks of MACEs and suicide in association with stock volatility indicators. We fit separate models on the current day (lag 0 d), the previous day (lag 1 d) and the previous two days (lag 2 d) to explore potential delayed effects. In models of daily stock returns, we adjusted for the current day opening index and intra-daily stock oscillations. Similarly, in models of intra-daily stock oscillations, we controlled for the current day opening index and daily stock returns.
We hypothesized a U- or V-shaped relationship for daily stock returns and mortality risks according to previous studies [13], [27]. We separately estimated the percentage changes in mortality risk associated with a 1% decrease or increase in daily stock returns, assuming a linear slope from the bottom to the extreme negatives or positives within the curves. The detailed calculation process of the mortality risk associated with stock falls is as follows:
First, we defined the extreme negative point (a) of the daily stock returns series. We obtained the coefficient (${{\beta }_{a}}$) and standard error ($\text{S}{{\text{E}}_{a}}$) of the point, the coefficient (${{\beta }_{c}}$) and standard error ($\text{S}{{\text{E}}_{c}}$) of the lowest point (c) of the curve, and the coefficient (${{\beta }_{0}}$) and standard error ($\text{S}{{\text{E}}_{0}}$) of the 0 value of the curve.
Second, c has three situations:
(1) c is on the left side of the 0 value: the falling sequence: ${{X}_{1}}$ point is a; ${{X}_{2}}$ point is c;
(2) c is on the right side of the 0 value: the falling sequence: ${{X}_{1}}$ point is a; ${{X}_{2}}$ point is 0;
(3) c = 0 or straight line (with 0 as c): falling sequence: ${{X}_{1}}$ point is a; ${{X}_{2}}$ point is 0.
Third, the slope between two points: $\beta $ and SE are obtained according to Eq. (3) and then used to calculate the percentage change in mortality according to Eqs. (2), (3), (4).
where ${{\hat{\beta }}_{1}}$ and ${{\hat{\beta }}_{2}}$ are coefficients; $\widehat{\text{SE}}_{1}^{2}$ and $\widehat{SE}_{2}^{2}$ are the standard errors; ${{X}_{1}}$ and ${{X}_{2}}$ are the exposure values; and CI refers to confidence interval. The mortality risk associated with stock increases was calculated similarly.
We assumed a linear relationship between intra-daily stock oscillations and mortality and thereby reported the percentage changes in mortality risk per 1% increase in oscillations.
In addition, we introduced a natural spline function of three degrees of freedom for stock indices in the above main models to flexibly visualize their relationships with mortality. From the curves, we derived the potential thresholds for stock indices, above which the mortality risk started to increase with statistical significance.
To explore potential effect modifications, we performed stratified analyses by gender (male vs female), age (18–64, 65–74, and ≥ 75), education (middle school or below vs high school or above), season (warm: May–October vs cold: November–April), and geographic region (eastern, central, and western China). The effect modification or interaction was explored by examining the between-stratum differences and their 95%CIs in the risk estimates. The difference was calculated as $\left( {{{\hat{Q}}}_{1}}-{{{\hat{Q}}}_{2}} \right)\pm 1.96\sqrt{\widehat{\text{SE}}_{1}^{2}+\widehat{\text{SE}}_{2}^{2}}$, where ${{\hat{Q}}_{1}}$ and ${{\hat{Q}}_{2}}$ are stratum-specific regression coefficients [28]. We then calculated the p value for interaction using the CIs of the between-strata difference as calculated above [29].
We conducted several sensitivity analyses. First, we additionally controlled for the three-day average values of fine particulate matter, temperature, and relative humidity to examine possible confounding by environmental conditions [17]. Second, using the above main models, we investigated the relationship of stock volatility with mortality due to rheumatic heart disease and myocardial disease as a negative control analysis. Third, we included deaths due to undetermined intent (codes Y10-Y34) in the main analysis.
All the statistical analyses were performed using R software (version 3.6.1; R Project for Statistical Computing) with the “survival” package. All analyses were two-sided with an alpha level of 0.05.
3. Results
3.1. Descriptive data
More than 12 million deaths were evaluated in this study, including 4 452 254 deaths from AMI, 4 126 286 from hemorrhagic stroke, 3 205 903 from ischemic stroke, 440 777 from suicide, and 366 326 from negative controls. Table 1 summarizes the basic characteristics of these deaths. More than half of the MACE deaths (N = 6 528 534) occurred in individuals over 75 years, with slightly more deaths occurring in the cold season, whereas more of the suicide deaths occurred in individuals between 18 and 65 years, with slightly more deaths occurring in the warm season. There were more deaths in males and in eastern China. As shown in Fig. S1 in Appendix A, the Shanghai Index, Shenzhen Index, and CSI 300 all experienced significant volatility during the study period. Taking the CSI 300 as an example, the daily stock returns varied from −8.7% to 6.7%, and the intra-daily stock oscillations ranged from 0.3% to 11.0% (Table S1 in Appendix A). There was a weak correlation between the two stock indices (Pearson's r = −0.06).
3.2. Regression results
For daily stock returns, we observed increased mortality risks of all MACEs and suicide for both stock rises and falls (Fig. 1), but the magnitude differed according to specific indices and their direction of volatility, outcomes, and lag days. Specifically, for stock indices, we observed that these associations were largely consistent, especially between the Shanghai Index and CSI 300, and the CSI 300 appears to be most closely associated with these mortality outcomes. For MACEs, the risks associated with stock falls were generally slightly greater than or similar to those associated with stock rises at lag 0 d. For suicide, stock falls showed larger risks than stock rises. Among the various mortality outcomes, suicide was most strongly associated with stock volatility; for three MACEs, hemorrhagic stroke showed a greater magnitude of mortality risks associated with stock volatility than AMI and ischemic stroke. The excess mortality risks occurred at lag 0 d and/or lag 1 d and attenuated substantially at lag 2 d (Table S2 in Appendix A). Taking the present-day CSI 300 index as an example, a 1% decrease in the daily returns was associated with increases of 0.74%-1.04% and 1.77% in the mortality risks of MACEs and suicide, respectively; the corresponding risk increments were 0.57%-0.85% and 0.92% for a 1% increase in the daily returns, respectively.
For intra-daily stock oscillations, we found consistent and positive associations with mortality due to MACEs and suicide (Fig. 2). The magnitude of associations was comparable for these outcomes. Notably, the risks at lag 0 d were the greatest, decreased at lag 1 d, and became nonsignificant at lag 2 d (Table S3 in Appendix A). For example, with a 1% increase in intra-daily stock oscillations of the CSI 300, the mortality risks of MACEs and suicide on the concurrent day increased by 0.67%-0.77% and 1.09%, respectively.
Fig. 3 and Fig. S2 in Appendix A show U-shaped curves for the associations between the daily returns of the three stock indices and mortality from MACEs at lag 0 d and lag 1 d, with the lowest approximately zero changes, illustrating that both stocks rises and falls could significantly increase the risk of MACE mortality. In contrast, there appeared to be an inverted J-shaped curve for daily stock returns and suicide mortality, indicating a much greater risk for stock falls than for stock rises. From the curves for the CSI 300, we predicted that the thresholds of daily stock returns that would significantly increase MACE risk were -1.73% to -2.56% for stock falls, which were slightly greater than those for stock rises (1.13%-1.59%). As indicated in Fig. 4 and Fig. S3 in Appendix A, there were almost linear curves for intra-daily stock oscillations and MACE mortality with a levelling-off trend at higher oscillations, whereas the increases in suicide mortality risk were more prominent at higher oscillations. Generally, the CSI 300 thresholds for suicide mortality risk (3.30%) were greater than those for MACE mortality risk (1.54%-1.61%).
Taking the CSI 300 as an example, we provided the results of stratified analyses for various mortality outcomes by age, gender, education level, region, and season (Figs. S4 and S5 in Appendix A). The patterns of between-stratum differences differed for daily stock rises, daily stock falls, and intra-daily stock oscillations. The excess mortality risks of MACEs associated with daily stock returns were higher among males and individuals with lower education levels. Additionally, these risks were more pronounced during the cool season when considering intra-daily stock oscillations. Regarding suicide, the excess mortality risk related to daily stock returns was greater among individuals aged 65-74 years and those residing in central China. Similarly, the excess mortality risk associated with intra-daily stock oscillations was elevated for individuals living in central China.
Sensitivity analyses revealed minimal alterations in our primary estimates when considering deaths due to undetermined intent or additionally adjusting for daily fine particulate matter concentrations and weather conditions (Tables S4-S6 in Appendix A). We did not find any significant associations between stock volatility indicators and mortality from rheumatic heart disease or myocardial disease (Figs. S6 and S7 in Appendix A).
4. Discussion
This case-crossover study examined more than 12 million deaths in China, the world’s largest developing country with the second-largest stock market turnover. We identified heightened mortality risks for all MACEs and suicide associated with stock market volatility. These risks persisted on the day of stock market volatility and the following day, with slightly higher risks observed during stock falls. Notably, the impact of stock market volatility on suicide risk was particularly pronounced. Those with vulnerable demographics, including individuals aged 65-74 years, males, and those with lower education levels, experienced more significant excess risks. Our nationwide analysis utilized individual-level data from a large death registry and incorporated negative controls, ensuring the representativeness and stability of our findings. Additionally, we identified thresholds of stock market volatility significantly linked to mortality risks, offering valuable insights for preventing MACEs and suicide among susceptible subpopulations.
Our findings revealed that stock market rises, falls, and intra-daily oscillations were significantly linked to increased risks of MACEs. The U-shaped associations of daily stock returns align with previous studies on mortality related to coronary heart disease and stroke [16], [17]. Importantly, our study distinguished between risks associated with stock market falls and rises, with the former showing slightly greater risks. This suggests that the psychological impact of wealth loss outweighs that of wealth gain. Moreover, we provided novel evidence that intra-daily stock oscillations independently contribute to the risk of MACEs, even after accounting for present-day stock returns. Although the exact mechanisms linking stock market volatility to cardiovascular health remain elusive, their association is plausible due to the acute psychological or mental stress induced by stock volatility. Stock volatility represents a rapid transfer of wealth among individual investors in stocks or related funds. Unexpected gains or losses in wealth can lead to considerable mental or psychological stress [30], [31]. Such stress has been shown to increase the risk of cardiovascular mortality and recurrent MACEs, such as AMI and stroke [8], [32], [33], [34]. Mental or psychological stress may induce cardiovascular events through multiple biological pathways, such as endothelial dysfunction, neuronal secretion changes, platelet aggregation, inflammatory responses, and reduced heart rate variability [32], [35], [36]. These physical stress responses could be mediated by increased activity of the sympathetic nervous system, hypothalamic release of pro-adrenocorticotropic hormones, cortisol inhibition of nitric oxide synthesis, and increased levels of proinflammatory cytokines. Acute emotional stress may also provoke severe catecholamine release, leading to direct myocyte injury due to calcium overload, coronary microvascular constriction, and increased left ventricular afterload [37], [38]. Several factors may explain the association of stock market rises with increased MACE risk. First, stress related to stock volatility remains significant even during upward trends, particularly for heavily invested individuals, potentially triggering cardiovascular events. Second, rapid changes in stock values can induce a fear of missing out, leading to impulsive decisions that escalate stress or result in regret and anxiety. Finally, the potential for sudden reversals in market upswings can cause uncertainty and fear, which are recognized risk factors for acute cardiovascular events.
Suicidal behaviors linked to stock market volatility have received limited attention in research. Our study revealed a significant association between stock market falls and suicide risk, consistent with findings from a time-series study in Ref. [14] indicating that stock falls may increase the risk of hospitalization for attempted suicide. Furthermore, the present study provided novel evidence that intra-daily stock oscillations independently correlate with increased suicide risk, with greater oscillations showing a more pronounced excess risk. These findings align with the broader literature on the relationship between abrupt economic changes and suicidal behaviors [39], [40]. The heightened risks associated with stock market volatility may be attributed to increased mental stress, which can lead to depression, a well-established precursor to suicide.
Our stratified analysis revealed that certain subgroups displayed heightened sensitivity to the impacts of stock market volatility. Individuals aged 65-74 years exhibited increased vulnerability, likely due to a greater prevalence of preexisting chronic diseases and greater investment exposure with reduced income postretirement. Males appeared more susceptible, possibly attributable to their greater prevalence of cardiovascular and mental disorders and increased involvement in the stock market compared to females [41]. Those with lower educational levels and lacking investment knowledge tend to engage in irrational behavior and expect disproportionate returns from the stock market, thereby amplifying mental stress from stock volatility. Furthermore, we observed a significantly greater excess mortality risk of suicide during the warm season, aligning with findings that high temperatures are associated with an increased risk of suicide [42].
Expanding our understanding of the potential influence of stock market volatility on health risks involves considering indirect exposure channels. First, recognizing the broad influence of stock market volatility on the overall economy and psychosocial climate is crucial. Economic downturns and market volatility can lead to financial strain, job insecurity, and reduced economic well-being, contributing to psychological distress and impacting health risks. Therefore, the observed associations between stock market volatility and mortality risk may reflect the broader psychosocial impact of economic instability. Second, it is important to acknowledge indirect exposure to the stock market through various channels, such as pension funds, retirement savings, or investments managed by family members or institutions. Media coverage and public discourse surrounding stock market volatility can also contribute to psychological stress and influence health risks, regardless of direct market involvement. A societal emphasis on economic indicators as measures of stability and success can exacerbate feelings of insecurity and stress during periods of market volatility, further impacting health outcomes.
To counteract the negative health effects of stock market volatility, a comprehensive approach is imperative. First, bolstering mental health services is essential for managing the psychological toll of market volatility. This involves increasing access to counseling and crisis intervention, particularly for vulnerable groups. Second, promoting financial literacy empowers people to wisely navigate market turbulence. Educating them on investment strategies and risk management reduces impulsive reactions. Third, fostering strong social support networks helps mitigate the health impacts of market swings. Encouraging community engagement and peer support groups provides emotional and practical aid during periods of uncertainty. Additionally, regulatory measures such as market surveillance and transparency can dampen volatility. Government intervention through fiscal stimulus and infrastructure investment can stabilize economies. Finally, public health campaigns should raise awareness about the health risks tied to market volatility and offer stress management tools. By implementing these measures collaboratively, stakeholders can safeguard individuals and communities during periods of market volatility.
Despite the limitations of our study, it is important to note the potential for exposure misclassification due to the varied participation in the stock market among decedents. Despite this, considering the generalized market influence can strengthen the findings. Various factors, such as economic conditions, media coverage, familial investments, and societal pressures during market volatility, collectively elevate stress levels, potentially impacting cardiovascular health and mental well-being. Notably, individuals not engaged in stock trading have zero exposure during both the case and control periods. Although their inclusion may dampen any true associations or inflate confidence intervals, it does not fundamentally alter observed relationships. Furthermore, although our study included all counties in China, the results might not be generalizable to countries with differing stock market characteristics and social health determinants. Additionally, the current time-stratified case-crossover design may still carry residual confounding, particularly from unmeasured physical stressors varying within a month. However, these stressors are unlikely to significantly bias our results due to speculated weak correlations with stock volatility. Finally, the lack of data on individual economic status limited our investigation into its modifying effects on the association between stock volatility and health outcomes. Including such economic metrics in future research could provide a more comprehensive assessment of the interactions and impacts.
5. Conclusions
This nationwide case-crossover study revealed a strong association between daily stock returns, intra-daily oscillations, and heightened risks of mortality due to MACEs and suicide. Our findings underscore the significant health hazards posed by sociopsychological stressors, emphasizing the need for government and public awareness to alleviate cardiovascular and mental health risks linked to stock market volatility. Targeted efforts in health education and mental support are essential, particularly during periods of pronounced stock market fluctuations.
Acknowledgments
This work is supported by the National Key Research and Development Program (2022YFC3702701), the Shanghai Municipal Science and Technology Commission (21TQ015), and the Shanghai International Science and Technology Partnership Project, China(21230780200).
We thank all staff in the 31 Provincial Centers for Disease Control and Prevention (CDC), and all other local CDCs in Disease Surveillance Point Systems for assisting with data collection, cleaning, and management.
Authors’ contribution
Maigeng Zhou and Renjie Chen contributed to the conceptualization of the study, funding acquisition, data curation, validation, review, editing, and supervision of the manuscript. Ya Gao and Peng Yin contributed to the data curation, formal analysis, methodology, validation, visualization, writing the original draft, and review and editing of the manuscript. Haidong Kan also contributed to the conceptualization of the study, funding acquisition. All authors had full access to all the data in the study and accept responsibility to submit for publication.
Compliance with ethics guidelines
Ya Gao, Peng Yin, Haidong Kan, Renjie Chen, and Maigeng Zhou declare that they have no conflict of interest or financial conflicts to disclose.
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