Spatiotemporal epidemiology and risk factors of scrub typhus in Hainan Province, China, 2011-2020

Associations of greenhouse gases, air pollutants and dynamics of scrub typhus incidence in China: a nationwide time-series study

BACKGROUND

Environmental factors have been identified as significant risk factors for scrub typhus. However, the impact of inorganic compounds such as greenhouse gases and air pollutants on the incidence of scrub typhus has not been evaluated.

METHODS

Our study investigated the correlation between greenhouse gases, air pollutants from the global atmospheric emissions database (2005-2018), and reported cases of scrub typhus from the Public Health Science Data Center. First, an early warning method was applied to estimate the epidemic threshold and the grading intensity threshold. Second, four statistical methods were used to assess the correlation and lag effects across different age groups and epidemic periods. Deep learning algorithms were employed to evaluate the predictive effect of environmental factors on the incidence of scrub typhus.

RESULTS

Using the Moving Epidemic Method (MEM) and Treed Distributed Lag Non-Linear Model (TDLNM), we found that the period from April to September is the epidemic season for scrub typhus in China. During this period, BC, CH4, NH3 and PM10 all reach key windows during their respective early warning lag periods. Interaction effects showed that increased CO exposure during the 0-2-month period led to an increased magnitude of the PM10 effect during the 3-7-month period. The Quantile-based G Computation (qgcomp) model revealed age-specific differences in susceptibility to environmental factors. In the Bayesian Kernel Machine Regression (BKMR) model, we identified NOx (RRmax (95% CI) = 103.14 (70.40, 135.87)) and NMVOC as the risk environmental factors for young adults, while CH4 (RRmax (95% CI) = 20.94 (9.26, 32.63)) was significantly associated with scrub typhus incidence in younger populations. For the elderly, N2O and NOx (RRmax (95% CI) = 30.23 (13.78, 46.68)) were identified as susceptibility factors for scrub typhus. The Weighted Quantile Sum (WQS) model revealed a significant risk effect of NOx on scrub typhus during periods of low risk, which are often overlooked (OR (95% CI) = 0.40 (0.23, 0.58)). During periods of medium to high risk, Convolutional Neural Networks (CNN) showed that environmental factors performed well in predicting the incidence of scrub typhus.

CONCLUSIONS

We found that most greenhouse gases and air pollutants increase the risk of contracting scrub typhus, mainly driven by CH4, NOx, and NMVOC. Among these, the primary high-level pollutants have long-term lag effects during the epidemic period. The correlation between environmental factors and scrub typhus incidence varies significantly across different age groups and risk periods. Among them, middle-aged and young individuals are more susceptible to the effects of exposure to mixed air pollutants. CNN algorithm can help develop a comprehensive early warning system for scrub typhus. These findings may have important implications for guiding effective public health interventions in the future. The primary interventions should focus on controlling greenhouse gas emissions and reducing air pollutants, which can, in turn, be used to support infectious disease monitoring systems through environmental monitoring. Moreover, given the cross-sectional approach of our study, these findings need to be confirmed through additional cohort studies.

Patient-centric analysis of Orientia tsutsugamushi spatial diversity patterns across Hainan Island, China

BACKGROUND

Scrub typhus, traditionally caused by Orientia tsutsugamushi, is a re-emerging public health concern within the Tsutsugamushi Triangle. Despite growing awareness, prevention strategies remain inadequate on Hainan Island, China, where scrub typhus poses a significant threat, especially in field-related environments.

METHODOLOGY/PRINCIPAL FINDINGS

Gene flow analysis of the tsa56 gene and multilocus sequence typing (MLST) were conducted on 156 previously confirmed scrub typhus cases from 2018 to 2021 across Hainan Island. By integrating published datasets, we identified 12 major sub-genotypes and traced their origins, revealing that these sub-genotypes share origins with isolates from Southeast Asia and coastal provinces and island of China, but also demonstrate unique local adaptations across all isolates. Alpha diversity index analysis was applied across administrative regions to identify hotspot regions. This analysis showed that nine out of the detected fourteen administrative regions, particularly along the northern and western coastlines and inland areas, exhibited relatively high genetic diversity, with the highest incidence observed in Qiongzhong, a centrally located city. Related major sequence types were mapped, and distances between locations were estimated, showing that identical MLST sequence types were observed to transfer across distances of 23 to 125 km between different sites on the island. Pathogen density was analyzed using quantitative real-time PCR targeting the tsa56 gene. Without accounting for potential confounding factors or dataset limitations, the Karp_B_2 sub-genotype showed a significant increasing trend in pathogen density with prolonged fever duration, while Gilliam sub-genotypes exhibited a slower or even declining trend.

CONCLUSIONS/SIGNIFICANCE

These findings emphasize the urgent need for targeted public health interventions, particularly focusing on vulnerable populations in rural and agricultural areas of nine key administrative regions where high genetic diversity and pathogen spread were observed. Additionally, this study provides valuable insights into the transmission dynamics and infection progression of scrub typhus, using gene flow analysis and multilocus sequence typing to identify major sub-genotypes.

Meteorological factors and normalized difference vegetation index drivers of scrub typhus incidence in Shandong Province based on a 16-year time-frequency analysis

OBJECTIVE

Scrub typhus, a natural epidemic disease that seriously impacts the health of the population, has imposed a substantial disease burden in Shandong Province. This study aimed to determine the periodicity of the scrub typhus incidence and identify the environmental risk factors affecting scrub typhus to help prevent and control its occurrence in Shandong Province.

METHODS

Monthly cases of scrub typhus, mean air temperature, relative humidity, cumulative precipitation, and Normalized Difference Vegetation Index (NDVI) data in Shandong Province from 2006 to 2021 were collected. Wavelet analysis was used to determine the incidence period of scrub typhus and to explore the relationships between environmental factors and the incidence of scrub typhus. Additionally, partial wavelet coherence (PWC) was employed to identify whether meteorological factors affect the association between NDVI and scrub typhus incidence.

RESULTS

Our results showed that scrub typhus incidence has a predominantly one-year period, followed by a less powerful six-month period. The wavelet coherence results revealed positive correlations between scrub typhus incidence and temperature, precipitation, relative humidity, and NDVI. Meteorological factors had a lagged effect of approximately 1-2 months (The phase angles of temperature, precipitation, and relative humidity were 59.15°, 56.57°, and 47.17° respectively) on scrub typhus incidence, whereas NDVI showed a lagged effect of approximately 1-2 weeks (The phase angle of NDVI was 18.11°). On the basis of partial wavelet analysis, we found that temperature and precipitation affected the association between NDVI and scrub typhus incidence.

CONCLUSION

Meteorological factors and NDVI play important roles in the occurrence of scrub typhus in Shandong Province. Moreover, temperature and precipitation can affect the role of NDVI. This study provides valuable recommendations and resources for the timely detection, mitigation, and management of scrub typhus in Shandong Province.

The application of the One Health approach in the management of five major zoonotic diseases using the World Bank domains: A scoping review

The international authorities, such as the Food and Agriculture Organization of the United Nations, World Health Organization, World Organization for Animal Health, United Nations Environment Programme, and World Bank, have endorsed the One Health concept as an effective approach to optimize the health of people, animals, and the environment. The One Health concept is considered as an integrated and unifying approach with the objective of sustainably balancing and optimizing the health of people, animals, and ecosystems. Despite variations in its definitions, the underlying principle remains consistent - recognizing the interconnected and interdependent health of humans, animals, and the environment, necessitating interdisciplinary collaboration to optimize health outcomes. The One Health approach has been applied in numerous countries for detecting, managing, and controlling diseases. Moreover, the concept has found application in various areas, including antimicrobial resistance, food safety, and ecotoxicology, with a growing demand. There is a growing consensus that the One Health concept and the United Nations Sustainable Development Goals mutually reinforce each other. The World Bank has recommended five domains as foundational building blocks for operationalising the One Health approach, which includes: i) One Health stakeholders, roles, and responsibilities; ii) financial and personal resources; iii) communication and information; iv) technical infrastructure; and v) governance. The domains provide a generalised overview of the One Health concept and guide to its application. We conducted a scoping review following the five-staged Arksey and O'Malley's framework. The objective of the review was to map and synthesise available evidence of application of the One Health approach to five major zoonotic diseases using the World Bank domains. Publications from the year 2004, marking the inception of the term 'One Health,' to 2022 were included. Information was charted and categorised against the World Bank domains identified as a priori. We included 1132 records obtained from three databases: Embase, Medline, and Global Health; as well as other sources. After excluding duplicates, screening for titles and abstracts, and full text screening, 20 articles that contained descriptions of 29 studies that implemented the One Health approach were selected for the review. We found that included studies varied in the extent to which the five domains were utilised. Less than half the total studies (45%) used all the five domains and none of the studies used all the sub-domains. The environmental sector showed an underrepresentation in the application of the One Health approach to zoonotic diseases as 14 (48%) studies in 10 articles did not mention it as a stakeholder. Sixty two percent of the studies mentioned receiving support from international partners in implementing the One Health approach and 76% of the studies were supported by international donors to conduct the studies. The review identified disparate funding mechanisms employed in the implementation of the One Health approach. However, there were limited discussions on plans for continuity and viability of these funding mechanisms in the future.