Background

Given the distinct transmission routes and modes of infection associated with respiratory infectious diseases (RIDs), many emerging and re-emerging RIDs have the potential to spread rapidly among susceptible populations worldwide. These RIDs persist as a significant public health challenge, imposing a global disease burden and jeopardizing individuals’ health and well-being [1, 2]. In the mainland of China, thirteen RIDs are subject to statutory reporting, with seasonal influenza and pulmonary tuberculosis (PTB) emerging as the most prevalent RIDs in recent decades [3]. In 2020, reported influenza morbidity stood at 81.58 per 100,000 individuals in the mainland of China, with a reported mortality rate of 0.005 per 100,000 [4]. Concurrently, PTB's notifiable incidence rate was 47.76 per 100,000, accompanied by a reported mortality rate of 0.14 per 100,000 [4]. Mumps, scarlet fever, pertussis, rubella and measles followed suit, representing five RIDs with comparatively elevated annual incidence rates that often clustered within specific regions [5,6,7,8,9,10,11,12]. Furthermore, the emergence and re-emergence of RIDs in recent years underscore the need for strengthen surveillance to enable early warnings and swift, effective responses. Consequently, a comprehensive elucidation of the demographic, temporal, seasonal, and spatial distribution characteristics of RIDs is crucial for the development of targeted, efficient interventions to curtail their propagation.

While several studies have explored the epidemiological features of various RIDs across different time frames and geographical locations in China [6,7,8,9,10,11,12,13,14], the present study stands apart due to its unique data sources and temporal scope. Leveraging data from the National Notifiable Disease Reporting System (NNDRS), this study provides a more comprehensive analysis, incorporating recent data and encompassing factors such as case categories (clinically diagnosed and laboratory-confirmed), gender, and occupation. The study’s focus centers on the demographic, temporal, seasonal, and spatial distribution characteristics of seven RIDs during the period 2017–2021, with the aim of furnishing valuable insights to inform effective control and prevention strategies.

Methods

Data collection

This study draws upon surveillance data for seven RIDs in the mainland of China spanning the years 2017 to 2021, sourced from the NNDRS. Among these RIDs, PTB, pertussis, measles, and scarlet fever are classified as category B infectious diseases, while seasonal influenza, mumps, and rubella fall under category C infectious diseases. The NNDRS is an internet-based real-time disease-reporting system that encompasses various healthcare facilities (community health centers, township health centers, and village clinics) across the mainland of China, boasting coverage of 55,077 health facilities in 31 provincial-level administrative divisions (PLADs) [15, 16]. The anonymized data for each reported case were compiled, encompassing demographic details (residential ID number, sex, age, and occupation) and clinical particulars (dates of symptom onset, diagnosis date, diagnosis category). Reported cases encompass both clinically diagnosed cases and laboratory-confirmed cases, aligned with the diagnosis criteria stipulated and disseminated by the National Health Commission of the People’s Republic of China [15, 16]. Clinically diagnosed cases are established based on primary symptoms, signs, and epidemiological links. In contrast, laboratory-confirmed cases entail a synthesis of clinical diagnosis and corroborating laboratory testing [15, 16].

Demographic data by age and sex for 31 PLADs and the country were culled from the National Bureau of Statistics of China (http://www.stats.gov.cn/english/Statisticaldata/AnnualData, accessed on April 20, 2023) [17]. The standard base map of China [GS(2019)1822] was sourced from the Standard Map Service (http://bzdt.ch.mnr.gov.cn/, accessed on April 20, 2023) under the Ministry of Natural Resources of the People's Republic of China.

Descriptive analysis

Incidence rates for both national and provincial levels were calculated on a monthly and annual basis, along with stratifications by sex and age groups. Visual representations in the form of bar graphs and scatter line plots were employed to depict trends in cases and incidence rates. Stacked plots and heat maps elucidated trends in occupation proportions and laboratory-confirmed cases, respectively. Seasonal attributes were visualized via radar charts, generated using SaTScan (version 10.1, Kulldorff and Information Management Services, Inc., Boston, MA, USA) outputs. Figures were crafted using R software with the ggplot2 package (version 4.0.0, R Development Core Team 2020) and OriginPro (version 2021, OriginLab Corporation, Northampton, MA, USA), while spatial characteristics were mapped using ArcGIS software (version 10.7, ESRI, Redlands, CA, USA).

Joinpoint regression analysis

Temporal trends were subjected to analysis using Joinpoint regression software (version 4.9.0.0, National Cancer Institute, Rockville, MD, USA) [18]. The default modeling method was the grid search method (GSM), while the Monte Carlo permutation test served as the default optimization strategy for the model. The Bayesian information criterion (BIC) was employed as a metric for gauging good fit [18]. The annual percent change (APC) serves as an indicator of the average annual percentage alteration in incidence rates and is represented by the slope of the fitted line. An APC > 0 (P < 0.05) denotes an increasing trend in incidence rates, whereas an APC < 0 (P < 0.05) signifies a decreasing trend. Conversely, trends lacking significant changes are denoted by APC values falling outside these ranges [13]. The APC is calculated using the following formula:

$$\ln (y) = \beta_{0} + \beta_{1} x$$
$$APC = \left[ {\frac{{{\text{y}}_{x + 1} - y_{x} }}{{y_{x} }}} \right] \times 100 = (e^{{\beta_{1} }} - 1) \times 100$$

Note: y is incidence rate, x is year, β1 is regression coefficient.

Seasonal and spatial analysis

The examination of seasonal and spatial characteristics was conducted using the SaTScan software. This software employed a Poisson probability model to identify clusters of RIDs in terms of seasonality, with a temporal window encompassing 50%. The software detected spatial clusters of RIDs in different regions for the years 2017 and 2021, employing the Poisson probability model and a spatial window covering 50% of the study areas [19]. In order to avoid overlap of PLADs in clusters, we adjust the default parameter to remove “Gini Optimized Cluster Collection” and set “No Geographical Overlap” in clusters [19]. By juxtaposing observed and predicted events within each location window, assuming a random distribution, probable clusters were pinpointed. The cluster exhibiting the highest log-likelihood ratio (LLR) was deemed the most likely cluster, while others were ranked as secondary clusters in a specific sequence [19]. The concept of relative risk (RR) denoted the ratio of estimated risks within and outside the cluster, serving as an indicator of the elevated infection risk faced by individuals residing within the cluster compared to those outside it [19].

In the context of the seasonal analysis, the 31 PLADs in the mainland of China were stratified into two distinct regions: southern China (encompassing Anhui, Fujian, Guangdong, Guangxi, Guizhou, Hainan, Hubei, Hunan, Jiangsu, Jiangxi, Shanghai, Sichuan, Yunnan, Zhejiang and Chongqing) and northern China (covering Bei**g, Gansu, Hebei, Henan, Heilongjiang, Jilin, Liaoning, Inner Mongolia, Ningxia, Qinghai, Shandong, Shanxi, Shaanxi, Tian**, Tibet and ** tailored strategies for RIDs control and prevention. However, the five-year duration of available surveillance data and potential variations in reporting across regions and levels present certain limitations.

Conclusions

The incidence rates of seasonal influenza, PTB, mumps, and scarlet fever remain high, whereas pertussis, rubella, and measles have reached lower levels. The reported incidence rates of PTB and measles have experienced significant declines between 2017 and 2021. Peaks in reported incidence rates for seasonal influenza, mumps, scarlet fever, pertussis, and rubella were observed in 2019. Each of the seven RIDs demonstrates distinct epidemiological characteristics. While significant progress has been made in RIDs control and prevention, challenges persist. Urgent measures are needed to strengthen surveillance efforts and develop effective digital/intelligent systems for precise RIDs surveillance, early detection of emerging or re-emerging events, and prompt responses.