El Niño and the shifting geography of cholera in Africa.

The El Niño Southern Oscillation (ENSO) and other climate patterns can have profound impacts on the occurrence of infectious diseases ranging from dengue to cholera. In Africa, El Niño conditions are associated with increased rainfall in East Africa and decreased rainfall in southern Africa, West Africa, and parts of the Sahel. Because of the key role of water supplies in cholera transmission, a relationship between El Niño events and cholera incidence is highly plausible, and previous research has shown a link between ENSO patterns and cholera in Bangladesh. However, there is little systematic evidence for this link in Africa. Using high-resolution mapping techniques, we find that the annual geographic distribution of cholera in Africa from 2000 to 2014 changes dramatically, with the burden shifting to continental East Africa-and away from Madagascar and portions of southern, Central, and West Africa-where almost 50,000 additional cases occur during El Niño years. Cholera incidence during El Niño years was higher in regions of East Africa with increased rainfall, but incidence was also higher in some areas with decreased rainfall, suggesting a complex relationship between rainfall and cholera incidence. Here, we show clear evidence for a shift in the distribution of cholera incidence throughout Africa in El Niño years, likely mediated by El Niño's impact on local climatic factors. Knowledge of this relationship between cholera and climate patterns coupled with ENSO forecasting could be used to notify countries in Africa when they are likely to see a major shift in their cholera risk.

A nine-year study on the benefits and risks of soil and water conservation practices in the humid highlands of Ethiopia: The Debre Mawi watershed.

A nine-year (2010-2018) field study in the Debre Mawi watershed was conducted to understand the effect of governmentally-imposed and farmer-initiated conservation practices. The watershed is in the sub-humid Ethiopian Highlands which experience high and increasing erosion rates despite years of conservation efforts. Consequently, reservoirs are filling up with sediment and soil degradation is enhanced, calling for the evaluation of conservation practices currently in use. The few past long-term experimental studies on structural practices are inconclusive. In addition, only anecdotal information is available for streamflow and sediment loss. Precipitation, stream discharge, and suspended sediment concentrations were recorded manually in the Debre Mawi watershed during the nine-year period. Groundwater depth and total saturated area measurements were taken for selected periods. From 2012 to 2014, government-mandated conservation practices were constructed, which consisted of 50-cm-deep infiltration furrows with bunds downslope. These furrows were filled in with sediment by 2018. At the same time, the acreage of eucalyptus trees planted by farmers on the most vulnerable lands tripled to 5% of the total area with most trees fully grown in 2018. Runoff coefficients and sediment concentrations decreased steadily throughout the nine years. In the saturated bottomlands, the observations suggested that government-sponsored infiltration furrows in the saturated bottomlands were ineffective and may concentrate flows and enhance gully erosion, while eucalyptus trees appear effective. The results of this observational study point to both the potential benefits of conservation practices in this sub-humid tropical highland region and to emerging long-term risks. If structural conservation is to be pursued in watersheds like Debre Mawi, due attention must be given to the safe removal of excess water from the valley bottoms. The vegetative farmer-initiated practice of planting eucalyptus trees effectively reduced streamflow and erosion, but at the same time, might dry up wells during the dry monsoon phase which should be investigated further.

Agroecosystem specific climate vulnerability analysis: application of the livelihood vulnerability index to a tropical highland region.

In topographically diverse highland terrain, socio-economic and environmental conditions can vary dramatically over relatively short distances. This presents a challenge for climate resilient development strategies, as exposure to climate variability and change, climate impacts, and adaptive capacity differ between communities located within common cultural and administrative units. The Livelihood Vulnerability Index (LVI) framed within the United Nations Intergovernmental Panel on Climate Change (IPCC) vulnerability framework (LVI-IPCC) offers a tool to assess climate vulnerability through direct household surveys. This makes it particularly appropriate for analyses at sub-community and community scales. Here we apply the LVI-IPCC to communities of Choke Mountain, located in the Blue Nile Highlands of Ethiopia. Recognizing the physiographic and climatic diversity that exists in this mountainous environment, we implement LVI-IPCC analysis for 793 mixed crop-livestock farming households using the five distinct agroecological systems (AES) that compose the populated area of Choke Mountain as a framework for analysis. For each AES, an LVI index, adaptive capacity metric, and LVI-IPCC vulnerability score was calculated. We found that each of these metrics varied systematically across AES. High elevation sloping lands and low elevation steep lands exhibited relatively low adaptive capacity and high vulnerability while midland AES had higher capacity and lower vulnerability. These results suggest that resilience building interventions for Choke Mountain ecosystems should be targeted to address the specific circumstances of each AES. The approach of applying LVI-IPCC at AES scale could be applicable to other climate vulnerable mountainous regions.

Facilitative plant interactions and climate simultaneously drive alpine plant diversity.

Interactions among species determine local-scale diversity, but local interactions are thought to have minor effects at larger scales. However, quantitative comparisons of the importance of biotic interactions relative to other drivers are rarely made at larger scales. Using a data set spanning 78 sites and five continents, we assessed the relative importance of biotic interactions and climate in determining plant diversity in alpine ecosystems dominated by nurse-plant cushion species. Climate variables related with water balance showed the highest correlation with richness at the global scale. Strikingly, although the effect of cushion species on diversity was lower than that of climate, its contribution was still substantial. In particular, cushion species enhanced species richness more in systems with inherently impoverished local diversity. Nurse species appear to act as a 'safety net' sustaining diversity under harsh conditions, demonstrating that climate and species interactions should be integrated when predicting future biodiversity effects of climate change.

Association between summertime emergency department visits and maximum daily heat index in rural and non-rural areas of Virginia (2015-2022).

There is growing evidence that high ambient temperatures are associated with a range of adverse health outcomes. Further evidence suggests differences in rural versus non-rural populations' vulnerability to heat-related adverse health outcomes. The current project aims to 1) refine estimated associations between maximum daily heat index (HI) and emergency department (ED) visits in regions of Virginia, and 2) compare associations between maximum daily HI and ED visits in rural versus non-rural areas of Virginia and within those areas, for persons 65 years of age and older versus those younger than 65 years. Our study utilized 16,873,213 healthcare visits from Virginia facilities reporting to the Virginia Department of Health syndromic surveillance system between May and September 2015-2022. Federal Office of Rural Health Policy defined rural areas were assigned to patient home ZIP code. The estimated daily maximum HI at which ED visits begin to rise varies between 25 °C and 33 °C across climate zones and regions of Virginia. Across all regions, estimated ED visits attributable to days with maximum HI above 25.7 °C were higher in rural areas (3.7%, 95% CI: 3.5%, 3.9%) versus in non-rural areas (3.1%, 95% CIs: 3.0%, 3.2%). Patients aged 0-64 years had a higher estimated heat attributable fraction of ED visits (4.2%, 95% CI: 4.0%, 4.3%) than patients 65 years and older (3.1%, 95% CI: 2.9%, 3.4%). Rural patients older than 65 have a higher estimated fraction of heat attributable ED visits (2.7%, 95% CI: 2.2%, 3.1%) compared to non-rural patients 65 years and older (1.5%, 95% CI: 1.3%, 1.8%). State-level syndromic surveillance data can be used to optimize heat warning messaging based on expected changes in healthcare visits given a set of meteorological variables, and can be further refined based on climate, rurality and age.

Health-Damaging Climate Events Highlight the Need for Interdisciplinary, Engaged Research.

In 2023 human populations experienced multiple record-breaking climate events, with widespread impacts on human health and well-being. These events include extreme heat domes, drought, severe storms, flooding, and wildfires. Due to inherent lags in the climate system, we can expect such extremes to continue for multiple decades after reaching net zero carbon emissions. Unfortunately, despite these significant current and future impacts, funding for research in climate and health has lagged behind that for other geoscience and biomedical research. While some initial efforts from funding agencies are evident, there is still a significant need to increase the resources available for multidisciplinary research in the face of this issue. As a group of experts at this important intersection, we call for a more concerted effort to encourage interdisciplinary and policy-relevant investigations into the detrimental health effects of continued climate change.

The Planetary Child Health & Enterics Observatory (Plan-EO): A protocol for an interdisciplinary research initiative and web-based dashboard for mapping enteric infectious diseases and their risk factors and interventions in LMICs.

BACKGROUND: Diarrhea remains a leading cause of childhood illness throughout the world that is increasing due to climate change and is caused by various species of ecologically sensitive pathogens. The emerging Planetary Health movement emphasizes the interdependence of human health with natural systems, and much of its focus has been on infectious diseases and their interactions with environmental and human processes. Meanwhile, the era of big data has engendered a public appetite for interactive web-based dashboards for infectious diseases. However, enteric infectious diseases have been largely overlooked by these developments. METHODS: The Planetary Child Health & Enterics Observatory (Plan-EO) is a new initiative that builds on existing partnerships between epidemiologists, climatologists, bioinformaticians, and hydrologists as well as investigators in numerous low- and middle-income countries. Its objective is to provide the research and stakeholder community with an evidence base for the geographical targeting of enteropathogen-specific child health interventions such as novel vaccines. The initiative will produce, curate, and disseminate spatial data products relating to the distribution of enteric pathogens and their environmental and sociodemographic determinants. DISCUSSION: As climate change accelerates there is an urgent need for etiology-specific estimates of diarrheal disease burden at high spatiotemporal resolution. Plan-EO aims to address key challenges and knowledge gaps by making and disseminating rigorously obtained, generalizable disease burden estimates. Pre-processed environmental and EO-derived spatial data products will be housed, continually updated, and made publicly available for download to the research and stakeholder communities. These can then be used as inputs to identify and target priority populations living in transmission hotspots and for decision-making, scenario-planning, and disease burden projection. STUDY REGISTRATION: PROSPERO protocol #CRD42023384709.

Study protocol: improving response to malaria in the Amazon through identification of inter-community networks and human mobility in border regions of Ecuador, Peru and Brazil.

INTRODUCTION: Understanding human mobility's role in malaria transmission is critical to successful control and elimination. However, common approaches to measuring mobility are ill-equipped for remote regions such as the Amazon. This study develops a network survey to quantify the effect of community connectivity and mobility on malaria transmission. METHODS: We measure community connectivity across the study area using a respondent driven sampling design among key informants who are at least 18 years of age. 45 initial communities will be selected: 10 in Brazil, 10 in Ecuador and 25 in Peru. Participants will be recruited in each initial node and administered a survey to obtain data on each community's mobility patterns. Survey responses will be ranked and the 2-3 most connected communities will then be selected and surveyed. This process will be repeated for a third round of data collection. Community network matrices will be linked with each country's malaria surveillance system to test the effects of mobility on disease risk. ETHICS AND DISSEMINATION: This study protocol has been approved by the institutional review boards of Duke University (USA), Universidad San Francisco de Quito (Ecuador), Universidad Peruana Cayetano Heredia (Peru) and Universidade Federal Minas Gerais (Brazil). Results will be disseminated in communities by the end of the study.

Adverse Health Outcomes Following Hurricane Harvey: A Comparison of Remotely-Sensed and Self-Reported Flood Exposure Estimates.

Remotely sensed inundation may help to rapidly identify areas in need of aid during and following floods. Here we evaluate the utility of daily remotely sensed flood inundation measures and estimate their congruence with self-reported home flooding and health outcomes collected via the Texas Flood Registry (TFR) following Hurricane Harvey. Daily flood inundation for 14 days following the landfall of Hurricane Harvey was acquired from FloodScan. Flood exposure, including number of days flooded and flood depth was assigned to geocoded home addresses of TFR respondents (N = 18,920 from 47 counties). Discordance between remotely-sensed flooding and self-reported home flooding was measured. Modified Poisson regression models were implemented to estimate risk ratios (RRs) for adverse health outcomes following flood exposure, controlling for potential individual level confounders. Respondents whose home was in a flooded area based on remotely-sensed data were more likely to report injury (RR = 1.5, 95% CI: 1.27-1.77), concentration problems (1.36, 95% CI: 1.25-1.49), skin rash (1.31, 95% CI: 1.15-1.48), illness (1.29, 95% CI: 1.17-1.43), headaches (1.09, 95% CI: 1.03-1.16), and runny nose (1.07, 95% CI: 1.03-1.11) compared to respondents whose home was not flooded. Effect sizes were larger when exposure was estimated using respondent-reported home flooding. Near-real time remote sensing-based flood products may help to prioritize areas in need of assistance when on the ground measures are not accessible.

Increased photosynthesis during spring drought in energy-limited ecosystems.

Drought is often thought to reduce ecosystem photosynthesis. However, theory suggests there is potential for increased photosynthesis during meteorological drought, especially in energy-limited ecosystems. Here, we examine the response of photosynthesis (gross primary productivity, GPP) to meteorological drought across the water-energy limitation spectrum. We find a consistent increase in eddy covariance GPP during spring drought in energy-limited ecosystems (83% of the energy-limited sites). Half of spring GPP sensitivity to precipitation was predicted solely from the wetness index (R2 = 0.47, p < 0.001), with weaker relationships in summer and fall. Our results suggest GPP increases during spring drought for 55% of vegetated Northern Hemisphere lands ( >30° N). We then compare these results to terrestrial biosphere model outputs and remote sensing products. In contrast to trends detected in eddy covariance data, model mean GPP always declined under spring precipitation deficits after controlling for air temperature and light availability. While remote sensing products captured the observed negative spring GPP sensitivity in energy-limited ecosystems, terrestrial biosphere models proved insufficiently sensitive to spring precipitation deficits.

Agreement in extreme precipitation exposure assessment is modified by race and social vulnerability.

Epidemiologic investigations of extreme precipitation events (EPEs) often rely on observations from the nearest weather station to represent individuals' exposures, and due to structural factors that determine the siting of weather stations, levels of measurement error and misclassification bias may differ by race, class, and other measures of social vulnerability. Gridded climate datasets provide higher spatial resolution that may improve measurement error and misclassification bias. However, similarities in the ability to identify EPEs among these types of datasets have not been explored. In this study, we characterize the overall and temporal patterns of agreement among three commonly used meteorological data sources in their identification of EPEs in all census tracts and counties in the conterminous United States over the 1991-2020 U.S. Climate Normals period and evaluate the association between sociodemographic characteristics with agreement in EPE identification. Daily precipitation measurements from weather stations in the Global Historical Climatology Network (GHCN) and gridded precipitation estimates from the Parameter-elevation Relationships on Independent Slopes Model (PRISM) and the North American Land Data Assimilation System (NLDAS) were compared in their ability to identify EPEs defined as the top 1% of precipitation events or daily precipitation >1 inch. Agreement among these datasets is fair to moderate from 1991 to 2020. There are spatial and temporal differences in the levels of agreement between ground stations and gridded climate datasets in their detection of EPEs in the United States from 1991 to 2020. Spatial variation in agreement is most strongly related to a location's proximity to the nearest ground station, with areas furthest from a ground station demonstrating the lowest levels of agreement. These areas have lower socioeconomic status, a higher proportion of Native American population, and higher social vulnerability index scores. The addition of ground stations in these areas may increase agreement, and future studies intending to use these or similar data sources should be aware of the limitations, biases, and potential for differential misclassification of exposure to EPEs. Most importantly, vulnerable populations should be engaged to determine their priorities for enhanced surveillance of climate-based threats so that community-identified needs are met by any future improvements in data quality.

Evolving Drivers of Brazilian SARS-CoV-2 Transmission: A Spatiotemporally Disaggregated Time Series Analysis of Meteorology, Policy, and Human Mobility.

Brazil has been severely affected by the COVID-19 pandemic. Temperature and humidity have been purported as drivers of SARS-CoV-2 transmission, but no consensus has been reached in the literature regarding the relative roles of meteorology, governmental policy, and mobility on transmission in Brazil. We compiled data on meteorology, governmental policy, and mobility in Brazil's 26 states and one federal district from June 2020 to August 2021. Associations between these variables and the time-varying reproductive number (R t ) of SARS-CoV-2 were examined using generalized additive models fit to data from the entire 15-month period and several shorter, 3-month periods. Accumulated local effects and variable importance metrics were calculated to analyze the relationship between input variables and R t . We found that transmission is strongly influenced by unmeasured sources of between-state heterogeneity and the near-recent trajectory of the pandemic. Increased temperature generally was associated with decreased transmission and increased specific humidity with increased transmission. However, the impacts of meteorology, policy, and mobility on R t varied in direction, magnitude, and significance across our study period. This time variance could explain inconsistencies in the published literature to date. While meteorology weakly modulates SARS-CoV-2 transmission, daily or seasonal weather variations alone will not stave off future surges in COVID-19 cases in Brazil. Investigating how the roles of environmental factors and disease control interventions may vary with time should be a deliberate consideration of future research on the drivers of SARS-CoV-2 transmission.

Effects of hydrometeorological and other factors on SARS-CoV-2 reproduction number in three contiguous countries of tropical Andean South America: a spatiotemporally disaggregated time series analysis.

Background: The COVID-19 pandemic has caused societal disruption globally, and South America has been hit harder than other lower-income regions. This study modeled the effects of six weather variables on district-level SARS-CoV-2 reproduction numbers (Rt ) in three contiguous countries of tropical Andean South America (Colombia, Ecuador, and Peru), adjusting for environmental, policy, healthcare infrastructural and other factors. Methods: Daily time-series data on SARS-CoV-2 infections were sourced from the health authorities of the three countries at the smallest available administrative level. Rt values were calculated and merged by date and unit ID with variables from a unified COVID-19 dataset and other publicly available sources for May-December, 2020. Generalized additive models were fitted. Findings: Relative humidity and solar radiation were inversely associated with SARS-CoV-2 Rt . Days with radiation above 1000 kJ/m2 saw a 1.3% reduction in Rt , and those with humidity above 50% recorded a 0.9% reduction in Rt . Transmission was highest in densely populated districts, and lowest in districts with poor healthcare access and on days with lowest population mobility. Wind speed, temperature, region, aggregate government policy response, and population age structure had little impact. The fully adjusted model explained 4.3% of Rt variance. Interpretation: Dry atmospheric conditions of low humidity increase district-level SARS-CoV-2 reproduction numbers, while higher levels of solar radiation decrease district-level SARS-CoV-2 reproduction numbers - effects that are comparable in magnitude to population factors like lockdown compliance. Weather monitoring could be incorporated into disease surveillance and early warning systems in conjunction with more established risk indicators and surveillance measures. Funding: NASA's Group on Earth Observations Work Programme (16-GEO16-0047).

The impact of sustained malaria control in the Loreto region of Peru: a retrospective, observational, spatially-varying interrupted time series analysis of the PAMAFRO program.

Background: Although malaria control investments worldwide have resulted in dramatic declines in transmission since 2000, progress has stalled. In the Amazon, malaria resurgence has followed withdrawal of Global Fund support of the Project for Malaria Control in Andean Border Areas (PAMAFRO). We estimate intervention-specific and spatially-explicit effects of the PAMAFRO program on malaria incidence across the Loreto region of Peru, and consider the influence of the environmental risk factors in the presence of interventions. Methods: We conducted a retrospective, observational, spatial interrupted time series analysis of malaria incidence rates among people reporting to health posts across Loreto, Peru between the first epidemiological week of January 2001 and the last epidemiological week of December 2016. Model inference is at the smallest administrative unit (district), where the weekly number of diagnosed cases of Plasmodium vivax and Plasmodium falciparum were determined by microscopy. Census data provided population at risk. We include as covariates weekly estimates of minimum temperature and cumulative precipitation in each district, as well as spatially- and temporally-lagged malaria incidence rates. Environmental data were derived from a hydrometeorological model designed for the Amazon. We used Bayesian spatiotemporal modeling techniques to estimate the impact of the PAMAFRO program, variability in environmental effects, and the role of climate anomalies on transmission after PAMAFRO withdrawal. Findings: During the PAMAFRO program, incidence of P. vivax declined from 42.8 to 10.1 cases/1000 people/year. Incidence for P. falciparum declined from 14.3 to 2.5 cases/1000 people/year over this same period. The effects of PAMAFRO-supported interventions varied both by geography and species of malaria. Interventions were only effective in districts where interventions were also deployed in surrounding districts. Further, interventions diminished the effects of other prevailing demographic and environmental risk factors. Withdrawal of the program led to a resurgence in transmission. Increasing minimum temperatures and variability and intensity of rainfall events from 2011 onward and accompanying population displacements contributed to this resurgence. Interpretation: Malaria control programs must consider the climate and environmental scope of interventions to maximize effectiveness. They must also ensure financial sustainability to maintain local progress and commitment to malaria prevention and elimination efforts, as well as to offset the effects of environmental change that increase transmission risk. Funding: National Aeronautics and Space Administration, National Institutes of Health, Bill and Melinda Gates Foundation.

Climate change, the environment, and rhinologic disease.

BACKGROUND: The escalating negative impact of climate change on our environment has the potential to result in significant morbidity of rhinologic diseases. METHODS: Evidence based review of examples of rhinologic diseases including allergic and nonallergic rhinitis, chronic rhinosinusitis, and allergic fungal rhinosinusitis was performed. RESULTS: The lower socioeconomic population, including historically oppressed groups, will be disproportionately affected. CONCLUSIONS: We need a systematic approach to improve healthcare database infrastructure and funding to promote diverse scientific collaboration to address these healthcare needs.

Unified real-time environmental-epidemiological data for multiscale modeling of the COVID-19 pandemic.

An impressive number of COVID-19 data catalogs exist. However, none are fully optimized for data science applications. Inconsistent naming and data conventions, uneven quality control, and lack of alignment between disease data and potential predictors pose barriers to robust modeling and analysis. To address this gap, we generated a unified dataset that integrates and implements quality checks of the data from numerous leading sources of COVID-19 epidemiological and environmental data. We use a globally consistent hierarchy of administrative units to facilitate analysis within and across countries. The dataset applies this unified hierarchy to align COVID-19 epidemiological data with a number of other data types relevant to understanding and predicting COVID-19 risk, including hydrometeorological data, air quality, information on COVID-19 control policies, vaccine data, and key demographic characteristics.

Network Profile: Improving Response to Malaria in the Amazon through Identification of Inter-Community Networks and Human Mobility in Border Regions of Ecuador, Peru, and Brazil.

Objectives: Understanding human mobility's role on malaria transmission is critical to successful control and elimination. However, common approaches to measuring mobility are ill-equipped for remote regions such as the Amazon. This study develops a network survey to quantify the effect of community connectivity and mobility on malaria transmission. Design: A community-level network survey. Setting: We collect data on community connectivity along three river systems in the Amazon basin: the Pastaza river corridor spanning the Ecuador-Peru border; and the Amazon and Javari river corridors spanning the Brazil-Peru border. Participants: We interviewed key informants in Brazil, Ecuador, and Peru, including from indigenous communities: Shuar, Achuar, Shiwiar, Kichwa, Ticuna, and Yagua. Key informants are at least 18 years of age and are considered community leaders. Primary outcome: Weekly, community-level malaria incidence during the study period. Methods: We measure community connectivity across the study area using a respondent driven sampling design. Forty-five communities were initially selected: 10 in Brazil, 10 in Ecuador, and 25 in Peru. Participants were recruited in each initial node and administered a survey to obtain data on each community's mobility patterns. Survey responses were ranked and the 2-3 most connected communities were then selected and surveyed. This process was repeated for a third round of data collection. Community network matrices will be linked with eadch country's malaria surveillance system to test the effects of mobility on disease risk. Findings: To date, 586 key informants were surveyed from 126 communities along the Pastaza river corridor. Data collection along the Amazon and Javari river corridors is ongoing. Initial results indicate that network sampling is a superior method to delineate migration flows between communities. Conclusions: Our study provides measures of mobility and connectivity in rural settings where traditional approaches are insufficient, and will allow us to understand mobility's effect on malaria transmission.

Assimilation of Remotely Sensed Leaf Area Index Enhances the Estimation of Anthropogenic Irrigation Water Use.

Representation of irrigation in Earth System Models has advanced over the past decade, yet large uncertainties persist in the effective simulation of irrigation practices, particularly over locations where the on-ground practices and climate impacts are less reliably known. Here we investigate the utility of assimilating remotely sensed vegetation data for improving irrigation water use and associated fluxes within a land surface model. We show that assimilating optical sensor-based leaf area index estimates significantly improves the simulation of irrigation water use when compared to the USGS ground reports. For heavily irrigated areas, assimilation improves the evaporative fluxes and gross primary production (GPP) simulations, with the median correlation increasing by 0.1-1.1 and 0.3-0.6, respectively, as compared to the reference datasets. Further, bias improvements in the range of 14-35 mm mo-1 and 10-82 g m-2 mo-1 are obtained in evaporative fluxes and GPP as a result of incorporating vegetation constraints, respectively. These results demonstrate that the use of remotely sensed vegetation data is an effective, observation-informed, globally applicable approach for simulating irrigation and characterizing its impacts on water and carbon states.

Estimating changes in emergency department visits associated with floods caused by Tropical Storm Imelda using satellite observations and syndromic surveillance.

BACKGROUND: Satellite observations following flooding coupled with electronic health data collected through syndromic surveillance systems (SyS) may be useful in efficiently characterizing and responding to health risks associated with flooding. RESULTS: There was a 10% (95% Confidence Interval (CI): 1%-19%) increase in asthma related ED visits and 22% (95% CI: 5%-41%) increase in insect bite related ED visits in the flooded ZCTAs compared to non-flooded ZCTAs during the flood period. One month following the floods, diarrhea related ED visits were increased by 15% (95% CI: 4%-27%) for flooded ZCTAs and children and adolescents from flooded ZCTAs had elevated risk for dehydration related ED visits. During the protracted period (2-3 months after the flood period), the risk for asthma, insect bite, and diarrhea related ED visits were elevated among the flooded ZCTAs. Effect modification by reported age, ethnicity and race was observed. CONCLUSION: Combining satellite observations with SyS data can be helpful in characterizing the location and timing of environmentally mediated adverse health outcomes, which may be useful for refining disaster resilience measures to mitigate health outcomes following flooding.