Power outage prediction using data streams: An adaptive ensemble learning approach with a feature- and performance-based weighting mechanism.

A wide variety of weather conditions, from windstorms to prolonged heat events, can substantially impact power systems, posing many risks and inconveniences due to power outages. Accurately estimating the probability distribution of the number of customers without power using data about the power utility system and environmental and weather conditions can help utilities restore power more quickly and efficiently. However, the critical shortcoming of current models lies in the difficulties of handling (i) data streams and (ii) model uncertainty due to combining data from various weather events. Accordingly, this article proposes an adaptive ensemble learning algorithm for data streams, which deploys a feature- and performance-based weighting mechanism to adaptively combine outputs from multiple competitive base learners. As a proof of concept, we use a large, real data set of daily customer interruptions to develop the first adaptive all-weather outage prediction model using data streams. We benchmark several approaches to demonstrate the advantage of our approach in offering more accurate probabilistic predictions. The results show that the proposed algorithm reduces the probabilistic predictions' error of the base learners between 4% and 22% with an average of 8%, which also result in substantially more accurate point predictions. The improvement made by our algorithm is enhanced as we exchange base learners with simpler models.

Identifying power outage hotspots to support risk management planning.

Secure and reliable power systems are vital for modern societies and economies. While there is a focus in the literature on predicting power outages caused by severe weather events, relatively little literature exists on identifying hot spots, locations where outages occur repeatedly and at a higher rate than expected. Reliably identifying hotspots can provide critical input for risk management efforts by power utilities, helping them to focus scarce resources on the most problematic portions of their system. In this article, we show how existing work on Moran's I spatial statistic can be adapted to identify power outage hotspots based on the types and quantities of data available to utilities in practice. The local Moran's I statistic was calculated on a grid cell level and a set of criteria were used to filter out which grid cells are considered hotspots. The hotspot identification approach utilized in this article is an easy method for utilities to use in practice, and it provides the type of information needed to directly support utility decisions about prioritizing areas of a power system to inspect and potentially reinforce.

Machine Learning Model Development to Predict Power Outage Duration (POD): A Case Study for Electric Utilities.

In the face of increasing climate variability and the complexities of modern power grids, managing power outages in electric utilities has emerged as a critical challenge. This paper introduces a novel predictive model employing machine learning algorithms, including decision tree (DT), random forest (RF), k-nearest neighbors (KNN), and extreme gradient boosting (XGBoost). Leveraging historical sensors-based and non-sensors-based outage data from a Turkish electric utility company, the model demonstrates adaptability to diverse grid structures, considers meteorological and non-meteorological outage causes, and provides real-time feedback to customers to effectively address the problem of power outage duration. Using the XGBoost algorithm with the minimum redundancy maximum relevance (MRMR) feature selection attained 98.433% accuracy in predicting outage durations, better than the state-of-the-art methods showing 85.511% accuracy on average over various datasets, a 12.922% improvement. This paper contributes a practical solution to enhance outage management and customer communication, showcasing the potential of machine learning to transform electric utility responses and improve grid resilience and reliability.

The immediate effects of winter storms and power outages on multiple health outcomes and the time windows of vulnerability.

BACKGROUND: While most prior research has focused on extreme heat, few assessed the immediate health effects of winter storms and associated power outages (PO), although severe storms have become more frequent. This study evaluates the joint and independent health effects of winter storms and PO, snow versus ice-storm, effects by time window (peak timing, winter/transitional months) and the impacts on critical care indicators including numbers of comorbidity, procedure, length of stay and cost. METHODS: We use distributed lag nonlinear models to assess the impacts of winter storm/PO on hospitalizations due to cardiovascular, lower respiratory diseases (LRD), respiratory infections, food/water-borne diseases (FWBD) and injuries in New York State on 0-6 lag days following storm/PO compared with non-storm/non-PO periods (references), while controlling for time-varying factors and PM2.5. The storm-related hospitalizations are described by time window. We also calculate changes in critical care indicators between the storm/PO and control periods. RESULTS: We found the joint effects of storm/PO are the strongest (risk ratios (RR) range: 1.01-1.90), followed by that of storm alone (1.02-1.39), but not during PO alone. Ice storms have stronger impacts (RRs: 1.04-3.15) than snowstorms (RRs: 1.03-2.21). The storm/PO-health associations, which occur immediately, and some last a whole week, are stronger in FWBD, October/November, and peak between 3:00-8:00 p.m. Comorbidity and medical costs significantly increase after storm/PO. CONCLUSION: Winter storms increase multiple diseases, comorbidity and medical costs, especially when accompanied by PO or ice storms. Early warnings and prevention may be critical in the transitional months and afternoon rush hours.

Power outage mediates the associations between major storms and hospital admission of chronic obstructive pulmonary disease.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is the third-leading cause of death worldwide with continuous rise. Limited studies indicate that COPD was associated with major storms and related power outages (PO). However, significant gaps remain in understanding what PO's role is on the pathway of major storms-COPD. This study aimed to examine how PO mediates the major storms-COPD associations. METHODS: In this time-series study, we extracted all hospital admissions with COPD as the principal diagnosis in New York, 2001-2013. Using distributed lag nonlinear models, the hospitalization rate during major storms and PO was compared to non-major storms and non-PO periods to determine the risk ratios (RRs) for COPD at each of 0-6 lag days respectively after controlling for time-varying confounders and concentration of fine particulate matter (PM2.5). We then used Granger mediation analysis for time series to assess the mediation effect of PO on the major storms-COPD associations. RESULTS: The RRs of COPD hospitalization following major storms, which mainly included flooding, thunder, hurricane, snow, ice, and wind, were 1.23 to 1.49 across lag 0-6 days. The risk was strongest at lag3 and lasted significantly for 4 days. Compared with non-outage periods, the PO period was associated with 1.23 to 1.61 higher risk of COPD admissions across lag 0-6 days. The risk lasted significantly for 2 days and was strongest at lag2. Snow, hurricane and wind were the top three contributors of PO among the major storms. PO mediated as much as 49.6 to 65.0% of the major storms-COPD associations. CONCLUSIONS: Both major storms and PO were associated with increased hospital admission of COPD. PO mediated almost half of the major storms-COPD hospitalization associations. Preparation of surrogate electric system before major storms is essential to reduce major storms-COPD hospitalization.

Quantifying Community Resilience Using Hierarchical Bayesian Kernel Methods: A Case Study on Recovery from Power Outages.

The ability to accurately measure recovery rate of infrastructure systems and communities impacted by disasters is vital to ensure effective response and resource allocation before, during, and after a disruption. However, a challenge in quantifying such measures resides in the lack of data as community recovery information is seldom recorded. To provide accurate community recovery measures, a hierarchical Bayesian kernel model (HBKM) is developed to predict the recovery rate of communities experiencing power outages during storms. The performance of the proposed method is evaluated using cross-validation and compared with two models, the hierarchical Bayesian regression model and the Poisson generalized linear model. A case study focusing on the recovery of communities in Shelby County, Tennessee after severe storms between 2007 and 2017 is presented to illustrate the proposed approach. The predictive accuracy of the models is evaluated using the log-likelihood and root mean squared error. The HBKM yields on average the highest out-of-sample predictive accuracy. This approach can help assess the recoverability of a community when data are scarce and inform decision making in the aftermath of a disaster. An illustrative example is presented demonstrating how accurate measures of community resilience can help reduce the cost of infrastructure restoration.

Power Outage Preparedness and Concern among Vulnerable New York City Residents.

Power outages can impact health, and certain populations may be more at risk. Personal preparedness may reduce impacts, but information on power outage preparedness and risk perception among vulnerable populations is limited. We examined power outage preparedness and concern among New York City residents, including vulnerable populations defined as older adults (≥ 65 years), and respondents with household members who require assistance with daily activities or depend on electric medical devices. A random sample telephone survey was conducted during November-December 2016. Preparedness was defined as having a three-day supply of drinking water, non-perishable food, and a working flashlight. Among all respondents (n = 887), 58% were prepared and 46% expressed concern about health. Respondents with electric-dependent household members (9% of all respondents) tended to have higher preparedness (70 vs. 56% of respondents without electric-dependent household members). Among this group, only 40% reported being registered with a utility company to receive early notification of outages. While the subgroup sample was small, respondents with registered electric-dependent household members had lower preparedness than those with non-registered users (59 vs. 76%). Respondents with household members who needed assistance had comparable levels of preparedness to respondents without someone who needed assistance (59 vs. 57%). Older adults had greater preparedness than younger adults (65 vs. 56%). Health concerns were greater among all vulnerable groups than the general population. Levels of preparedness varied among vulnerable respondents, and awareness of power outage notification programs was low. Our findings highlight the need to increase awareness and preparedness among at-risk people.

Power outages and refrigerated medicines: The need for better guidelines, awareness and planning.

WHAT IS KNOWN AND OBJECTIVE: Vaccines and other pharmaceuticals are essential medical supplies that require continuous storage at specific temperatures to maintain viability. Power outages can lead to a break in the cold chain, resulting in the degradation of essential medicines. COMMENT: After a power outage, the stability of vaccines and other medicines can be difficult to ascertain. Many public health guidelines therefore recommend discarding potentially compromised pharmaceuticals unless the cold chain can be guaranteed-a costly endeavour. There are government guidelines aimed at minimizing exposure to high temperatures in the event of a power outage; however, the usefulness of these guidelines is uncertain. WHAT IS NEW AND CONCLUSION: The actual cost of vaccine and pharmaceutical loss due to a break in the cold chain is poorly studied and requires further research. Additional recommendations regarding the stability of specific medicines would also be a valuable resource.

Power outage estimation for tropical cyclones: improved accuracy with simpler models.

In this article, we discuss an outage-forecasting model that we have developed. This model uses very few input variables to estimate hurricane-induced outages prior to landfall with great predictive accuracy. We also show the results for a series of simpler models that use only publicly available data and can still estimate outages with reasonable accuracy. The intended users of these models are emergency response planners within power utilities and related government agencies. We developed our models based on the method of random forest, using data from a power distribution system serving two states in the Gulf Coast region of the United States. We also show that estimates of system reliability based on wind speed alone are not sufficient for adequately capturing the reliability of system components. We demonstrate that a multivariate approach can produce more accurate power outage predictions.

[Nationwide Questionnaire Survey on Disaster Power Outage Countermeasures in the Radiology Departments of Hospitals].

PURPOSE: To identify the countermeasures and current status of disaster power outages in the radiology departments of hospitals. METHODS: A web-based questionnaire survey of 600 hospitals nationwide was conducted. The questionnaire survey covered 34 items, including availability of power in the radiology department in the event of a disaster and the impact of power outages on medical equipment in the radiology department. RESULTS: In all, 242 facilities (40.3%) responded to our survey. During power outages, 55.8%-68.2% of facilities were able to use CT, digital radiography, and angiography systems with their private generators. In 28.1%-40.7% of facilities, medical information systems were not available in all laboratories. In addition, power outages caused equipment malfunctions in 81.4% of facilities' radiology departments. CONCLUSION: We have identified the power supplied by private generators to the radiology department's medical equipment and medical information systems. Many medical equipment have malfunctioned due to power outages. Therefore, drills should be conducted to simulate various situations caused by power outages.

Heat-related mortality and ambulance transport after a power outage in the Tokyo metropolitan area.

Background: Air conditioners can prevent heat-related illness and mortality, but the increased use of air conditioners may enhance susceptibility to heat-related illnesses during large-scale power failures. Here, we examined the risks of heat-related illness ambulance transport (HIAT) and mortality associated with typhoon-related electricity reduction (ER) in the summer months in the Tokyo metropolitan area. Methods: We conducted event study analyses to compare temperature-HIAT and mortality associations before and after the power outage (July to September 2019). To better understand the role of temperature during the power outage, we then examined whether the temperature-HIAT and mortality associations were modified by different power outage levels (0%, 10%, and 20% ER). We computed the ratios of relative risks to compare the risks associated with various ER values to the risks associated without ER. Results: We analyzed the data of 14,912 HIAT cases and 74,064 deaths. Overall, 93,200 power outage cases were observed when the typhoon hit. Event study results showed that the incidence rate ratio was 2.01 (95% confidence interval [CI] = 1.42, 2.84) with effects enduring up to 6 days, and 1.11 (95% CI = 1.02, 1.22) for mortality on the first 3 days after the typhoon hit. Comparing 20% to 0% ER, the ratios of relative risks of heat exposure were 2.32 (95% CI = 1.41, 3.82) for HIAT and 0.95 (95% CI = 0.75, 1.22) for mortality. Conclusions: A 20% ER was associated with a two-fold greater risk of HIAT because of summer heat during the power outage, but there was little evidence for the association with all-cause mortality.

Optimal real-time power dispatch of power grid with wind energy forecasting under extreme weather.

With breakthroughs in the power electronics industry, the stability and rapid power regulation of wind power generation have been improved. Its power generation technology is becoming more and more mature. However, there are still weaknesses in the operation and control of power systems under the influence of extreme weather events, especially in real-time power dispatch. To optimally distribute the power of the regulation resources in a more stable manner, a wind energy forecasting-based power dispatch model with time-control intervals optimization is proposed. In this model, the outage of the wind energy under extreme weather is analyzed by an autoregressive integrated moving average model (ARIMA). Additionally, the other regulation resources are used to balance the corresponding wind power drop and power mismatch. Meanwhile, an algorithm names weighted mean of vectors (INFO) is employed to solve the real-time power dispatch and minimize the power deviation between the power command and real output. Lastly, the performance of the proposed optimal real-time power dispatch is executed in a simulation model with ten regulation resources. The simulation tests show that the combination of ARIMA and INFO can effectively improve the power control performance of the PD-WEF system.

The 2021 Texas Power Crisis: distribution, duration, and disparities.

BACKGROUND: Precipitated by an unusual winter storm, the 2021 Texas Power Crisis lasted February 10 to 27 leaving millions of customers without power. Such large-scale outages can have severe health consequences, especially among vulnerable subpopulations such as those reliant on electricity to power medical equipment, but limited studies have evaluated sociodemographic disparities associated with outages. OBJECTIVE: To characterize the 2021 Texas Power Crisis in relation to distribution, duration, preparedness, and issues of environmental justice. METHODS: We used hourly Texas-wide county-level power outage data to estimate geographic clustering and association between outage exposure (distribution and duration) and six measures of racial, social, political, and/or medical vulnerability: Black and Hispanic populations, the Centers for Disease Control and Prevention (CDC) Social Vulnerability Index (SVI), Medicare electricity-dependent durable medical equipment (DME) usage, nursing homes, and hospitals. To examine individual-level experience and preparedness, we used a preexisting and non-representative internet survey. RESULTS: At the peak of the Texas Power Crisis, nearly 1/3 of customers statewide (N = 4,011,776 households/businesses) lost power. We identified multiple counties that faced a dual burden of racial/social/medical vulnerability and power outage exposure, after accounting for multiple comparisons. County-level spatial analyses indicated that counties where more Hispanic residents resided tended to endure more severe outages (OR = 1.16, 95% CI: 1.02, 1.40). We did not observe socioeconomic or medical disparities. With individual-level survey data among 1038 respondents, we found that Black respondents were more likely to report outages lasting 24+ hours and that younger individuals and those with lower educational attainment were less likely to be prepared for outages. SIGNIFICANCE: Power outages can be deadly, and medically vulnerable, socioeconomically vulnerable, and marginalized groups may be disproportionately impacted or less prepared. Climate and energy policy must equitably address power outages, future grid improvements, and disaster preparedness and management.

Cascading disasters and mental health: The February 2021 winter storm and power crisis in Texas, USA.

In February 2021, the state of Texas and large parts of the US were affected by a severe cold air outbreak and winter weather event. This event resulted in large-scale power outages and cascading impacts, including limited access to potable water, multiple days without electricity, and large-scale infrastructure damage. Little is known about the mental health implications of these events, as most research has predominantly focused on the mental health effects of exposure to hurricanes, wildfires, or other natural disasters that are more commonly found in the summer months. This study aimed to analyze the crisis responses from the 2021 winter weather event in Texas using Crisis Text Line, a text-based messaging service that provides confidential crisis counseling nationwide. To date, Crisis Text Line is the largest national crisis text service, with over 8 million crisis conversations since its inception in 2013. We employed multiple analytic techniques, including segmented regression, interrupted time series, autoregressive integrated moving average (ARIMA), and difference-in-difference (DID), to investigate distinct time periods of exposure for all crisis conversations. ARIMA and DID were further utilized to examine specific crisis outcomes, including depression, stress/anxiety, and thoughts of suicide. Results found increases in total crisis conversations and for thoughts of suicide after the initial winter weather event; however, crisis outcomes varied in time. Thoughts of suicide in high-impact regions were higher across multiple time periods (e.g., 4-weeks, 3-months, 6-months, 9-months and 11-months) compared to low-impact regions and were elevated compared to pre-event time periods for 6-months and 11-months from the initial event. Total crisis volume also remained elevated for high-impact regions compared to low-impact regions up to 11-months after the beginning of the winter event. Our work highlights that cascading winter weather events, like the Texas 2021 Winter storm, negatively impacted mental health. Future research is needed across different disaster types (e.g., cascading, concurrent events) and for specific crisis outcomes (e.g., depression, suicidal ideation) to understand the optimal timing of crisis intervention post-disaster.

A Friend in Need Is a Friend Indeed? Analysis of the Willingness to Share Self-Produced Electricity During a Long-lasting Power Outage.

Will private households owning a photovoltaic system share their electricity during a long-lasting power outage? Prior research has shown that our energy systems need to become more resilient by using dispersed energy sources-a role that could well be performed by these private photovoltaic systems, but only if their owners decide to share the produced electricity, and not consume it themselves. Considering the potential of this approach, it is indispensable to better understand incentives and motives that facilitate such cooperative behaviour. Drawing on theories of social dilemmas as well as prosocial behaviour, we hypothesize that both, structural solutions such as increased rewards as well as individual motives such as empathy-elicited altruism and norms predict cooperation. We test these hypotheses against a dataset of 80 households in Germany which were asked about their sharing behaviour towards four different recipient groups. We show that the effectiveness of motives differs significantly across recipient groups: Individual (intrinsic) motivations such as empathy-elicited altruism and altruistic norms serve as a strong predictor for cooperative behaviour towards related recipients as well as critical infrastructure, whereas higher rewards partially even reduce cooperation depending on the donor's social value orientation. For the recipient groups neighbours and public infrastructure, no significant effect for any of the tested incentives is found. Contributing to literature on social dilemmas and energy resilience, these results demonstrate the relevance of individual rather than structural incentives for electricity sharing during a power outage to render our energy provision more resilient. Practical implications for policymakers are given.

How neighborhood environment modified the effects of power outages on multiple health outcomes in New York state?

Background: Although power outage (PO) is one of the most important consequences of increasing weather extremes and the health impact of POs has been reported previously, studies on the neighborhood environment underlying the population vulnerability in such situations are limited. This study aimed to identify dominant neighborhood environmental predictors which modified the impact of POs on multiple health outcomes in New York State. Methods: We applied a two-stage approach. In the first stage, we used time series analysis to determine the impact of POs (versus non-PO periods) on multiple health outcomes in each power operating division in New York State, 2001-2013. In the second stage, we classified divisions as risk-elevated and non-elevated, then developed predictive models for the elevation status based on 36 neighborhood environmental factors using random forest and gradient boosted trees. Results: Consistent across different outcomes, we found predictors representing greater urbanization, particularly, the proportion of residents having access to public transportation (importance ranging from 4.9-15.6%), population density (3.3-16.1%), per capita income (2.3-10.7%), and the density of public infrastructure (0.8-8.5%), were associated with a higher possibility of risk elevation following power outages. Additionally, the percent of minority (-6.3-27.9%) and those with limited English (2.2-8.1%), the percent of sandy soil (6.5-11.8%), and average soil temperature (3.0-15.7%) were also dominant predictors for multiple outcomes. Spatial hotspots of vulnerability generally were located surrounding New York City and in the northwest, the pattern of which was consistent with socioeconomic status. Conclusion: Population vulnerability during power outages was dominated by neighborhood environmental factors representing greater urbanization.

Blackout and supply chains: Cross-structural ripple effect, performance, resilience and viability impact analysis.

Increased electricity consumption along with the transformations of the energy systems and interruptions in energy supply can lead to a blackout, i.e., the total loss of power in an area (or a set of areas) of a longer duration. This disruption can be fatal for production, logistics, and retail operations. Depending on the scope of the affected areas and the blackout duration, supply chains (SC) can be impacted to different extent. In this study, we perform a simulation analysis using anyLogistix digital SC twin to identify potential impacts of blackouts on SCs for scenarios of different severity. Distinctively, we triangulate the design and evaluation of experiments with consideration of SC performance, resilience, and viability. The results allow for some generalizations. First, we conceptualize blackout as a special case of SC risks which is distinctively characterized by a simultaneous shutdown of several SC processes, disruption propagations (i.e., the ripple effect), and a danger of viability losses for entire ecosystems. Second, we demonstrate how simulation-based methodology can be used to examine and predict the impacts of blackouts, mitigation and recovery strategies. The major observation from the simulation experiments is that the dynamics of the power loss propagation across different regions, the blackout duration, simultaneous unavailability of supply and logistics along with the unpredictable customer behavior might become major factors that determine the blackout impact and influence selection of an appropriate recovery strategy. The outcomes of this research can be used by decision-makers to predict the operative and long-term impacts of blackouts on the SCs and viability and develop mitigation and recovery strategies. The paper is concluded by summarizing the most important insights and outlining future research agenda toward SC viability, reconfigurable SC, multi-structural SC dynamics, intertwined supply networks, and cross-structural ripple effects.

The independent and synergistic impacts of power outages and floods on hospital admissions for multiple diseases.

Highly destructive disasters such as floods and power outages (PO) are becoming more commonplace in the U.S. Few studies examine the effects of floods and PO on health, and no studies examine the synergistic effects of PO and floods, which are increasingly co-occurring events. We examined the independent and synergistic impacts of PO and floods on cardiovascular diseases, chronic respiratory diseases, respiratory infections, and food-/water-borne diseases (FWBD) in New York State (NYS) from 2002 to 2018. We obtained hospitalization data from the NYS discharge database, PO data from the NYS Department of Public Service, and floods events from NOAA. Distributed lag nonlinear models were used to evaluate the PO/floods-health association while controlling for time-varying confounders. We identified significant increased health risks associated with both the independent effects from PO and floods, and their synergistic effects. Generally, the Rate Ratios (RRs) for the co-occurrence of PO and floods were the highest, followed by PO alone, and then floods alone, especially when PO coverage is >75th percentile of its distribution (1.72% PO coverage). For PO and floods combined, immediate effects (lag 0) were observed for chronic respiratory diseases (RR:1.58, 95%CI: 1.24, 2.00) and FWBD (RR:3.02, 95%CI: 1.60, 5.69), but delayed effects were found for cardiovascular diseases (lag 3, RR:1.13, 95%CI: 1.03, 1.24) and respiratory infections (lag 6, RR:1.85, 95%CI: 1.35, 2.53). The risk association was slightly stronger among females, whites, older adults, and uninsured people but not statistically significant. Improving power system resiliency could be a very effective way to alleviate the burden on hospitals during co-occurring floods. We conclude that PO and floods have independently and jointly led to increased hospitalization for multiple diseases, and more research is needed to confirm our findings.

Increased risk of multiple pregnancy complications following large-scale power outages during Hurricane Sandy in New York State.

BACKGROUND: Large-scale power outages (PO) are increasing in the context of climate change. Although some research has been conducted into the adverse health impacts of POs, significant gaps remain regarding whether POs would affect the health of pregnant women. We investigated the association between ED visits due to pregnancy complications and the occurence, intensity, and duration of large-scale POs in eight Sandy-affected counties in New York State (NYS). METHODS: In this cross-sectional study, daily ED visits for pregnancy complications and large-scale PO data in eight counties in NYS from October to December in 2005-2014 were collected. Using time-series analysis, we estimated the relative increase in ED visits for pregnancy complications during POs compared with non-PO periods at lag 0-7 days. Short-term health impacts of PO intensity and PO duration were investigated. Estimations were also stratified by sociodemographic characteristics and disease subtypes including threatened or spontaneous abortion, threatened or early labor, hypertension complications, infections of genitourinary tract, renal diseases, gestational diabetes mellitus, mental illnesses, and cardiovascular diseases during pregnancy. RESULTS: From October to December in 2005-2014, there were 307,739 ED visits for pregnancy complications in the eight counties. We found significant increases in ED visits for overall pregnancy complications (16.6%, 95% confidence interval [CI]: 10.3%, 23.2%) during the Hurricane-PO period at lag 0-7 days. The ED visits increased by 8.8% per level increase in PO intensity and 1.4% per day increase in PO duration. Specifically, threatened/early delivery and gestational diabetes mellitus during the PO period increased by 26.7% (95% CI: 8.2%, 48.4%) and 111.8% (95% CI: 16.7%, 284.4%), respectively. Young adult, Black, Hispanic, and uninsured individuals were at higher risk of complications. CONCLUSIONS: POs may adversely impact pregnancy, especially for certain pregnancy complications and among low sociodemographic women.

Who's at Risk in a Changing Climate? Mapping Electricity-Dependent Patient Populations in a Coastal City.

BACKGROUND: Climate change is causing increasingly frequent extreme weather events. This pilot study demonstrates a GIS-based approach for assessing risk to electricity-dependent patients of a coastal academic medical center during future hurricanes.  Methods: A single-center retrospective chart review was conducted and the spatial distribution of patients with prescriptions for nebulized medications was mapped. Census blocks at risk of flooding in future hurricanes were identified; summary statistics describing proportion of patients at risk are reported.  Results: Out of a local population of 2,101 patients with prescriptions for nebulized medications in the preceding year, 521 (24.8%) were found to live in a hurricane flood zone.  Conclusions: Healthcare systems can assess risk to climate-vulnerable patient populations using publicly available data in combination with hospital medical records. The approach described here could be applied to a variety of environmental hazards and can inform institutional and individual disaster preparedness efforts.