Seasonality of rotavirus in South Asia: a meta-analysis approach assessing associations with temperature, precipitation, and vegetation index

Application of improved harmonic Poisson segmented regression model in evaluating the effectiveness of Kala-Azar intervention in Yangquan City, China

Background

The Centre for Disease Control and Prevention in Yangquan, China, has taken a series of preventive and control measures in response to the increasing trend of Kala-Azar. In response, we propose a new model to more scientifically evaluate the effectiveness of these interventions.

Methods

We obtained the incidence data of Kala-Azar from 2017 to 2021 from the Centre for Disease Control and Prevention (CDC) in Yangquan. We constructed Poisson segmented regression model, harmonic Poisson segmental regression model, and improved harmonic Poisson segmented regression model, and used the three models to explain the intervention effect, respectively. Finally, we selected the optimal model by comparing the fitting effects of the three models.

Results

The primary analysis showed an underlying upward trend of Kala-Azar before intervention [incidence rate ratio (IRR): 1.045, 95% confidence interval (CI): 1.027-1.063, p < 0.001]. In terms of long-term effects, the rise of Kala-Azar slowed down significantly after the intervention (IRR:0.960, 95%CI:0.927-0.995, p = 0.026), and the risk of Kala-Azar increased by 0.3% for each additional month after intervention (β1  + β3  = 0.003, IRR = 1.003). The results of the model fitting effect showed that the improved harmonic Poisson segmental regression model had the best fitting effect, and the values of MSE, MAE, and RMSE were the lowest, which were 0.017, 0.101, and 0.130, respectively.

Conclusion

In the long term, the intervention measures taken by the Yangquan CDC can well curb the upward trend of Kala-Azar. The improved harmonic Poisson segmented regression model has higher fitting performance, which can provide a certain scientific reference for the evaluation of the intervention effect of seasonal infectious diseases.

Urban wastewater-based epidemiology for multi-viral pathogen surveillance in the Valencian region, Spain

Wastewater-based epidemiology (WBE) has lately arised as a promising tool for monitoring and tracking viral pathogens in communities. In this study, we analysed WBE's role as a multi-pathogen surveillance strategy to detect the presence of several viral illness causative agents. Thus, an epidemiological study was conducted from October 2021 to February 2023 to estimate the weekly levels of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), Respiratory Syncytial virus (RSV), and Influenza A virus (IAV) in influent wastewater samples (n = 69). In parallel, a one-year study (October 2021 to October 2022) was performed to assess the presence of pathogenic human enteric viruses. Besides, monitoring of proposed viral fecal contamination indicators crAssphage and Pepper mild mottle virus (PMMoV) was also assessed, along with plaque counting of somatic coliphages. Genetic material of rotavirus (RV), human astrovirus (HAStV), and norovirus genogroup I (GI) and GII was found in almost all samples, while hepatitis A and E viruses (HAV and HEV) only tested positive in 3.77 % and 22.64 % of the samples, respectively. No seasonal patterns were overall found for enteric viruses, although RVs had a peak prevalence in the winter months. All samples tested positive for SARS-CoV-2 RNA, with a mean concentration of 5.43 log genome copies per liter (log GC/L). The tracking of the circulating SARS-CoV-2 variants of concern (VOCs) was performed by both duplex RT-qPCR and next generation sequencing (NGS). Both techniques reliably showed how the dominant VOC transitioned from Delta to Omicron during two weeks in Spain in December 2021. RSV and IAV viruses peaked in winter months with mean concentrations 6.40 and 4.10 log GC/L, respectively. Moreover, the three selected respiratory viruses strongly correlated with reported clinical data when normalised by wastewater physico-chemical parameters and presented weaker correlations when normalising sewage concentration levels with crAssphage or somatic coliphages titers. Finally, predictive models were generated for each respiratory virus, confirming high reliability on WBE data as an early-warning system and communities illness monitoring system. Overall, this study presents WBE as an optimal tool for multi-pathogen tracking reflecting viral circulation and diseases trends within a selected area, its value as a multi-pathogen early-warning tool stands out due to its public health interest.

Association between ambient temperature and risk of notifiable infectious diseases in China from 2011 to 2019

Previous studies on temperature and infectious diseases primarily focused on individual disease types, yielding inconsistent conclusions. This study collected monthly data on notifiable infectious disease cases and meteorological variables across 7 provinces in China from 2011 to 2019. A distributed lag nonlinear model was used to evaluate the association between ambient temperature and infectious diseases within each province, and random meta-analysis was applied to evaluate the pooled effect. Extreme hot temperature (the 97.5th percentile) was positively associated with the risk of respiratory infectious diseases with the relative risk (RR) of 1.45 (95%CI: 1.01-2.08). Conversely, extreme cold temperature (the 2.5th percentile) was negatively associated with intestinal infectious diseases and zoonotic diseases and vector-borne diseases, reporting RRs of 0.43 (95%CI: 0.30-0.60) and 0.46 (95%CI: 0.38-0.57), respectively. This study described the nonlinear association between ambient temperature and infectious diseases with different transmission routes, informing comprehensive prevention and control strategies for temperature-related infectious diseases.

Associations between meteorological variation and hospitalisations for rotavirus infections in Kuala Lumpur, Malaysia

Objectives

Rotavirus (RoV) infections have emerged as a significant public health concern around the world. Understanding the relationship between climatic conditions and hospitalisations due to RoV infections can help engage effective prevention strategies. This study aims to investigate the potential associations between meteorological variability and RoV-related hospitalisations in Kuala Lumpur, Malaysia.

Methods

Hospitalization data from a tertiary teaching hospital in Kuala Lumpur over a twelve-year period were retrospectively collected. Concurrently, meteorological data were obtained from the Malaysian Meteorological Department (MetMalaysia) including variables of temperature, humidity, precipitation, and atmospheric pressure to further demonstrate relationship with RoV-associated hospitalisations.

Results

The results indicated positive correlations between increased rainfall, rainy days, humidity, and RoV-related hospitalisations, suggesting the influence of environmental factors on the transmission of RoV.

Conclusions

This study highlights positive associations between meteorological variations and hospitalizations for RoV infections in Kuala Lumpur, Malaysia. Further investigations, including national-level data, are needed to deepen our understanding of these associations, particularly within the context of Malaysia and to develop targeted interventions for disease prevention and control.

Temporal dynamic effects of meteorological factors and air quality on the physical health of the older adults in Shenzhen, China

Introduction

Meteorological and environmental factors can affect people's lives and health, which is crucial among the older adults. However, it is currently unclear how they specifically affect the physical condition of older adults people.

Methods

We collected and analyzed the basic physical examination indicators of 41 older adults people for two consecutive years (2021 and 2022), and correlated them with meteorological and environmental factors. Partial correlation was also conducted to exclude unrelated factors as well.

Results

We found that among the physical examination indicators of the older adults for two consecutive years, five indicators (HB, WBC, HbAlc, CB, LDL-C) showed significant differences across the population, and they had significantly different dynamic correlation patterns with six meteorological (air pressure, temperature, humidity, precipitation, wind speed, and sunshine duration) and seven air quality factors (NO2, SO2, PM10, O3-1h, O3-8h, CO, PM2.5).

Discussion

Our study has discovered for the first time the dynamic correlation between indicators in normal basic physical examinations and meteorological factors and air quality indicators, which will provide guidance for the future development of policies that care for the healthy life of the older adults.

Peak timing of slowest growth velocity among young children coincides with highest ambient temperatures in Burkina Faso: a longitudinal study

BACKGROUND

Seasonal cycles in climatic factors affect drivers of child growth and contribute to seasonal fluctuations in undernutrition. Current growth seasonality models are limited by categorical definitions of seasons that rely on assumptions about their timing and fail to consider their magnitude.

OBJECTIVE

We disentangle the relationship between climatic factors and growth indicators, using harmonic regression to determine how child growth is related to peaks in temperature, precipitation, and vegetation.

METHODS

Longitudinal anthropometric data collected between August 2014 and December 2016 from 5039 Burkinabè children measured monthly from age 6 to 28 mo (108,580 observations) were linked with remotely sensed daily precipitation, vegetation, and maximum air temperature. Our models parsimoniously extract a cyclic signal with multiple potential peaks, to compare the magnitude and timing of seasonal peaks in climatic factors and morbidity with that of nadirs in growth velocity (cm/mo, kg/mo).

RESULTS

Length and weight velocity were slowest twice a year, coinciding both times with the highest temperatures, and peak fever incidence. Length velocity is slowest 13 d after the first temperature peak in April, and 5 d after the second. Similarly, weight velocity is slowest 13 d before the first temperature peak, and 11 d before the second. The statistical relationship between temperature and anthropometry shows that when the current temperature is higher, weight velocity is lower (β = -0.0048; 95% CI: -0.0059, -0.0038), and length velocity is higher (β = 0.0088; 95% CI: 0.0070, 0.0105).

CONCLUSIONS

Results suggest that child health and development are more affected by high temperatures than by other aspects of climatic seasonality such as rainfall. Emerging shifts in climatic conditions will pose challenges to optimal growth, highlighting the importance of changes that optimize the timing of nutrition interventions and address environmental growth-limiting conditions.

CLINICAL TRIAL REGISTRY

Clinicaltrials.gov: NCT02071563.

The Built Environment and Pediatric Health

Buildings, parks, and roads are all elements of the "built environment," which can be described as the human-made structures that comprise the neighborhoods and communities where people live, work, learn, and recreate (https://www.epa.gov/smm/basic-information-about-built-environment). The design of communities where children and adolescents live, learn, and play has a profound impact on their health. Moreover, the policies and practices that determine community design and the built environment are a root cause of disparities in the social determinants of health that contribute to health inequity. An understanding of the links between the built environment and pediatric health will help to inform pediatricians' and other pediatric health professionals' care for patients and advocacy on their behalf. This technical report describes the range of pediatric physical and mental health conditions influenced by the built environment, as well as historical and persistent effects of the built environment on health disparities. The accompanying policy statement outlines community design solutions that can improve pediatric health and health equity, including opportunities for pediatricians and the health care sector to incorporate this knowledge in patient care, as well as to play a role in advancing a health-promoting built environment for all children and families.

The Complexity of the Seasonality of Nutritional Status: Two Annul Peaks in Child Wasting in Eastern Chad

BACKGROUND

Understanding seasonal patterns in nutritional status is critical for achieving and tracking global nutrition goals. However, the majority of nutrition seasonality research design draws on 2 or 3 within-year time points based on existing assumptions of seasonality, missing a more nuanced pattern.

OBJECTIVE

We aimed to identify the intra-year variability of childhood wasting, severe wasting, and weight-for-height z-scores (WHZ) in a dryland single wet-season context and illustrate an analytical approach for improving analysis of the seasonality of nutritional status.

METHODS

To quantify the intra-year variability in nutritional status, we use data from a 23-month panel study (May 2018 to March 2020) following 231 children (6-59 months of age) in eastern Chad. We apply a mixed-effects harmonic regression with child- and village-level fixed effects on the odds of being wasted, severely wasted, and on WHZ, testing for multiple and nonsymmetrical seasonal peaks, adjusted for child sex and age. We triangulate our findings using climate data on temperature, vegetation, and precipitation.

RESULTS

We identify 2 annual peaks of wasting and severe wasting. Wasting peaks at 14.7% (confidence interval [CI], 11.8-18.2) at the end of the dry season, while the smaller peak corresponds to the start of the harvest period at 13.4% (CI, 10.7-16.6). The odds of being wasted decline during the rainy season to 11.8% (CI, 9.4-14.7), with the lowest prevalence of 8.8% (CI, 6.9-11.1) occurring during the start of the dry season. In addition, a 1°C monthly increase in temperature is significantly associated with a 5% (CI, 1.4-8.7) and 12% (CI, 3.0-20.3) increase in the odds that a child is wasted and severely wasted, respectively.

CONCLUSIONS

Intra-year variability of child wasting is far more complex and nuanced than identified by the literature, with 2 peaks, as opposed to 1, likely corresponding to different seasonal drivers, such as food insecurity, disease, water contamination, and care practices at different times of year. Better seasonality analysis can go a long way in improving the timing and content of programming with the goal of reducing child wasting.

Seasonality of Acute Malnutrition in African Drylands: Evidence From 15 Years of SMART Surveys

Reduction of wasting, or low weight-for-height, is a critical target for the Zero Hunger Sustainable Development Goal, yet robust evidence establishing continuous seasonal patterns of wasting is presently lacking. The current consensus of greatest hunger during the preharvest period is based on survey designs and analytical methods, which discretize time frame into preharvest/postharvest, dry/wet, or lean/plenty seasons. We present a spatiotemporally nuanced study of acute malnutrition seasonality in African drylands using a 15-year data set of Standardized Monitoring and Assessment of Relief and Transition surveys (n = 412,370). Climatological similarity was ensured by selecting subnational survey regions with 1 rainy season and by spatially matching each survey to aridity and livelihood zones. Harmonic logit regression models indicate 2 peaks of wasting during the calendar year. Greatest wasting prevalence is estimated in April to May, coincident with the primary peak of temperature. A secondary peak of wasting is observed in August to October, coinciding with the primary peak of rainfall and secondary peak of temperature. This pattern is retained across aridity and livelihood zones and is sensitive to temperature, precipitation, and vegetation. Improved subnational estimation of acute malnutrition seasonality can thus assist decision makers and practitioners in data-sparse settings and facilitate global progress toward Zero Hunger.

Prevalence and genetic diversity of rotavirus in Bangladesh during pre-vaccination period, 1973-2023: a meta-analysis

Introduction

Rotavirus infection is a major cause of mortality among children under 5 years in Bangladesh. There is lack of integrated studies on rotavirus prevalence and genetic diversity during 1973 to 2023 in Bangladesh.

Methods

This meta-analysis was conducted to determine the prevalence, genotypic diversity and seasonal distribution of rotavirus during pre-vaccination period in Bangladesh. This study included published articles on rotavirus A, rotavirus B and rotavirus C. We used Medline, Scopus and Google Scholar for published articles. Selected literatures were published between 1973 to 2023.

Results

This study detected 12431 research articles published on rotavirus. Based on the inclusion criteria, 29 of 75 (30.2%) studies were selected. Molecular epidemiological data was taken from 29 articles, prevalence data from 29 articles, and clinical symptoms from 19 articles. The pooled prevalence of rotavirus was 30.1% (95% CI: 22%-45%, p = 0.005). Rotavirus G1 (27.1%, 2228 of 8219) was the most prevalent followed by G2 (21.09%, 1733 of 8219), G4 (11.58%, 952 of 8219), G9 (9.37%, 770 of 8219), G12 (8.48%, 697 of 8219), and G3 (2.79%, 229 of 8219), respectively. Genotype P[8] (40.6%, 2548 of 6274) was the most prevalent followed by P[4] (12.4%, 777 of 6274) and P[6] (6.4%, 400 of 6274), respectively. Rotavirus G1P[8] (19%) was the most frequent followed by G2P [4] (9.4%), G12P[8] (7.2%), and G9P[8], respectively. Rotavirus infection had higher odds of occurrence during December and February (aOR: 2.86, 95% CI: 2.43-3.6, p = 0.001).

Discussion

This is the first meta-analysis including all the studies on prevalence, molecular epidemiology, and genetic diversity of rotavirus from 1973 to 2023, pre-vaccination period in Bangladesh. This study will provide overall scenario of rotavirus genetic diversity and seasonality during pre-vaccination period and aids in policy making for rotavirus vaccination program in Bangladesh. This work will add valuable knowledge for vaccination against rotavirus and compare the data after starting vaccination in Bangladesh.

Influence of sanitation facilities on diarrhea prevalence among children aged below 5 years in flood-prone areas of Bangladesh: a multilevel analysis

Although the improvement of sanitation facilities has been a major contributor to improving public health, it is not guaranteed to prevent negative health outcomes. This is especially true in areas affected by severe natural disasters, such as flooding or extreme rainfall. Previous studies have examined the association between catastrophic natural disasters and negative health outcomes. However, studies on disaster-prone areas are limited. This study focused on the impact of flood risks and examined whether the improvement of sanitation facilities would be sufficient to suppress the prevalence of diarrhea in flood-prone areas. Two secondary datasets including geodata on flood-prone areas were used for the analysis: one each was obtained from the Bangladesh Demographic and Health Survey and Bangladesh Agricultural Research Council. Two models with categorizations of sanitation facilities based on containment type and excreta flow were applied for analysis. Results showed that the severe flood-prone areas and "diffused" type of sanitation, where the feces are diffused without any containment, had significant positive associations with diarrhea prevalence; however, the interaction between them was negative. Moderate flood-prone areas had a significant positive association with diarrhea prevalence; however, the interaction with unimproved sanitation, which includes containment without clear partition from feces, was significantly negative. These findings indicate that improved sanitation or containment type of sanitation may not positively contribute to the prevention of diarrhea in these severe- and moderate-flood prone areas. The urgent need for alternative sanitation technologies should be addressed in flood-prone regions.

Seasonal pattern in occurrence of rotavirus infection (RV) in diarrheic children, calves and piglets from Bareilly, India

Rapid and reliable diagnosis for diarrhoeal disease is critically important for the differentiation of etiological agents and subsequent suitable treatment modalities. The objective of the study is to reveal the seasonal pattern in the occurrence of rotavirus in diarrheic children, calves and piglets from Bareilly, Uttar Pradesh, India. A total of 115 diarrhoeal samples were collected, out of which 51 were collected during post-monsoon/autumn (September 2018-November 2018) and 64 during the winter season (December 2018-February 2019). The samples were collected from children <5 years (n = 50), piglets <3 months (n = 35) and calves <6 months of age (n = 30). These samples were screened by ribonucleic acid-polyacrylamide gel electrophoresis (RNA-PAGE) and reverse transcriptase-polymerase chain reaction (RT-PCR) by targeting the VP6 gene of rotavirus A (RVA) and the two were compared. In RNA-PAGE 29.4% (5/17), 6.3% (1/16) and 0% (0/18) samples collected from children, calves and piglets, respectively were rotavirus positive during the autumn season while 45.5% (15/33), 21.4% (3/14) and 17.7% (3/17) samples in the winter season. In RT-PCR, 41.2% (7/17), 12.5% (2/16) and 0% (0/18) samples were rotavirus positive in the autumn season while 51.5% (17/33), 28.6% (4/14) and 29.4% (5/17) samples in winter season collected from children, calves and piglets, respectively. On statistical analysis, no significant difference between the season and number of positives in children and calves (p > 0.05) was observed, however in piglets significantly higher number of RVA positives were detected in the winter season than autumn (p < 0.01). The diagnostic test comparison of RNA-PAGE and RT-PCR showed no statistically significant difference in detecting the RVA positives (p > 0.05). Overall the percent positivity showed a seasonal pattern with higher positivity in winter as compared to autumn season.

The influence of demographic and meteorological factors on temporal patterns of rotavirus infection in Dhaka, Bangladesh

To quantify the potential impact of rotavirus vaccines and identify strategies to improve vaccine performance in Bangladesh, a better understanding of the drivers of pre-vaccination rotavirus patterns is required. We developed and fitted mathematical models to 23 years (1990-2012) of weekly rotavirus surveillance data from Dhaka with and without incorporating long-term and seasonal variation in the birth rate and meteorological factors. We performed external model validation using data between 2013 and 2019 from the regions of Dhaka and Matlab. The models showed good agreement with the observed age distribution of rotavirus cases and captured the observed shift in seasonal patterns of rotavirus hospitalizations from biannual to annual peaks. The declining long-term trend in the birth rate in Bangladesh was the key driver of the observed shift from biannual to annual winter rotavirus patterns. Meteorological indices were also important: a 1°C, 1% and 1 mm increase in diurnal temperature range, surface water presence and degree of wetness were associated with a 19%, 3.9% and 0.6% increase in the transmission rate, respectively. The model demonstrated reasonable predictions for both Dhaka and Matlab, and can be used to evaluate the impact of rotavirus vaccination in Bangladesh against changing patterns of disease incidence.

The effect and attributable risk of daily temperature on category C infectious diarrhea in Guangdong Province, China

Previous studies have explored the effect between ambient temperature and infectious diarrhea (ID) mostly using relative risk, which provides limited information in practical applications. Few studies have focused on the disease burden of ID caused by temperature, especially for different subgroups and cities in a multi-city setting. This study aims to estimate the effects and attributable risks of temperature on category C ID and explore potential modifiers among various cities in Guangdong. First, distributed lag non-linear models (DLNMs) were used to explore city-specific associations between daily mean temperature and category C ID from 2014 to 2016 in Guangdong and pooled by applying multivariate meta-analysis. Then, multivariate meta-regression was implemented to analyze the potential heterogeneity among various cities. Finally, we assessed the attributable burden of category C ID due to temperature, low (below the 5th percentile of temperature) and high temperature (above the 95th percentile of temperature) for each city and subgroup population. Compared with the 50th percentile of daily mean temperature, adverse effects on category C ID were found when the temperature was lower than 12.27 ℃ in Guangdong Province. Some city-specific factors (longitude, urbanization rate, population density, disposable income per capita, and the number of medical technicians and beds per thousand persons) could modify the relationship of temperature-category C ID. During the study period, there were 60,505 category C ID cases (17.14% of total cases) attributable to the exposure of temperature, with the attributable fraction (AF) of low temperature (4.23%, 95% empirical confidence interval (eCI): 1.79-5.71%) higher than high temperature (1.34%, 95% eCI: 0.86-1.64%). Males, people under 5 years, and workers appeared to be more vulnerable to temperature, with AFs of 29.40%, 19.25%, and 21.49%, respectively. The AF varied substantially at the city level, with the largest AF of low temperature occurring in Shaoguan (9.58%, 95% eCI: 8.36-10.09%), and that of high temperature occurring in Shenzhen (3.16%, 95% eCI: 2.70-3.51%). Low temperature was an important risk factor for category C ID in Guangdong Province, China. The exposure-response relationship could be modified by city-specific characteristics. Considering the whole population, the attributable risk of low temperature was much higher than that of high temperature, and males, people under 5 years, and workers were vulnerable populations.

Norovirus Genogroup II Epidemics and the Potential Effect of Climate Change on Norovirus Transmission in Taiwan

The activity of norovirus varies from season to season, and the effect of climate change on the incidence of norovirus outbreaks is a widely recognized yet poorly understood phenomenon. Investigation of the possible association between climatic factors and the incidence of norovirus is key to a better understanding of the epidemiology of norovirus and early prediction of norovirus outbreaks. In this study, clinical stool samples from acute gastroenteritis outbreaks were collected from January 2015 to June 2019 in Taiwan. Data analysis from our study indicated that more than half of the cases were reported in the winter and spring seasons, including those caused by norovirus of genotypes GII (genogroup II).2, GII.3, GII.6, and GII.17, and 45.1% of the patients who tested positive for norovirus were infected by the GII.4 norovirus in autumn. However, GII.6 norovirus accounted for a higher proportion of the cases reported in summer than any other strain. Temperature is a crucial factor influencing patterns of epidemic outbreaks caused by distinct genotypes of norovirus. The results of this study may help experts predict and issue early public warnings of norovirus transmission and understand the effect of climate change on norovirus outbreaks caused by different genotypes and occurring in different locations.

Evaluating Completeness of Foodborne Outbreak Reporting in the United States, 1998-2019

Public health agencies routinely collect time-referenced records to describe and compare foodborne outbreak characteristics. Few studies provide comprehensive metadata to inform researchers of data limitations prior to conducting statistical modeling. We described the completeness of 103 variables for 22,792 outbreaks publicly reported by the United States Centers for Disease Control and Prevention’s (US CDC’s) electronic Foodborne Outbreak Reporting System (eFORS) and National Outbreak Reporting System (NORS). We compared monthly trends of completeness during eFORS (1998−2008) and NORS (2009−2019) reporting periods using segmented time series analyses adjusted for seasonality. We quantified the overall, annual, and monthly completeness as the percentage of outbreaks with blank records per our study period, calendar year, and study month, respectively. We found that outbreaks of unknown genus (n = 7401), Norovirus (n = 6414), Salmonella (n = 2872), Clostridium (n = 944), and multiple genera (n = 779) accounted for 80.77% of all outbreaks. However, crude completeness ranged from 46.06% to 60.19% across the 103 variables assessed. Variables with the lowest crude completeness (ranging 3.32−6.98%) included pathogen, specimen etiological testing, and secondary transmission traceback information. Variables with low (<35%) average monthly completeness during eFORS increased by 0.33−0.40%/month after transitioning to NORS, most likely due to the expansion of surveillance capacity and coverage within the new reporting system. Examining completeness metrics in outbreak surveillance systems provides essential information on the availability of data for public reuse. These metadata offer important insights for public health statisticians and modelers to precisely monitor and track the geographic spread, event duration, and illness intensity of foodborne outbreaks.

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.

The Epidemiological and clinical profiles of acute diarrhea due rotavirus in children aged 0-71 months hospitalized at Kalembe-lembe pediatric hospital in Kinshasa, Democratic Republic of Congo

This research is based on a retrospective analysis of medical records filed in the archives of the emergency departments of Kalembe-lembe Hospital in the provincial city of Kinshasa in the Democratic Republic of Congo. The study involved 324 records of patients aged 0-71 months admitted to the emergency departments and hospitalized for acute diarrhea from January 1 to December 31, 2015. The aim was to inventory the cases of rotavirus diarrhea and/or other germs (individually or in combination) to study their epidemiological and clinical aspects. Thus, the epidemiological and clinical parameters (age, sex, season, symptoms, frequency and physical aspects of stools, dehydration status and duration of hospitalization) of diarrheic children diagnosed as positive for rotavirus were compared with those infected with other germs (individually or in combination with rotavirus or other viruses). The search for the etiological agents of the diarrhea was performed in 56.48% of the cases. The results of this work allowed us to show: (i) a predominance of infections by viruses (69.94%) including rotavirus (48.08%), (ii) high rates of infections by etiological agents of diarrhea including rotavirus in children under 12 months, (iii) a high proportion of vomiting, (iii) a high proportion of vomiting, fever, physical asthenia and restlessness or frequent and liquid stools or moderate dehydration in children infected with rotavirus, (iv) specific clinical pictures according to the etiological agents of diarrhea or their combinations.

Seasonal variation of diarrheal diseases and drinking water quality in resettlement colony in Delhi, India: A community-based cross-sectional study

Seasonal variation plays an important role in the occurrence of diarrheal diseases and distinct seasonal occurrence of diarrheal diseases, with bacterial diarrhea occurring more during the warm seasons and viral diarrhea occurring during the drier cool months, has been observed due to seasonal variation. Time-series cross-sectional study was conducted from January to December 2018 among 553 under-five children to assess seasonal variation of diarrheal diseases and its association with the drinking water quality in a resettlement colony of Delhi. The prevalence of diarrhea was 40.7% and was highest during the rainy season (67.6%). Majority of the water samples were found to be unsatisfactory for human consumption during the rainy and summer seasons. Significant and moderate relationship was found between the seasonal variation and occurrence of diarrhea (r = 0.728, P < 0.05) and most probable number count (r = 0.50, P < 0.05), respectively. Understanding the environmental factors that influences the occurrence of diarrheal diseases is warranted.

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.

Meteorological and social conditions contribute to infectious diarrhea in China

Infectious diarrhea in China showed a significant pattern. Many researchers have tried to reveal the drivers, yet usually only meteorological factors were taken into consideration. Furthermore, the diarrheal data they analyzed were incomplete and the algorithms they exploited were inefficient of adapting realistic relationships. Here, we investigate the impacts of meteorological and social factors on the number of infectious diarrhea cases in China. A machine learning algorithm called the Random Forest is utilized. Our results demonstrate that nearly half of infectious diarrhea occurred among children under 5 years old. Generally speaking, increasing temperature or relative humidity leads to increased cases of infectious diarrhea in China. Nevertheless, people from different age groups or different regions own different sensitivities to meteorological factors. The weight of feces that are harmfully treated could be a possible reason for infectious diarrhea of the elderly as well as children under 5 years old. These findings indicate that infectious diarrhea prevention for children under 5 years old remains a primary task in China. Personalized prevention countermeasures ought to be provided to different age groups and different regions. It is essential to bring the weight of feces that are harmfully treated to the forefront when considering infectious diarrhea prevention.

Temperature and risk of infectious diarrhea: a systematic review and meta-analysis

Infectious diarrhea (ID) is an intestinal infectious disease including cholera, typhoid and paratyphoid fever, bacterial and amebic dysentery, and other infectious diarrhea. There are many studies that have explored the relationship between ambient temperature and the spread of infectious diarrhea, but the results are inconsistent. It is necessary to systematically evaluate the impact of temperature on the incidence of ID. This study was based on the PRISMA statement to report this systematic review. We conducted literature searches from CNKI, VIP databases, CBM, PubMed, Web of Science, Cochrane Library, and other databases. The number registered in PROSPERO is CRD42021225472. After searching a total of 4915 articles in the database and references, 27 studies were included. The number of people involved exceeded 7.07 million. The overall result demonstrated when the temperature rises, the risk of infectious diarrhea increases significantly (RR_cumulative=1.42, 95%CI: 1.07-1.88, RR_single-day=1.08, 95%CI: 1.03-1.14). Subgroup analysis found the effect of temperature on the bacillary dysentery group (RR_cumulative=1.85, 95%CI: 1.48-2.30) and unclassified diarrhea groups (RR_cumulative=1.18, 95%CI: 0.59-2.34). The result of the single-day effect subgroup analysis was similar to the result of the cumulative effect. And the sensitivity analysis proved that the results were robust. This systematic review and meta-analysis support that temperature will increase the risk of ID, which is helpful for ID prediction and early warning in the future.

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.

A potential risk assessment tool to monitor pathogens circulation in coastal waters

The present study reports data on a 20 months campaign monitoring enteric viruses (hepatitis A, norovirus, rotavirus, astrovirus, sapovirus, and aichivirus) and bacteria (Salmonella spp.) in seawater. The aim of this work was to assess the potential correlation among the presence of viruses/bacteria and different environmental factors like seasonality, water discharge sources (treated and untreated wastewater, mixed waters and raw water) as well as influence of the Italian lockdown measure against COVID-19 pandemic. Results showed different prevalence of the investigated viruses with values equal to 16 % for norovirus GI, 15.1 % for norovirus GII, followed by 13.8 % for astrovirus, and 13.3 % for sapovirus. Rotavirus was detected in the 8.4 % of samples and aichivirus was detected with the lowest prevalence of 3.5 %. Hepatitis A virus was never identified in the monitoring campaign. Salmonella spp. was detected with a prevalence of 36.6 %. Statistical analysis displayed a high correlation for the two noroviruses simultaneous detection (NGI and NGII) while a lower correlation was found for co-presence of noroviruses with astrovirus, sapovirus or Salmonella spp. A significant decrease of enteric pathogens in seawater was observed during the restrictions period. Results on seasonality highlighted a higher viral prevalence correlated to the wet season for all the pathogens but rotavirus and aichivirus, which instead showed an opposite trend and a higher incidence in the dry season. With respect to discharge typology, some viruses displayed a higher prevalence in treated waters (astrovirus, rotavirus, sapovirus and aichivirus) while the other investigated pathogens (noroviruses and Salmonella spp.) showed a higher prevalence in mixed waters. The main observations of this work were used to define a potential monitoring strategy that could be useful for sanitary Authorities to implement surveillance plans aimed at preventing possible sanitary outbreaks and/or environmental quality deterioration.

Global projections of temperature-attributable mortality due to enteric infections: a modelling study

BACKGROUND

Mortality due to enteric infections is projected to increase because of global warming; however, the different temperature sensitivities of major enteric pathogens have not yet been considered in projections on a global scale. We aimed to project global temperature-attributable enteric infection mortality under various future scenarios of sociodemographic development and climate change.

METHODS

In this modelling study, we generated global projections in two stages. First, we forecasted baseline mortality from ten enteropathogens (non-typhoidal salmonella, Shigella, Campylobacter, cholera, enteropathogenic Escherichia coli, enterotoxigenic E coli, typhoid, rotavirus, norovirus, and Cryptosporidium) under several future sociodemographic development and health investment scenarios (ie, pessimistic, intermediate, and optimistic). We then estimated the mortality change from baseline attributable to global warming using the product of projected annual temperature anomalies and pathogen-specific temperature sensitivities.

FINDINGS

We estimated that in the period 2080-95, the global mean number of temperature-attributable deaths due to enteric infections could be as low as 6599 (95% empirical CI 5441-7757) under the optimistic sociodemographic development and climate change scenario, or as high as 83 888 (67 760-100 015) under the pessimistic scenario. Most of the projected temperature-attributable deaths were from shigellosis, cryptosporidiosis, and typhoid fever in sub-Saharan Africa and South Asia. Considerable reductions in the number of attributable deaths were from viral infections, such as rotaviral and noroviral enteritis, which resulted in net reductions in attributable enteric infection mortality under optimistic scenarios for Latin America and the Caribbean and East Asia and the Pacific.

INTERPRETATION

Temperature-attributable mortality could increase under warmer climate and unfavourable sociodemographic conditions. Mitigation policies for limiting global warming and sociodemographic development policies for low-income and middle-income countries might help reduce mortality from enteric infections in the future.

FUNDING

Japan Society for the Promotion of Science, Japan Science and Technology Agency, and Spanish Ministry of Economy, Industry, and Competitiveness.

The Short-term Effects of Temperature on Infectious Diarrhea among Children under 5 Years Old in Jiangsu, China: A Time-series Study (2015-2019)

The association between meteorological factors and infectious diarrhea has been widely studied in many countries. However, investigation among children under 5 years old in Jiangsu, China remains quite limited. Data including infectious diarrhea cases among children under five years old and daily meteorological indexes in Jiangsu, China from 2015 to 2019 were collected. The lag-effects up to 21 days of daily maximum temperature (Tmax) on infectious diarrhea were explored using a quasi-Poisson regression with a distributed lag non-linear model (DLNM) approach. The cases number of infectious diarrhea was significantly associated with seasonal variation of meteorological factors, and the burden of disease mainly occurred among children aged 0–2 years old. Moreover, when the reference value was set at 16.7°C, Tmax had a significant lag-effect on cases of infectious diarrhea among children under 5 years old in Jiangsu Province, which was increased remarkably in cold weather with the highest risk at 8°C. The results of DLNM analysis implicated that the lag-effect of Tmax varied among the 13 cities in Jiangsu and had significant differences in 8 cities. The highest risk of Tmax was presented at 5 lag days in Huaian with a maximum RR of 1.18 (95% CI: 1.09, 1.29). Suzhou which had the highest number of diarrhea cases (15830 cases), had a maximum RR of 1.04 (95% CI:1.03, 1.05) on lag 15 days. Tmax is a considerable indicator to predict the epidemic of infectious diarrhea among 13 cities in Jiangsu, which reminds us that in cold seasons, more preventive strategies and measures should be done to prevent infectious diarrhea.

A 21-year retrospective analysis of environmental impacts on paediatric acute gastroenteritis in an affluent setting

BACKGROUND

Extreme weather events happen more frequently along with global warming and they constitute a challenge for public health preparedness. For example, many investigations showed heavy rainfall was associated with an increased risk of acute gastroenteritis. In this study, we examined the associations between different meteorological factors and paediatric acute gastroenteritis in an affluent setting in China controlling for pollutant effects.

METHODS

Aggregated total weekly number of intestinal infection-related hospital admissions, and meteorological and air pollution data during 1998-2018 in Hong Kong were collected and analysed by a combination of quasi-Poisson generalized additive model and distributed lag nonlinear model. Study population was restricted to children under 5 years of age at the time of admission.

RESULTS

While heavy rainfall did not exhibit a statistically significant association with the risk of paediatric admission due to intestinal infections, low temperature and humidity extremes (both relative humidity and vapour pressure) did. Compared with the temperature at which the lowest risk was detected (i.e. 22.5 °C), the risk was 6.4% higher (95% confidence interval: 0.0% to 13.0% at 15.1 °C (i.e. the 5th percentile)). We also found the risk of paediatric admission was statistically significantly associated with an increase in the number of extreme cold days in a week over the study period.

CONCLUSION

Cold condition may have greater impact on disease transmission through increased stability and infectivity of enteric viruses in affluent settings like Hong Kong and thus resulted in an increased risk for paediatric acute gastroenteritis. On the contrary, an insignificant impact from heavy rainfall and high temperature may indicate a minor effect on disease transmission through bacterial growth in contaminated food and water. With the identified impacts of weather factors, extreme weather events are likely to distort the prevalence and seasonal pattern of diarrhoeal diseases in the future.

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.

Epidemiology and Genotype Distribution of Rotavirus Gastroenteritis in Under-Five Children of South Rajasthan, India

OBJECTIVE

To determine the epidemiology of rotavirus diarrhea and its genotypes distribution among under five children from Udaipur, Rajasthan.

METHODS

Hospital-based prospective study among children aged 0-59 mo of age hospitalized due to acute gastroenteritis and assess the clinical and epidemiological profile. Stool samples collected during 2017-2019 from 734 children, were tested by enzyme immune assay (EIA) to identify rotavirus and the samples that were positive were subjected to genotyping using published methods.

RESULTS

Rotavirus was detected in 12.94% (95/734) of the stool samples. Maximum positivity (38.9%) was seen in children aged 12-23 mo followed by 34.7% in 6-11 mo of age. Detection rates were higher (46.31%, 44/95) during winter months of December-February; 65% children with rotavirus diarrhea had severe dehydration followed by moderate dehydration in 32% of cases. G3P[8] (40, 43.01%) was the commonest genotype followed by G9P[4] (10, 10.75%).

CONCLUSION

Among under-five children hospitalized due to acute gastroenteritis in a tertiary care setting in Udaipur, Rajasthan, 12.9% of children were rotavirus positive with predominant (43.01%) circulation of G3P[8] strains.

Rotavirus Diarrhea and its Determinants Among Under-Five Children Admitted in a Tertiary Care Hospital of Southern Haryana, India

OBJECTIVE

To estimate the prevalence of rotavirus diarrhea and its demographic, social, and clinical characteristics among children less than five years of age admitted in a rural tertiary care institute.

METHODS

This prospective hospital-based observational study was carried out during February 2016 to June 2019. Diarrheal admissions of children aged 0-59 mo were screened and those who met the inclusion criteria were included in the study. Sociodemographic and clinical information was collected using a case report form. Stool samples were collected within 48 h of admission and transported in cold chain every month to the referral laboratory situated at Christian Medical College for testing.

RESULTS

Among the children admitted with acute diarrhea, 148 (11.02%) were positive for rotavirus in the study. As per Vesikari scoring system, around three fourth (76.2%) of children were having severe or very severe diarrhea. Severity of diarrhea was more among rotavirus positive cases as assessed by the Vesikari scoring system. The rotavirus diarrhea showed a peak during November to February.

CONCLUSION

Rotavirus diarrhea is an issue of public health importance, particularly due to its association with the severe diarrhea. As evidenced from similar settings in the world, rotavirus vaccine introduction and increased coverage is the most important strategy towards prevention and control of rotavirus diarrhea.

Rotavirus Gastroenteritis Hospitalizations Among Under-5 Children in Northern India

OBJECTIVE

To study epidemiological profile, prevalence, and molecular epidemiology of RVGE in hospitalized under-5 children at a tertiary care teaching rural hospital located in sub-Himalayan belt of Northern India.

METHODS

This was a hospital-based surveillance study done over 4 y (2016-2019) including under-5 children hospitalized with acute gastroenteritis (AGE). Demographic and clinical parameters were recorded in a pre-designed performa. After consent, stool samples were collected and sent to Christian Medical College (CMC), Vellore for RV screening by enzyme immunoassay (EIA). Each EIA-positive sample was further subjected to G and P typing using published methods.

RESULTS

Out of total 851 included children, rotavirus gastroenteritis (RVGE) was detected in 23.03% (196/851) cases by EIA. The highest incidence for RVGE-positive cases (40.43%) was observed in 2016 with gradual decline over next 3 y. Maximum cases of diarrhea were observed in 12-23 mo age group along with highest rotavirus detection. G3P[8] was most common genotype (46.94%) found, followed by G1P[8] (13.78%), G2P[4] (4.59%), G1P[6] (8.16%) and G9P[4] (3.57%). Mixed genotype was seen in 13.78% of total cases.

CONCLUSION

This study summarizes the changing trends in the epidemiology of RVGE in Northern India along with the major circulating genotypes postvaccine introduction.

Rotavirus Gastroenteritis Hospitalizations Among Under-Five Children in Bhubaneswar, Odisha, India

OBJECTIVE

To report rotavirus-associated AGE (AGE) profile of admitted children among vaccine-introduced areas in Bhubaneswar, Odisha.

METHODS

This study was conducted between 2016 and 2019 at the Capital Hospital, Jagannath hospital and Hi-Tech Medical College and Hospital. All AGE patients below five years old, hospitalized in the study facilities were enrolled. A stool sample was collected and transported to the central laboratory (Christian Medical College, Vellore) for rotavirus antigen detection and genotyping. Clinical and demographic information was collected using a predesigned case report format (CRF).

RESULTS

Out of the 1213 stool samples, 447 (36.9%) were identified to have rotavirus with 350 (78.3%) of them belonging to 6 mo to 2 y age group. Rotavirus gastroenteritis exhibited a single peak from November to February. A total of 414 (92.6%) of rotavirus gastroenteritis children had severe or very severe dehydration with odds of rotavirus diarrhea leading to severe dehydration being 1.5 (95% CI, 1-2.4) compared to moderate dehydration. The common genotype combination was G3P[8] (46%) followed by G1P[8] (19%), G2P[4] (5%), and G9P[4] (3%).

CONCLUSION

Rotavirus gastroenteritis peaked between December to February while the most common genotype combination among the rotavirus GE was G3P[8] and G1P[8]. The present study provides the clinical profile of admitted children in the hospitals with diarrhea and the circulating strains of rotavirus which will help in documenting the epidemiology and the performance of vaccine against the disease in the state.

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.

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.

Molecular epidemiology and surveillance of circulating rotavirus among children with gastroenteritis in Bangladesh during 2014-2019

Acute gastroenteritis is one of the major health problems in children aged <5 years around the world. Rotavirus A (RVA) is an important pathogen of acute gastroenteritis. The burden of rotavirus disease in the pediatric population is still high in Bangladesh. This study investigated the prevalence of group A, B, and C rotavirus (RAV, RBV, RCV), norovirus, adenovirus (AdV) and human bocavirus (HBoV) infections in children with acute gastroenteritis in Bangladesh from February 2014 to January 2019. A total of 574 fecal specimens collected from children with diarrhea in Bangladesh during the period of February 2014-January 2019 were examined for RAV, RBV and RCV by reverse transcriptase- multiplex polymerase chain reaction (RT- multiplex PCR). RAV was further characterized to G-typing and P-typing by RT-multiplex PCR and sequencing method. It was found that 24.4% (140 of 574) fecal specimens were positive for RVA followed by AdV of 4.5%. RBV and RCV could not be detected in this study. Genotype G1P[8] was the most prevalent (43%), followed by G2P[4] (18%), and G9P[8] (3%). Among other genotypes, G9P[4] was most frequent (12%), followed by G1P[6] (11%), G9P[6] (3%), and G11P[25] (3%). We found that 7% RVA were nontypeable. Mutations at antigenic regions of the VP7 gene were detected in G1P[8] and G2P[4] strains. Incidence of rotavirus infection had the highest peak (58.6%) during November to February with diarrhea (90.7%) as the most common symptom. Children aged 4-11 months had the highest rotavirus infection percentage (37.9%). By providing baseline data, this study helps to assess efficacy of currently available RVA vaccine. This study revealed a high RVA detection rate, supporting health authorities in planning strategies such as introduction of RVA vaccine in national immunization program to reduce the disease burden.

G3 and G9 Rotavirus genotypes in waste water circulation from two major metropolitan cities of Pakistan

Rotavirus A (RVA) is a diarrheal pathogen affecting children under age five, particularly in developing and underdeveloped regions of the world due to malnutrition, poor healthcare and hygienic conditions. Water and food contamination are found to be major sources of diarrheal outbreaks. Pakistan is one of the countries with high RVA related diarrhea burden but with insufficient surveillance system. The aim of this study was to gauge the RVA contamination of major open sewerage collecting streams and household water supplies in two major metropolitan cities of Pakistan. Three concentration methods were compared using RNA purity and concentration as parameters, and detection efficiency of the selected method was estimated. Water samples were collected from 21 sites in Islamabad and Rawalpindi in two phases during the year 2014-2015. Meteorological conditions were recorded for each sampling day and site from Pakistan Meteorological Department (PMD). Nested PCR was used to detect the presence of RVA in samples targeting the VP7 gene. Logistic regression was applied to assess the association of weather conditions with RVA persistence in water bodies. Statistical analysis hinted at a temporal and seasonal pattern of RVA detection in water. Phylogenetic analysis of selected isolates showed a close association of environmental strains with clinical RVA isolates from hospitalized children with acute diarrhea during the same period. This is the first scientific report cataloging the circulating RVA strains in environmental samples from the region. The study highlights the hazards of releasing untreated sewerage containing potentially infectious viral particles into collecting streams, which could become a reservoir of multiple pathogens and a risk to exposed communities. Moreover, routine testing of these water bodies can present an effective surveillance system of circulating viral strains in the population.

Rotavirus infection in children in Southeast Asia 2008-2018: disease burden, genotype distribution, seasonality, and vaccination

Background

Rotaviruses (RVs) are recognized as a major cause of acute gastroenteritis (AGE) in infants and young children worldwide. Here we summarize the virology, disease burden, prevalence, distribution of genotypes and seasonality of RVs, and the current status of RV vaccination in Southeast Asia (Cambodia, Indonesia, Lao People’s Democratic Republic, Malaysia, Myanmar, Philippines, Singapore, Thailand, and Vietnam) from 2008 to 2018.

Methods

Rotavirus infection in Children in Southeast Asia countries was assessed using data from Pubmed and Google Scholars. Most countries in Southeast Asia have not yet introduced national RV vaccination programs. We exclude Brunei Darussalam, and Timor Leste because there were no eligible studies identified during that time.

Results

According to the 2008–2018 RV surveillance data for Southeast Asia, 40.78% of all diarrheal disease in children were caused by RV infection, which is still a major cause of morbidity and mortality in children under 5 years old in Southeast Asia. Mortality was inversely related to socioeconomic status. The most predominant genotype distribution of RV changed from G1P[8] and G2P[4] into the rare and unusual genotypes G3P[8], G8P[8], and G9P[8]. Although the predominat strain has changed, but the seasonality of RV infection remains unchanged. One of the best strategies for decreasing the global burden of the disease is the development and implementation of effective vaccines.

Conclusions

The most predominant genotype distribution of RV was changed time by time. Rotavirus vaccine is highly cost effective in Southeast Asian countries because the ratio between cost per disability-adjusted life years (DALY) averted and gross domestic product (GDP) per capita is less than one. These data are important for healthcare practitioners and officials to make appropriate policies and recommendations about RV vaccination.

Assessing Seasonality Variation with Harmonic Regression: Accommodations for Sharp Peaks

The use of the harmonic regression model is well accepted in the epidemiological and biostatistical communities as a standard procedure to examine seasonal patterns in disease occurrence. While these models may provide good fit to periodic patterns with relatively symmetric rises and falls, for some diseases the incidence fluctuates in a more complex manner. We propose a two-step harmonic regression approach to improve the model fit for data exhibiting sharp seasonal peaks. To capture such specific behavior, we first build a basic model and estimate the seasonal peak. At the second step, we apply an extended model using sine and cosine transform functions. These newly proposed functions mimic a quadratic term in the harmonic regression models and thus allow us to better fit the seasonal spikes. We illustrate the proposed method using actual and simulated data and recommend the new approach to assess seasonality in a broad spectrum of diseases manifesting sharp seasonal peaks.

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.

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.

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

Rotavirus is a leading cause of acute diarrhea in children worldwide. Costa Rica recently started universal rotavirus vaccinations for infants with a two-dose schedule in February 2019. We aimed to study the seasonality of rotavirus during the pre-vaccination era. We retrospectively studied a six-year period of hospital admissions due to rotavirus gastroenteritis. We estimated seasonal peak timing and relative intensities using trend-adjusted negative binomial regression models with the δ-method. We assessed the relationship between rotavirus cases and weather characteristics and estimated their effects for the current month, one-month prior and two months prior, by using Pearson correlation coefficients. A total of 798 cases were analyzed. Rotavirus cases predominated in the first five months of the year. On average, the peak of admissions occurred between late-February and early-March. During the seasonal peaks, the monthly count tended to increase 2.5-2.75 times above the seasonal nadir. We found the strongest negative association of monthly hospitalizations and joint percentiles of precipitation and minimal temperature at a lag of two months (R = -0.265, p = 0.027) and we detected correlations of -0.218, -0.223, and -0.226 (p < 0.05 for all three estimates) between monthly cases and the percentile of precipitation at lags 0, 1, and 2 months. In the warm tropical climate of Costa Rica, the increase in rotavirus hospitalizations coincided with dry and cold weather conditions with a two-month lag. The findings serve as the base for predictive modeling and estimation of the impact of a nation-wide vaccination campaign on pediatric rotaviral infection morbidity.

The use of remotely sensed environmental parameters for spatial and temporal schistosomiasis prediction across climate zones in Ghana

Schistosomiasis control in sub-Saharan Africa is enacted primarily through preventive chemotherapy. Predictive models can play an important role in filling knowledge gaps in the distribution of the disease and help guide the allocation of limited resources. Previous modeling approaches have used localized cross-sectional survey data and environmental data typically collected at a discrete point in time. In this analysis, 8 years (2008-2015) of monthly schistosomiasis cases reported into Ghana's national surveillance system were used to assess temporal and spatial relationships between disease rates and three remotely sensed environmental variables: land surface temperature (LST), normalized difference vegetation index (NDVI), and accumulated precipitation (AP). Furthermore, the analysis was stratified by three major and nine minor climate zones, defined using a new climate classification method. Results showed a downward trend in reported disease rates (~ 1% per month) for all climate zones. Seasonality was present in the north with two peaks (March and September), and in the middle of the country with a single peak (July). Lowest disease rates were observed in December/January across climate zones. Seasonal patterns in the environmental variables and their associations with reported schistosomiasis infection rates varied across climate zones. Precipitation consistently demonstrated a positive association with disease outcome, with a 1-cm increase in rainfall contributing a 0.3-1.6% increase in monthly reported schistosomiasis infection rates. Generally, surveillance of neglected tropical diseases (NTDs) in low-income countries continues to suffer from data quality issues. However, with systematic improvements, our approach demonstrates a way for health departments to use routine surveillance data in combination with publicly available remote sensing data to analyze disease patterns with wide geographic coverage and varying levels of spatial and temporal aggregation.

Climate Change Impacts on Waterborne Diseases: Moving Toward Designing Interventions

PURPOSE

Climate change threatens progress achieved in global reductions of infectious disease rates over recent decades. This review summarizes literature on potential impacts of climate change on waterborne diseases, organized around a framework of questions that can be addressed depending on available data.

RECENT FINDINGS

A growing body of evidence suggests that climate change may alter the incidence of waterborne diseases, and diarrheal diseases in particular. Much of the existing work examines historical relationships between weather and diarrhea incidence, with a limited number of studies projecting future disease rates. Some studies take social and ecological factors into account in considerations of historical relationships, but few have done so in projecting future conditions. The field is at a point of transition, toward incorporating social and ecological factors into understanding the relationships between climatic factors and diarrheal diseases and using this information for future projections. The integration of these components helps identify vulnerable populations and prioritize adaptation strategies.

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.

Associations between High Temperature, Heavy Rainfall, and Diarrhea among Young Children in Rural Tamil Nadu, India: A Prospective Cohort Study

BACKGROUND

The effects of weather on diarrhea could influence the health impacts of climate change. Children have the highest diarrhea incidence, especially in India, where many lack safe water and sanitation.

OBJECTIVES

In a prospective cohort of 1,284 children under 5 y of age from 900 households across 25 villages in rural Tamil Nadu, India, we examined whether high temperature and heavy rainfall was associated with increased all-cause diarrhea and water contamination.

METHODS

Seven-day prevalence of diarrhea was assessed monthly for up to 12 visits from January 2008 to April 2009, and hydrogen sulfide ([Formula: see text]) presence in drinking water, a fecal contamination indicator, was tested in a subset of households. We estimated associations between temperature and rainfall exposures and diarrhea and [Formula: see text] using binomial regressions, adjusting for potential confounders, random effects for village, and autoregressive-1 error terms for study week.

RESULTS

There were 259 cases of diarrhea. The prevalence of diarrhea during the 7 d before visits was 2.95 times higher (95% CI: 1.99, 4.39) when mean temperature in the week before the 7-d recall was in the hottest versus the coolest quartile of weekly mean temperature during 1 December 2007 to 15 April 2009. Diarrhea prevalence was 1.50 times higher when the 3 weeks before the diarrhea recall period included [Formula: see text] (vs. 0 d) with rainfall of [Formula: see text] (95% CI: 1.12, 2.02), and 2.60 times higher (95% CI: 1.55, 4.36) for heavy rain weeks following a 60-d dry period. The [Formula: see text] prevalence in household water was not associated with heavy rain prior to sample collection.

CONCLUSIONS

The results suggest that, in rural Tamil Nadu, heavy rainfall may wash pathogens that accumulate during dry weather into child contact. Higher temperatures were positively associated with diarrhea 1-3 weeks later. Our findings suggest that diarrhea morbidity could worsen under climate change without interventions to reduce enteric pathogen transmission through multiple pathways. https://doi.org/10.1289/EHP3711.

Waterborne Diseases Arising From Climate Change

As a result of increased frequency and intensity of heat waves, increased floods and droughts, change in climate will affect biological, physical, and chemical components of water through different paths thus enhancing the risk of waterborne diseases. Identifying the role of weather in waterborne infection is a priority public health research issue as climate change is predicted to increase the frequency of extreme precipitation and temperature events. This chapter provides evidence that precipitation and temperature can affect directly or indirectly water quality and consequently affect the health human. This chapter also highlights the complex relationship between precipitation or temperature and transmission of waterborne disease such as diarrheal disease, gastroenteritis, cryptosporidiosis, giardiasis, and cholera.

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.

Hydrometeorology and flood pulse dynamics drive diarrheal disease outbreaks and increase vulnerability to climate change in surface-water-dependent populations: A retrospective analysis

BACKGROUND

The impacts of climate change on surface water, waterborne disease, and human health remain a growing area of concern, particularly in Africa, where diarrheal disease is one of the most important health threats to children under 5 years of age. Little is known about the role of surface water and annual flood dynamics (flood pulse) on waterborne disease and human health nor about the expected impact of climate change on surface-water-dependent populations.

METHODS AND FINDINGS

Using the Chobe River in northern Botswana, a flood pulse river-floodplain system, we applied multimodel inference approaches assessing the influence of river height, water quality (bimonthly counts of Escherichia coli and total suspended solids [TSS], 2011-2017), and meteorological variability on weekly diarrheal case reports among children under 5 presenting to health facilities (n = 10 health facilities, January 2007-June 2017). We assessed diarrheal cases by clinical characteristics and season across age groups using monthly outpatient data (January 1998-June 2017). A strong seasonal pattern was identified, with 2 outbreaks occurring regularly in the wet and dry seasons. The timing of outbreaks diverged from that at the level of the country, where surface water is largely absent. Across age groups, the number of diarrheal cases was greater, on average, during the dry season. Demographic and clinical characteristics varied by season, underscoring the importance of environmental drivers. In the wet season, rainfall (8-week lag) had a significant influence on under-5 diarrhea, with a 10-mm increase in rainfall associated with an estimated 6.5% rise in the number of cases. Rainfall, minimum temperature, and river height were predictive of E. coli concentration, and increases in E. coli in the river were positively associated with diarrheal cases. In the dry season, river height (1-week lag) and maximum temperature (1- and 4-week lag) were significantly associated with diarrheal cases. During this period, a 1-meter drop in river height corresponded to an estimated 16.7% and 16.1% increase in reported diarrhea with a 1- and 4-week lag, respectively. In this region, as floodwaters receded from the surrounding floodplains, TSS levels increased and were positively associated with diarrheal cases (0- and 3-week lag). Populations living in this region utilized improved water sources, suggesting that hydrological variability and rapid water quality shifts in surface waters may compromise water treatment processes. Limitations include the potential influence of health beliefs and health seeking behaviors on data obtained through passive surveillance.

CONCLUSIONS

In flood pulse river-floodplain systems, hydrology and water quality dynamics can be highly variable, potentially impacting conventional water treatment facilities and the production of safe drinking water. In Southern Africa, climate change is predicted to intensify hydrological variability and the frequency of extreme weather events, amplifying the public health threat of waterborne disease in surface-water-dependent populations. Water sector development should be prioritized with urgency, incorporating technologies that are robust to local environmental conditions and expected climate-driven impacts. In populations with high HIV burdens, expansion of diarrheal disease surveillance and intervention strategies may also be needed. As annual flood pulse processes are predominantly influenced by climate controls in distant regions, country-level data may be inadequate to refine predictions of climate-health interactions in these systems.

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.