A spatial-temporal statistical analysis of health seasonality: explaining HFMD infections within a children population along the Vietnamese south central coast

Potential therapeutic substances for hand-foot-and-mouth disease in the interplay of enteroviruses and type I interferon

OBJECTIVES

Hand-foot-and-mouth disease (HFMD) is widespread in the world. Severe HFMD can lead to complications like pneumonia, encephalitis, myocarditis, transverse myelitis and even death. Since HFMD is caused by at least 20 types of enteroviruses, there is an urgent need for broad-spectrum antiviral drugs to help control the spread of HFMD outbreaks.

METHODS

Type I interferon (IFN), as an indispensable part of the immune response, plays a key role in the inhibition of the enterovirus replication cycle without species specificity, and regulation of the innate immune system by inducing the activation of the IFN-stimulated genes.

CONCLUSIONS

Here, the interplay of enteroviruses and type I IFN was systematically summarized, including pathways for the activation and evasion of type I IFN. Besides, we proposed promising anti-enterovirus agents with therapeutic potential.

Spatiotemporal filtering modeling of hand, foot, and mouth disease: a case study from East China, 2009-2015

Hand, foot, and mouth disease (HFMD) shows spatiotemporal heterogeneity in China. A spatiotemporal filtering model was constructed and applied to HFMD data to explore the underlying spatiotemporal structure of the disease and determine the impact of different spatiotemporal weight matrices on the results. HFMD cases and covariate data in East China were collected between 2009 and 2015. The different spatiotemporal weight matrices formed by Rook, K-nearest neighbour (KNN; K = 1), distance, and second-order spatial weight matrices (SO-SWM) with first-order temporal weight matrices in contemporaneous and lagged forms were decomposed, and spatiotemporal filtering model was constructed by selecting eigenvectors according to MC and the AIC. We used MI, standard deviation of the regression coefficients, and five indices (AIC, BIC, DIC, R2, and MSE) to compare the spatiotemporal filtering model with a Bayesian spatiotemporal model. The eigenvectors effectively removed spatial correlation in the model residuals (Moran's I < 0.2, p > 0.05). The Bayesian spatiotemporal model's Rook weight matrix outperformed others. The spatiotemporal filtering model with SO-SWM was superior, as shown by lower AIC (92,029.60), BIC (92,681.20), and MSE (418,022.7) values, and higher R2 (0.56) value. All spatiotemporal contemporaneous structures outperformed the lagged structures. Additionally, eigenvector maps from the Rook and SO-SWM closely resembled incidence patterns of HFMD.

Influencing factors of hand, foot, and mouth disease based on structural equation modeling in Hubei, China

Hand, foot and mouth disease (HFMD) is a major public health issue in Hubei Province; however, research on the direct and indirect effects of factors affecting HFMD is limited. This study employed structural equation modeling (SEM) and geographically weighted regression (GWR) to investigate the various impacts and spatial variations in the factors influencing the HFMD epidemic in Hubei Province from 2016 to 2018. The results indicated that (1) with respect to the direct effects, the number of primary school students had the greatest positive direct effect on the number of HFMD cases, with a coefficient of 0.542. Socioeconomic factors strongly influence HFMD cases more directly than natural factors. (2) Concerning indirect effects, the minimum temperature, combined with the per capita disposable income of urban residents, had the greatest positive indirect effect on HFMD cases, with a coefficient of 0.022. Both natural and social factors had more substantial direct impacts on the HFMD epidemic than indirect impacts. (3) Regarding total effects, the number of primary school students, through various natural and social factors, had a total effect coefficient of 0.503 on HFMD incidence. (4) The number of primary school students, per capita GDP, and the number of hospital beds per thousand people had the most significant spatial impacts on HFMD cases. In underdeveloped regions, the HFMD epidemic is more heavily influenced by economic factors.

Global, regional, national epidemiology and trends of neglected tropical diseases in youths and young adults aged 15-39 years from 1990 to 2019: findings from the global burden of disease study 2019

BACKGROUND

In recent years, the escalating concern for neglected tropical diseases (NTDs) has been recognized as a pressing global health issue. This concern is acutely manifested in low- and middle-income countries, where there is an escalating prevalence among adolescents and young adults. The burgeoning of these conditions threatens to impair patients' occupational capabilities and overall life quality. Despite the considerable global impact of NTDs, comprehensive studies focusing on their impact in younger populations remain scarce. Our study aims to describe the global prevalence of neglected tropical diseases among people aged 15 to 39 years over the 30-year period from 1990 to 2019, and to project the disease burden of the disease up to 2040.

METHODS

Annual data on incident cases, mortality, and disability-adjusted life years (DALYs) for NTDs were procured from the Global Burden of Disease Study 2019 (GBD 2019). These data were stratified by global and regional distribution, country, social development index (SDI), age, and sex. We computed age-standardized rates (ASRs) and the numbers of incident cases, mortalities, and DALYs from 1990 to 2019. The estimated annual percentage change (EAPC) in the ASRs was calculated to evaluate evolving trends.

RESULTS

In 2019, it was estimated that there were approximately 552 million NTD cases globally (95% Uncertainty Interval [UI]: 519.9 million to 586.3 million), a 29% decrease since 1990. South Asia reported the highest NTD prevalence, with an estimated 171.7 million cases (95% UI: 150.4 million to 198.6 million). Among the five SDI categories, the prevalence of NTDs was highest in the moderate and low SDI regions in 1990 (approximately 270.5 million cases) and 2019 (approximately 176.5 million cases). Sub-Saharan Africa recorded the most significant decline in NTD cases over the past three decades. Overall, there was a significant inverse correlation between the disease burden of NTDs and SDI.

CONCLUSION

NTDs imposed over half a billion incident cases and 10.8 million DALYs lost globally in 2019-exerting an immense toll rivaling major infectious and non-communicable diseases. Encouraging declines in prevalence and disability burdens over the past three decades spotlight the potential to accelerate progress through evidence-based allocation of resources. Such strategic integration could substantially enhance public awareness about risk factors and available treatment options.

The 2023 report of the MJA-Lancet Countdown on health and climate change: sustainability needed in Australia's health care sector

The MJA-Lancet Countdown on health and climate change in Australia was established in 2017 and produced its first national assessment in 2018 and annual updates in 2019, 2020, 2021 and 2022. It examines five broad domains: health hazards, exposures and impacts; adaptation, planning and resilience for health; mitigation actions and health co-benefits; economics and finance; and public and political engagement. In this, the sixth report of the MJA-Lancet Countdown, we track progress on an extensive suite of indicators across these five domains, accessing and presenting the latest data and further refining and developing our analyses. Our results highlight the health and economic costs of inaction on health and climate change. A series of major flood events across the four eastern states of Australia in 2022 was the main contributor to insured losses from climate-related catastrophes of $7.168 billion - the highest amount on record. The floods also directly caused 23 deaths and resulted in the displacement of tens of thousands of people. High red meat and processed meat consumption and insufficient consumption of fruit and vegetables accounted for about half of the 87 166 diet-related deaths in Australia in 2021. Correction of this imbalance would both save lives and reduce the heavy carbon footprint associated with meat production. We find signs of progress on health and climate change. Importantly, the Australian Government released Australia's first National Health and Climate Strategy, and the Government of Western Australia is preparing a Health Sector Adaptation Plan. We also find increasing action on, and engagement with, health and climate change at a community level, with the number of electric vehicle sales almost doubling in 2022 compared with 2021, and with a 65% increase in coverage of health and climate change in the media in 2022 compared with 2021. Overall, the urgency of substantial enhancements in Australia's mitigation and adaptation responses to the enormous health and climate change challenge cannot be overstated. Australia's energy system, and its health care sector, currently emit an unreasonable and unjust proportion of greenhouse gases into the atmosphere. As the Lancet Countdown enters its second and most critical phase in the leadup to 2030, the depth and breadth of our assessment of health and climate change will be augmented to increasingly examine Australia in its regional context, and to better measure and track key issues in Australia such as mental health and Aboriginal and Torres Strait Islander health and wellbeing.

Climate change and human health in Vietnam: a systematic review and additional analyses on current impacts, future risk, and adaptation

This study aims to investigate climate change's impact on health and adaptation in Vietnam through a systematic review and additional analyses of heat exposure, heat vulnerability, awareness and engagement, and projected health costs. Out of 127 reviewed studies, findings indicated the wider spread of infectious diseases, and increased mortality and hospitalisation risks associated with extreme heat, droughts, and floods. However, there are few studies addressing health cost, awareness, engagement, adaptation, and policy. Additional analyses showed rising heatwave exposure across Vietnam and global above-average vulnerability to heat. By 2050, climate change is projected to cost up to USD1-3B in healthcare costs, USD3-20B in premature deaths, and USD6-23B in work loss. Despite increased media focus on climate and health, a gap between public and government publications highlighted the need for more governmental engagement. Vietnam's climate policies have faced implementation challenges, including top-down approaches, lack of cooperation, low adaptive capacity, and limited resources.

Changing epidemiology of human enteroviruses (HEV) in a hand, foot and mouth disease outbreak in Vellore, south India

PURPOSE

Hand Foot and mouth disease (HFMD) is a major childhood exanthematous disease causing outbreaks that have become a major public health threat in recent years. In Vellore district of Tamil Nadu, south India, occasional outbreaks are common among the paediatric age group, most commonly in those under 5years of age (U5s). CoxsackieA6, A4, A5, A9, A10, B2 and B5 are the common serotypes causing outbreaks. This study aimed to identify the molecular serotype of the causative agent, co-circulating in this region.

METHODS

Adapting the WHO case definition, cases during an HFMD outbreak between October and December 2017, were identified by a clinical criterion of fever, mouth ulcers and rash in the extremities. Vesicle fluid from these lesions were collected in viral transport medium and transported cold to the Clinical Virology laboratory of a tertiary care hospital in Vellore. Identification of the causative agent was undertaken by two real time PCRs (EV1 and EV2) followed by sequencing the VP1-2C region and constructing a phylogenetic tree.

RESULTS

Among the 31 HFMD patients included in this study, 23 (74.2%) were U5s, 3 (9.7%) were between 6 and 15 years and the remaining 5 (16.1%) were adolescents (>15 ​yrs). The outbreak ran a mild clinical course, with 22(71%) patients having fever as a prodromal symptom. Papulovesicular lesions characteristic of HFMD were present on all 31 (100%) patients' palms and soles, buttocks of 19 (61.3%), oral mucosa of 12 (38.7%), and all over the body in 4 (12.9%) patients. Coxsackie A6(75%) and Coxsackie A16(25%) were the pathogens associated with this outbreak.

CONCLUSIONS

Changing epidemiology of HFMD was seen in this outbreak since; other serotypes apart from the classical Coxsackievirus serotypes causing HFMD outbreak were also found co-circulating. EV1 PCR was a better screening assay than EV2 PCR in this region. Continued surveillance and molecular serotyping are necessary for HFMD outbreaks in any region.

Infectious Disease Relational Data Analysis Using String Grammar Non-Euclidean Relational Fuzzy C-Means

Statistical analysis in infectious diseases is becoming more important, especially in prevention policy development. To achieve that, the epidemiology, a study of the relationship between the occurrence and who/when/where, is needed. In this paper, we develop the string grammar non-Euclidean relational fuzzy C-means (sgNERF-CM) algorithm to determine a relationship inside the data from the age, career, and month viewpoint for all provinces in Thailand for the dengue fever, influenza, and Hepatitis B virus (HBV) infection. The Dunn's index is used to select the best models because of its ability to identify the compact and well-separated clusters. We compare the results of the sgNERF-CM algorithm with the string grammar relational hard C-means (sgRHCM) algorithm. In addition, their numerical counterparts, i.e., relational hard C-means (RHCM) and non-Euclidean relational fuzzy C-means (NERF-CM) algorithms are also applied in the comparison. We found that the sgNERF-CM algorithm is far better than the numerical counterparts and better than the sgRHCM algorithm in most cases. From the results, we found that the month-based dataset does not help in relationship-finding since the diseases tend to happen all year round. People from different age ranges in different regions in Thailand have different numbers of dengue fever infections. The occupations that have a higher chance to have dengue fever are student and teacher groups from the central, north-east, north, and south regions. Additionally, students in all regions, except the central region, have a high risk of dengue infection. For the influenza dataset, we found that a group of people with the age of more than 1 year to 64 years old has higher number of influenza infections in every province. Most occupations in all regions have a higher risk of infecting the influenza. For the HBV dataset, people in all regions with an age between 10 to 65 years old have a high risk in infecting the disease. In addition, only farmer and general contractor groups in all regions have high chance of infecting HBV as well.

Using deep learning to predict the hand-foot-and-mouth disease of enterovirus A71 subtype in Beijing from 2011 to 2018

Hand-foot-and-month disease (HFMD), especially the enterovirus A71 (EV-A71) subtype, is a major health problem in Beijing, China. Previous studies mainly used regressive models to forecast the prevalence of HFMD, ignoring its intrinsic age groups. This study aims to predict HFMD of EV-A71 subtype in three age groups (0–3, 3–6 and > 6 years old) from 2011 to 2018 using residual-convolutional-recurrent neural network (CNNRNN-Res), convolutional-recurrent neural network (CNNRNN) and recurrent neural network (RNN). They were compared with auto-regressio, global auto-regression and vector auto-regression on both short-term and long-term prediction. Results showed that CNNRNN-Res and RNN had higher accuracies on point forecast tasks, as well as robust performances in long-term prediction. Three deep learning models also had better skills in peak intensity forecast, and CNNRNN-Res achieved the best results in the peak month forecast. We also found that three age groups had consistent outbreak trends and similar patterns of prediction errors. These results highlight the superior performance of deep learning models in HFMD prediction and can assist the decision-makers to refine the HFMD control measures according to age groups.