Seasonality of Rotavirus Hospitalizations at Costa Rica's National Children's Hospital in 2010-2015

The impact of increasing temperatures due to climate change on infectious diseases

Global temperatures will continue to rise due to climate change, with high temperature periods expected to increase in intensity, frequency, and duration. Infectious diseases, including vector-borne diseases such as dengue fever and malaria, waterborne diseases such as cholera, and foodborne diseases such as salmonellosis are influenced by temperature and other climatic variables, thus contributing to higher disease burden and associated healthcare costs, particularly in socioeconomically disadvantaged regions. Targeted efforts and investments are therefore needed to support low and middle income countries to prepare for and respond to the increasing infectious disease threats posed by rising temperatures. This can be facilitated by the development and refinement of robust disease and entomological surveillance and early warning systems with integration of climatic information that promote enhanced understanding of the geographic distribution of disease risk. To enhance healthcare workforce capacity and capability to respond to these public health threats, medical curricula and continuing professional education programmes for healthcare providers must include evidence based components on the impacts of climate change on infectious diseases.

Rotavirus A Infection Prevalence and Spatio-Temporal Genotype Shift among Under-Five Children in Amhara National Regional State, Ethiopia: A Multi-Center Cross-Sectional Study

Background: Globally, rotavirus (RV) A (RVA) is the most common cause of severe and sometimes fatal diarrhea in young children. It is also the major cause of acute gastroenteritis among children in Ethiopia. Currently, the WHO has prequalified four RVA vaccines for universal childhood immunization. Ethiopia introduced the monovalent Rotarix vaccine into its national immunization program in 2013. Since then, only a few studies on the burden and genotype distribution of RVA infection post-vaccine introduction have been conducted (mostly at sentinel surveillance sites). Therefore, this study aimed to assess RVA prevalence and genotype distribution among children under five years in Ethiopia (February 2021-December 2022). Methods: This multi-center hospital-based cross-sectional study involved 537 diarrheic children under-five years old. Rotavirus A detection was conducted using a one-step reverse-transcriptase polymerase chain reaction (RT-PCR). Genotyping was conducted by Sanger sequencing of the VP7 (complete) and VP4 (partial) genes. Descriptive analysis and Pearson's chi-squared test were carried out using SPSS version 29. Phylogenetic analysis with 1000 bootstrap replicates was performed using MEGA version 11 software. Statistical significance was set at p < 0.05 for all analyses. Results: The prevalence of RVA infection among diarrheic children was 17.5%. The most prevalent G-types identified were G3 (37%), the previously uncommon G12 (28%), and G1 (20%). The predominant P-types were P[8] (51%), P[6] (29%), and P[4] (14%). The three major G/P combinations observed were G3P[8] (32.8%), G12P[6] (28.4%), and G1P[8] (19.4%). Phylogenetic analysis revealed clustering of Ethiopian strains with the globally reported strains. Many strains exhibited amino acid differences in the VP4 (VP8* domain) and VP7 proteins compared to vaccine strains, potentially affecting virus neutralization. Conclusions: Despite the high RVA vaccination rate, the prevalence of RVA infection remains significant among diarrheic children in Ethiopia. There is an observable shift in circulating RVA genotypes from G1 to G3, alongside the emergence of unusual G/P genotype combinations such as G9P[4]. Many of these circulating RVA strains have shown amino acid substitutions that may allow for neutralization escape. Therefore, further studies are warranted to comprehend the emergence of these unusual RVA strains and the diverse factors influencing the vaccine's diminished effectiveness in developing countries.

Global seasonal and pandemic patterns in influenza: An application of longitudinal study designs

The confluence of growing analytic capacities and global surveillance systems for seasonal infections has created new opportunities to further develop statistical methodology and advance the understanding of the global disease dynamics. We developed a framework to characterise the seasonality of infectious diseases for publicly available global health surveillance data. Specifically, we aimed to estimate the seasonal characteristics and their uncertainty using mixed effects models with harmonic components and the δ-method and develop multi-panel visualisations to present complex interplay of seasonal peaks across geographic locations. We compiled a set of 2 422 weekly time series of 14 reported outcomes for 173 Member States from the World Health Organization's (WHO) international influenza virological surveillance system, FluNet, from 02 January 1995 through 20 June 2021. We produced an analecta of data visualisations to describe global travelling waves of influenza while addressing issues of data completeness and credibility. Our results offer directions for further improvements in data collection, reporting, analysis and development of statistical methodology and predictive approaches.

Seasonal Patterns of Enteric Pathogens in Colombian Indigenous People—A More Pronounced Effect on Bacteria Than on Parasites

Enteric pathogens, which are frequently food- and waterborne transmitted, are highly abundant in Indigenous people living in remote rural areas of Colombia. As the frequency of gastroenteritis in the tropics shows seasonal differences, we analyzed variations of pathogen patterns in the stool samples of a Colombian Indigenous tribe called Wiwa during the dry (n = 105) and the rainy (n = 227) season, applying real-time PCR from stool samples and statistical analysis based on a multi-variable model. Focusing on bacterial pathogens, increased detection rates could be confirmed for enteropathogenic, enterotoxigenic and enteroaggregative Escherichia coli with a tendency for an increase in Campylobacter jejuni detections during the rainy season, while there was no seasonal effect on the carriage of Tropheryma whipplei. Salmonellae were recorded during the rainy season only. A differentiated pattern was seen for the assessed parasites. Entamoeba histolytica, Necator americanus and Trichuris trichiura were increasingly detected during the rainy season, but not Ascaris lumbricoides, Giardia duodenalis, Hymenolepis nana, Strongyloides stercoralis, and Taenia solium, respectively. Increased detection rates during the dry season were not recorded. Negative associations were found for Campylobacter jejuni and Giardia duodenalis with age and for Tropheryma whipplei with the body mass index, respectively. Positive associations of enteropathogenic Escherichia coli and Taenia solium detections were observed with age. In conclusion, facilitating effects of the tropical rainy season were more pronounced on bacterial enteric pathogens compared to enteropathogenic parasites.

Associations Between Eight Earth Observation-Derived Climate Variables and Enteropathogen Infection: An Independent Participant Data Meta-Analysis of Surveillance Studies With Broad Spectrum Nucleic Acid Diagnostics

Diarrheal disease, still a major cause of childhood illness, is caused by numerous, diverse infectious microorganisms, which are differentially sensitive to environmental conditions. Enteropathogen-specific impacts of climate remain underexplored. Results from 15 studies that diagnosed enteropathogens in 64,788 stool samples from 20,760 children in 19 countries were combined. Infection status for 10 common enteropathogens-adenovirus, astrovirus, norovirus, rotavirus, sapovirus, Campylobacter, ETEC, Shigella, Cryptosporidium and Giardia-was matched by date with hydrometeorological variables from a global Earth observation dataset-precipitation and runoff volume, humidity, soil moisture, solar radiation, air pressure, temperature, and wind speed. Models were fitted for each pathogen, accounting for lags, nonlinearity, confounders, and threshold effects. Different variables showed complex, non-linear associations with infection risk varying in magnitude and direction depending on pathogen species. Rotavirus infection decreased markedly following increasing 7-day average temperatures-a relative risk of 0.76 (95% confidence interval: 0.69-0.85) above 28°C-while ETEC risk increased by almost half, 1.43 (1.36-1.50), in the 20-35°C range. Risk for all pathogens was highest following soil moistures in the upper range. Humidity was associated with increases in bacterial infections and decreases in most viral infections. Several virus species' risk increased following lower-than-average rainfall, while rotavirus and ETEC increased with heavier runoff. Temperature, soil moisture, and humidity are particularly influential parameters across all enteropathogens, likely impacting pathogen survival outside the host. Precipitation and runoff have divergent associations with different enteric viruses. These effects may engender shifts in the relative burden of diarrhea-causing agents as the global climate changes.

Short-Term Impacts of Meteorology, Air Pollution, and Internet Search Data on Viral Diarrhea Infection among Children in Jilin Province, China

The influence of natural environmental factors and social factors on children’s viral diarrhea remains inconclusive. This study aimed to evaluate the short-term effects of temperature, precipitation, air quality, and social attention on children’s viral diarrhea in temperate regions of China by using the distribution lag nonlinear model (DLNM). We found that low temperature affected the increase in children’s viral diarrhea infection for about 1 week, while high temperature and heavy precipitation affected the increase in children’s viral diarrhea infection risk for at least 3 weeks. As the increase of the air pollution index may change the daily life of the public, the infection of children’s viral diarrhea can be restrained within 10 days, but the risk of infection will increase after 2 weeks. The extreme network search may reflect the local outbreak of viral diarrhea, which will significantly improve the infection risk. The above factors can help the departments of epidemic prevention and control create early warnings of high-risk outbreaks in time and assist the public to deal with the outbreak of children’s viral diarrhea.

The association between short-term exposure to air pollutants and rotavirus infection in Wuhan, China

BACKGROUND

The impact of various meteorological factors on rotavirus (RV) infection has been previously studied; however, few studies have explored the association between short-term exposure to air pollutants and RV infection.

METHODS

Daily RV positive cases among children aged 0-6 years were collected from July 2014 to August 2019 in Tongji hospital (Wuhan, China). Daily data on air temperature and air pollutants were obtained from the China Meteorological Network. A distributed lag model to explore the lagged effects of short-term exposure to air pollutants and RV infection was performed. The distribution lag model was used to study the lag effect of short-term exposure to air pollutants and RV infection.

RESULTS

RV infection was negatively correlated with mean air temperature and O₃ concentration. The RV infection risk decreased by 5.2% and 0.47% for every 1℃ increase in average temperature and 1 ug/m³ increase in O₃ concentration, respectively. Increased PM_2.5 , SO₂ , and NO₂ concentrations were independent risk factors for an increase in positive rates; their relative risk values were 1.0014 (95% confidence interval [CI], 1.0013-1.0015), 1.0050 (95% CI, 1.0047-1.0053), and 1.0030 (95% CI, 1.0028-1.0032), respectively. The highest RV-positive rates were from January to March and November to December. Additionally, children <18 months of age and boys were more vulnerable to infection.

CONCLUSIONS

Air pollutants were important factors impacting the RV-positivity of children in Wuhan. These findings may help develop an early environment-based warning system to prevent and control RV infection.

Etiological, epidemiological, and clinical features of acute diarrhea in China

National-based prospective surveillance of all-age patients with acute diarrhea was conducted in China between 2009‒2018. Here we report the etiological, epidemiological, and clinical features of the 152,792 eligible patients enrolled in this analysis. Rotavirus A and norovirus are the two leading viral pathogens detected in the patients, followed by adenovirus and astrovirus. Diarrheagenic Escherichia coli and nontyphoidal Salmonella are the two leading bacterial pathogens, followed by Shigella and Vibrio parahaemolyticus. Patients aged <5 years had higher overall positive rate of viral pathogens, while bacterial pathogens were more common in patients aged 18‒45 years. A joinpoint analysis revealed the age-specific positivity rate and how this varied for individual pathogens. Our findings fill crucial gaps of how the distributions of enteropathogens change across China in patients with diarrhea. This allows enhanced identification of the predominant diarrheal pathogen candidates for diagnosis in clinical practice and more targeted application of prevention and control measures.

Effect of ambient temperatures on category C notifiable infectious diarrhea in China: An analysis of national surveillance data

BACKGROUND

Many studies have explored the association between meteorological factors and infectious diarrhea (ID) transmission but with inconsistent results, in particular the roles from temperatures. We aimed to explore the effects of temperatures on the transmission of category C ID, to identify its potential heterogeneity in different climate zones of China, and to provide scientific evidence to health authorities and local communities for necessary public health actions.

METHODS

Daily category C ID counts and meteorological variables were collected from 270 cities in China over the period of 2014-16. Distributed lag non-linear models (DLNMs) were applied in each city to obtain the city-specific temperature-disease associations, then a multivariate meta-analysis was implemented to pool the city-specific effects. Multivariate meta-regression was conducted to explore the potential effect modifiers. Attributable fraction was calculated for both low and high temperatures, defined as temperatures below the 5th percentile of temperature or above the 95th percentile of temperature.

RESULTS

A total of 2,715,544 category C ID cases were reported during the study period. Overall, a M-shaped curve relationship was observed between temperature and category C ID, with a peak at the 81st percentile of temperatures (RR = 1.723, 95% CI: 1.579-1.881) compared to 50th percentile of temperatures. The pooled associations were generally stronger at high temperatures compared to low ambient temperatures, and the attributable fraction due to heat was higher than cold. Latitude was identified as a possible effect modifier.

CONCLUSIONS

The overall positive pooled associations between temperature and category C ID in China suggest the increasing temperature could bring about more category C infectious diarrhea cases, which warrants further public health measurements.

How Seasonality of Malnutrition Is Measured and Analyzed

Seasonality is a critical source of vulnerability across most human activities and natural processes, including the underlying and immediate drivers of acute malnutrition. However, while there is general agreement that acute malnutrition is highly variable within and across years, the evidence base is limited, resulting in an overreliance on assumptions of seasonal peaks. We review the design and analysis of 24 studies exploring the seasonality of nutrition outcomes in Africa's drylands, providing a summary of approaches and their advantages and disadvantages. Over half of the studies rely on two to four time points within the year and/or the inclusion of time as a categorical variable in the analysis. While such approaches simplify interpretation, they do not correspond to the climatic variability characteristic of drylands or the relationship between climatic variability and human activities. To better ground our understanding of the seasonality of acute malnutrition in a robust evidence base, we offer recommendations for study design and analysis, including drawing on participatory methods to identify community perceptions of seasonality, use of longitudinal data and panel analysis with approaches borrowed from the field of infectious diseases, and linking oscillations in nutrition data with climatic data.

Understanding the Impact of Rainfall on Diarrhea: Testing the Concentration-Dilution Hypothesis Using a Systematic Review and Meta-Analysis

BACKGROUND

Projected increases in extreme weather may change relationships between rain-related climate exposures and diarrheal disease. Whether rainfall increases or decreases diarrhea rates is unclear based on prior literature. The concentration-dilution hypothesis suggests that these conflicting results are explained by the background level of rain: Rainfall following dry periods can flush pathogens into surface water, increasing diarrhea incidence, whereas rainfall following wet periods can dilute pathogen concentrations in surface water, thereby decreasing diarrhea incidence.

OBJECTIVES

In this analysis, we explored the extent to which the concentration-dilution hypothesis is supported by published literature.

METHODS

To this end, we conducted a systematic search for articles assessing the relationship between rain, extreme rain, flood, drought, and season (rainy vs. dry) and diarrheal illness.

RESULTS

A total of 111 articles met our inclusion criteria. Overall, the literature largely supports the concentration-dilution hypothesis. In particular, extreme rain was associated with increased diarrhea when it followed a dry period [incidence rate ratio ( IRR ) = 1.26 ; 95% confidence interval (CI): 1.05, 1.51], with a tendency toward an inverse association for extreme rain following wet periods, albeit nonsignificant, with one of four relevant studies showing a significant inverse association (IRR = 0.911 ; 95% CI: 0.771, 1.08). Incidences of bacterial and parasitic diarrhea were more common during rainy seasons, providing pathogen-specific support for a concentration mechanism, but rotavirus diarrhea showed the opposite association. Information on timing of cases within the rainy season (e.g., early vs. late) was lacking, limiting further analysis. We did not find a linear association between nonextreme rain exposures and diarrheal disease, but several studies found a nonlinear association with low and high rain both being associated with diarrhea.

DISCUSSION

Our meta-analysis suggests that the effect of rainfall depends on the antecedent conditions. Future studies should use standard, clearly defined exposure variables to strengthen understanding of the relationship between rainfall and diarrheal illness. https://doi.org/10.1289/EHP6181.

Effects of Data Aggregation on Time Series Analysis of Seasonal Infections

Time series analysis in epidemiological studies is typically conducted on aggregated counts, although data tend to be collected at finer temporal resolutions. The decision to aggregate data is rarely discussed in epidemiological literature although it has been shown to impact model results. We present a critical thinking process for making decisions about data aggregation in time series analysis of seasonal infections. We systematically build a harmonic regression model to characterize peak timing and amplitude of three respiratory and enteric infections that have different seasonal patterns and incidence. We show that irregularities introduced when aggregating data must be controlled during modeling to prevent erroneous results. Aggregation irregularities had a minimal impact on the estimates of trend, amplitude, and peak timing for daily and weekly data regardless of the disease. However, estimates of peak timing of the more common infections changed by as much as 2.5 months when controlling for monthly data irregularities. Building a systematic model that controls for data irregularities is essential to accurately characterize temporal patterns of infections. With the urgent need to characterize temporal patterns of novel infections, such as COVID-19, this tutorial is timely and highly valuable for experts in many disciplines.

Prevalence, risk factors, and clinical characteristics of rotavirus and adenovirus among Lebanese hospitalized children with acute gastroenteritis

BACKGROUND

Acute gastroenteritis is a very common infectious disease facing all age groups worldwide, especially the pediatric population. Viruses, bacteria, and parasites are all possible causes of infectious gastroenteritis; however, viruses have become more frequently identified with the advances in the ability to diagnose viral infections, particularly rotavirus and adenovirus. We aimed in our study to compare between the prevalence, risk factors, and clinical characteristics of rotavirus and adenovirus among children with viral gastroenteritis in Lebanon.

MATERIALS AND METHODS

A 12-months retrospective study was performed between January 1st and December 31st, 2018 including 308 children aged 1 month to 12 years, who were admitted to three tertiary healthcare centers in South Lebanon. Medical data were retrieved from patients' files, including clinical and laboratory information.

RESULTS

Rotavirus was found in stool of 204 patients (66.23 %), followed by adenovirus in 78 cases (25.32 %), and mixed group (rotavirus and adenovirus) in 26 cases (8.44%). The highest prevalence of rotavirus in our present study was seen among children between 12 and 23 months old, whereas patients infected with adenovirus were mainly aged between 24-35 months or 4-11 months. Majority of patients in the adenovirus and mixed groups had high-grade fever compared to the rotavirus group. Laboratory findings presented significantly higher average of white blood cells (WBCs), absolute neutrophil count (ANC), and C-reactive protein (CRP) in the mixed group compared to the two other groups. Monthly distribution of rotavirus and adenovirus infection revealed a biennial pattern of rotavirus incidence during January and July-August while frequency of adenovirus infection was highest during July-August.

CONCLUSION

Due to the high prevalence of viral diarrhea among the pediatric age group in our region, particularly rotavirus and adenovirus, along with the associated non-specific signs and symptoms, we highly recommend that medical laboratories be equipped for virus detection. Also, vaccination against rotavirus should be considered as a prevention strategy.

Rotavirus Seasonality: An Application of Singular Spectrum Analysis and Polyharmonic Modeling

The dynamics of many viral infections, including rotaviral infections (RIs), are known to have a complex non-linear, non-stationary structure with strong seasonality indicative of virus and host sensitivity to environmental conditions. However, analytical tools suitable for the identification of seasonal peaks are limited. We introduced a two-step procedure to determine seasonal patterns in RI and examined the relationship between daily rates of rotaviral infection and ambient temperature in cold climates in three Russian cities: Chelyabinsk, Yekaterinburg, and Barnaul from 2005 to 2011. We described the structure of temporal variations using a new class of singular spectral analysis (SSA) models based on the "Caterpillar" algorithm. We then fitted Poisson polyharmonic regression (PPHR) models and examined the relationship between daily RI rates and ambient temperature. In SSA models, RI rates reached their seasonal peaks around 24 February, 5 March, and 12 March (i.e., the 55.17 ± 3.21, 64.17 ± 5.12, and 71.11 ± 7.48 day of the year) in Chelyabinsk, Yekaterinburg, and Barnaul, respectively. Yet, in all three cities, the minimum temperature was observed, on average, to be on 15 January, which translates to a lag between the peak in disease incidence and time of temperature minimum of 38-40 days for Chelyabinsk, 45-49 days in Yekaterinburg, and 56-59 days in Barnaul. The proposed approach takes advantage of an accurate description of the time series data offered by the SSA-model coupled with a straightforward interpretation of the PPHR model. By better tailoring analytical methodology to estimate seasonal features and understand the relationships between infection and environmental conditions, regional and global disease forecasting can be further improved.