The Effect of Rainfall on the Incidence of Cholera in Bangladesh

Assessment of water scarcity as a risk factor for cholera outbreaks

INTRODUCTION

Increasing aridity and incidence of droughts pose a significant threat to human health, primarily in exacerbating water scarcity, and is projected to become more frequent and severe as a result of related environmental changes in many regions globally. Concomitantly, water scarcity will force populations to utilize potentially contaminated water sources, hence increasing exposure to waterborne diseases, notably cholera. Proliferation of Vibrio cholerae, causative agent of cholera, is driven by environmental factors. Notably, temperature and precipitation have been employed in providing predictive awareness of cholera, allowing early warning and mitigation. The impact of droughts on incidence and spread of cholera is less understood.

METHODS

This study aimed to quantify relationships among temperature, precipitation, and droughts as a basis for establishing the connection of environmental parameters and outbreaks of cholera. Thirteen cholera outbreaks between 2003 and 2023 in four African countries (Ethiopia, Kenya, Nigeria, and Senegal) were assessed using odds ratio and k-means clustering analysis.

RESULTS

Cholera outbreaks were 3.07 (95 % CI: [0.95, 9.88]) times more likely when drought conditions (negative precipitation anomalies, positive temperature anomalies, and negative Standardized Precipitation-Evapotranspiration Index) were present, compared to their absence. When excess rainfall was also considered, the odds ratio increased to 3.50 (95 % CI: [1.03, 11.90]). Complementary evidence obtained using k-means clustering analysis supported the conclusion that outbreaks of cholera were common during drought conditions.

CONCLUSIONS

Considering the last few decades with increased severity and frequency of droughts in cholera-impacted regions, climate projections indicate the threat of cholera outbreaks will continue, especially noting increasing reports of cholera globally. Hence, predictive intelligence systems for rapid risk assessment, with respect to climate, drought, and human health, are warranted.

Trends of cholera epidemics and associated mortality factors in Cameroon: 2018-2023: a cross-sectional study

BACKGROUND

Cameroon has faced frequent and severe cholera outbreaks since 1971, with case-fatality rates (CFRs) ranging from 12% in 1991, to 5.3% in 2014, all higher than the less than 1% cholera CFR target set by WHO. However, not many studies providing insight on context-specific risk factors have been published. The purpose of this study was to describe the recent cholera outbreaks in Cameroon and to determine factors associated with mortality.

METHODS

This was an analytical cross-sectional study that employed a retrospective design exploiting Ministry of Public Health cholera line-lists from 2018-2023. These line lists were obtained from the Public Health Emergency Operations Coordination and Control Center, compiled into a single Microsoft Excel Sheet, cleaned and analyzed using Microsoft Excel 2016 and SPSS version 20. Cholera cases were defined as those confirmed in reference laboratories via stool culture and suspected cases with proven epidemiological link to laboratory-confirmed cases (suspected cases in health districts with active laboratory-confirmed cases). Factors associated with cholera mortality were identified using binary logistic regression (adjusted odds ratios), after socio-demographic, clinical, and geographical distribution of cholera cases were described. Maps were generated using QGIS version 3.28.14.

RESULTS

Between May 2018 and March 2023, Cameroon experienced four cholera epidemics resulting in 18,986 reported cases and affecting 8 out of 10 administrative regions. The three coastal regions (Littoral, South and South-West Region) reported 83.4% (15,839/18,986) of all the cases while the remaining five affected regions jointly reported 16.6% (3,147/18,986) cases. The most represented age group were those aged 25-35 years (21.9%; 4,163/1,876) and the male: female sex ratio was 1.27. The overall CFR was 2.7% (478 deaths/17,967 cases with known outcome) and was highest among persons > 65 years (6.8%; 59/869). Urban areas notified more cases than rural areas (13,267 vs 5,484). Factors associated with increased mortality were male sex (aOR 1.61, 95% CI: 1.30-2.04), dry season (aOR 1.67, 95% CI: 1.28-2.22), age above 45 years (aOR 1.79, 95% CI: 1.45-2.22) and severe dehydration at consultation (aOR 12.76, 95% CI: 7.66-21.25).

CONCLUSIONS

Cholera outbreaks occurred in eight out of the ten administrative regions in Cameroon during the study period and mortality appeared to be driven by multiple factors notably severe dehydration at time of consultation, advanced age, male sex and the dry season. The high caseloads and case-fatality rates reiterate the need for further strengthening of existing cholera surveillance and outbreak response mechanisms.

Causal inference concepts can guide research into the effects of climate on infectious diseases

A pressing question resulting from global warming is how climate change will affect infectious diseases. Answering this question requires research into the effects of weather on the population dynamics of transmission and infection; elucidating these effects, however, has proved difficult due to the challenges of assessing causality from the predominantly observational data available in epidemiological research. Here we show how concepts from causal inference-the sub-field of statistics aiming at inferring causality from data-can guide that research. Through a series of case studies, we illustrate how such concepts can help assess study design and strategically choose a study's location, evaluate and reduce the risk of bias, and interpret the multifaceted effects of meteorological variables on transmission. More broadly, we argue that interdisciplinary approaches based on explicit causal frameworks are crucial for reliably estimating the effect of weather and accurately predicting the consequences of climate change.

Short-term associations of diarrhoeal diseases in children with temperature and precipitation in seven low- and middle-income countries from Sub-Saharan Africa and South Asia in the Global Enteric Multicenter Study

BACKGROUND

Diarrhoeal diseases cause a heavy burden in developing countries. Although studies have described the seasonality of diarrhoeal diseases, the association of weather variables with diarrhoeal diseases has not been well characterized in resource-limited settings where the burden remains high. We examined short-term associations between ambient temperature, precipitation and hospital visits due to diarrhoea among children in seven low- and middle-income countries.

METHODOLOGY

Hospital visits due to diarrhoeal diseases under 5 years old were collected from seven sites in The Gambia, Mali, Mozambique, Kenya, India, Bangladesh, and Pakistan via the Global Enteric Multicenter Study from December 2007 to March 2011. Daily weather data during the same period were downloaded from the ERA5-Land. We fitted time-series regression models to examine the relationships of daily diarrhoea cases with daily ambient temperature and precipitation. Then, we used meta-analytic tools to examine the heterogeneity between the site-specific estimates.

PRINCIPAL FINDINGS

The cumulative relative risk (RR) of diarrhoea for temperature exposure (95th percentile vs. 1st percentile) ranged from 0.24 to 8.07, with Mozambique and Bangladesh showing positive associations, while Mali and Pakistan showed negative associations. The RR for precipitation (95th percentile vs. 1st percentile) ranged from 0.77 to 1.55, with Mali and India showing positive associations, while the only negative association was observed in Pakistan. Meta-analysis showed substantial heterogeneity in the association between temperature-diarrhoea and precipitation-diarrhoea across sites, with I2 of 84.2% and 67.5%, respectively.

CONCLUSIONS

Child diarrhoea and weather factors have diverse and complex associations across South Asia and Sub-Saharan Africa. Diarrhoeal surveillance system settings should be conceptualized based on the observed pattern of climate change in these locations.

Mechanisms of cholera transmission via environment in India and Bangladesh: state of the science review

OBJECTIVES

Cholera has a long history in India and Bangladesh, the region where six out of the past seven global pandemics have been seeded. The changing climate and growing population have led to global cholera cases remaining high despite a consistent improvement in the access to clean water and sanitation. We aim to provide a holistic overview of variables influencing environmental cholera transmission within the context of India and Bangladesh, with a focus on the mechanisms by which they act.

CONTENT

We identified 56 relevant texts (Bangladesh n = 40, India n = 7, Other n = 5). The results of the review found that cholera transmission is associated with several socio-economic and environmental factors, each associated variable is suggested to have at least one mediating mechanism. Increases in ambient temperature and coastal sea surface temperature support cholera transmission via increases in plankton and a preference of Vibrio cholerae for warmer waters. Increased rainfall can potentially support or reduce transmission via several mechanisms.

SUMMARY AND OUTLOOK

Common issues in the literature are co-variance of seasonal factors, limited access to high quality cholera data, high research bias towards research in Dhaka and Matlab (Bangladesh). A specific and detailed understanding of the relationship between SST and cholera incidence remains unclear.

Cholera rages in Africa and the Middle East: A narrative review on challenges and solutions

Background and Aim

Cholera is a life-threatening infectious disease that is still one of the most common acute watery diarrheal diseases in the world today. Acute diarrhea and severe dehydration brought on by cholera can cause hypovolemic shock, which can be fatal in minutes. Without competent clinical therapy, the rate of case fatality surpasses 50%. The purpose of this review was to highlight cholera challenges in Africa and the Middle East and explain the reasons for why this region is currently a fertile environment for cholera. We investigated cholera serology, epidemiology, and the geographical distribution of cholera in Africa and the Middle East in 2022 and 2023. We reviewed detection methods, such as rapid diagnostic tests (RDTs), and treatments, such as antibiotics and phage therapy. Finally, this review explored oral cholera vaccines (OCVs), and the vaccine shortage crisis.

Methods

We carried out a systematic search in multiple databases, including PubMed, Web of Science, Google Scholar, Scopus, MEDLINE, and Embase, for studies on cholera using the following keywords: ((Cholera) OR (Vibrio cholera) and (Coronavirus) OR (COVID-19) OR (SARS-CoV2) OR (The Middle East) OR (Africa)).

Results and Conclusions

Cholera outbreaks have increased dramatically, mainly in Africa and many Middle Eastern countries. The COVID-19 pandemic has reduced the attention devoted to cholera and disrupted diagnosis and treatment services, as well as vaccination initiatives. Most of the cholera cases in Africa and the Middle East were reported in Malawi and Syria, respectively, in 2022. RDTs are effective in the early detection of cholera epidemics, especially with limited advanced resources, which is the case in much of Africa. By offering both direct and indirect protection, expanding the use of OCV will significantly reduce the burden of current cholera outbreaks in Africa and the Middle East.

Anticipatory decision-making for cholera in Malawi

Climate change raises an old disease to a new level of public health threat. The causative agent, Vibrio cholerae, native to aquatic ecosystems, is influenced by climate and weather processes. The risk of cholera is elevated in vulnerable populations lacking access to safe water and sanitation infrastructure. Predictive intelligence, employing mathematical algorithms that integrate earth observations and heuristics derived from microbiological, sociological, and weather data, can provide anticipatory decision-making capabilities to reduce the burden of cholera and save human lives. An example offered here is the recent outbreak of cholera in Malawi, predicted in advance by such algorithms.

Mitigating the effects of climate change on human health with vaccines and vaccinations

Climate change represents an unprecedented threat to humanity and will be the ultimate challenge of the 21st century. As a public health consequence, the World Health Organization estimates an additional 250,000 deaths annually by 2030, with resource-poor countries being predominantly affected. Although climate change's direct and indirect consequences on human health are manifold and far from fully explored, a growing body of evidence demonstrates its potential to exacerbate the frequency and spread of transmissible infectious diseases. Effective, high-impact mitigation measures are critical in combating this global crisis. While vaccines and vaccination are among the most cost-effective public health interventions, they have yet to be established as a major strategy in climate change-related health effect mitigation. In this narrative review, we synthesize the available evidence on the effect of climate change on vaccine-preventable diseases. This review examines the direct effect of climate change on water-related diseases such as cholera and other enteropathogens, helminthic infections and leptospirosis. It also explores the effects of rising temperatures on vector-borne diseases like dengue, chikungunya, and malaria, as well as the impact of temperature and humidity on airborne diseases like influenza and respiratory syncytial virus infection. Recent advances in global vaccine development facilitate the use of vaccines and vaccination as a mitigation strategy in the agenda against climate change consequences. A focused evaluation of vaccine research and development, funding, and distribution related to climate change is required.

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.

The persistence of time: the lifespan of Bacillus anthracis spores in environmental reservoirs

Anthrax is a lethal bacterial zoonosis primarily affecting herbivorous wildlife and livestock. Upon host death Bacillus anthracis vegetative cells form spores capable of surviving for years in soil. Anthrax transmission requires host exposure to large spore doses. Thus, conditions that facilitate higher spore concentrations or promote spore survival will increase the probability that a pathogen reservoir infects future hosts. We investigated abiotic and pathogen genomic variation in relation to spore concentrations in surface soils (0-1 cm depth) at 40 plains zebra (Equus quagga) anthrax carcass sites in Namibia. Specifically, how initial spore concentrations and spore survival were affected by seasonality associated with the timing of host mortality, local soil characteristics, and pathogen genomic variation. Zebras dying of anthrax in wet seasons-the peak season for anthrax in Etosha National Park-had soil spore concentrations 1.36 orders of magnitude higher than those that died in dry seasons. No other variables considered affected spore concentrations, and spore survival rates did not differ among sites. Surface soils at these pathogen reservoirs remained culture positive for a range of 3.8-10.4 years after host death. Future research could evaluate if seasonal patterns in spore concentrations are driven by differences in sporulation success or levels of terminal bacteremia.

Combating cholera by building predictive capabilities for pathogenic Vibrio cholerae in Yemen

Cholera remains a global public health threat in regions where social vulnerabilities intersect with climate and weather processes that impact infectious Vibrio cholerae. While access to safe drinking water and sanitation facilities limit cholera outbreaks, sheer cost of building such infrastructure limits the ability to safeguard the population. Here, using Yemen as an example where cholera outbreak was reported in 2016, we show how predictive abilities for forecasting risk, employing sociodemographical, microbiological, and climate information of cholera, can aid in combating disease outbreak. An epidemiological analysis using Bradford Hill Criteria was employed in near-real-time to understand a predictive model's outputs and cholera cases in Yemen. We note that the model predicted cholera risk at least four weeks in advance for all governorates of Yemen with overall 72% accuracy (varies with the year). We argue the development of anticipatory decision-making frameworks for climate modulated diseases to design intervention activities and limit exposure of pathogens preemptively.

An Assessment of Household Knowledge and Practices during a Cholera Epidemic- Dar es Salaam, Tanzania, 2016

From August 15, 2015 to March 5, 2016, Tanzania reported 16,521 cholera cases and 251 deaths, with 4,596 cases and 44 deaths in its largest city, Dar es Salaam. To evaluate outbreak response efforts, we conducted a household survey with drinking water testing in the five most affected wards in Dar es Salaam. We interviewed 641 households 6 months after the beginning of the outbreak. Although most respondents knew that cholera causes diarrhea (90%) and would seek care if suspecting cholera (95%), only 45% were aware of the current outbreak in the area and only 5% would use oral rehydration salts (ORS) if ill. Of 200 (31%) respondents reporting no regular water treatment, 46% believed treatment was unnecessary and 18% believed treatment was too expensive. Fecal contamination was found in 45% of water samples and was associated with water availability (P = 0.047). Only 11% of samples had detectable free chlorine residual, which was associated with water availability (P = 0.025), reported current water treatment (P = 0.006), and observed free chlorine product in the household (P = 0.015). The provision of accessible, adequately chlorinated water supply, and implementation of social mobilization campaigns advocating household water treatment and use of ORS should be prioritized to address gaps in cholera prevention and treatment activities.

Predictive Intelligence for Cholera in Ukraine?

Cholera, an ancient waterborne diarrheal disease, remains a threat to public health, especially when climate/weather processes, microbiological parameters, and sociological determinants intersect with population vulnerabilities of loss of access to safe drinking water and sanitation infrastructure. The ongoing war in Ukraine has either damaged or severely crippled civil infrastructure, following which the human population is at risk of health disasters. This editorial highlights a perspective on using predictive intelligence to combat potential (and perhaps impending) cholera outbreaks in various regions of Ukraine. Reliable and judicious use of existing earth observations inspired mathematical algorithms integrating heuristic understanding of microbiological, sociological, and weather parameters have the potential to save or reduce the disease burden.

Uncovering the Research Gaps to Alleviate the Negative Impacts of Climate Change on Food Security: A Review

Climatic variability has been acquiring an extensive consideration due to its widespread ability to impact food production and livelihoods. Climate change has the potential to intersperse global approaches in alleviating hunger and undernutrition. It is hypothesized that climate shifts bring substantial negative impacts on food production systems, thereby intimidating food security. Vast developments have been made addressing the global climate change, undernourishment, and hunger for the last few decades, partly due to the increase in food productivity through augmented agricultural managements. However, the growing population has increased the demand for food, putting pressure on food systems. Moreover, the potential climate change impacts are still unclear more obviously at the regional scales. Climate change is expected to boost food insecurity challenges in areas already vulnerable to climate change. Human-induced climate change is expected to impact food quality, quantity, and potentiality to dispense it equitably. Global capabilities to ascertain the food security and nutritional reasonableness facing expeditious shifts in biophysical conditions are likely to be the main factors determining the level of global disease incidence. It can be apprehended that all food security components (mainly food access and utilization) likely be under indirect effect via pledged impacts on ménage, incomes, and damages to health. The corroboration supports the dire need for huge focused investments in mitigation and adaptation measures to have sustainable, climate-smart, eco-friendly, and climate stress resilient food production systems. In this paper, we discussed the foremost pathways of how climate change impacts our food production systems as well as the social, and economic factors that in the mastery of unbiased food distribution. Likewise, we analyze the research gaps and biases about climate change and food security. Climate change is often responsible for food insecurity issues, not focusing on the fact that food production systems have magnified the climate change process. Provided the critical threats to food security, the focus needs to be shifted to an implementation oriented-agenda to potentially cope with current challenges. Therefore, this review seeks to have a more unprejudiced view and thus interpret the fusion association between climate change and food security by imperatively scrutinizing all factors.

Mapping climate change's impact on cholera infection risk in Bangladesh

Several studies have investigated how Vibrio cholerae infection risk changes with increased rainfall, temperature, and water pH levels for coastal Bangladesh, which experiences seasonal surges in cholera infections associated with heavy rainfall events. While coastal environmental conditions are understood to influence V. cholerae propagation within brackish waters and transmission to and within human populations, it remains unknown how changing climate regimes impact the risk for cholera infection throughout Bangladesh. To address this, we developed a random forest species distribution model to predict the occurrence probability of cholera incidence within Bangladesh for 2015 and 2050. We developed a random forest model trained on cholera incidence data and spatial environmental raster data to be predicted to environmental data for the year of training (2015) and 2050. From our model's predictions, we generated risk maps for cholera occurrence for 2015 and 2050. Our best-fitting model predicted cholera occurrence given elevation and distance to water. Generally, we find that regions within every district in Bangladesh experience an increase in infection risk from 2015 to 2050. We also find that although cells of high risk cluster along the coastline predominantly in 2015, by 2050 high-risk areas expand from the coast inland, conglomerating around surface waters across Bangladesh, reaching all but the northwestern-most district. Mapping the geographic distribution of cholera infections given projected environmental conditions provides a valuable tool for guiding proactive public health policy tailored to areas most at risk of future disease outbreaks.

Climate Precursors of Satellite Water Marker Index for Spring Cholera Outbreak in Northern Bay of Bengal Coastal Regions

Cholera is a water-borne infectious disease that affects 1.3 to 4 million people, with 21,000 to 143,000 reported fatalities each year worldwide. Outbreaks are devastating to affected communities and their prospects for development. The key to support preparedness and public health response is the ability to forecast cholera outbreaks with sufficient lead time. How Vibrio cholerae survives in the environment outside a human host is an important route of disease transmission. Thus, identifying the environmental and climate drivers of these pathogens is highly desirable. Here, we elucidate for the first time a mechanistic link between climate variability and cholera (Satellite Water Marker; SWM) index in the Bengal Delta, which allows us to predict cholera outbreaks up to two seasons earlier. High values of the SWM index in fall were associated with above-normal summer monsoon rainfalls over northern India. In turn, these correlated with the La Niña climate pattern that was traced back to the summer monsoon and previous spring seasons. We present a new multi-linear regression model that can explain 50% of the SWM variability over the Bengal Delta based on the relationship with climatic indices of the El Niño Southern Oscillation, Indian Ocean Dipole, and summer monsoon rainfall during the decades 1997–2016. Interestingly, we further found that these relationships were non-stationary over the multi-decadal period 1948–2018. These results bear novel implications for developing outbreak-risk forecasts, demonstrating a crucial need to account for multi-decadal variations in climate interactions and underscoring to better understand how the south Asian summer monsoon responds to climate variability.

COVID-19 pandemic, dengue epidemic, and climate change vulnerability in Bangladesh: Scenario assessment for strategic management and policy implications

Bangladesh is one of the most vulnerable countries to climate change impacts also struck by the COVID-19 pandemic. The lockdown measures were ineffective with no sign of flattening the curve. Therefore, the high risk of transmission is evident with an increasing number of affected people. Under this circumstance, a multiple hazards scenario can be developed in this country due to climatic hazards such as cyclones, floods, landslides, heat waves, and the outbreak of infectious diseases such as dengue, cholera, and diarrhoea. The country experiences simultaneously the global pandemic, exceptionally prolonged flood along with the recovery stage from the damages due to the cyclone (Amphan). Therefore, these multiple factors have been putting pressure on losing millions of homes, livelihoods, and agricultural crops. This study aimed to assess the potential impact of a simultaneous strike of climatic hazards and infectious disease outbreaks and their possible strategic management in Bangladesh under different scenarios. A mixed methodological approach was followed in this study including a questionnaire survey, in-depth discussion with experts, and extensive literature review to assess the multi-hazard scenario in a resource-limited setting with high population density. A set of statistical techniques were used to analyze the responses (n = 1590) from different social groups (healthcare professionals, academicians, students, Government and NGO officials, and businessman) under three scenarios. The results revealed the high possibility of aggravating the impact of COVID-19 pandemic if there is a climatic hazard such as flood, cyclone have appeared. The majority of the respondents agreed that the situation will become more devastating if there is another outbreak of diseases such as dengue, cholera, and diarrhoea. The poor and fragile healthcare system of this country cannot bear such unprecedented pressure. The lack of risk assessment and communication, lack of sectoral coordination might restrict the contingency plan of the government. Therefore, considering the unprecedented worst cases a stringent strategic plan for emergency response, short term and long-term management should have to be formulated. Resilience building through proactive planning and implementation of integrated, inclusive and sustainable strategies will be effective to ensure the health and socio-economic security for multi-hazard threats in the country.

How climate change can affect cholera incidence and prevalence? A systematic review

Although the number of cholera infection decreased universally, climate change can potentially affect both incidence and prevalence rates of disease in endemic regions. There is considerable consistent evidence, explaining the associations between cholera and climatic variables. However, it is essentially required to compare and interpret these relationships globally. The aim of the present study was to carry out a systematic review in order to identify and appraise the literature concerning the relationship between nonanthropogenic climatic variabilities such as extreme weather- and ocean-related variables and cholera infection rates. The systematic literature review of studies was conducted by using determined search terms via four major electronic databases (PubMed, Web of Science, Embase, and Scopus) according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach. This search focused on published articles in English-language up to December 31, 2018. A total of 43 full-text studies that met our criteria have been identified and included in our analysis. The reviewed studies demonstrated that cholera incidence is highly attributed to climatic variables, especially rainfall, temperature, sea surface temperature (SST) and El Niño Southern Oscillation (ENSO). The association between cholera incidence and climatic variables has been investigated by a variety of data analysis methodologies, most commonly time series analysis, generalized linear model (GLM), regression analysis, and spatial/GIS. The results of this study assist the policy-makers who provide the efforts for planning and prevention actions in the face of changing global climatic variables.

Geographical disparities and determinants of childhood diarrheal illness in Ethiopia: further analysis of 2016 Ethiopian Demographic and Health Survey

Background

Childhood diarrheal illness is the second leading cause of child mortality in sub-Saharan Africa, including Ethiopia. Epidemiology of diarrhea has long-term implications with respect to medical, social, and economic consequences. Studies hypothesize that there have been regional differences, and this study aimed to examine the spatial variations and identify the determinants of childhood diarrhea in Ethiopia.

Methods

Data from the 2016 Demographic and Health Survey of Ethiopia (EDHS), which included 10,337 aged under 5 years were analyzed. The survey was conducted using a two-stage stratified sampling design. The study attempted to detect and test the clustering of diarrhea cases using global Moran’s I and LISA. Descriptive statistics followed by mixed-effect logistic regressions were used to identify factors related to the prevalence of diarrhea.

Results

Overall, 11.87% of the children experienced childhood diarrheal illness. The study showed that the risk was high in the southern and central parts and low in the eastern and western regions of the country. Children aged 6–12 (AOR = 2.66, [95% CI 2.01, 3.52]), 12–23 (AOR = 2.45, [95% CI 1.89, 3.17]), and 24–35 (AOR = 1.53, [95% CI 1.17, 2.01]) months were more likely to suffer from childhood diarrhea than those aged less than 6 months. Children in Tigray (AOR = 1.69 [95% CI 1.01, 2.83]), Amhara (AOR = 1.80, [95% CI 1.06, 3.06]), SNNPR (AOR = 2.04, [95% CI 1.22, 3.42]), and Gambella (AOR = 2.05, [95% CI 1.22, 3.42]) were at higher risk than those in Addis Ababa. The odds of getting diarrhea decreased by 24% among households with ≥ 3 under-five children compared to those with only one under-five child (AOR = 0.76 [95% CI 0.61, 0.94]). The odds of getting diarrheal illness for the children of employed mothers increased by 19% compared to those children of non-employed mothers (AOR = 1.19 [95% CI 1.03, 1.38]).

Conclusions

Childhood diarrheal disease is prevalent among under-five children, particularly in the regions of SNNP, Gambella, Oromia, and Benishangul Gumuz, while the regions are generally making progress in reducing childhood illness. Capacity building programs with the best experience sharing and better home environments can be effective in reducing the incidence of childhood diarrhea in Ethiopia.

Association Between Seasonal Influenza and Absolute Humidity: Time-Series Analysis with Daily Surveillance Data in Japan

Seasonal influenza epidemics are associated with various meteorological factors. Recently absolute humidity (AH) has garnered attention, and some epidemiological studies show an association between AH and human influenza infection. However, they mainly analyzed weekly surveillance data, and daily data remains largely unexplored despite its potential benefits. In this study, we analyze daily influenza surveillance data using a distributed lag non-linear model to examine the association of AH with the number of influenza cases and the magnitude of the association. Additionally, we investigate how adjustment for seasonality and autocorrelation in the model affect results. All models used in the study showed a significant increase in the number of influenza cases as AH decreased, although the magnitude of the association differed substantially by model. Furthermore, we found that relative risk reached a peak at lag 10–14 with extremely low AH. To verify these findings, further analysis should be conducted using data from other locations.

High genetic diversity of Vibrio parahaemolyticus isolated from tidal water and mud of southern coast of South Korea

A horizontal, fluorophore-enhanced, repetitive extragenic palindromic-polymerase chain reaction (rep-PCR) DNA fingerprinting technique was adapted to examine the genotypic richness and source differentiation of Vibrio parahaemolyticus (n = 1749) isolated from tidal water and mud of southern coast of South Korea. The number of unique genotypes observed from June (163, 51.9%), September (307, 63.9%), December (205, 73.8%) and February (136, 74.7%), indicating a high degree of genetic diversity. Contrary, lower genetic diversity was detected in April (99, 46.8%), including predominant genotypes comprised >30 V. parahaemolyticus isolates. Jackknife analysis indicated that 65.1% tidal water isolates and 87.1% mud isolates were correctly assigned to their source groups. Sixty-nine isolates of pathogenic V. parahaemolyticus were clustered into two groups, separated by sampling month, source of isolation and serogroups. Serotypes O1, O4, O5, O10/O12 and O11 were the dominant serovariants, while serotypes O3/O13 were highly detected in April where there were no pathogenic V. parahaemolyticus isolates. Most of the V. parahaemolyticus isolates were resistant to ampicillin, ceftazidime and sulfamethoxazole. Interestingly, four V. parahaemolyticus isolates resistant to carbepenem did not contain the known carbapenemase-encoding gene, but possess an extended-spectrum β-lactamase blaTEM.

Developing a forecasting model for cholera incidence in Dhaka megacity through time series climate data

Cholera, an acute diarrheal disease spread by lack of hygiene and contaminated water, is a major public health risk in many countries. As cholera is triggered by environmental conditions influenced by climatic variables, establishing a correlation between cholera incidence and climatic variables would provide an opportunity to develop a cholera forecasting model. Considering the auto-regressive nature and the seasonal behavioral patterns of cholera, a seasonal-auto-regressive-integrated-moving-average (SARIMA) model was used for time-series analysis during 2000-2013. As both rainfall (r = 0.43) and maximum temperature (r = 0.56) have the strongest influence on the occurrence of cholera incidence, single-variable (SVMs) and multi-variable SARIMA models (MVMs) were developed, compared and tested for evaluating their relationship with cholera incidence. A low relationship was found with relative humidity (r = 0.28), ENSO (r = 0.21) and SOI (r = -0.23). Using SVM for a 1 °C increase in maximum temperature at one-month lead time showed a 7% increase of cholera incidence (p < 0.001). However, MVM (AIC = 15, BIC = 36) showed better performance than SVM (AIC = 21, BIC = 39). An MVM using rainfall and monthly mean daily maximum temperature with a one-month lead time showed a better fit (RMSE = 14.7, MAE = 11) than the MVM with no lead time (RMSE = 16.2, MAE = 13.2) in forecasting. This result will assist in predicting cholera risks and better preparedness for public health management in the future.

Environmental Reservoirs of Vibrio cholerae: Challenges and Opportunities for Ocean-Color Remote Sensing

The World Health Organization has estimated the burden of the on-going pandemic of cholera at 1.3 to 4 million cases per year worldwide in 2016, and a doubling of case-fatality-rate to 1.8% in 2016 from 0.8% in 2015. The disease cholera is caused by the bacterium Vibrio cholerae that can be found in environmental reservoirs, living either in free planktonic form or in association with host organisms, non-living particulate matter or in the sediment, and participating in various biogeochemical cycles. An increasing number of epidemiological studies are using land- and water-based remote-sensing observations for monitoring, surveillance, or risk mapping of Vibrio pathogens and cholera outbreaks. Although the Vibrio pathogens cannot be sensed directly by satellite sensors, remotely-sensed data can be used to infer their presence. Here, we review the use of ocean-color remote-sensing data, in conjunction with information on the ecology of the pathogen, to map its distribution and forecast risk of disease occurrence. Finally, we assess how satellite-based information on cholera may help support the Sustainable Development Goals and targets on Health (Goal 3), Water Quality (Goal 6), Climate (Goal 13), and Life Below Water (Goal 14).

The effect of climate change on cholera disease: The road ahead using artificial neural network

Climate change has been described to raise outbreaks of water-born infectious diseases and increases public health concerns. This study aimed at finding out these impacts on cholera infections by using Artificial Neural Networks (ANNs) from 2021 to 2050. Daily data for cholera infection cases in Qom city, which is located in the center of Iran, were analyzed from 1998 to 2016. To determine the best lag time and combination of inputs, Gamma Test (GT) was applied. General circulation model outputs were utilized to project future climate pattern under two scenarios of Representative Concentration Pathway (RCP2.6 and RCP8.5). Statistical downscaling was done to produce high-resolution synthetic time series weather dataset. ANNs were applied for simulating the impact of climate change on cholera. The observed climate variables including maximum and minimum temperatures and precipitation were tagged as predictors in ANNs. Cholera cases were considered as the target outcome variable. Projected future (2020–2050) climate in previous step was carried out to assess future cholera incidence. A seasonal trend in cholera infection was seen. Our results elucidated that the best lag time was 21 days. According to the results of downscaling tool, future climate in the study area by 2050 will be warmer and wetter. Simulation of cholera cases indicated that there is a clear trend of increasing cholera cases under the worst scenario (RCP8.5) by the year 2050 and the highest cholera cases observe in warmer months. The precipitation was recognized as the most effective input variable by sensitivity analysis. We observed a significant correlation between low precipitation and cholera infection. There is a strong evidence to show that cholera disease is correlated with environment variables, as low precipitation and high temperatures in warmer months could provide the swifter bacterial replication. These conditions in Iran, especially in the central parts, may raise the cholera infection rates. Furthermore, ANNs is an executive tool to simulate the impact of climate change on cholera to estimate the future trend of cholera incidence for adopting protective measures in endemic areas.

The Future of Climate Epidemiology: Opportunities for Advancing Health Research in the Context of Climate Change

In the coming decades, climate change is expected to dramatically affect communities worldwide, altering the patterns of many ambient exposures and disasters, including extreme temperatures, heat waves, wildfires, droughts, and floods. These exposures, in turn, can affect risks for a variety of human diseases and health outcomes. Climate epidemiology plays an important role in informing policy related to climate change and its threats to public health. Climate epidemiology leverages deep, integrated collaborations between epidemiologists and climate scientists to understand the current and potential future impacts of climate-related exposures on human health. A variety of recent and ongoing developments in climate science are creating new avenues for epidemiologic contributions. Here, we discuss the contributions of climate epidemiology and describe some key current research directions, including research to better characterize uncertainty in climate health projections. We end by outlining 3 developing areas of climate science that are creating opportunities for high-impact epidemiologic advances in the near future: 1) climate attribution studies, 2) subseasonal to seasonal forecasts, and 3) decadal predictions.

Rainfall as a driver of epidemic cholera: Comparative model assessments of the effect of intra-seasonal precipitation events

The correlation between cholera epidemics and climatic drivers, in particular seasonal tropical rainfall, has been studied in a variety of contexts owing to its documented relevance. Several mechanistic models of cholera transmission have included rainfall as a driver by focusing on two possible transmission pathways: either by increasing exposure to contaminated water (e.g. due to worsening sanitary conditions during water excess), or water contamination by freshly excreted bacteria (e.g. due to washout of open-air defecation sites or overflows). Our study assesses the explanatory power of these different modeling structures by formal model comparison using deterministic and stochastic models of the type susceptible-infected-recovered-bacteria (SIRB). The incorporation of rainfall effects is generalized using a nonlinear function that can increase or decrease the relative importance of the large precipitation events. Our modelling framework is tested against the daily epidemiological data collected during the 2015 cholera outbreak within the urban context of Juba, South Sudan. This epidemic is characterized by a particular intra-seasonal double peak on the incidence in apparent relation with particularly strong rainfall events. Our results show that rainfall-based models in both their deterministic and stochastic formulations outperform models that do not account for rainfall. In fact, classical SIRB models are not able to reproduce the second epidemiological peak, thus suggesting that it was rainfall-driven. Moreover we found stronger support across model types for rainfall acting on increased exposure rather than on exacerbated water contamination. Although these results are context-specific, they stress the importance of a systematic and comprehensive appraisal of transmission pathways and their environmental forcings when embarking in the modelling of epidemic cholera.

Increase in Reported Cholera Cases in Haiti Following Hurricane Matthew: An Interrupted Time Series Model

Matthew, a category 4 hurricane, struck Haiti on October 4, 2016, causing widespread flooding and damage to buildings and crops, and resulted in many deaths. The damage caused by Matthew raised concerns of increased cholera transmission particularly in Sud and Grand'Anse departments, regions which were hit most heavily by the storm. To evaluate the change in reported cholera cases following Hurricane Matthew on reported cholera cases, we used interrupted time series regression models of daily reported cholera cases, controlling for the impact of both rainfall, following a 4-week lag, and seasonality, from 2013 through 2016. Our results indicate a significant increase in reported cholera cases after Matthew, suggesting that the storm resulted in an immediate surge in suspect cases, and a decline in reported cholera cases in the 46-day post-storm period, after controlling for rainfall and seasonality. Regression models stratified by the department indicate that the impact of the hurricane was regional, with larger surges in the two most highly storm-affected departments: Sud and Grand'Anse. These models were able to provide input to the Ministry of Health in Haiti on the national and regional impact of Hurricane Matthew and, with further development, could provide the flexibility of use in other emergency situations. This article highlights the need for continued cholera prevention and control efforts, particularly in the wake of natural disasters such as hurricanes, and the continued need for intensive cholera surveillance nationally.

Experience and Future Perceived Risk of Floods and Diarrheal Disease in Urban Poor Communities in Accra, Ghana

Diarrheal disease is a critical health condition in urban areas of developing countries due to increasing urbanization and its associated problems of sanitation and poor access to good drinking water. Increasing floods in cities have been linked to the risk of diarrheal disease. There are few studies that specifically link flooding with diarrhea diseases. This may be due to the fact that secondary data mainly hospital recorded cases, and not individual cases at the household level are used. Furthermore, of the few papers that consider the flood-diarrheal diseases nexus, none have considered risk perceptions in general, and more specifically, whether households that have experienced floods which resulted in a reported case of diarrhea, have higher perceived risks of future occurrences of the two phenomena compared to households that had different experiences. Yet, this is critical for the development of interventions that seek to increase protective behaviors and reduce the risk of contracting diarrhea. We surveyed 401 households in some selected urban poor communities in Accra, the capital of Ghana. Results show that households that experienced floods which resulted in a reported case of diarrhea, have higher perceived risk of future occurrence of the two phenomena compared to other households. We recommend public education that reduces the risk of exposure to flood and diarrhea through flood mitigation measures, including the construction of drains in communities and educating communities on good sanitation.

Influences of heatwave, rainfall, and tree cover on cholera in Bangladesh

Cholera is a severe diarrheal disease and remains a global threat to public health. Climate change and variability have the potential to increase the distribution and magnitude of cholera outbreaks. However, the effect of heatwave on the occurrence of cholera at individual level is still unclear. It is also unknown whether the local vegetation could potentially mitigate the effects of extreme heat on cholera outbreaks. In this study, we designed a case-crossover study to examine the association between the risk of cholera and heatwaves as well as the modification effects of rainfall and tree cover. The study was conducted in Matlab, a cholera endemic area of rural Bangladesh, where cholera case data were collected between January 1983 and April 2009. The association between the risk of cholera and heatwaves was examined using conditional logistic regression models. The results showed that there was a higher risk of cholera two days after heatwaves (OR = 1.53, 95% CI: 1.07-2.19) during wet days (rainfall > 0 mm). For households with less medium-dense tree cover, the heatwave after a 2-day lag was positively associated (OR = 1.80, 95% CI: 1.01-3.22) with the risk of cholera during wet days. However, for households with more medium-dense tree cover, the association between the risk of cholera and heatwave in 2-day lag was not significant. These findings suggest that heatwaves might promote the occurrence of cholera, while this relationship was modified by rainfall and tree cover. Further investigations are needed to explore major mechanisms underlying the association between heatwaves and cholera as well as the beneficial effects of tree cover.

Vibrio spp. infections

Vibrio is a genus of ubiquitous bacteria found in a wide variety of aquatic and marine habitats; of the >100 described Vibrio spp., ~12 cause infections in humans. Vibrio cholerae can cause cholera, a severe diarrhoeal disease that can be quickly fatal if untreated and is typically transmitted via contaminated water and person-to-person contact. Non-cholera Vibrio spp. (for example, Vibrio parahaemolyticus, Vibrio alginolyticus and Vibrio vulnificus) cause vibriosis - infections normally acquired through exposure to sea water or through consumption of raw or undercooked contaminated seafood. Non-cholera bacteria can lead to several clinical manifestations, most commonly mild, self-limiting gastroenteritis, with the exception of V. vulnificus, an opportunistic pathogen with a high mortality that causes wound infections that can rapidly lead to septicaemia. Treatment for Vibrio spp. infection largely depends on the causative pathogen: for example, rehydration therapy for V. cholerae infection and debridement of infected tissues for V. vulnificus-associated wound infections, with antibiotic therapy for severe cholera and systemic infections. Although cholera is preventable and effective oral cholera vaccines are available, outbreaks can be triggered by natural or man-made events that contaminate drinking water or compromise access to safe water and sanitation. The incidence of vibriosis is rising, perhaps owing in part to the spread of Vibrio spp. favoured by climate change and rising sea water temperature.

Near real-time forecasting for cholera decision making in Haiti after Hurricane Matthew

Computational models of cholera transmission can provide objective insights into the course of an ongoing epidemic and aid decision making on allocation of health care resources. However, models are typically designed, calibrated and interpreted post-hoc. Here, we report the efforts of a team from academia, field research and humanitarian organizations to model in near real-time the Haitian cholera outbreak after Hurricane Matthew in October 2016, to assess risk and to quantitatively estimate the efficacy of a then ongoing vaccination campaign. A rainfall-driven, spatially-explicit meta-community model of cholera transmission was coupled to a data assimilation scheme for computing short-term projections of the epidemic in near real-time. The model was used to forecast cholera incidence for the months after the passage of the hurricane (October-December 2016) and to predict the impact of a planned oral cholera vaccination campaign. Our first projection, from October 29 to December 31, predicted the highest incidence in the departments of Grande Anse and Sud, accounting for about 45% of the total cases in Haiti. The projection included a second peak in cholera incidence in early December largely driven by heavy rainfall forecasts, confirming the urgency for rapid intervention. A second projection (from November 12 to December 31) used updated rainfall forecasts to estimate that 835 cases would be averted by vaccinations in Grande Anse (90% Prediction Interval [PI] 476-1284) and 995 in Sud (90% PI 508-2043). The experience gained by this modeling effort shows that state-of-the-art computational modeling and data-assimilation methods can produce informative near real-time projections of cholera incidence. Collaboration among modelers and field epidemiologists is indispensable to gain fast access to field data and to translate model results into operational recommendations for emergency management during an outbreak. Future efforts should thus draw together multi-disciplinary teams to ensure model outputs are appropriately based, interpreted and communicated.

Exploring Droughts and Floods and Their Association with Cholera Outbreaks in Sub-Saharan Africa: A Register-Based Ecological Study from 1990 to 2010

Cholera outbreaks in Africa have been attributed to both droughts and floods, but whether the risk of a cholera outbreak is elevated during droughts is unknown. We estimated the risk of cholera outbreaks during droughts and floods compared with drought- and flood-free periods in 40 sub-Saharan African countries during 1990-2010 based on data from Emergency Events Database: the Office of Foreign Disaster Assistance /Centre for Research on the Epidemiology of Disasters International Disaster Database (www.emdat.be). A cholera outbreak was registered in one of every three droughts and one of every 15 floods. We observed an increased incidence rate of cholera outbreaks during drought periods (incidence rate ratio [IRR] = 4.3, 95% confidence interval [CI] = 2.9-7.2) and during flood periods (IRR = 144, 95% CI = 101-208) when compared with drought/flood-free periods. Floods are more strongly associated with cholera outbreaks, yet the prevalence of cholera outbreaks is higher during droughts because of droughts' long durations. The results suggest that droughts in addition to floods call for increased cholera preparedness.

Natural Disasters and Cholera Outbreaks: Current Understanding and Future Outlook

PURPOSE OF REVIEW

Diarrheal diseases remain a serious global public health threat, especially for those populations lacking access to safe water and sanitation infrastructure. Although association of several diarrheal diseases, e.g., cholera, shigellosis, etc., with climatic processes has been documented, the global human population remains at heightened risk of outbreak of diseases after natural disasters, such as earthquakes, floods, or droughts. In this review, cholera was selected as a signature diarrheal disease and the role of natural disasters in triggering and transmitting cholera was analyzed.

RECENT FINDINGS

Key observations include identification of an inherent feedback loop that includes societal structure, prevailing climatic processes, and spatio-temporal seasonal variability of natural disasters. Data obtained from satellite-based remote sensing are concluded to have application, although limited, in predicting risks of a cholera outbreak(s). We argue that with the advent of new high spectral and spatial resolution data, earth observation systems should be seamlessly integrated in a decision support mechanism to be mobilize resources when a region suffers a natural disaster. A framework is proposed that can be used to assess the impact of natural disasters with response to outbreak of cholera, providing assessment of short- and long-term influence of climatic processes on disease outbreaks.

River networks as ecological corridors: A coherent ecohydrological perspective

This paper draws together several lines of argument to suggest that an ecohydrological framework, i.e. laboratory, field and theoretical approaches focused on hydrologic controls on biota, has contributed substantially to our understanding of the function of river networks as ecological corridors. Such function proves relevant to: the spatial ecology of species; population dynamics and biological invasions; the spread of waterborne disease. As examples, we describe metacommunity predictions of fish diversity patterns in the Mississippi-Missouri basin, geomorphic controls imposed by the fluvial landscape on elevational gradients of species' richness, the zebra mussel invasion of the same Mississippi-Missouri river system, and the spread of proliferative kidney disease in salmonid fish. We conclude that spatial descriptions of ecological processes in the fluvial landscape, constrained by their specific hydrologic and ecological dynamics and by the ecosystem matrix for interactions, i.e. the directional dispersal embedded in fluvial and host/pathogen mobility networks, have already produced a remarkably broad range of significant results. Notable scientific and practical perspectives are thus open, in the authors' view, to future developments in ecohydrologic research.

Bagtzoglou A, Anagnostou E. Effect of climatic variability on childhood diarrhea and its high risk periods in northwestern parts of Ethiopia

Background

Increasing climate variability as a result of climate change will be one of the public health challenges to control infectious diseases in the future, particularly in sub-Saharan Africa including Ethiopia.

Objective

To investigate the effect of climate variability on childhood diarrhea (CDD) and identify high risk periods of diarrheal diseases.

Methods

The study was conducted in all districts located in three Zones (Awi, West and East Gojjam) of Amhara Region in northwestern parts of Ethiopia. Monthly CDD cases for 24 months (from July 2013 to June 2015) reported to each district health office from the routine surveillance system were used for the study. Temperature, rainfall and humidity data for each district were extracted from satellite precipitation estimates and global atmospheric reanalysis. The space-time permutation scan statistic was used to identify high risk periods of CDD. A negative binomial regression was used to investigate the relationship between cases of CDD and climate variables. Statistical analyses were conducted using SaTScan program and StataSE v. 12.

Results

The monthly average incidence rate of CDD was 11.4 per 1000 (95%CI 10.8–12.0) with significant variation between males [12.5 per 1000 (95%CI 11.9 to 13.2)] and females [10.2 per 1000 (95%CI 9.6 to 10.8)]. The space-time permutation scan statistic identified the most likely high risk period of CDD between March and June 2014 located in Huletej Enese district of East Gojjam Zone. Monthly average temperature and monthly average rainfall were positively associated with the rate of CDD, whereas the relative humidity was negatively associated with the rate of CDD.

Conclusions

This study found that the most likely high risk period is in the beginning of the dry season. Climatic factors have an association with the occurrence of CDD. Therefore, CDD prevention and control strategy should consider local weather variations to improve programs on CDD.

Socioenvironmental factors associated with heat and cold-related mortality in Vadu HDSS, western India: a population-based case-crossover study

Ambient temperatures (heat and cold) are associated with mortality, but limited research is available about groups most vulnerable to these effects in rural populations. We estimated the effects of heat and cold on daily mortality among different sociodemographic groups in the Vadu HDSS area, western India. We studied all deaths in the Vadu HDSS area during 2004-2013. A conditional logistic regression model in a case-crossover design was used. Separate analyses were carried out for summer and winter season. Odds ratios (OR) and 95% confidence intervals (CI) were estimated for total mortality and population subgroups. Temperature above a threshold of 31 °C was associated with total mortality (OR 1.48, CI = 1.05-2.09) per 1 °C increase in daily mean temperature. Odds ratios were higher among females (OR 1.93; CI = 1.07-3.47), those with low education (OR 1.65; CI = 1.00-2.75), those owing larger agricultural land (OR 2.18; CI = 0.99-4.79), and farmers (OR 1.70; CI = 1.02-2.81). In winter, per 1 °C decrease in mean temperature, OR for total mortality was 1.06 (CI = 1.00-1.12) in lag 0-13 days. High risk of cold-related mortality was observed among people occupied in housework (OR = 1.09; CI = 1.00-1.19). Our study suggests that both heat and cold have an impact on mortality particularly heat, but also, to a smaller degree, cold have an impact. The effects may differ partly by sex, education, and occupation. These findings might have important policy implications in preventing heat and cold effects on particularly vulnerable groups of the rural populations in low and middle-income countries with hot semi-arid climate.

Long-term impact of changing childhood malnutrition on rotavirus diarrhoea: Two decades of adjusted association with climate and socio-demographic factors from urban Bangladesh

Background

There is strong association between childhood rotavirus, diarrhoea, climate factors and malnutrition. Conversely, a significant nutritional transition (reduced under-nutrition) with a concurrent increasing trend of rotavirus infection in last decade was also observed among under 5 children, especially in developing countries including Bangladesh. Considering the pathophysiology of rotavirus, there might be an interaction of this nutrition transition which plays a pivotal role in increasing rotavirus infection in addition to climate and other man-made factors in urban areas such as Dhaka, Bangladesh.

Methods

Relevant monthly data from 1993–2012 were extracted from the archive of the Diarrhoeal Disease Surveillance System of icddr, b and linked with data collected from the Dhaka station of the Bangladesh Meteorological Department (mean temperature, rainfall, sea level pressure and humidity). Seasonal autoregressive integrated moving average time series models were deployed to determine the association between the monthly proportion of rotavirus infection and underweight, stunting and wasting adjusting for climate, socio-demographic and sanitation factors.

Finding

The proportion of rotavirus cases among all causes diarrhoea increased from 20% in 1993 to 43% in 2012 (Chi squared for trend p = 0.010). In contrast, underweight, stunting and wasting decreased from 59%-29% (p<0.001); 53%-21% (p<0.001) and 32%-22% (p<0.001) respectively over the same period. Mean ambient temperature increased from 25.76°C-26.62°C (p = 0.07); mean rainfall, sea level pressure and mean humidity decreased from 234.92–111.75 mm (p = 0.5), 1008.30–1006.61 mm of hg (p = 0.02) and 76.63%-70.26% (p<0.001), respectively. In the adjusted model, a decrease in monthly proportion of underweight [coef.: -0.189 (95% CI:-0.376, -0.003)] and wasting [-0.265 (-0.455, -0.075)] were significantly and inversely associated with rotavirus infection. However, an inverse but insignificant association was observed for stunting [-0.070 (-0.249, 0.109)].

Interpretation

The reduction of acute childhood malnutrition is significantly associated with increasing rotavirus diarrhoea among under-5 children. Thus mass vaccination in addition to interventions directed at man-made modifiable predictors for prevention and control is warranted.

Modeling Key Drivers of Cholera Transmission Dynamics Provides New Perspectives for Parasitology

Hydroclimatological and anthropogenic factors are key drivers of waterborne disease transmission. Information on human settlements and host mobility on waterways along which pathogens and hosts disperse, and relevant hydroclimatological processes, can be acquired remotely and included in spatially explicit mathematical models of disease transmission. In the case of epidemic cholera, such models allowed the description of complex disease patterns and provided insight into the course of ongoing epidemics. The inclusion of spatial information in models of disease transmission can aid in emergency management and the assessment of alternative interventions. Here, we review the study of drivers of transmission via spatially explicit approaches and argue that, because many parasitic waterborne diseases share the same drivers as cholera, similar principles may apply.

Cholera forecast for Dhaka, Bangladesh, with the 2015-2016 El Niño: Lessons learned

A substantial body of work supports a teleconnection between the El Niño-Southern Oscillation (ENSO) and cholera incidence in Bangladesh. In particular, high positive anomalies during the winter (Dec-Feb) in sea surface temperatures (SST) in the tropical Pacific have been shown to exacerbate the seasonal outbreak of cholera following the monsoons from August to November. Climate studies have indicated a role of regional precipitation over Bangladesh in mediating this long-distance effect. Motivated by this previous evidence, we took advantage of the strong 2015-2016 El Niño event to evaluate the predictability of cholera dynamics for the city in recent times based on two transmission models that incorporate SST anomalies and are fitted to the earlier surveillance records starting in 1995. We implemented a mechanistic temporal model that incorporates both epidemiological processes and the effect of ENSO, as well as a previously published statistical model that resolves space at the level of districts (thanas). Prediction accuracy was evaluated with "out-of-fit" data from the same surveillance efforts (post 2008 and 2010 for the two models respectively), by comparing the total number of cholera cases observed for the season to those predicted by model simulations eight to twelve months ahead, starting in January each year. Although forecasts were accurate for the low cholera risk observed for the years preceding the 2015-2016 El Niño, the models also predicted a high probability of observing a large outbreak in fall 2016. Observed cholera cases up to Oct 2016 did not show evidence of an anomalous season. We discuss these predictions in the context of regional and local climate conditions, which show that despite positive regional rainfall anomalies, rainfall and inundation in Dhaka remained low. Possible explanations for these patterns are given together with future implications for cholera dynamics and directions to improve their prediction for the city.

Untangling the Impacts of Climate Change on Waterborne Diseases: a Systematic Review of Relationships between Diarrheal Diseases and Temperature, Rainfall, Flooding, and Drought

Global climate change is expected to affect waterborne enteric diseases, yet to date there has been no comprehensive, systematic review of the epidemiological literature examining the relationship between meteorological conditions and diarrheal diseases. We searched PubMed, Embase, Web of Science, and the Cochrane Collection for studies describing the relationship between diarrheal diseases and four meteorological conditions that are expected to increase with climate change: ambient temperature, heavy rainfall, drought, and flooding. We synthesized key areas of agreement and evaluated the biological plausibility of these findings, drawing from a diverse, multidisciplinary evidence base. We identified 141 articles that met our inclusion criteria. Key areas of agreement include a positive association between ambient temperature and diarrheal diseases, with the exception of viral diarrhea and an increase in diarrheal disease following heavy rainfall and flooding events. Insufficient evidence was available to evaluate the effects of drought on diarrhea. There is evidence to support the biological plausibility of these associations, but publication bias is an ongoing concern. Future research evaluating whether interventions, such as improved water and sanitation access, modify risk would further our understanding of the potential impacts of climate change on diarrheal diseases and aid in the prioritization of adaptation measures.

Gastrointestinal, influenza-like illness and dermatological complaints following exposure to floodwater: a cross-sectional survey in The Netherlands

Extreme rainfall events may cause pluvial flooding, increasing the transmission of several waterborne pathogens. However, the risk of experiencing clinically overt infections following exposure to pluvial floodwater is poorly estimated. A retrospective cross-sectional survey was performed to quantify the occurrence of self-reported gastrointestinal, influenza-like illness (ILI) and dermatological complaints, and the frequency of visits to the general practitioner (GP), during a 4-week observation period following pluvial flooding at seven locations in The Netherlands. Questionnaires were sent to 817 flooded households, 149 (17%) of which returned the questionnaire reporting information for 199 participants. Contact with floodwater was significantly associated with increased occurrence of gastrointestinal [odds ratio (OR 4·44)], ILI (OR 2·75) and dermatological (OR 6·67) complaints, and GP visits (OR 2·72). Having hand contact with floodwater was associated with gastrointestinal and dermatological complaints, whereas ILI complaints were associated with being engaged in post-flooding cleaning operations and having walked/cycled through floodwater. This study shows that floodwater-associated diseases occur in urban settings following extreme rainfall events in a high-income country. As pluvial floods are expected to escalate in the future due to global climate change, further research is warranted to determine the disease burden of pluvial flooding and to assess the effect of different interventions, including raising awareness among stakeholders.

Spatiotemporal Dynamics of Vibrio spp. within the Sydney Harbour Estuary

Vibrio are a genus of marine bacteria that have substantial environmental and human health importance, and there is evidence that their impact may be increasing as a consequence of changing environmental conditions. We investigated the abundance and composition of the Vibrio community within the Sydney Harbour estuary, one of the most densely populated coastal areas in Australia, and a region currently experiencing rapidly changing environmental conditions. Using quantitative PCR (qPCR) and Vibrio-specific 16S rRNA amplicon sequencing approaches we observed significant spatial and seasonal variation in the abundance and composition of the Vibrio community. Total Vibrio spp. abundance, derived from qPCR analysis, was higher during the late summer than winter and within locations with mid-range salinity (5-26 ppt). In addition we targeted three clinically important pathogens: Vibrio cholerae, V. Vulnificus, and V. parahaemolyticus. While toxigenic strains of V. cholerae were not detected in any samples, non-toxigenic strains were detected in 71% of samples, spanning a salinity range of 0-37 ppt and were observed during both late summer and winter. In contrast, pathogenic V. vulnificus was only detected in 14% of samples, with its occurrence restricted to the late summer and a salinity range of 5-26 ppt. V. parahaemolyticus was not observed at any site or time point. A Vibrio-specific 16S rRNA amplicon sequencing approach revealed clear shifts in Vibrio community composition across sites and between seasons, with several Vibrio operational taxonomic units (OTUs) displaying marked spatial patterns and seasonal trends. Shifts in the composition of the Vibrio community between seasons were primarily driven by changes in temperature, salinity and NO2, while a range of factors including pH, salinity, dissolved oxygen (DO) and NOx (Nitrogen Oxides) explained the observed spatial variation. Our evidence for the presence of a spatiotemporally dynamic Vibrio community within Sydney Harbour is notable given the high levels of human use of this waterway, and the significant increases in seawater temperature predicted for this region.

El Niño, Climate, and Cholera Associations in Piura, Peru, 1991-2001: A Wavelet Analysis

In Peru, it was hypothesized that epidemic cholera in 1991 was linked to El Niño, the warm phase of El Niño-Southern Oscillation. While previous studies demonstrated an association in 1997-1998, using cross-sectional data, they did not assess the consistency of this relationship across the decade. Thus, how strong or variable an El Niño-cholera relationship was in Peru or whether El Niño triggered epidemic cholera early in the decade remains unknown. In this study, wavelet and mediation analyses were used to characterize temporal patterns among El Niño, local climate variables (rainfall, river discharge, and air temperature), and cholera incidence in Piura, Peru from 1991 to 2001 and to estimate the mediating effects of local climate on El Niño-cholera relationships. The study hypothesis is that El Niño-related connections with cholera in Piura were transient and interconnected via local climate pathways. Overall, our findings provide evidence that a strong El Niño-cholera link, mediated by local hydrology, existed in the latter part of the 1990s but found no evidence of an El Niño association in the earlier part of the decade, suggesting that El Niño may not have precipitated cholera emergence in Piura. Further examinations of cholera epicenters in Peru are recommended to support these results in Piura. For public health planning, the results may improve existing efforts that utilize El Niño monitoring for preparedness during future climate-related extremes in the region.

Time series regression model for infectious disease and weather

Time series regression has been developed and long used to evaluate the short-term associations of air pollution and weather with mortality or morbidity of non-infectious diseases. The application of the regression approaches from this tradition to infectious diseases, however, is less well explored and raises some new issues. We discuss and present potential solutions for five issues often arising in such analyses: changes in immune population, strong autocorrelations, a wide range of plausible lag structures and association patterns, seasonality adjustments, and large overdispersion. The potential approaches are illustrated with datasets of cholera cases and rainfall from Bangladesh and influenza and temperature in Tokyo. Though this article focuses on the application of the traditional time series regression to infectious diseases and weather factors, we also briefly introduce alternative approaches, including mathematical modeling, wavelet analysis, and autoregressive integrated moving average (ARIMA) models. Modifications proposed to standard time series regression practice include using sums of past cases as proxies for the immune population, and using the logarithm of lagged disease counts to control autocorrelation due to true contagion, both of which are motivated from "susceptible-infectious-recovered" (SIR) models. The complexity of lag structures and association patterns can often be informed by biological mechanisms and explored by using distributed lag non-linear models. For overdispersed models, alternative distribution models such as quasi-Poisson and negative binomial should be considered. Time series regression can be used to investigate dependence of infectious diseases on weather, but may need modifying to allow for features specific to this context.

Effect of Temperature on Fimbrial Gene Expression and Adherence of Enteroaggregative Escherichia coli

The influence of temperature on bacterial virulence has been studied worldwide from the viewpoint of climate change and global warming. The bacterium enteroaggregative Escherichia coli (EAEC) is the causative agent of watery diarrhea and shows an increasing incidence worldwide. Its pathogenicity is associated with the virulence factors aggregative adherence fimbria type I and II (AAFI and AAFII), encoded by aggA and aafA in EAEC strains 17-2 and 042, respectively. This study focused on the effect of temperature increases from 29 °C to 40 °C on fimbrial gene expression using real-time PCR, and on its virulence using an aggregative adherence assay and biofilm formation assay. Incubation at 32 °C caused an up-regulation in both EAEC strains 17-2 and strain 042 virulence gene expression. EAEC strain 042 cultured at temperature above 32 °C showed down-regulation of aafA expression except at 38 °C. Interestingly, EAEC cultured at a high temperature showed a reduced adherence to cells and an uneven biofilm formation. These results provide evidence that increases in temperature potentially affect the virulence of pathogenic EAEC, although the response varies in each strain.

Climate and socioeconomic influences on interannual variability of cholera in Nigeria

Cholera is one of the most important climate sensitive diseases in Nigeria that pose a threat to public health because of its fatality and endemic nature. This study aims to investigate the influences of meteorological and socioeconomic factors on the spatiotemporal variability of cholera morbidity and mortality in Nigeria. Stepwise multiple regression and generalised additive models were fitted for individual states as well as for three groups of the states based on annual precipitation. Different meteorological variables were analysed, taking into account socioeconomic factors that are potentially enhancing vulnerability (e.g. absolute poverty, adult literacy, access to pipe borne water). Results quantify the influence of both climate and socioeconomic variables in explaining the spatial and temporal variability of the disease incidence and mortality. Regional importance of different factors is revealed, which will allow further insight into the disease dynamics. Additionally, cross validated models suggest a strong possibility of disease prediction, which will help authorities to put effective control measures in place which depend on prevention, and or efficient response.

Assessment of Climate-sensitive Infectious Diseases in the Federated States of Micronesia

Background: The health impacts of climate change are an issue of growing concern in the Pacific region. Prior to 2010, no formal, structured, evidence-based approach had been used to identify the most significant health risks posed by climate change in Pacific island countries. During 2010 and 2011, the World Health Organization supported the Federated States of Micronesia (FSM) in performing a climate change and health vulnerability and adaptation assessment. This paper summarizes the priority climate-sensitive health risks in FSM, with a focus on diarrheal disease, its link with climatic variables and the implications of climate change.

Methods: The vulnerability and adaptation assessment process included a review of the literature, extensive stakeholder consultations, ranking of climate-sensitive health risks, and analysis of the available long-term data on climate and climate-sensitive infectious diseases in FSM, which involved examination of health information data from the four state hospitals in FSM between 2000 and 2010; along with each state’s rainfall, temperature and El Niño-Southern Oscillation data. Generalized linear Poisson regression models were used to demonstrate associations between monthly climate variables and cases of climate-sensitive diseases at differing temporal lags.

Results: Infectious diseases were among the highest priority climate-sensitive health risks identified in FSM, particularly diarrheal diseases, vector-borne diseases and leptospirosis. Correlation with climate data demonstrated significant associations between monthly maximum temperature and monthly outpatient cases of diarrheal disease in Pohnpei and Kosrae at a lag of one month and 0 to 3 months, respectively; no such associations were observed in Chuuk or Yap. Significant correlations between disease incidence and El Niño-Southern Oscillation cycles were demonstrated in Kosrae state.

Conclusions: Analysis of the available data demonstrated significant associations between climate variables and climate-sensitive infectious diseases. This information should prove useful in implementing health system and community adaptation strategies to avoid the most serious impacts of climate change on health in FSM.

A systematic review of methodology: time series regression analysis for environmental factors and infectious diseases

BACKGROUND

Time series analysis is suitable for investigations of relatively direct and short-term effects of exposures on outcomes. In environmental epidemiology studies, this method has been one of the standard approaches to assess impacts of environmental factors on acute non-infectious diseases (e.g. cardiovascular deaths), with conventionally generalized linear or additive models (GLM and GAM). However, the same analysis practices are often observed with infectious diseases despite of the substantial differences from non-infectious diseases that may result in analytical challenges.

METHODS

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, systematic review was conducted to elucidate important issues in assessing the associations between environmental factors and infectious diseases using time series analysis with GLM and GAM. Published studies on the associations between weather factors and malaria, cholera, dengue, and influenza were targeted.

FINDINGS

Our review raised issues regarding the estimation of susceptible population and exposure lag times, the adequacy of seasonal adjustments, the presence of strong autocorrelations, and the lack of a smaller observation time unit of outcomes (i.e. daily data). These concerns may be attributable to features specific to infectious diseases, such as transmission among individuals and complicated causal mechanisms.

CONCLUSION

The consequence of not taking adequate measures to address these issues is distortion of the appropriate risk quantifications of exposures factors. Future studies should pay careful attention to details and examine alternative models or methods that improve studies using time series regression analysis for environmental determinants of infectious diseases.