scholarly journals Disaster-Related Surveillance Among US Virgin Islands (USVI) Shelters During the Hurricanes Irma and Maria Response

2019 ◽  
Vol 13 (1) ◽  
pp. 38-43 ◽  
Author(s):  
Amy Helene Schnall ◽  
Joseph (Jay) Roth ◽  
Lisa LaPlace Ekpo ◽  
Irene Guendel ◽  
Michelle Davis ◽  
...  
Keyword(s):  
Public Health ◽  
Large Scale ◽  
Critical Infrastructure ◽  
Gastrointestinal Symptoms ◽  
Virgin Islands ◽  
Health Surveillance ◽  
Health Needs ◽  
Health Issues ◽  
Us Virgin Islands ◽  
Reported Data

AbstractObjectivesTwo Category 5 storms, Hurricane Irma and Hurricane Maria, hit the U.S. Virgin Islands (USVI) within 13 days of each other in September 2017. These storms caused catastrophic damage across the territory, including widespread loss of power, destruction of homes, and devastation of critical infrastructure. During large scale disasters such as Hurricanes Irma and Maria, public health surveillance is an important tool to track emerging illnesses and injuries, identify at-risk populations, and assess the effectiveness of response efforts. The USVI Department of Health (DoH) partnered with shelter staff volunteers to monitor the health of the sheltered population and help guide response efforts.MethodsShelter volunteers collect data on the American Red Cross Aggregate Morbidity Report form that tallies the number of client visits at a shelter’s health services every 24 hours. Morbidity data were collected at all 5 shelters on St. Thomas and St. Croix between September and October 2017. This article describes the health surveillance data collected in response to Hurricanes Irma and Maria.ResultsFollowing Hurricanes Irma and Maria, 1130 health-related client visits were reported, accounting for 1655 reasons for the visits (each client may have more than 1 reason for a single visit). Only 1 shelter reported data daily. Over half of visits (51.2%) were for health care management; 17.7% for acute illnesses, which include respiratory conditions, gastrointestinal symptoms, and pain; 14.6% for exacerbation of chronic disease; 9.8% for mental health; and 6.7% for injury. Shelter volunteers treated many clients within the shelters; however, reporting of the disposition (eg, referred to physician, pharmacist) was often missed (78.1%).ConclusionShelter surveillance is an efficient means of quickly identifying and characterizing health issues and concerns in sheltered populations following disasters, allowing for the development of evidence-based strategies to address identified needs. When incorporated into broader surveillance strategies using multiple data sources, shelter data can enable disaster epidemiologists to paint a more comprehensive picture of community health, thereby planning and responding to health issues both within and outside of shelters. The findings from this report illustrated that managing chronic conditions presented a more notable resource demand than acute injuries and illnesses. Although there remains room for improvement because reporting was inconsistent throughout the response, the capacity of shelter staff to address the health needs of shelter residents and the ability to monitor the health needs in the sheltered population were critical resources for the USVI DoH overwhelmed by the disaster. (Disaster Med Public Health Preparedness. 2019;13:38-43)

Sexual and Gender Minorities, Public Health, and Ethics

2019 ◽  
pp. 231-244
Author(s):  
Tonya Littlejohn ◽  
Tonia Poteat ◽  
Chris Beyrer
Keyword(s):  
Public Health ◽  
Public Health Surveillance ◽  
Health Surveillance ◽  
Health Needs ◽  
Health Issues ◽  
Sexual And Gender Minorities ◽  
Ethical Challenges ◽  
Gender Minorities ◽  
The Social ◽  
And Gender

Sexual and gender minorities (LGBT persons) are more visible and mobilized than ever. In some countries, that visibility and activism have contributed to the advancement of sexual and gender rights. Nevertheless, and despite those gains, stigma, discrimination, and criminalization of these populations persist and have impeded efforts to address their public health needs. As a result, sexual and gender minorities continue to experience a range of health disparities, and overall face a disproportionately high burden of mental health issues, HIV/AIDS, and other illnesses. This chapter explores core ethical challenges and debates that impact health promotion and prevention efforts with sexual and gender minorities, with a focus on issues arising in public health surveillance and interventions, and on understanding the social and political context that impacts the lived reality of sexual and gender minorities.

scholarly journals Disaster-Related Shelter Surveillance During the Hurricane Harvey Response – Texas 2017

2019 ◽  
Vol 14 (1) ◽  
pp. 49-55
Author(s):  
Amy Helene Schnall ◽  
Arianna Hanchey ◽  
Nicole Nakata ◽  
Alice Wang ◽  
Zuha Jeddy ◽  
...  
Keyword(s):  
Public Health ◽  
Critical Infrastructure ◽  
Gastrointestinal Symptoms ◽  
Red Cross ◽  
Local Public Health ◽  
Public Health Agencies ◽  
Morbidity Data ◽  
Control And Prevention ◽  
At Risk Populations ◽  
Critical Resource

ABSTRACTObjectives:Hurricane Harvey left a path of destruction in its wake, resulting in over 100 deaths and damaging critical infrastructure. During a disaster, public health surveillance is necessary to track emerging illnesses and injuries, identify at-risk populations, and assess the effectiveness of response efforts. The Centers for Disease Control and Prevention (CDC) and American Red Cross collaborate on shelter surveillance to monitor the health of the sheltered population and help guide response efforts.Methods:We analyzed data collected from 24 Red Cross shelters between August 25, 2017, and September 14, 2017. We described the aggregate morbidity data collected during Harvey compared with previous hurricanes (Gustav, Ike, and Sandy).Results:Over one-third (38%) of reasons for visit were for health care maintenance; 33% for acute illnesses, which includes respiratory conditions, gastrointestinal symptoms, and pain; 19% for exacerbation of chronic disease; 7% for mental health; and 4% for injury. The Red Cross treated 41% of clients within the shelters; however, reporting of disposition was often missed. These results are comparable to previous hurricanes.Conclusion:The capacity of Red Cross shelter staff to address the acute health needs of shelter residents is a critical resource for local public health agencies overwhelmed by the disaster. However, there remains room for improvement because reporting remained inconsistent.

scholarly journals Understanding User Behavior Through the Use of Unsupervised Anomaly Detection: Proof of Concept Using Internet of Things Smart Home Thermostat Data for Improving Public Health Surveillance

10.2196/21209 ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. e21209
Author(s):  
Niloofar Jalali ◽  
Kirti Sundar Sahu ◽  
Arlene Oetomo ◽  
Plinio Pelegrini Morita
Keyword(s):  
Public Health ◽  
Older Adults ◽  
Internet Of Things ◽  
Activity Pattern ◽  
Smart Home ◽  
Large Scale ◽  
Public Health Surveillance ◽  
Activity Patterns ◽  
Health Surveillance ◽  
Easy Access

Background One of the main concerns of public health surveillance is to preserve the physical and mental health of older adults while supporting their independence and privacy. On the other hand, to better assist those individuals with essential health care services in the event of an emergency, their regular activities should be monitored. Internet of Things (IoT) sensors may be employed to track the sequence of activities of individuals via ambient sensors, providing real-time insights on daily activity patterns and easy access to the data through the connected ecosystem. Previous surveys to identify the regular activity patterns of older adults were deficient in the limited number of participants, short period of activity tracking, and high reliance on predefined normal activity. Objective The objective of this study was to overcome the aforementioned challenges by performing a pilot study to evaluate the utilization of large-scale data from smart home thermostats that collect the motion status of individuals for every 5-minute interval over a long period of time. Methods From a large-scale dataset, we selected a group of 30 households who met the inclusion criteria (having at least 8 sensors, being connected to the system for at least 355 days in 2018, and having up to 4 occupants). The indoor activity patterns were captured through motion sensors. We used the unsupervised, time-based, deep neural-network architecture long short-term memory-variational autoencoder to identify the regular activity pattern for each household on 2 time scales: annual and weekday. The results were validated using 2019 records. The area under the curve as well as loss in 2018 were compatible with the 2019 schedule. Daily abnormal behaviors were identified based on deviation from the regular activity model. Results The utilization of this approach not only enabled us to identify the regular activity pattern for each household but also provided other insights by assessing sleep behavior using the sleep time and wake-up time. We could also compare the average time individuals spent at home for the different days of the week. From our study sample, there was a significant difference in the time individuals spent indoors during the weekend versus on weekdays. Conclusions This approach could enhance individual health monitoring as well as public health surveillance. It provides a potentially nonobtrusive tool to assist public health officials and governments in policy development and emergency personnel in the event of an emergency by measuring indoor behavior while preserving privacy and using existing commercially available thermostat equipment.

scholarly journals Understanding User Behavior Through the Use of Unsupervised Anomaly Detection: Proof of Concept Using Internet of Things Smart Home Thermostat Data for Improving Public Health Surveillance (Preprint)

2020 ◽  
Author(s):  
Niloofar Jalali ◽  
Kirti Sundar Sahu ◽  
Arlene Oetomo ◽  
Plinio Pelegrini Morita
Keyword(s):  
Public Health ◽  
Older Adults ◽  
Internet Of Things ◽  
Activity Pattern ◽  
Smart Home ◽  
Large Scale ◽  
Public Health Surveillance ◽  
Activity Patterns ◽  
Health Surveillance ◽  
Easy Access

BACKGROUND One of the main concerns of public health surveillance is to preserve the physical and mental health of older adults while supporting their independence and privacy. On the other hand, to better assist those individuals with essential health care services in the event of an emergency, their regular activities should be monitored. Internet of Things (IoT) sensors may be employed to track the sequence of activities of individuals via ambient sensors, providing real-time insights on daily activity patterns and easy access to the data through the connected ecosystem. Previous surveys to identify the regular activity patterns of older adults were deficient in the limited number of participants, short period of activity tracking, and high reliance on predefined normal activity. OBJECTIVE The objective of this study was to overcome the aforementioned challenges by performing a pilot study to evaluate the utilization of large-scale data from smart home thermostats that collect the motion status of individuals for every 5-minute interval over a long period of time. METHODS From a large-scale dataset, we selected a group of 30 households who met the inclusion criteria (having at least 8 sensors, being connected to the system for at least 355 days in 2018, and having up to 4 occupants). The indoor activity patterns were captured through motion sensors. We used the unsupervised, time-based, deep neural-network architecture long short-term memory-variational autoencoder to identify the regular activity pattern for each household on 2 time scales: annual and weekday. The results were validated using 2019 records. The area under the curve as well as loss in 2018 were compatible with the 2019 schedule. Daily abnormal behaviors were identified based on deviation from the regular activity model. RESULTS The utilization of this approach not only enabled us to identify the regular activity pattern for each household but also provided other insights by assessing sleep behavior using the sleep time and wake-up time. We could also compare the average time individuals spent at home for the different days of the week. From our study sample, there was a significant difference in the time individuals spent indoors during the weekend versus on weekdays. CONCLUSIONS This approach could enhance individual health monitoring as well as public health surveillance. It provides a potentially nonobtrusive tool to assist public health officials and governments in policy development and emergency personnel in the event of an emergency by measuring indoor behavior while preserving privacy and using existing commercially available thermostat equipment.

scholarly journals CDC Surveillance Strategy – A Strategy for Improving CDC Activities in Public Health Surveillance

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Chesley Richards ◽  
Brian Lee
Keyword(s):  
Public Health ◽  
Large Scale ◽  
Public Health Surveillance ◽  
Health Surveillance ◽  
Policy Changes ◽  
Surveillance Strategy ◽  
The Public ◽  
Public Health Professionals ◽  
Health Surveillance Data ◽  
The Impact

Public health surveillance guides efforts to detect and monitor disease and injuries, assess the impact of interventions and assist in the management of and recovery from large-scale public health incidents. Actions informed by surveillance information take many forms, such as policy changes, new program interventions, public communications and investments in research. Local, state and federal public health professionals, government leaders, public health partners and the public are dependent on high quality, timely and actionable public health surveillance data. This Surveillance Strategy aims to improve overall surveillance capabilities, and by extension those of the public health system at large.

scholarly journals Ignoring transmission dynamics leads to underestimation of the impact of a novel intervention against mosquito-borne disease

2021 ◽  
Author(s):  
Sean M. Cavany ◽  
John H Huber ◽  
Annaliese Wieler ◽  
Margaret Elliott ◽  
Quan Minh Tran ◽  
...  
Keyword(s):  
Public Health ◽  
Mathematical Models ◽  
Spatial Scale ◽  
Large Scale ◽  
Intracellular Bacterium ◽  
Health Needs ◽  
Transmission Dynamics ◽  
Multiple Sources ◽  
Recent Trial ◽  
The Impact

Wolbachia is an intracellular bacterium that many hope could have a major impact on dengue and other mosquito-borne diseases that are notoriously difficult to control. The balance of future investments in Wolbachia versus other public health needs will be informed to a great extent by efficacy estimates from large-scale trials, which can be affected by multiple sources of bias. We used mathematical models to quantify the possible magnitude of these biases, finding that efficacy would have been severely underestimated in a recent trial in Indonesia if the spatial scale of clusters had been smaller than it was. We also identified a previously unrecognized source of bias owing to the coupled nature of transmission dynamics across clusters. This too led to a consistent underestimate of the protection afforded by Wolbachia. Taken together, our findings suggest that this intervention may be even more promising than currently recognized.

scholarly journals NextGen Public Health Surveillance and the Internet of Things (IoT)

2021 ◽  
Vol 9 ◽  
Author(s):  
Kirti Sundar Sahu ◽  
Shannon E. Majowicz ◽  
Joel A. Dubin ◽  
Plinio Pelegrini Morita
Keyword(s):  
Public Health ◽  
Internet Of Things ◽  
Large Scale ◽  
Public Health Surveillance ◽  
Health Surveillance ◽  
Current Data ◽  
Data Sources ◽  
Surveillance Systems ◽  
Data Generation ◽  
Data Source

Recent advances in technology have led to the rise of new-age data sources (e.g., Internet of Things (IoT), wearables, social media, and mobile health). IoT is becoming ubiquitous, and data generation is accelerating globally. Other health research domains have used IoT as a data source, but its potential has not been thoroughly explored and utilized systematically in public health surveillance. This article summarizes the existing literature on the use of IoT as a data source for surveillance. It presents the shortcomings of current data sources and how NextGen data sources, including the large-scale applications of IoT, can meet the needs of surveillance. The opportunities and challenges of using these modern data sources in public health surveillance are also explored. These IoT data ecosystems are being generated with minimal effort by the device users and benefit from high granularity, objectivity, and validity. Advances in computing are now bringing IoT-based surveillance into the realm of possibility. The potential advantages of IoT data include high-frequency, high volume, zero effort data collection methods, with a potential to have syndromic surveillance. In contrast, the critical challenges to mainstream this data source within surveillance systems are the huge volume and variety of data, fusing data from multiple devices to produce a unified result, and the lack of multidisciplinary professionals to understand the domain and analyze the domain data accordingly.

scholarly journals A National Framework to Improve Mortality, Morbidity, and Disparities Data for COVID-19 and Other Large-Scale Disasters

2021 ◽  
Vol 111 (S2) ◽  
pp. S93-S100
Author(s):  
Michael A. Stoto ◽  
Charles Rothwell ◽  
Maureen Lichtveld ◽  
Matthew K. Wynia
Keyword(s):  
Public Health ◽  
Health Economic ◽  
Large Scale ◽  
Public Health Surveillance ◽  
Health Surveillance ◽  
Policy Implications ◽  
Accurate Data ◽  
Health Surveillance Data ◽  
National Framework ◽  
Significant Health

Timely and accurate data on COVID-19 cases and COVID-19‒related deaths are essential for making decisions with significant health, economic, and policy implications. A new report from the National Academies of Sciences, Engineering, and Medicine proposes a uniform national framework for data collection to more accurately quantify disaster-related deaths, injuries, and illnesses. This article describes how following the report’s recommendations could help improve the quality and timeliness of public health surveillance data during pandemics, with special attention to addressing gaps in the data necessary to understand pandemic-related health disparities.

scholarly journals Large-scale Analysis of Counseling Conversations: An Application of Natural Language Processing to Mental Health

2016 ◽  
Vol 4 ◽  
pp. 463-476 ◽  
Author(s):  
Tim Althoff ◽  
Kevin Clark ◽  
Jure Leskovec
Keyword(s):  
Public Health ◽  
Language Processing ◽  
Large Scale ◽  
Language Model ◽  
Text Message ◽  
Scale Analysis ◽  
Health Issues ◽  
Large Scale Data ◽  
Large Scale Analysis ◽  
Scale Data

Mental illness is one of the most pressing public health issues of our time. While counseling and psychotherapy can be effective treatments, our knowledge about how to conduct successful counseling conversations has been limited due to lack of large-scale data with labeled outcomes of the conversations. In this paper, we present a large-scale, quantitative study on the discourse of text-message-based counseling conversations. We develop a set of novel computational discourse analysis methods to measure how various linguistic aspects of conversations are correlated with conversation outcomes. Applying techniques such as sequence-based conversation models, language model comparisons, message clustering, and psycholinguistics-inspired word frequency analyses, we discover actionable conversation strategies that are associated with better conversation outcomes.

A New Opportunity for Public Health Development: Athens 2004

2004 ◽  
Vol 19 (2) ◽  
pp. 130-132 ◽  
Author(s):  
Jeffrey Levett
Keyword(s):  
Public Health ◽  
Large Scale ◽  
Olympic Games ◽  
Health Issues ◽  
Organization And Management ◽  
Laboratory Services ◽  
Transient Population ◽  
Future Events ◽  
Geographical Space ◽  
The Olympic Games

A number of organized and universally publicized, large-scale events take place each year in many parts of the world that involve a population at greater risk. Large gatherings provide a theater of operations for public health and more thought now is being given to these issues. The Olympic Games is the largest, single event that is concentrated into one significant geographical space that unfolds over a period of weeks and involves a transient population. From Atlanta to Sydney, a growing awareness of public health issues has occurred, and there is a clear recognition that much more preparation is necessary for all future events. Therefore, it is mandatory that we recognize that the Olympic Games, Athens 2004 is a potential venue for accidents as well as for purposefully precipitated acts leading to suffering, disability, and death. The organization and management of public health is a major hurdle for Athens 2004. At a minimum, hospital and emergency medical services must be in an optimal state of readiness, a network of public health laboratory services must be deployed, and human resources must be retooled.

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