Are hospitalizations for rotavirus gastroenteritis associated with meteorologic factors?

Outdoor Absolute Humidity Predicts the Start of Norovirus GII Epidemics

Seasonal variation of viral gastroenteritis is related to weather conditions, but the relationship with the incidence of viral gastroenteritis (GE) is not fully understood. This study examined the impact of outdoor climate factors on seasonal variation in detection rates of gastroenteritis viruses, with emphasis on norovirus. Weekly detection rates of norovirus genogroup I (GI) and II (GII), rotavirus, adenovirus, astrovirus, and sapovirus were analyzed in relation to average weekly means of meteorological parameters. Associations between rates of PCR detection of the viral GE pathogens and climate factors were investigated with generalized linear models. Low absolute humidity was correlated with increased detection of adenovirus (P = 0.007), astrovirus (P = 0.005), rotavirus (P = 0.004), norovirus GI (P = 0.001), and sapovirus (P = 0.002). In each investigated season, a drop in absolute humidity preceded the increase in norovirus GII detections. We found a correlation between declining absolute humidity and increasing norovirus GII detection rate. Absolute humidity was a better predictor of gastrointestinal virus seasonality compared to relative humidity. IMPORTANCE Viral gastroenteritis causes considerable morbidity, especially in vulnerable groups such as the elderly and chronically ill. Predicting the beginning of seasonal epidemics is important for the health care system to withstand increasing demands. In this paper we studied the association of outdoor climate factors on the detection rates of gastrointestinal viruses and the association between these factors and the onset of annual norovirus epidemics. Declining absolute humidity preceded the increase in diagnosed norovirus GII cases by approximately 1 week. These findings contribute to the understanding of norovirus epidemiology and allow health care services to install timely preventive measures and can help the public avoid transmission.

Associations between ambient temperature and enteric infections by pathogen: a systematic review and meta-analysis

BACKGROUND

Numerous studies have quantified the associations between ambient temperature and enteric infections, particularly all-cause enteric infections. However, the temperature sensitivity of enteric infections might be pathogen dependent. Here, we sought to identify pathogen-specific associations between ambient temperature and enteric infections.

METHODS

We did a systematic review and meta-analysis by searching PubMed, Web of Science, and Scopus for peer-reviewed research articles published from Jan 1, 2000, to Dec 31, 2019, and also hand searched reference lists of included articles and excluded reviews. We included studies that quantified the effects of ambient temperature increases on common pathogen-specific enteric infections in humans. We excluded studies that expressed ambient temperature as a categorical or diurnal range, or in a standardised format. Two authors screened the search results, one author extracted data from eligible studies, and four authors verified the data. We obtained the overall risks by pooling the relative risks of enteric infection by pathogen for each 1°C temperature rise using random-effects modelling and robust variance estimation for the correlated effect estimates. Between-study heterogeneity was measured using I², τ², and Q-statistic. Publication bias was determined using funnel plot asymmetry and the trim-and-fill method. Differences among pathogen-specific pooled estimates were determined using subgroup analysis of taxa-specific meta-analysis. The study protocol was not registered but followed the PRISMA guidelines.

FINDINGS

We identified 2981 articles via database searches and 57 articles from scanning reference lists of excluded reviews and included articles, of which 40 were eligible for pathogen-specific meta-analyses. The overall increased risks of incidence per 1°C temperature rise, expressed as relative risks, were 1·05 (95% CI 1·04-1·07; I² 97%) for salmonellosis, 1·07 (1·04-1·10; I² 99%) for shigellosis, 1·02 (1·01-1·04; I² 98%) for campylobacteriosis, 1·05 (1·04-1·07; I² 36%) for cholera, 1·04 (1·01-1·07; I² 98%) for Escherichia coli enteritis, and 1·15 (1·07-1·24; I² 0%) for typhoid. Reduced risks per 1°C temperature increase were 0·96 (95% CI 0·90-1·02; I² 97%) for rotaviral enteritis and 0·89 (0·81-0·99; I² 96%) for noroviral enteritis. There was evidence of between-pathogen differences in risk for bacterial infections but not for viral infections.

INTERPRETATION

Temperature sensitivity of enteric infections can vary according to the enteropathogen causing the infection, particularly for bacteria. Thus, we encourage a pathogen-specific health adaptation approach, such as vaccination, given the possibility of increasingly warm temperatures in the future.

FUNDING

Japan Society for the Promotion of Science (Kakenhi) Grant-in-Aid for Scientific Research.

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.

Childhood Rotavirus Infection Associated with Temperature and Particulate Matter 2.5 µm: A Retrospective Cohort Study

No study has ever investigated how ambient temperature and PM_2.5 mediate rotavirus infection (RvI) in children. We used insurance claims data from Taiwan in 2006–2012 to evaluate the RvI characteristics in children aged ≤ 9. The RvI incidence rates were higher in colder months, reaching the highest in March (117.0/100 days), and then declining to the lowest in July (29.2/100 days). The age–sex-specific average incident cases were all higher in boys than in girls. Stratified analysis by temperature (<20, 20–24, and ≥25 °C) and PM_2.5 (<17.5, 17.5–31.4, 31.5–41.9, and ≥42.0 μg/m³) showed that the highest incidence was 16.4/100 days at average temperatures of <20 °C and PM_2.5 of 31.5–41.9 μg/m³, with Poisson regression analysis estimating an adjusted relative risk (aRR) of 1.26 (95% confidence interval (CI) = 1.11–1.43), compared to the incidence at the reference condition (<20 °C and PM_2.5 < 17.5 μg/m³). As the temperature increased, the incident RvI cases reduced to 4.84 cases/100 days (aRR = 0.40, 95% CI = 0.35–0.45) when it was >25 °C with PM_2.5 < 17.5 μg/m³, or to 9.84/100 days (aRR = 0.81, 95% CI = 0.77–0.93) when it was >25 °C with PM_2.5 > 42 μg/m³. The seasonal RvI is associated with frequent indoor personal contact among children in the cold months. The association with PM_2.5 could be an alternative assessment due to temperature inversion.

Association between climate and infectious diseases among children in Varanasi city, India: A prospective cohort study

The effects of climate on infectious diseases could influence the health impacts, particularly in children in countries with the unfair socioeconomic conditions. In a prospective cohort of 461 children under 16-years-of-age in Varanasi city, India, the association of maximum-temperature (Tmax), relative humidity (RH), absolute humidity (AH), rainfall (RF), wind-speed (WS), and solar radiation (SLR) with prevalent infectious diseases (Diarrhea, Common cold and flu, Pneumonia, Skin-disease and Malaria, and Dengue) was examined using binomial-regression, adjusting for confounders and effect modifiers (socioeconomic-status; SES and child anthropometry), from January 2017 to January 2020. Attributable-fraction (AFx) was calculated due to each climate variable for each infectious disease. The result showed that each unit (1 °C) rise in Tmax was associated with an increase in diarrhea and skin-disease cases by 3.97% (95% CI: 2.92, 5.02) and 3.94% (95% CI: 1.67, 6.22), respectively, whereas, a unit decline in Tmax was associated with an increase in cold and flu cases by 3.87% (95% CI: 2.97, 4.76). Rise in humidity (RH) was associated with increase in cases of cold and flu by 0.73% (95% CI: 0.38, 1.08) and malaria (AH) by 7.19% (95% CI: 1.51, 12.87) while each unit (1 g/m³) decrease in humidity (AH) observed increase in pneumonia cases by 3.02% (95% CI: 0.75, 5.3). WS was positively associated with diarrhea (14.16%; 95% CI: 6.52, 21.80) and negatively with dengue (17.40%; 12.32, 22.48) cases for each unit change (kmph). RF showed marginal association while SLR showed no association at all. The combined AFx due to climatic factors ranged from 9 to 18%. SES and anthropometric parameters modified the climate-morbidity association in children with a high proportion of children found suffering from stunting, wasting, and underweight conditions. Findings from this study draw the attention of government and policymakers to prioritize effective measures for child health as the present association may increase disease burden in the future under climate-change scenarios in already malnourished paediatric population through multiple pathways.

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.

Impact analysis of rotavirus vaccination in various geographic regions in Western Europe

BACKGROUND

Universal mass vaccination (UMV) against rotavirus has been implemented in many but not all European countries. This study investigated the impact of UMV on rotavirus incidence trends by comparing European countries with UMV: Belgium, England/Wales and Germany versus countries without UMV: Denmark and the Netherlands.

METHODS

For this observational retrospective cohort study, time series data (2001-2016) on rotavirus detections, meteorological factors and population demographics were collected. For each country, several meteorological and population factors were investigated as possible predictors of rotavirus incidence. The final set of predictors were incorporated in negative binomial models accounting for seasonality and serial autocorrelation, and time-varying incidence rate ratios (IRR) were calculated for each age group and country separately. The overall vaccination impact two years after vaccine implementation was estimated by pooling the results using a random effects meta-analyses. Independent t-tests were used to compare annual epidemics in the pre-vaccination and post-vaccination era to explore any changes in the timing of rotavirus epidemics.

RESULTS

The population size and several meteorological factors were predictors for the rotavirus epidemiology. Overall, we estimated a 42% (95%-CI 23;56%) reduction in rotavirus incidence attributable to UMV. Strongest reductions were observed for age-groups 0-, 1- and 2-years (IRR 0.47, 0.48 and 0.63, respectively). No herd effect induced by UMV in neighbouring countries was observed. In all UMV countries, the start and/or stop and corresponding peak of the rotavirus season was delayed by 4-7 weeks.

CONCLUSIONS

The introduction of rotavirus UMV resulted in an overall reduction of 42% in rotavirus incidence in Western European countries two years after vaccine introduction and caused a change in seasonal pattern. No herd effect induced by UMV neighbouring countries was observed for Denmark and the Netherlands.

Non-linear effect of temperature variation on childhood rotavirus infection: A time series study from Kathmandu, Nepal

INTRODUCTION

This study aimed to investigate the effects of temperature variability on rotavirus infections among children under 5 years of age in Kathmandu, Nepal. Findings may inform infection control planning, especially in relation to the role of environmental factors in the transmission of rotavirus infection.

METHODS

Generalized linear Poisson regression equations with distributed lag non-linear model were fitted to estimate the effect of temperature (maximum, mean and minimum) variation on weekly counts of rotavirus infections among children under 5 years of age living in Kathmandu, Nepal, over the study period (2013 to 2016). Seasonality and long-term effects were adjusted in the model using Fourier terms up to the seventh harmonic and a time function, respectively. We further adjusted the model for the confounding effects of rainfall and relative humidity.

RESULTS

During the study period, a total of 733 cases of rotavirus infection were recorded, with a mean of 3 cases per week. We detected an inverse non-linear association between rotavirus infection and average weekly mean temperature, with increased risk (RR: 1.52; 95% CI: 1.08-2.15) at the lower quantile (10th percentile) and decreased risk (RR: 0.64; 95% CI: 0.43-0.95) at the higher quantile (75th percentile). Similarly, we detected an increased risk [(RR: 1.93; 95% CI: 1.40-2.65) and (RR: 1.42; 95% CI: 1.04-1.95)] of rotavirus infection for both maximum and minimum temperature at their lower quantile (10th percentile). We estimated that 344 (47.01%) cases of rotavirus diarrhoea among the children under 5 years of age were attributable to minimum temperature. The significant effect of temperature on rotavirus infection was not observed beyond lag zero week.

CONCLUSION

An inverse non-linear association was estimated between rotavirus incidence and all three indices of temperature, indicating a higher risk of infection during the cooler times of the year, and suggesting that transmission of rotavirus in Kathmandu, Nepal may be influenced by temperature.

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.

The association between ambient temperature and clinical visits for inflammation-related diseases in rural areas in China

BACKGROUND

The association between temperature and mortality has been widely reported. However, it remains largely unclear whether inflammation-related diseases, caused by excessive or inappropriate inflammatory reaction, may be affected by ambient temperature, particularly in low-income areas.

OBJECTIVES

To explore the association between ambient temperature and clinical visits for inflammation-related diseases in rural villages in the Ningxia Hui Autonomous Region, China, during 2012─2015.

METHODS

Daily data on inflammation-related diseases and weather conditions were collected from 258 villages in Haiyuan (161 villages) and Yanchi (97 villages) counties during 2012─2015. A Quasi-Poisson regression with distributed lag non-linear model was used to examine the association between temperature and clinical visits for inflammation-related diseases. Stratified analyses were performed by types of diseases including arthritis, gastroenteritis, and gynecological inflammations.

RESULTS

During the study period, there were 724,788 and 288,965 clinical visits for inflammation-related diseases in Haiyuan and Yanchi, respectively. Both exposure to low (RR: 2.045, 95% CI: 1.690, 2.474) and high temperatures (RR: 1.244, 95% CI: 1.107, 1.399) were associated with increased risk of total inflammation-related visits in Haiyuan county. Low temperatures were associated with increased risks of all types of inflammation-related diseases in Yanchi county (RR: 4.344, 95% CI: 2.887, 6.535), while high temperatures only affected gastroenteritis (RR: 1.274, 95% CI: 1.040, 1.561). Moderate temperatures explained approximately 26% and 33% of clinical visits due to inflammation-related diseases in Haiyuan and Yanchi, respectively, with the burden attributable to cold exposure higher than hot exposure. The reference temperature values ranged from 17 to 19 in Haiyuan, and 12 to 14 in Yanchi for all types of clinical visits.

CONCLUSIONS

Our findings add additional evidence for the adverse effect of suboptimal ambient temperature and provide useful information for public health programs targeting people living in rural villages.

Association of meteorological factors with infectious diarrhea incidence in Guangzhou, southern China: A time-series study (2006-2017)

BACKGROUND

Infectious diarrhea (ID) has exerted a severe disease burden on the world. The seasonal ID patterns suggest that meteorological factors (MFs) may influence ID incidence. The aim of this study was to evaluate the effect of MFs on ID, and to provide scientific evidence to the relevant health authorities for disease control and prevention.

METHODS

Data from ID cases and daily MFs (including mean temperature, diurnal temperature range, relative humidity, precipitation, atmospheric pressure, and wind velocity) in Guangzhou, Southern China from 2006 to 2017 were collected. Using a distributed lag non-linear model approach, we assessed the relationship between MFs and ID incidence.

RESULTS

Compared with the lowest ID risk values, low mean temperature, relative humidity, and precipitation were associated with an increased risk for ID, while higher diurnal temperature range and atmospheric pressure were also associated with increased risk. Maximum atmospheric pressure and minimum relative humidity had larger cumulative effects within 21 lag days, yielding relative risks of 133.11 (95% CI: 61.29-289.09) and 18.17 (14.42-22.89), respectively. The cumulative effect within 21 lag days of minimum temperature was higher than that from maximum temperature in all sub-populations. The cumulative effects of minimum temperature for men, teenagers, and young adults (10-29 years) were higher than those for other populations.

CONCLUSIONS

MFs should be considered when developing prevention and surveillance programs for ID. Special attention should be paid to vulnerable populations, such as teenagers and young adults.

Use of earth observation-derived hydrometeorological variables to model and predict rotavirus infection (MAL-ED): a multisite cohort study

BACKGROUND

Climate change threatens to undermine recent progress in reducing global deaths from diarrhoeal disease in children. However, the scarcity of evidence about how individual environmental factors affect transmission of specific pathogens makes prediction of trends under different climate scenarios challenging. We aimed to model associations between daily estimates of a suite of hydrometeorological variables and rotavirus infection status ascertained through community-based surveillance.

METHODS

For this analysis of multisite cohort data, rotavirus infection status was ascertained through community-based surveillance of infants in the eight-site MAL-ED cohort study, and matched by date with earth observation estimates of nine hydrometeorological variables from the Global Land Data Assimilation System: daily total precipitation volume (mm), daily total surface runoff (mm), surface pressure (mbar), wind speed (m/s), relative humidity (%), soil moisture (%), solar radiation (W/m2), specific humidity (kg/kg), and average daily temperatures (°C). Lag relationships, independent effects, and interactions were characterised by use of modified Poisson models and compared with and without adjustment for seasonality and between-site variation. Final models were created with stepwise selection of main effects and interactions and their validity assessed by excluding each site in turn and calculating Tjur's Coefficients of Determination.

FINDINGS

All nine hydrometeorological variables were significantly associated with rotavirus infection after adjusting for seasonality and between-site variation over multiple consecutive or non-consecutive lags, showing complex, often non-linear associations that differed by symptom status and showed considerable mutual interaction. The final models explained 5·9% to 6·2% of the variability in rotavirus infection in the pooled data and their predictions explained between 0·0% and 14·1% of the variability at individual study sites.

INTERPRETATION

These results suggest that the effect of climate on rotavirus transmission was mediated by four independent mechanisms: waterborne dispersal, airborne dispersal, virus survival on soil and surfaces, and host factors. Earth observation data products available at a global scale and at subdaily resolution can be combined with longitudinal surveillance data to test hypotheses about routes and drivers of transmission but showed little potential for making predictions in this setting.

FUNDING

Bill & Melinda Gates Foundation; Foundation for the National Institutes of Health, National Institutes of Health, Fogarty International Center; Sherrilyn and Ken Fisher Center for Environmental Infectious Diseases, Johns Hopkins School of Medicine; and NASA's Group on Earth Observations Work Programme.

A time-series study of the association of rainfall, relative humidity and ambient temperature with hospitalizations for rotavirus and norovirus infection among children in Hong Kong

BACKGROUND

Rotavirus and norovirus are infectious pathogens primarily affecting children under 5 years old. The impact of rainfall on diarrheal diseases remains inconclusive. This study aimed to evaluate the association between short-term variation in rainfall, temperature and humidity, and rotavirus and norovirus hospitalizations among young children in Hong Kong.

METHODS

Generalized additive negative binomial regression models with distributed lag non-linear terms, were fit with daily counts of hospital admissions due to rotavirus and norovirus infection as the outcomes and daily total rainfall and other meteorological variables as predictors, adjusting for seasonality and trend.

RESULTS

Generally, greater rainfall was associated with fewer rotavirus, but more norovirus hospitalizations. Extreme precipitation (99.5 mm, 99th percentile) was found to be associated with 0.40 (95% confidence interval (CI) 0.20-0.79) and 1.93 (95% CI 1.21-3.09) times the risk of hospitalization due to rotavirus and norovirus infection respectively, relative to trace rainfall. Stronger associations were observed in winter for rotavirus and in summer for norovirus. The duration of association with rotavirus was notably longer than norovirus. Higher temperatures were found to be associated with fewer hospitalizations for both rotavirus and norovirus infection, while higher relative humidity was generally associated with more norovirus, but fewer rotavirus, hospitalizations.

CONCLUSIONS

Both rotavirus and norovirus hospitalizations were strongly associated with recent precipitation variation but in opposite directions. With the introduction of the rotavirus vaccine norovirus is likely to become a greater threat than rotavirus and thus greater precipitation may become more clearly associated with more childhood diarrhea.

Evaluating meteorological data from weather stations, and from satellites and global models for a multi-site epidemiological study

BACKGROUND

Longitudinal and time series analyses are needed to characterize the associations between hydrometeorological parameters and health outcomes. Earth Observation (EO) climate data products derived from satellites and global model-based reanalysis have the potential to be used as surrogates in situations and locations where weather-station based observations are inadequate or incomplete. However, these products often lack direct evaluation at specific sites of epidemiological interest.

METHODS

Standard evaluation metrics of correlation, agreement, bias and error were applied to a set of ten hydrometeorological variables extracted from two quasi-global, commonly used climate data products - the Global Land Data Assimilation System (GLDAS) and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) - to evaluate their performance relative to weather-station derived estimates at the specific geographic locations of the eight sites in a multi-site cohort study. These metrics were calculated for both daily estimates and 7-day averages and for a rotavirus-peak-season subset. Then the variables from the two sources were each used as predictors in longitudinal regression models to test their association with rotavirus infection in the cohort after adjusting for covariates.

RESULTS

The availability and completeness of station-based validation data varied depending on the variable and study site. The performance of the two gridded climate models varied considerably within the same location and for the same variable across locations, according to different evaluation criteria and for the peak-season compared to the full dataset in ways that showed no obvious pattern. They also differed in the statistical significance of their association with the rotavirus outcome. For some variables, the station-based records showed a strong association while the EO-derived estimates showed none, while for others, the opposite was true.

CONCLUSION

Researchers wishing to utilize publicly available climate data - whether EO-derived or station based - are advised to recognize their specific limitations both in the analysis and the interpretation of the results. Epidemiologists engaged in prospective research into environmentally driven diseases should install their own weather monitoring stations at their study sites whenever possible, in order to circumvent the constraints of choosing between distant or incomplete station data or unverified EO estimates.

Seasonality and within-subject clustering of rotavirus infections in an eight-site birth cohort study

Improving understanding of the pathogen-specific seasonality of enteric infections is critical to informing policy on the timing of preventive measures and to forecast trends in the burden of diarrhoeal disease. Data obtained from active surveillance of cohorts can capture the underlying infection status as transmission occurs in the community. The purpose of this study was to characterise rotavirus seasonality in eight different locations while adjusting for age, calendar time and within-subject clustering of episodes by applying an adapted Serfling model approach to data from a multi-site cohort study. In the Bangladesh and Peru sites, within-subject clustering was high, with more than half of infants who experienced one rotavirus infection going on to experience a second and more than 20% experiencing a third. In the five sites that are in countries that had not introduced the rotavirus vaccine, the model predicted a primary peak in prevalence during the dry season and, in three of these, a secondary peak during the rainy season. The patterns predicted by this approach are broadly congruent with several emerging hypotheses about rotavirus transmission and are consistent for both symptomatic and asymptomatic rotavirus episodes. These findings have practical implications for programme design, but caution should be exercised in deriving inferences about the underlying pathways driving these trends, particularly when extending the approach to other pathogens.

Rotavirus and adenovirus gastroenteritis: time series analysis

BACKGROUND

This study investigated the effects of changes in weather conditions (monthly average temperature, monthly minimum temperature, monthly average humidity) on rotavirus and adenovirus gastroenteritis frequency and whether there was a seasonal correlation.

METHODS

Between 2006 and 2012, 4702 fecal samples were taken from patients ≤ 5 years of age with acute gastroenteritis; these samples were analyzed in terms of rotavirus group A and adenovirus serotype 40-41 antigens using time-series and negative binomial regression analysis.

RESULTS

Rotavirus antigens were found in 797 samples (17.0%), adenovirus antigens in 113 samples (2.4%), and rotavirus and adenovirus antigens together in 16 samples (0.3%). There was a seasonal change in rotavirus gastroenteritis (P < 0.001), and a 1°C decrease in average temperature increased the ratio of rotavirus cases in those with diarrhea by 0.523%. In addition, compared with data from other years, the number of patients was lower in the first month of 2008 and in the second month of 2012, when the temperature was below -20°C (monthly minimum temperature). There was no statistically significant relationship between adenovirus infection and change in weather conditions.

CONCLUSION

Various factors such as change in weather conditions, as well as the population's sensitivity and associated changes in activity, play a role in the spread of rotavirus infection.