The Effect of Rainfall on the Incidence of Cholera in Bangladesh

Influence of hydrologic and anthropogenic factors on the abundance variability of enteropathogens in the Ganges estuary, a cholera endemic region

This study deals with the influence of water physico-chemical properties, tides, rainfall and fecal pollution on the abundance of enteropathogens in a main distributary of the Ganges, in the endemic cholera belt of West Bengal. Between January and June 2011, water and sediments were sampled from two sites of the Hooghly River by Kolkata and Diamond Harbour. Counts of cultivable Vibrio (CVC, from~10(2) to~10(5)CFU/L) and total bacteria (TBC, from~10(5) to~10(9)CFU/L) increased with water temperature (17°C to 37°C). A combination of variations in tidal height, salinity and turbidity had a distinct influence on CVC, TBC and coliform counts. At Diamond Harbour, a salinity increase from 0.6 to 7.9 was accompanied by a 1000-fold amplification of initial CVC~10(2)CFU/L, whereas higher prevalence of coliforms in Kolkata was related to greater disposal of untreated sewage into the river. Turbidity-dependent variation of CVC was noteworthy, particularly at Diamond Harbour, where CVC in intertidal surface sediments showed an analogous trend as in surface waters, suggesting bentho-pelagic coupling of Vibrio dynamics. Besides the influence of salinity variation with tidal cycles, sediment re-suspension from tidal flats can play a role on Vibrio abundance in aquatic ecosystems.

Climate change and waterborne diarrhoea in northern India: impacts and adaptation strategies

Although several studies show the vulnerability of human health to climate change, a clear comprehensive quantification of the increased health risks attributable to climate change is lacking. Even more complicated are assessments of adaptation measures for this sector. We discuss the impact of climate change on diarrhoea as a representative of a waterborne infectious disease affecting human health in the Ganges basin of northern India. A conceptual framework is presented for climate exposure response relationships based on studies from different countries, as empirical studies and appropriate epidemiological data sets for India are lacking. Four climate variables are included: temperature, increased/extreme precipitation, decreased precipitation/droughts and relative humidity. Applying the conceptual framework to the latest regional climate projections for northern India shows increases between present and future (2040s), varying spatially from no change to an increase of 21% in diarrhoea incidences, with 13.1% increase on average for the Ganges basin. We discuss three types of measures against diarrhoeal disease: reactive actions, preventive actions and national policy options. Preventive actions have the potential to counterbalance this expected increase. However, given the limited progress in reducing incidences over the past decade consorted actions and effective implementation and integration of existing policies are needed.

Examining rainfall and cholera dynamics in Haiti using statistical and dynamic modeling approaches

Haiti has been in the midst of a cholera epidemic since October 2010. Rainfall is thought to be associated with cholera here, but this relationship has only begun to be quantitatively examined. In this paper, we quantitatively examine the link between rainfall and cholera in Haiti for several different settings (including urban, rural, and displaced person camps) and spatial scales, using a combination of statistical and dynamic models. Statistical analysis of the lagged relationship between rainfall and cholera incidence was conducted using case crossover analysis and distributed lag nonlinear models. Dynamic models consisted of compartmental differential equation models including direct (fast) and indirect (delayed) disease transmission, where indirect transmission was forced by empirical rainfall data. Data sources include cholera case and hospitalization time series from the Haitian Ministry of Public Health, the United Nations Water, Sanitation and Health Cluster, International Organization for Migration, and Hôpital Albert Schweitzer. Rainfall data was obtained from rain gauges from the U.S. Geological Survey and Haiti Regeneration Initiative, and remote sensing rainfall data from the National Aeronautics and Space Administration Tropical Rainfall Measuring Mission. A strong relationship between rainfall and cholera was found for all spatial scales and locations examined. Increased rainfall was significantly correlated with increased cholera incidence 4-7 days later. Forcing the dynamic models with rainfall data resulted in good fits to the cholera case data, and rainfall-based predictions from the dynamic models closely matched observed cholera cases. These models provide a tool for planning and managing the epidemic as it continues.

Environmental factors influencing epidemic cholera

Cholera outbreak following the earthquake of 2010 in Haiti has reaffirmed that the disease is a major public health threat. Vibrio cholerae is autochthonous to aquatic environment, hence, it cannot be eradicated but hydroclimatology-based prediction and prevention is an achievable goal. Using data from the 1800s, we describe uniqueness in seasonality and mechanism of occurrence of cholera in the epidemic regions of Asia and Latin America. Epidemic regions are located near regional rivers and are characterized by sporadic outbreaks, which are likely to be initiated during episodes of prevailing warm air temperature with low river flows, creating favorable environmental conditions for growth of cholera bacteria. Heavy rainfall, through inundation or breakdown of sanitary infrastructure, accelerates interaction between contaminated water and human activities, resulting in an epidemic. This causal mechanism is markedly different from endemic cholera where tidal intrusion of seawater carrying bacteria from estuary to inland regions, results in outbreaks.

Cholera Outbreaks in Urban Bangladesh In 2011

BACKGROUND

In 2011, a multidisciplinary team investigated two diarrhoea outbreaks affecting urban Bangladeshi communities from the districts of Bogra and Kishorganj to identify etiology, pathways of transmission, and factors contributing to these outbreaks.

METHODS

We defined case-patients with severe diarrhoea as residents from affected communities admitted with ≥3 loose stools per day. We listed case-patients, interviewed and examined them, and collected rectal swabs. We visited the affected communities to explore the water and sanitation infrastructure. We tested the microbial load of water samples from selected case household taps, tube wells, and pump stations. We conducted anthropological investigations to understand community perceptions regarding the outbreaks.

RESULTS

We identified 21 case-patients from Bogra and 84 from Kishorganj. The median age in Bogra was 23 years, and 21 years in Kishorganj. There were no reported deaths. We isolated Vibrio in 29% (5/17) of rectal swabs from Bogra and in 40% (8/20) from Kishorganj. We found Vibrio in 1/8 tap water samples from Bogra and in both of the samples from Kishorganj. We did not find Vibrio in water samples from pumps or tube wells in either outbreak. Ground water extracted through deep tube wells was supplied intermittently through interconnected pipes without treatment in both areas. We found leakages in the water pipes in Bogra, and in Kishorganj water pipes passed through open sewers.

CONCLUSION

The rapid onset of severe diarrhoea predominantly affecting adults and the isolation of cholera in rectal swabs confirmed that these outbreaks were caused by Vibrio cholerae. The detection of Vibrio in water samples organisms from taps but not from pumps or tube wells, suggested contamination within the pipes. Safe water provision is difficult in municipalities where supply is intermittent, and where pipes commonly leak. Research to develop and evaluate water purification strategies could identify appropriate approaches for ensuring safe drinking water in resource-poor cities.

A differential effect of Indian ocean dipole and El Niño on cholera dynamics in Bangladesh

Background

A stationary (i.e., constant through time) association between El Niño Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD) and epidemics of cholera in Bangladesh has been widely assumed. However, whether or not elements of the local climate that are relevant for cholera transmission have stationary signatures of the IOD on their dynamics over different time scales is still not clear. Here we report results on the time-varying relationships between the various remote and local environmental drivers and cholera incidence in Bangladesh.

Methodology/Principal Findings

We performed a cross wavelet coherency analysis to examine patterns of association between monthly cholera cases in the hospitals in Dhaka and Matlab (1983–2008) and indices for both IOD and ENSO. Our results showed that the strength of both the IOD and ENSO associations with cholera hospitalizations changed across time scales during the study period. In Dhaka, 4-year long coherent cycles were observed between cholera and the index of IOD in 1988–1997. In Matlab, the effect of ENSO was more dominant while there was no evidence for an IOD effect on cholera hospitalizations.

Conclusions/Significance

Our results call for the consideration of non-stationary, possibly non-linear, patterns of association between cholera hospitalizations and climatic factors in cholera epidemic early warning systems.

The association of weather and mortality in Bangladesh from 1983-2009

Introduction

The association of weather and mortality have not been widely studied in subtropical monsoon regions, particularly in Bangladesh. This study aims to assess the association of weather and mortality (measured with temperature and rainfall), adjusting for time trend and seasonal patterns in Abhoynagar, Bangladesh.

Material and methods

A sample vital registration system (SVRS) was set up in 1982 to facilitate operational research in family planning and maternal and child health. SVRS provided data on death counts and population from 1983–2009. The Bangladesh Meteorological Department provided data on daily temperature and rainfall for the same period. Time series Poisson regression with cubic spline functions was used, allowing for over-dispersion, including lagged weather parameters, and adjusting for time trends and seasonal patterns. Analysis was carried out using R statistical software.

Results

Both weekly mean temperature and rainfall showed strong seasonal patterns. After adjusting for seasonal pattern and time trend, weekly mean temperatures (lag 0) below the 25th percentile and between the 25th and 75th percentiles were associated with increased mortality risk, particularly in females and adults aged 20–59 years by 2.3–2.4% for every 1°C decrease. Temperature above the 75th percentile did not increase the risk. Every 1 mm increase in rainfall up to 14 mm of weekly average rainfall over lag 0–4 weeks was associated with decreased mortality risks. Rainfall above 14 mm was associated with increased mortality risk.

Conclusion

The relationships between temperature, rainfall and mortality reveal the importance of understanding the current factors contributing to adaptation and acclimatization, and how these can be enhanced to reduce negative impacts from weather.

The association of meteorological factors and mortality in rural Bangladesh, 1983-2009

INTRODUCTION

While the association of weather and mortality has been well documented for moderate climate zones, little is known about sub-tropical zones, particularly Bangladesh. This study aims to assess the short-term relationship of temperature and rainfall on daily mortality after controlling for seasonality and time-trends. The study used data from Matlab, Bangladesh, where a rigorous health and demographic surveillance system (HDSS) has been operational since 1966.

MATERIAL AND METHODS

Matlab HDSS data on mortality and population for the period 1983-2009 were used. Weather data for the same period were obtained from a nearby government weather station. Time series Poisson regression with cubic spline functions was applied allowing for lagged effects of weather and extreme weather events on mortality, and controlling for time trends and seasonal patterns. Analysis was carried out using R statistical software.

RESULTS

Both temperature and rainfall showed strong seasonal patterns, explaining a significant part of mortality in all age groups. After adjusting for seasonality and trend, mortality and temperature show a U-shaped pattern; below a temperature of around 29°C, a decrease in temperature resulted in an increase in mortality, whereas above 29°C, increased temperature resulted in increased mortality. The strongest negative mortality temperature association was observed in the elderly (5.4% increase with every 1°C decrease in temperature at temperatures below 23°C), and the opposite trend was observed in the age groups 1-4 and 5-19 years old. At aggregate level, the rainfall-mortality association is statistically weak. However in the age group 5-19, a 0.6% increase in mortality per 1 mm additional rainfall was found, at rainfall levels over 100 mm per day. Multivariate analysis showed high mortality risks for women aged 20-59 years of age during cyclone episodes.

DISCUSSION

Weather and extreme weather were associated with mortality with differential impacts in age and sex sub-groups. Further studies should investigate these findings more closely and develop policy recommendations targeted at improving public health and protecting population groups susceptible to environmental stressors.

Estimated effect of climatic variables on the transmission of Plasmodium vivax malaria in the Republic of Korea

BACKGROUND

Climate change may affect Plasmodium vivax malaria transmission in a wide region including both subtropical and temperate areas.

OBJECTIVES

We aimed to estimate the effects of climatic variables on the transmission of P. vivax in temperate regions.

METHODS

We estimated the effects of climatic factors on P. vivax malaria transmission using data on weekly numbers of malaria cases for the years 2001-2009 in the Republic of Korea. Generalized linear Poisson models and distributed lag nonlinear models (DLNM) were adopted to estimate the effects of temperature, relative humidity, temperature fluctuation, duration of sunshine, and rainfall on malaria transmission while adjusting for seasonal variation, between-year variation, and other climatic factors.

RESULTS

A 1°C increase in temperature was associated with a 17.7% [95% confidence interval (CI): 16.9, 18.6%] increase in malaria incidence after a 3-week lag, a 10% rise in relative humidity was associated with 40.7% (95% CI: -44.3, -36.9%) decrease in malaria after a 7-week lag, a 1°C increase in the diurnal temperature range was associated with a 24.1% (95% CI: -26.7, -21.4%) decrease in malaria after a 7-week lag, and a 10-hr increase in sunshine per week was associated with a 5.1% (95% CI: -8.4, -1.7%) decrease in malaria after a 2-week lag. The cumulative relative risk for a 10-mm increase in rainfall (≤ 350 mm) on P. vivax malaria was 3.61 (95% CI: 1.69, 7.72) based on a DLNM with a 10-week maximum lag.

CONCLUSIONS

Our findings suggest that malaria transmission in temperate areas is highly dependent on climate factors. In addition, lagged estimates of the effect of rainfall on malaria are consistent with the time necessary for mosquito development and P. vivax incubation.

Highly localized sensitivity to climate forcing drives endemic cholera in a megacity

The population dynamics of endemic cholera in urban environments--in particular interannual variation in the size and distribution of seasonal outbreaks--remain poorly understood and highly unpredictable. In part, this situation is due to the considerable demographic, socioeconomic, and environmental heterogeneity of large and growing urban centers. Despite this heterogeneity, the influence of climate variability on the population dynamics of infectious diseases is considered a large-scale, regional, phenomenon, and as such has been previously addressed for cholera only with temporal models that do not incorporate spatial structure. Here we show that a probabilistic spatial model can explain cholera dynamics in the megacity of Dhaka, Bangladesh, and afford a basis for cholera forecasts at lead times of 11 mo. Critically, we find that the action of climate variability (El Niño southern oscillation and flooding) is quite localized: There is a climate-sensitive urban core that acts to propagate risk to the rest of the city. The modeling framework presented here should be applicable to cholera in other cities, as well as to other infectious diseases in urban settings and other biological systems with spatiotemporal interactions.

Association of Heavy Rainfall on Genotypic Diversity in V. cholerae Isolates from an Outbreak in India

The outbreak of waterborne disease cholera has been associated with rainfall and flooding events by contamination of potable water with environmental Vibrio cholerae. The continuation of the epidemic in a region, however, is often due to secondary transmission of the initial outbreak strain through human waste. This paper reports, on the contrary, a rapid shift of genotype from one V. cholerae strain to another one in an epidemic region. V. cholerae isolated from patients during 2005 cholera epidemic in Chennai, India were characterized using PCR identification of toxin genes, antibiogram, and genomic fingerprinting analysis. The results showed that in spite of the similarity of toxin genes and antibiogram, the Vibrio isolates grouped into two different clusters based on the ERIC-PCR fingerprinting. Each cluster corresponded to a distinct peak of cholera outbreak, which occurred after separate heavy rainfall. The results suggest that the rainfall event can bring various genotypes of V. cholerae strains causing multiple outbreaks.

The simultaneous effects of spatial and social networks on cholera transmission

This study uses social network and spatial analytical methods simultaneously to understand cholera transmission in rural Bangladesh. Both have been used separately to incorporate context into health studies, but using them together is a new and recent approach. Data include a spatially referenced longitudinal demographic database consisting of approximately 200,000 people and a database of all laboratory-confirmed cholera cases from 1983 to 2003. A complete kinship-based network linking households is created, and distance matrices are also constructed to model spatial relationships. A spatial error-social effects model tested for cholera clustering in socially linked households while accounting for spatial factors. Results show that there was social clustering in five out of twenty-one years while accounting for both known and unknown environmental variables. This suggests that environmental cholera transmission is significant and social networks also influence transmission, but not as consistently. Simultaneous spatial and social network analysis may improve understanding of disease transmission.

Vulnerability of newborns to environmental factors: findings from community based surveillance data in Bangladesh

Infection is the major cause of neonatal deaths. Home born newborns in rural Bangladeshi communities are exposed to environmental factors increasing their vulnerability to a number of disease agents that may compromise their health. The current analysis was conducted to assess the association of very severe disease (VSD) in newborns in rural communities with temperature, rainfall, and humidity. A total of 12,836 newborns from rural Sylhet and Mirzapur communities were assessed by trained community health workers using a sign based algorithm. Records of temperature, humidity, and rainfall were collected from the nearest meteorological stations. Associations between VSD and environmental factors were estimated. Incidence of VSD was found to be associated with higher temperatures (odds ratios: 1.14, 95% CI: 1.08 to 1.21 in Sylhet and 1.06, 95% CI: 1.04 to 1.07 in Mirzapur) and heat humidity index (odds ratios: 1.06, 95% CI: 1.04 to 1.08 in Sylhet and, 1.03, 95% CI: 1.01 to 1.04 in Mirzapur). Four months (June–September) in Sylhet, and six months in Mirzapur (April–September) had higher odds ratios of incidence of VSD as compared to the remainder of the year (odds ratios: 1.72, 95% CI: 1.32 to 2.23 in Sylhet and, 1.62, 95% CI: 1.33 to 1.96 in Mirzapur). Prevention of VSD in neonates can be enhanced if these interactions are considered in health intervention strategies.

Climate variability and the outbreaks of cholera in Zanzibar, East Africa: a time series analysis

Global cholera incidence is increasing, particularly in sub-Saharan Africa. We examined the impact of climate and ocean environmental variability on cholera outbreaks, and developed a forecasting model for outbreaks in Zanzibar. Routine cholera surveillance reports between 1997 and 2006 were correlated with remotely and locally sensed environmental data. A seasonal autoregressive integrated moving average (SARIMA) model determined the impact of climate and environmental variability on cholera. The SARIMA model shows temporal clustering of cholera. A 1°C increase in temperature at 4 months lag resulted in a 2-fold increase of cholera cases, and an increase of 200 mm of rainfall at 2 months lag resulted in a 1.6-fold increase of cholera cases. Temperature and rainfall interaction yielded a significantly positive association (P < 0.04) with cholera at a 1-month lag. These results may be applied to forecast cholera outbreaks, and guide public health resources in controlling cholera in Zanzibar.

The Indian Ocean dipole and cholera incidence in Bangladesh: a time-series analysis

BACKGROUND

It has been reported that the El Niño-Southern Oscillation (ENSO) influences the interannual variation of endemic cholera in Bangladesh. There is increased interest in the influence of the Indian Ocean dipole (IOD), a climate mode of coupled ocean-atmosphere variability, on regional ocean climate in the Bay of Bengal and on Indian monsoon rainfall.

OBJECTIVES

We explored the relationship between the IOD and the number of cholera patients in Bangladesh, controlling for the effects of ENSO.

METHODS

Time-series regression was performed. Negative binomial models were used to estimate associations between the monthly number of hospital visits for cholera in Dhaka and Matlab (1993-2007) and the dipole mode index (DMI) controlling for ENSO index [NINO3, a measure of the average sea surface temperature (SST) in the Niño 3 region], seasonal, and interannual variations. Associations between cholera cases and SST and sea surface height (SSH) of the northern Bay of Bengal were also examined.

RESULTS

A 0.1-unit increase in average DMI during the current month through 3 months before was associated with an increase in cholera incidence of 2.6% [(95% confidence interval (CI), 0.0-5.2; p = 0.05] in Dhaka and 6.9% (95% CI, 3.2-10.8; p < 0.01) in Matlab. Cholera incidence in Dhaka increased by 2.4% (95% CI, 0.0-5.0; p = 0.06) after a 0.1-unit decrease in DMI 4-7 months before. Hospital visits for cholera in both areas were positively associated with SST 0-3 months before, after adjusting for SSH (p < 0.01).

CONCLUSIONS

These findings suggest that both negative and positive dipole events are associated with an increased incidence of cholera in Bangladesh with varying time lags.

Aquatic Ecosystems, Human Health, and Ecohydrology

This chapter treats two main topics: the relationship between human health, aquatic ecosystems, and water use; and the necessity of interdisciplinary approaches for the development of water management policies and disease control.

Main waterborne diseases, mostly affecting developing countries and relevant in terms of water management and changes in land use, such as malaria, schistosomiasis, or cholera, are discussed stressing links to the global water crisis. Also, the role of artificial and natural wetlands in influenza epidemics is treated. The effects of increasing water use and scarcity on human health are discussed considering historical and contemporary incidence of diarrheal diseases in European and South Asian megacities, relationships between dams and on waterborne diseases in Asia and Africa, and intensive agri- and aquaculture resulting in man-made ecotones, fragmented aquatic ecosystems, and pathogen mutations.

It is emphasized that the comprehension of the multiple interactions among changes in environmental settings, land use, and human health requires a new synthesis of ecohydrology, biomedical sciences, and water management for surveillance and control of waterborne diseases in basin-based, transboundary health systems. Surveillance systems should monitor changes in water management, ecotones, and hydrological cycles and shifts in, for example, the outbreak timing of strongly seasonal diseases. These indicators would provide criteria for the development of innovative water management policies, combining methods of vector control and the safe creation of water reservoirs, irrigation systems, and wetland habitats.

The effect of global warming on infectious diseases

Global warming has various effects on human health. The main indirect effects are on infectious diseases. Although the effects on infectious diseases will be detected worldwide, the degree and types of the effect are different, depending on the location of the respective countries and socioeconomical situations.

Among infectious diseases, water- and foodborne infectious diseases and vector-borne infectious diseases are two main categories that are forecasted to be most affected. The effect on vector-borne infectious diseases such as malaria and dengue fever is mainly because of the expansion of the infested areas of vector mosquitoes and increase in the number and feeding activity of infected mosquitoes. There will be increase in the number of cases with water- and foodborne diarrhoeal diseases.

Even with the strongest mitigation procedures, global warming cannot be avoided for decades. Therefore, implementation of adaptation measures to the effect of global warming is the most practical action we can take. It is generally accepted that the impacts of global warming on infectious diseases have not been apparent at this point yet in East Asia. However, these impacts will appear in one form or another if global warming continues to progress in future. Further research on the impacts of global warming on infectious diseases and on future prospects should be conducted.

Cholera in Bangladesh: climatic components of seasonal variation

BACKGROUND

The mechanisms underlying the seasonality of cholera are still not fully understood, despite long-standing recognition of clear bimodal seasonality in Bangladesh. We aimed to quantify the contribution of climatic factors to seasonal variations in cholera incidence.

METHODS

We investigated the association of seasonal and weather factors with the weekly number of cholera patients in Dhaka, Bangladesh, using Poisson regression models. The contribution of each weather factor (temperature and high and low rainfall) to seasonal variation was estimated as the mean over the study period (1983-2008) for each week of the year of each weather term. Fractions of the number of cholera patients attributed to each weather factor, assuming all values were constant at their minimum risk levels throughout the year, were estimated for spring and monsoon seasons separately.

RESULTS

Lower temperature predicted a lower incidence of cholera in the first 15 weeks of the year. Low rainfall predicted a peak in spring, and high rainfall predicted a peak at the end of the monsoon. The risk predicted from all the weather factors combined showed a broadly bi-modal pattern, as observed in the raw data. Low rainfall explained 18% of the spring peak, and high rainfall explained 25% of the peak at the end of the monsoon.

CONCLUSIONS

Seasonal variation in temperature and rainfall contribute to cholera incidence in complex ways, presumably in interaction with unmeasured environmental or behavioral factors.

Tracking Cholera in Coastal Regions using Satellite Observations

Cholera remains a significant health threat across the globe. The pattern and magnitude of the seven global pandemics suggest that cholera outbreaks primarily originate in coastal regions and then spread inland through secondary means. Cholera bacteria show strong association with plankton abundance in coastal ecosystems. This review study investigates relationship(s) between cholera incidence and coastal processes and explores utility of using remote sensing data to track coastal plankton blooms, using chlorophyll as a surrogate variable for plankton abundance, and subsequent cholera outbreaks. Most studies over the last several decades have primarily focused on the microbiological and epidemiological understanding of cholera outbreaks. Accurate identification and mechanistic understanding of large scale climatic, geophysical and oceanic processes governing cholera-chlorophyll relationship is important for developing cholera prediction models. Development of a holistic understanding of these processes requires long and reliable chlorophyll dataset(s), which are beginning to be available through satellites. We have presented a schematic pathway and a modeling framework that relate cholera with various hydroclimatic and oceanic variables for understanding disease dynamics using latest advances in remote sensing. Satellite data, with its unprecedented spatial and temporal coverage, have potentials to monitor coastal processes and track cholera outbreaks in endemic regions.

Environmental exposures and invasive meningococcal disease: an evaluation of effects on varying time scales

Invasive meningococcal disease (IMD) is an important cause of meningitis and bacteremia worldwide. Seasonal variation in IMD incidence has long been recognized, but mechanisms responsible for this phenomenon remain poorly understood. The authors sought to evaluate the effect of environmental factors on IMD risk in Philadelphia, Pennsylvania, a major urban center. Associations between monthly weather patterns and IMD incidence were evaluated using multivariable Poisson regression models controlling for seasonal oscillation. Short-term weather effects were identified using a case-crossover approach. Both study designs control for seasonal factors that might otherwise confound the relation between environment and IMD. Incidence displayed significant wintertime seasonality (for oscillation, P < 0.001), and Poisson regression identified elevated monthly risk with increasing relative humidity (per 1% increase, incidence rate ratio = 1.04, 95% confidence interval: 1.004, 1.08). Case-crossover methods identified an inverse relation between ultraviolet B radiation index 1–4 days prior to onset and disease risk (odds ratio = 0.54, 95% confidence interval: 0.34, 0.85). Extended periods of high humidity and acute changes in ambient ultraviolet B radiation predict IMD occurrence in Philadelphia. The latter effect may be due to decreased pathogen survival or virulence and may explain the wintertime seasonality of IMD in temperate regions of North America.

The Indian Ocean Dipole and malaria risk in the highlands of western Kenya

Epidemics of malaria in the East African highlands in the last 2 decades have often been associated with climate variability, particularly the El Niño-Southern Oscillation (ENSO). However, there are other factors associated with malaria risk and there is increased interest in the influences of the Indian Ocean Dipole (IOD), a climate mode of coupled ocean-atmosphere variability, on East African rainfall. This study explores the relationship between IOD and the number of malaria patients in 7 hospitals from 2 districts in the western Kenyan highlands, controlling for the effects of ENSO. We examined temporal patterns (1982-2001) in the number of malaria cases in relation to the dipole mode index (DMI), defined as the difference in sea surface temperature anomaly between the western (10 degrees S-10 degrees N, 50 degrees-70 degrees E) and eastern (10 degrees S-0 degrees, 90 degrees-110 degrees E) tropical Indian Ocean. We used Poisson regression models, adjusted for ENSO index Niño 3 region (NINO3), seasonal and interannual variations. The number of malaria patients per month increased by 3.4%-17.9% for each 0.1 increase above a DMI threshold (3-4 months lag). Malaria cases increased by 1.4%-10.7% per month, for each 10 mm increase in monthly rainfall (2-3 months lag). In 6 of 7 places, there was no evidence of an association between NINO3 and the number of malaria cases after adjusting for the effect of DMI. This study suggests that the number of malaria cases in the western Kenyan highlands increases with high DMI in the months preceding hospital visits.