Epidemiological Characteristics and Trends of Scarlet Fever in Zhejiang Province of China: Population-Based Surveillance during 2004-2022.

Background: Over the past two decades, scarlet fever has resurged in some countries or areas. Nationwide nonpharmaceutical interventions changed the patterns of other infectious diseases, but its effects on the spread of scarlet fever were rarely studied. This study aimed to evaluate the changes in scarlet fever incidence in Zhejiang Province, China, before and during the COVID-19 pandemic periods and to provide references for scarlet fever prevention and control. Methods: Scarlet fever surveillance data in Zhejiang, China (2004-2022), were analyzed in three stages. Two-sample z test, ANOVA, and Tukey's test were used to compare and analyze the characteristics of disease spread at different stages. The ARIMA model was used to predict the overall trend. The data were obtained from the National Infectious Disease Reporting Information System. Results: A total of 28,652 cases of scarlet fever were reported across Zhejiang Province during the study period, with the lowest average monthly incidences in 2020 (0.111/100,000). The predominant areas affected were the northern and central regions of Zhejiang, and all regions of Zhejiang experienced a decrease in incidence in 2020. The steepest decline in incidence in 2020 was found in children aged 0-4 years (67.3% decrease from 23.8/100,000 to 7.8/100,000). The seasonal pattern changed, with peak occurrences in April to June and November to January during 2004-2019 and 2021 and a peak in January in 2020. The median duration from diagnosis to confirmation was highest before COVID-19 (4 days); however, it decreased to 1 day in 2020-2022, matching the other two medians. Conclusions: In 2020, Zhejiang experienced an unprecedented decrease in scarlet fever, with the lowest incidence in nearly 18 years, but it rebounded in 2021 and 2022. The seasonal epidemiologic characteristics of scarlet fever also changed with the COVID-19 outbreaks. This suggested that nationwide nonpharmaceutical interventions greatly depressed the spread of scarlet fever. With the relaxation of non-pharmaceutical intervention restrictions, scarlet fever may reappear. Government policymakers should prioritize the control of future scarlet fever outbreaks for public health.

Epidemiological Features of Hepatitis C in China From 2015 to 2021: Insights From National Surveillance Data.

The COVID-19 pandemic overwhelmed national health care systems, not least in the context of hepatitis elimination. This study investigates the effects of the pandemic response on the incidence rate, mortality rate, and case fatality rate (CFR) for hepatitis C virus (HCV) cases in China. We extracted the number of hepatitis C cases and HCV-related deaths by month and year for 2015 to 2021 in China and applied two proportional tests to analyze changes in the average yearly incidence rates, mortality rates, and CFRs for 2015 to 2020. We used the autoregressive integrated moving average model to predict these three rates for 2020 based on 2015 to 2019 HCV data. The incidence of hepatitis C decreased by 7.11% and 1.42% (P < .001) in 2020 and 2021, respectively, compared with 2015 to 2019, while it increased by 6.13% (P < .001) in 2021 relative to 2020. The monthly observed incidence in 2020 was significantly lower (-26.07%) than predicted. Meanwhile, no differences in mortality rate or CFR were observed between 2021, 2020, and 2015 to 2019. Our findings suggest that nonpharmaceutical interventions and behavioral changes to mitigate COVID-19 could have reduced hepatitis C incidence and accelerated China's implementation of a plan to eliminate HCV infection.

Impact of the COVID-19 pandemic and the dynamic COVID-zero strategy on HIV incidence and mortality in China.

BACKGROUND: In response to the coronavirus disease 2019 (COVID-19) pandemic, the Chinese government implemented the dynamic COVID-zero strategy. We hypothesized that pandemic mitigation measures might have reduced the incidence, mortality rates, and case fatality ratios (CFRs) of the human immunodeficiency virus (HIV) in 2020-2022. METHOD: We collected HIV incidence and mortality data from the website of the National Health Commission of the People's Republic of China from January 2015 to December 2022. We compared the observed and predicted HIV values in 2020-2022 with those in 2015-2019 using a two-ratio Z-test. RESULTS: From January 1, 2015, to December 31, 2022, a total of 480,747 HIV incident cases were reported in mainland China, of which 60,906 (per year) and 58,739 (per year) were reported in 2015-2019 (pre-COVID-19 stage) and 2020-2022 (post-COVID-19 stage), respectively. The average yearly HIV incidence decreased by 5.2450% (from 4.4143 to 4.1827 per 100,000 people, p <  0.001) in 2020-2022 compared with that in 2015-2019. However, the average yearly HIV mortality rates and CFRs increased by 14.1076 and 20.4238%, respectively (all p <  0.001), in 2020-2022 compared with those in 2015-2019. During the emergency phase in January 2020 to April 2020, the monthly incidence was significantly lower (23.7158%) than that during the corresponding period in 2015-2019, while the incidence during the routine stage in May 2020-December 2022 increased by 27.4334%, (all p <  0.001). The observed incidence and mortality rates for HIV decreased by 16.55 and 18.1052% in 2020, by 25.1274 and 20.2136% in 2021, and by 39.7921 and 31.7535% in 2022, respectively, compared with the predicted values, (all p <  0.001). CONCLUSIONS: The findings suggest that China's dynamic COVID-zero strategy may have partly disrupted HIV transmission and further slowed down its growth. Without China's dynamic COVID-zero strategy, HIV incidence and deaths in the country would have likely remained high in 2020-2022. There is an urgent need to expand and improve HIV prevention, care, and treatment, as well as surveillance in the future.

Spatio-temporally smoothed deep survival neural network.

The analysis of registry data has important implications for cancer monitoring, control, and treatment. In such analysis, (semi)parametric models, such as the Cox Proportional Hazards model, have been routinely adopted. In recent years, deep neural network (DNN) has been shown to excel in many fields with its flexibility and superior prediction performance, and it has been applied to the analysis of cancer survival data. Cancer registry data usually has a broad spatial and temporal coverage, leading to significant heterogeneity. Published studies have suggested that it is not sensible to fit one model for all spatial and temporal locations combined. On the other hand, it is inefficient to fit one model for each spatial/temporal location separately. Motivated by such considerations, in this study, we develop a spatio-temporally smoothed DNN approach for the analysis of cancer registry data with a (censored) survival outcome. This approach can accommodate the significant differences across time and space, while recognizing that the spatial and temporal changes are smooth. It is effectively realized via cutting-edge optimization techniques. To draw more definitive conclusions, we also develop an approach for assessing the importance of each individual input variable. Data on head and neck cancer (HNC) and pancreatic cancer from the Surveillance, Epidemiology, and End Results (SEER) database is analyzed. Compared to direct competitors, the proposed approach leads to network architectures that are smoother. Evaluated using the time-dependent Concordance-Index, it has a better prediction performance. The important variables are also biomedically sensible. Overall, this study can deliver a new and effective tool for deciphering cancer survival at the population level.

Collateral Impact of COVID-19 Prevention Measures on Re-Emergence of Scarlet Fever and Pertussis in Mainland China and Hong Kong China.

The incidence of scarlet fever and pertussis has increased significantly in China in recent years. During the COVID-19 pandemic, stringent non-pharmaceutical intervention measures were widely adopted to contain the spread of the virus, which may also have essential collateral impacts on other infectious diseases, such as scarlet fever and pertussis. We compared the incidence data of scarlet fever and pertussis in Mainland China and Hong Kong from 2004 to 2021 before and after the COVID-19 pandemic. The results show that the incidence of both diseases decreased significantly in 2020-2021 compared to the after-re-emergence stage in these two locations. Specifically, in 2020, scarlet fever decreased by 73.13% and pertussis by 76.63% in Mainland China, and 83.70% and 76.10%, respectively, in Hong Kong. In the absence of COVID-19, the predicted incidence of both diseases was much higher than the actual incidence in Mainland China and Hong Kong in 2020-2021. This study demonstrates that non-pharmaceutical measures implemented during the COVID-19 pandemic can partially reduce scarlet fever and pertussis re-emergence in Mainland China and Hong Kong.

The impact of COVID-19 continuous containment and mitigation strategy on the epidemic of vector-borne diseases in China.

BACKGROUND: This study explored the effect of a continuous mitigation and containment strategy for coronavirus disease 2019 (COVID-19) on five vector-borne diseases (VBDs) in China from 2020 to 2021. METHODS: Data on VBDs from 2015 to 2021 were obtained from the National Health Commission of the People's Republic of China, and the actual trend in disease activity in 2020-2021 was compared with that in 2015-2019 using a two-ratio Z-test and two proportional tests. Similarly, the estimated trend in disease activity was compared with the actual trend in disease activity in 2020. RESULTS: There were 13,456 and 3684 average yearly cases of VBDs in 2015-2019 and 2020, respectively. This represents a decrease in the average yearly incidence of total VBDs of 72.95% in 2020, from 0.9753 per 100,000 population in 2015-2019 to 0.2638 per 100,000 population in 2020 (t = 75.17, P < 0.001). The observed morbidity rates of the overall VBDs were significantly lower than the predicted rates (47.04% reduction; t = 31.72, P < 0.001). The greatest decline was found in dengue, with a 77.13% reduction (observed rate vs predicted rate: 0.0574 vs. 0.2510 per 100,000; t = 41.42, P < 0.001). Similarly, the average yearly mortality rate of total VBDs decreased by 77.60%, from 0.0064 per 100,000 population in 2015-2019 to 0.0014 per 100,000 population in 2020 (t = 6.58, P < 0.001). A decreasing trend was also seen in the monthly incidence of total VBDs in 2021 compared to 2020 by 43.14% (t = 5.48, P < 0.001). CONCLUSIONS: The results of this study verify that the mobility and mortality rates of VBDs significantly decreased from 2015-2019 to 2020-2021, and that they are possibly associated to the continuous COVID-19 mitigation and contamination strategy implemented in China in 2020-2021.

Analysis of cancer omics data: a selective review of statistical techniques.

Cancer is an omics disease. The development in high-throughput profiling has fundamentally changed cancer research and clinical practice. Compared with clinical, demographic and environmental data, the analysis of omics data-which has higher dimensionality, weaker signals and more complex distributional properties-is much more challenging. Developments in the literature are often 'scattered', with individual studies focused on one or a few closely related methods. The goal of this review is to assist cancer researchers with limited statistical expertise in establishing the 'overall framework' of cancer omics data analysis. To facilitate understanding, we mainly focus on intuition, concepts and key steps, and refer readers to the original publications for mathematical details. This review broadly covers unsupervised and supervised analysis, as well as individual-gene-based, gene-set-based and gene-network-based analysis. We also briefly discuss 'special topics' including interaction analysis, multi-datasets analysis and multi-omics analysis.

The impact of the COVID-19 pandemic on the incidence and mortality of zoonotic diseases in China.

BACKGROUND: It has been reported that strict non-pharmaceutical measures can significantly reduce the incidence and mortality of respiratory and intestinal infectious diseases during the COVID-19 pandemic, but there are limited reports on the impact in terms of the rates of zoonotic diseases. METHODS: We extracted the incidence and mortality data of eight notifiable infectious zoonotic diseases from the website of the National Health Commission of the People's Republic of China for the period of January 2015 to April 2021. RESULTS: First, the overall incidence of zoonotic diseases decreased from 0.3714 per 100 000 in 2015-2019 to 0.2756 in 2020 (25.79% reduction, p<0.001); however, a dramatic increase in activity was seen in 2021 compared with 2020 (0.4478 per 100 000 in 2021, 62.47% increase, p<0.001). Anthrax, brucellosis, leptospirosis and hydatid disease exhibited significant upward trends in 2021. Second, analysed further by stages, the monthly incidence in the routine stage (from May to December 2020) was much higher than that in the emergency stage of the COVID-19 (from January to April 2020) (55.33% increase, p<0.001). We also found that the monthly observed incidence was significantly lower than the predicted incidence of a 10.29% reduction in the emergency stage. Third, no differences were seen in mortality between 2021 and 2020, while a significant decline was found in 2020 compared with the previous 5 years (72.70%, p<0.001). CONCLUSIONS: Strict containment and feasible suppression strategies during the 2020 period of the COVID-19 pandemic had positive impacts on the overall incidence of zoonotic diseases in China. However, anthrax, brucellosis, leptospirosis and hydatid diseases might increase with the relaxation of non-pharmacological interventions in 2021.

Associations of physical activity and screen time with suboptimal health status and sleep quality among Chinese college freshmen: A cross-sectional study.

This study aimed to investigate the associations of physical activity (PA) and screen time (ST) with physiological, psychological, and social health-particularly regarding effects on sleep quality-among Chinese college freshmen. A cross-sectional survey was conducted at Renmin University of China, in Beijing. A total of 5,233 students were surveyed in September 2015. Participants completed a self-report questionnaire on their demographic characteristics, tobacco and alcohol use, PA, ST, sleep quality, and health status. Multivariate logistic regression was performed to examine the independent and interactive associations between PA and ST with sleep quality and suboptimal health status. In total, 10.43%, 13.18%, and 13.26% of the 5,233 students had physiological, psychological, and social suboptimal health status, respectively. The prevalence of poor sleep quality was 37.94%. High ST and high PA were significantly associated with physiological suboptimal health status (aOR = 1.39, 95% CI: 1.16-1.68, and aOR = 0.55, 95% CI: 0.45-0.71), psychological suboptimal health status (aOR = 1.43, 95% CI: 1.21-1.69, and aOR = 0.57, 95% CI: 0.47-0.69), social suboptimal health status (aOR = 1.27, 95% CI: 1.08-1.50, and aOR = 0.63, 95% CI: 0.52-0.77), and poor sleep quality (aOR = 1.20, 95% CI: 1.03-1.39, and aOR = 0.64, 95% CI: 0.55-0.76). Additionally, low ST and high PA were interactively negatively associated with poor sleep quality (aOR = 0.56, 95% CI: 0.45-0.70), physiological suboptimal health status (aOR = 0.49, 95% CI: 0.40-0.59), psychological suboptimal health status (aOR = 0.48, 95% CI: 0.39-0.58), and social suboptimal health status (aOR = 0.49, 95% CI: 0.40-0.59). These findings suggested there are independent and interactive associations of low ST and high PA with poor sleep quality and suboptimal health status among Chinese college freshmen.

Healthcare, Insurance, and Medical Expenditure of the Floating Population in Beijing, China.

Background: China has a large floating population created by the fast urbanization and unique hukou system. With low socioeconomic status, labor-intensive jobs, and the lack of portability of health insurance, the floating population are often disadvantageous in healthcare. However, there is often insufficient attention to healthcare of the floating population. Method: To provide an informative description of certain aspects of the floating population under healthcare, particularly including demographic characteristics, illness conditions, insurance utilization, and medical expenditure, a survey study was conducted in Beijing, China, collecting data on 437 subjects. Characteristics of the floating population and treatments of their illness conditions are examined using univariate and multivariate regression analysis. Results: Personal characteristics and healthcare of the floating population are examined in detail. It is found that the floating population has low insurance coverage and utilization rates. Multiple personal characteristics are identified as significantly associated with insurance utilization and medical expenditure. Conclusions: This study suggests the necessity of further improving healthcare and health insurance protection for the floating population. The identified significant characteristics may assist healthcare providers and other stakeholders identifying the less advantaged.

Human disease cost network analysis.

Diseases can be interconnected. In the recent years, there has been a surge of multidisease studies. Among them, HDN (human disease network) analysis takes a system perspective, examines the interconnections among diseases along with their individual properties, and has demonstrated great potential. Most of the existing HDN analyses are based on either molecular information (which may be unreliable and have limited clinical relevance) or phenotypic measures (which may have limited implications for disease management and not directly reflect disease severity). In this study, we take advantage of the uniquely valuable Taiwan NHIRD (National Health Insurance Research Database) data and conduct an HDN analysis of disease treatment cost. Complementing the existing literature, treatment cost can serve as a surrogate of disease severity (and hence be clinically highly relevant) and also directly describe the financial burden of illness (and hence be uniquely informative for disease management). With inpatient and outpatient treatment data on close to 1 million randomly selected subjects and collected during the period of 2000 to 2013, the human disease cost network is constructed using a novel copula-based approach and the weighted correlation-based network construction technique. Extensive analysis is conducted, and the results are found to be biomedically sensible.

Identification of gene-environment interactions with marginal penalization.

Gene-environment (G-E) interaction analysis has been extensively conducted for complex diseases. In marginal analysis, the common practice is to conduct likelihood-based (and other "standard") estimation with each marginal model, and then select significant G-E interactions and main effects based on p values and multiple comparisons adjustment. One limitation of this approach is that the identification results often do not respect the "main effects, interactions" hierarchy, which has been stressed in recent G-E interaction analyses. There is some recent effort tackling this problem, however, with very complex formulations. Another limitation of the common practice is that it may not perform well when regularization is needed, for example, because of "non-normal" distributions. In this article, we propose a marginal penalization approach which adopts a novel penalty to directly tackle the aforementioned problems. The proposed approach has a framework more coherent with that of the recently developed joint analysis methods and an intuitive formulation, and can be effectively realized. In simulation, it outperforms the popular significance-based analysis and simple penalization-based alternatives. Promising findings are made in the analysis of a single-nucleotide polymorphism and a gene expression data.

Population-Based Brain Tumor Survival Analysis via Spatial- and Temporal-Smoothing.

In cancer research, population-based survival analysis has played an important role. In this article, we conduct survival analysis on patients with brain tumors using the SEER (Surveillance, Epidemiology, and End Results) database from the NCI (National Cancer Institute). It has been recognized that cancer survival models have spatial and temporal variations which are caused by multiple factors, but such variations are usually not "abrupt" (that is, they should be smooth). As such, spatially and temporally pooling all data and analyzing each spatial/temporal point separately are either inappropriate or ineffective. In this article, we develop and implement a spatial- and temporal-smoothing technique, which can effectively accommodate spatial/temporal variations and realize information borrowing across spatial/temporal points. Simulation demonstrates effectiveness of the proposed approach in improving estimation. Data on a total of 123,571 patients with brain tumors diagnosed between 1911 and 2010 from 16 SEER sites is analyzed. Findings different from separate estimation and simple pooling are made. Overall, this study may provide a practically useful way for modeling the survival of brain tumor (and other cancers) using population data.

Medical expenditure for middle-aged and elderly in Beijing.

BACKGROUND: High medical expenditures serve as a major obstacle for many people trying to access healthcare. Our goals are to provide an updated and comprehensive description of each category of medical expenditure in inpatient and outpatient treatment, and to identify factors associated with medical expenditures. METHODS: A survey of the middle-aged and elderly was conducted in August 2016 in Beijing, China. Data were collected from 808 random samples. Each participant had reported at least one inpatient or outpatient treatment episode and was 45 years old or older, were collected. Chi-squared tests, t-tests, multivariate analysis, and a linear regression were conducted in the data analysis. RESULTS: A total of 452 and 734 subjects had at least one inpatient and outpatient treatment, respectively. Even though insurance covered a significant amount of the total cost, the remaining out-of-pocket cost was still high, possibly resulting in financial difficulties for a number of the subjects. Demographic and socioeconomic factors were found to be associated with various costs. CONCLUSIONS: Our findings suggest that the government may need to further adjust health care and health insurance systems to alleviate financial burdens caused by illness and improve the effective utilization of healthcare services.

Descriptions of self-treatment for the middle-aged and elderly in Shanxi, China.

OBJECTIVES: Self-treatment is a widespread practice among patients with common symptoms and ailments; it is necessary to explore multiple aspects of it. Notably, there is little research into self-treatment among middle-aged and elderly people, who are more likely to fall ill. Our goals are to provide a comprehensive description of self-treatment and explore associated factors with insurance utilization and expenditures among the middle-aged and elderly populations in China. METHODS: A survey was conducted in July 2016 in Shanxi, China. A stratified sampling scheme was applied to achieve representativeness. A total of 972 subjects were surveyed. Descriptive statistics, t- and Chi-squared tests, multivariate logistic regression, and multivariate linear regression were utilized. RESULTS: In our study, 772 (79.4%) of the surveyed subjects self-treated during the previous twelve months. Among them, 253 (32.8%) used health insurance. Subjects' characteristics were associated with insurance utilization and expenditures for self-treatment. Total cost was positively associated with insurance utilization. The subjects with a junior high education (p-value < 0.001, aOR = 0.049) and senior high education (p-value = 0.020, aOR = 0.146) had a lower probability of using insurance. For both total costs and out-of-pocket costs, subjects who were 51 to 60 years old had lower costs. The subjects who were seriously sick and had a primary school education, as well as enterprise occupations, had higher costs. Self-treatment times were also positively associated with costs. Finally, it was found that subjects who didn't use insurance had lower total costs. CONCLUSIONS: The prevalence of self-treatment was high (79.4%). Some characteristics were associated with insurance utilization and expenditures in self-treatment. Our results may be helpful for policy interventions, which are needed to further improve the effectiveness of health insurance in China.

Association between lifestyle factors and suboptimal health status among Chinese college freshmen: a cross-sectional study.

BACKGROUND: Suboptimal health status (SHS) is the third state between good health and disease. SHS is the clinical or pre-disease status of psychosomatic disease and a major global public health challenge. Although its underlying causes remain unclear, lifestyle is one of the most important factors affecting health status. METHODS: A cross-sectional survey was conducted at Renmin University of China in September of 2015. Data were collected from college freshmen using a questionnaire covering characteristics, lifestyle, nutrition status, and health status. A total of 6025 questionnaires were distributed during the study period, and 5344 completed responses were received. RESULTS: The prevalence rates for the "healthy," "SHS," and "disease" groups of college freshmen were 46.7% (2433), 51.2% (2667), and 2.1% (111), respectively. It is notable that health status was significantly positively correlated with lifestyle (Spearman's r = 0.4435, p < 0.001). The multivariate Logistic regression results showed that students who were relatively younger and students from rural areas had a higher percentage of SHS. Good sleep quality (aOR = 0.650, 95%CI = 0.612-0.690), abundant physical exercise (aOR = 0.889, 95%CI = 0.845-0.933), and adequate nutrition intake (aOR = 0.868, 95%CI = 0.864-0.908) are negatively associated with SHS. Overuse of electronic devices (aOR = 1.066, 95%CI = 1.013-1.121), smoking (aOR = 1.824, 95%CI = 1.195-2.755), and weight loss (aOR = 1.255, 95%CI = 1.043-1.509) are positively associated with SHS. CONCLUSIONS: Poor lifestyle behaviors are associated with SHS. In particular, the overuse of electronic devices is one of underlying causes of SHS. By altering lifestyle behaviors for the better, the health statuses of these college freshmen can be effectively improved.