Jyotsna S. Jagai

Seasonality in six enterically transmitted diseases and ambient temperature

We propose an analytical and conceptual framework for a systematic and comprehensive assessment of disease seasonality to detect changes and to quantify and compare temporal patterns. To demonstrate the proposed technique, we examined seasonal patterns of six enterically transmitted reportable diseases (EDs) in Massachusetts collected over a 10-year period (1992-2001). We quantified the timing and intensity of seasonal peaks of ED incidence and examined the synchronization in timing of these peaks with respect to ambient temperature. All EDs, except hepatitis A, exhibited well-defined seasonal patterns which clustered into two groups. The peak in daily incidence of Campylobacter and Salmonella closely followed the peak in ambient temperature with the lag of 2-14 days. Cryptosporidium, Shigella, and Giardia exhibited significant delays relative to the peak in temperature (approximately 40 days, P<0.02). The proposed approach provides a detailed quantification of seasonality that enabled us to detect significant differences in the seasonal peaks of enteric infections which would have been lost in an analysis using monthly or weekly cumulative information. This highly relevant to disease surveillance approach can be used to generate and test hypotheses related to disease seasonality and potential routes of transmission with respect to environmental factors.

Seasonality of cryptosporidiosis: A meta-analysis approach.

OBJECTIVES We developed methodology for and conducted a meta-analysis to examine how seasonal patterns of cryptosporidiosis, a primarily waterborne diarrheal illness, relate to precipitation and temperature fluctuations worldwide. METHODS Monthly cryptosporidiosis data were abstracted from 61 published epidemiological studies that cover various climate regions based on the Köppen Climate Classification. Outcome data were supplemented with monthly aggregated ambient temperature and precipitation for each study location. We applied a linear mixed-effect model to relate the monthly normalized cryptosporidiosis incidence with normalized location-specific temperature and precipitation data. We also conducted a sub-analysis of associations between the Normalized Difference Vegetation Index (NDVI), a remote sensing measure for the combined effect of temperature and precipitation on vegetation, and cryptosporidiosis in Sub-Saharan Africa. RESULTS Overall, and after adjusting for distance from the equator, increases in temperature and precipitation predict an increase in cryptosporidiosis; the strengths of relationship vary by climate subcategory. In moist tropical locations, precipitation is a strong seasonal driver for cryptosporidiosis whereas temperature is in mid-latitude and temperate climates. When assessing lagged relationships, temperature and precipitation remain strong predictors. In Sub-Saharan Africa, after adjusting for distance from the equator, low NDVI values are predictive of an increase in cryptosporidiosis in the following month. DISCUSSION In this study we propose novel methodology to assess relationships between disease outcomes and meteorological data on a global scale. Our findings demonstrate that while climatic conditions typically define a pathogen habitat area, meteorological factors affect timing and intensity of seasonal outbreaks. Therefore, meteorological forecasts can be utilized to develop focused prevention programs for waterborne cryptosporidiosis.

Seasonality of Rotavirus in South Asia: A Meta-Analysis Approach Assessing Associations with Temperature, Precipitation, and Vegetation Index

Background Rotavirus infection causes a significant proportion of diarrhea in infants and young children worldwide leading to dehydration, hospitalization, and in some cases death. Rotavirus infection represents a significant burden of disease in developing countries, such as those in South Asia. Methods We conducted a meta-analysis to examine how patterns of rotavirus infection relate to temperature and precipitation in South Asia. Monthly rotavirus data were abstracted from 39 published epidemiological studies and related to monthly aggregated ambient temperature and cumulative precipitation for each study location using linear mixed-effects models. We also considered associations with vegetation index, gathered from remote sensing data. Finally, we assessed whether the relationship varied in tropical climates and humid mid-latitude climates. Results Overall, as well as in tropical and humid mid-latitude climates, low temperature and precipitation levels are significant predictors of an increased rate of rotaviral diarrhea. A 1°C decrease in monthly ambient temperature and a decrease of 10 mm in precipitation are associated with 1.3% and 0.3% increase above the annual level in rotavirus infections, respectively. When assessing lagged relationships, temperature and precipitation in the previous month remained significant predictors and the association with temperature was stronger in the tropical climate. The same association was seen for vegetation index; a seasonal decline of 0.1 units results in a 3.8% increase in rate of rotavirus. Conclusions In South Asia the highest rate of rotavirus was seen in the colder, drier months. Meteorological characteristics can be used to better focus and target public health prevention programs.

Emergency room visits for respiratory conditions in children increased after Guagua Pichincha volcanic eruptions in April 2000 in Quito, Ecuador observational study: time series analysis.

BackgroundThis study documented elevated rates of emergency room (ER) visits for acute upper and lower respiratory infections and asthma-related conditions in the children of Quito, Ecuador associated with the eruption of Guagua Pichincha in April of 2000.MethodsWe abstracted 5169 (43% females) ER records with primary respiratory conditions treated from January 1 – December 27, 2000 and examined the change in pediatric ER visits for respiratory conditions before, during, and after exposure events of April, 2000. We applied a Poisson regression model adapted to time series of cases for three non-overlapping disease categories: acute upper respiratory infection (AURI), acute lower respiratory infection (ALRI), and asthma-related conditions in boys and girls for three age groups: 0–4, 5–9, and 10–15 years.ResultsAt the main pediatric medical facility, the Baca Ortiz Pediatric Hospital, the rate of emergency room (ER) visits due to respiratory conditions substantially increased in the three weeks after eruption (RR = 2.22, 95%CI = [1.95, 2.52] and RR = 1.72 95%CI = [1.49, 1.97] for lower and upper respiratory tract infections respectively. The largest impact of eruptions on respiratory distress was observed in children younger than 5 years (RR = 2.21, 95%CI = [1.79, 2.73] and RR = 2.16 95%CI = [1.67, 2.76] in boys and girls respectively). The rate of asthma and asthma-related diagnosis doubled during the period of volcano fumarolic activity (RR = 1.97, 95%CI = [1.19, 3.24]). Overall, 28 days of volcanic activity and ash releases resulted in 345 (95%CI = [241, 460]) additional ER visits due to respiratory conditions.ConclusionThe study has demonstrated strong relationship between ash exposure and respiratory effects in children.

Extreme Precipitation and Emergency Room Visits for Gastrointestinal Illness in Areas with and without Combined Sewer Systems: An Analysis of Massachusetts Data, 2003-2007.

Background Combined sewer overflows (CSOs) occur in combined sewer systems when sewage and stormwater runoff are released into water bodies, potentially contaminating water sources. CSOs are often caused by heavy precipitation and are expected to increase with increasing extreme precipitation associated with climate change. Objectives The aim of this study was to assess whether the association between heavy rainfall and rate of emergency room (ER) visits for gastrointestinal (GI) illness differed in the presence of CSOs. Methods For the study period 2003–2007, time series of daily rate of ER visits for GI illness and meteorological data were organized for three exposure regions: a) CSOs impacting drinking water sources, b) CSOs impacting recreational waters, c) no CSOs. A distributed lag Poisson regression assessed cumulative effects for an 8-day lag period following heavy (≥ 90th and ≥ 95th percentile) and extreme (≥ 99th percentile) precipitation events, controlling for temperature and long-term time trends. Results The association between extreme rainfall and rate of ER visits for GI illness differed among regions. Only the region with drinking water exposed to CSOs demonstrated a significant increased cumulative risk for rate (CRR) of ER visits for GI for all ages in the 8-day period following extreme rainfall: CRR: 1.13 (95% CI: 1.00, 1.28) compared with no rainfall. Conclusions The rate of ER visits for GI illness was associated with extreme precipitation in the area with CSO discharges to a drinking water source. Our findings suggest an increased risk for GI illness among consumers whose drinking water source may be impacted by CSOs after extreme precipitation. Citation Jagai JS, Li Q, Wang S, Messier KP, Wade TJ, Hilborn ED. 2015. Extreme precipitation and emergency room visits for gastrointestinal illness in areas with and without combined sewer systems: an analysis of Massachusetts data, 2003–2007. Environ Health Perspect 123:873–879; http://dx.doi.org/10.1289/ehp.1408971

Data Sources for an Environmental Quality Index: Availability, Quality, and Utility

OBJECTIVES An environmental quality index (EQI) for all counties in the United States is under development to explore the relationship between environmental insults and human health. The EQI is potentially useful for investigators researching health disparities to account for other concurrent environmental conditions. This article focused on the identification and assessment of data sources used in developing the EQI. Data source strengths, limitations, and utility were addressed. METHODS Five domains were identified that contribute to environmental quality: air, water, land, built, and sociodemographic environments. An inventory of possible data sources was created. Data sources were evaluated for appropriate spatial and temporal coverage and data quality. RESULTS The overall data inventory identified multiple data sources for each domain. From the inventory (187 sources, 617 records), the air, water, land, built environment, and sociodemographic domains retained 2, 9, 7, 4, and 2 data sources for inclusion in the EQI, respectively. However, differences in data quality, geographic coverage, and data availability existed between the domains. CONCLUSIONS The data sources identified for use in the EQI may be useful to researchers, advocates, and communities to explore specific environmental quality questions.

Hospitalization of the Elderly in the United States for Nonspecific Gastrointestinal Diseases: A Search for Etiological Clues

The frequency of hospitalization among the elderly in the United States caused by gastrointestinal diseases between 1991 and 2004 increased dramatically, especially hospitalization of elderly individuals with nonspecific diagnoses. We analyzed 6 640 304 gastrointestinal disease-associated hospitalization records in this 14-year period by comparing the peak times of nonspecific gastrointestinal diseases with those of specific diseases. We found that most nonspecific gastrointestinal diseases peak concurrently with viral enteritis, suggesting a lack of diagnostic testing for viruses, which may adversely affect the efficiency of prevention, surveillance, and treatment efforts.

Patterns of Protozoan Infections: Spatiotemporal Associations with Cattle Density

Waste from cattle production contains protozoa, such as Cryptosporidium spp. and Giardia, which can be transmitted to humans. People residing in areas of high cattle density may be at increased risk for protozoan infections. The objective of this study was to assess spatial and temporal associations between cattle density and hospitalizations for protozoan infections in the U.S. elderly. Data on protozoan infections were abstracted from Centers for Medicare and Medicaid Services datasets for a 14-year period (1991–2004). Cattle inventory data were abstracted from the 2002 U.S. Census of Agriculture. Counties were classified into one of five exposure categories based on both cattle density and human density. Our analyses considered differences in rates, trends, and variations in seasonal patterns based on exposure categories. Cryptosporidiosis demonstrated a trend of increasing annual rates related to increased potential exposure to cattle. Both cryptosporidiosis and giardiasis demonstrated significant seasonal patterns peaking during the fourth week of October in areas of high cattle/low population density and the second week of September in counties with low cattle/low human density, respectively. Counties with low human population density (regardless of cattle density) had the highest rate of all protozoan infections, peaking in the summer. These results demonstrate the elderly population is at increased risk of protozoan infections in areas of high cattle density, particularly cryptosporidiosis. The seasonal patterns and higher annual rates seen in rural areas suggest time-variant environmental exposures, which may be affected with geographical and temporal targeting of agricultural policies and interventions to improve public health.

The SEEDs of two gastrointestinal diseases: socioeconomic, environmental, and demographic factors related to cryptosporidiosis and giardiasis in Massachusetts.

OBJECTIVES We assessed associations between community-level socioeconomic, demographic, and environmental characteristics, and the presence of two potentially waterborne infectious diseases, cryptosporidiosis and giardiasis, as reported to the Massachusetts Department of Public Health. METHODS We created a series of maps showing the spatial distribution of cryptosporidiosis and giardiasis in Massachusetts (1993-2002) overall and by age, using logistic regression to analyze associations between community-level characteristics and the presence of at least one reported case of each disease. This analysis was repeated for communities with predominantly private water supplies. RESULT After adjusting for population size, higher population density and larger than average household sizes were associated with increased odds of reported cases of cryptosporidiosis. Giardiasis was also associated with high population density, but was not associated with household size. In the elderly, income was positively associated with the presence of giardiasis. DISCUSSION These findings suggest that greater population density and larger household sizes may increase the likelihood of protozoan gastrointestinal infection. The results emphasize the necessity to account for distal factors, such as demographic characteristics, that may ultimately play a role in the transmission or reporting of disease.

Seasonal Patterns of Gastrointestinal Illness and Streamflow along the Ohio River

Waterborne gastrointestinal (GI) illnesses demonstrate seasonal increases associated with water quality and meteorological characteristics. However, few studies have been conducted on the association of hydrological parameters, such as streamflow, and seasonality of GI illnesses. Streamflow is correlated with biological contamination and can be used as proxy for drinking water contamination. We compare seasonal patterns of GI illnesses in the elderly (65 years and older) along the Ohio River for a 14-year period (1991–2004) to seasonal patterns of streamflow. Focusing on six counties in close proximity to the river, we compiled weekly time series of hospitalizations for GI illnesses and streamflow data. Seasonal patterns were explored using Poisson annual harmonic regression with and without adjustment for streamflow. GI illnesses demonstrated significant seasonal patterns with peak timing preceding peak timing of streamflow for all six counties. Seasonal patterns of illness remain consistent after adjusting for streamflow. This study found that the time of peak GI illness precedes the peak of streamflow, suggesting either an indirect relationship or a more direct path whereby pathogens enter water supplies prior to the peak in streamflow. Such findings call for interdisciplinary research to better understand associations among streamflow, pathogen loading, and rates of gastrointestinal illnesses.