scholarly journals Usability and Feasibility of a Smartphone App to Assess Human Behavioral Factors Associated with Tick Exposure (The Tick App): Quantitative and Qualitative Study

10.2196/14769 ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. e14769 ◽  
Author(s):  
Maria P Fernandez ◽  
Gebbiena M. Bron ◽  
Pallavi A Kache ◽  
Scott R Larson ◽  
Adam Maus ◽  
...  
Keyword(s):  
United States ◽  
New York ◽  
Disease Incidence ◽  
Age Groups ◽  
User Profile ◽  
The United States ◽  
Epidemiological Methods ◽  
Survey Tool ◽  
Outdoor Activities ◽  
Effective Manner

Background Mobile health (mHealth) technology takes advantage of smartphone features to turn them into research tools, with the potential to reach a larger section of the population in a cost-effective manner, compared with traditional epidemiological methods. Although mHealth apps have been widely implemented in chronic diseases and psychology, their potential use in the research of vector-borne diseases has not yet been fully exploited. Objective This study aimed to assess the usability and feasibility of The Tick App, the first tick research–focused app in the United States. Methods The Tick App was designed as a survey tool to collect data on human behaviors and movements associated with tick exposure while engaging users in tick identification and reporting. It consists of an enrollment survey to identify general risk factors, daily surveys to collect data on human activities and tick encounters (Tick Diaries), a survey to enter the details of tick encounters coupled with tick identification services provided by the research team (Report a Tick), and educational material. Using quantitative and qualitative methods, we evaluated the enrollment strategy (passive vs active), the user profile, location, longitudinal use of its features, and users’ feedback. Results Between May and September 2018, 1468 adult users enrolled in the app. The Tick App users were equally represented across genders and evenly distributed across age groups. Most users owned a pet (65.94%, 962/1459; P<.001), did frequent outdoor activities (recreational or peridomestic; 75.24%, 1094/1454; P<.001 and 64.58%, 941/1457; P<.001, respectively), and lived in the Midwest (56.55%, 824/1457) and Northeast (33.0%, 481/1457) regions in the United States, more specifically in Wisconsin, southern New York, and New Jersey. Users lived more frequently in high-incidence counties for Lyme disease (incidence rate ratio [IRR] 3.5, 95% CI 1.8-7.2; P<.001) and in counties with cases recently increasing (IRR 1.8, 95% CI 1.1-3.2; P=.03). Recurring users (49.25%, 723/1468) had a similar demographic profile to all users but participated in outdoor activities more frequently (80.5%, 575/714; P<.01). The number of Tick Diaries submitted per user (median 2, interquartile range [IQR] 1-11) was higher for older age groups (aged >55 years; IRR 3.4, 95% CI 1.5-7.6; P<.001) and lower in the Northeast (IRR[NE] 0.4, 95% CI 0.3-0.7; P<.001), whereas the number of tick reports (median 1, IQR 1-2) increased with the frequency of outdoor activities (IRR 1.5, 95% CI 1.3-1.8; P<.001). Conclusions This assessment allowed us to identify what fraction of the population used The Tick App and how it was used during a pilot phase. This information will be used to improve future iterations of The Tick App and tailor potential tick prevention interventions to the users’ characteristics.

scholarly journals Usability and Feasibility of a Smartphone App to Assess Human Behavioral Factors Associated with Tick Exposure (The Tick App): Quantitative and Qualitative Study (Preprint)

2019 ◽  
Author(s):  
Maria P Fernandez ◽  
Gebbiena M. Bron ◽  
Pallavi A Kache ◽  
Scott R Larson ◽  
Adam Maus ◽  
...  
Keyword(s):  
United States ◽  
New York ◽  
Disease Incidence ◽  
Age Groups ◽  
User Profile ◽  
The United States ◽  
Epidemiological Methods ◽  
Survey Tool ◽  
Outdoor Activities ◽  
Effective Manner

BACKGROUND Mobile health (mHealth) technology takes advantage of smartphone features to turn them into research tools, with the potential to reach a larger section of the population in a cost-effective manner, compared with traditional epidemiological methods. Although mHealth apps have been widely implemented in chronic diseases and psychology, their potential use in the research of vector-borne diseases has not yet been fully exploited. OBJECTIVE This study aimed to assess the usability and feasibility of The Tick App, the first tick research–focused app in the United States. METHODS The Tick App was designed as a survey tool to collect data on human behaviors and movements associated with tick exposure while engaging users in tick identification and reporting. It consists of an enrollment survey to identify general risk factors, daily surveys to collect data on human activities and tick encounters (Tick Diaries), a survey to enter the details of tick encounters coupled with tick identification services provided by the research team (Report a Tick), and educational material. Using quantitative and qualitative methods, we evaluated the enrollment strategy (passive vs active), the user profile, location, longitudinal use of its features, and users’ feedback. RESULTS Between May and September 2018, 1468 adult users enrolled in the app. The Tick App users were equally represented across genders and evenly distributed across age groups. Most users owned a pet (65.94%, 962/1459; <italic>P</italic>&lt;.001), did frequent outdoor activities (recreational or peridomestic; 75.24%, 1094/1454; <italic>P</italic>&lt;.001 and 64.58%, 941/1457; <italic>P</italic>&lt;.001, respectively), and lived in the Midwest (56.55%, 824/1457) and Northeast (33.0%, 481/1457) regions in the United States, more specifically in Wisconsin, southern New York, and New Jersey. Users lived more frequently in high-incidence counties for Lyme disease (incidence rate ratio [IRR] 3.5, 95% CI 1.8-7.2; <italic>P</italic>&lt;.001) and in counties with cases recently increasing (IRR 1.8, 95% CI 1.1-3.2; <italic>P</italic>=.03). Recurring users (49.25%, 723/1468) had a similar demographic profile to all users but participated in outdoor activities more frequently (80.5%, 575/714; <italic>P</italic>&lt;.01). The number of Tick Diaries submitted per user (median 2, interquartile range [IQR] 1-11) was higher for older age groups (aged &gt;55 years; IRR 3.4, 95% CI 1.5-7.6; <italic>P</italic>&lt;.001) and lower in the Northeast (IRR[NE] 0.4, 95% CI 0.3-0.7; <italic>P</italic>&lt;.001), whereas the number of tick reports (median 1, IQR 1-2) increased with the frequency of outdoor activities (IRR 1.5, 95% CI 1.3-1.8; <italic>P</italic>&lt;.001). CONCLUSIONS This assessment allowed us to identify what fraction of the population used The Tick App and how it was used during a pilot phase. This information will be used to improve future iterations of The Tick App and tailor potential tick prevention interventions to the users’ characteristics.

scholarly journals Trends in incidence and epidemiologic characteristics of cerebral venous thrombosis in the United States

Neurology ◽  
2020 ◽  
Vol 95 (16) ◽  
pp. e2200-e2213 ◽  
Author(s):  
Fadar Oliver Otite ◽  
Smit Patel ◽  
Richa Sharma ◽  
Pushti Khandwala ◽  
Devashish Desai ◽  
...  
Keyword(s):  
United States ◽  
New York ◽  
Venous Thrombosis ◽  
Cerebral Venous Thrombosis ◽  
Census Data ◽  
Age Groups ◽  
The United States ◽  
Joinpoint Regression ◽  
Over Time ◽  
Age And Sex

ObjectiveTo test the hypothesis that race-, age-, and sex-specific incidence of cerebral venous thrombosis (CVT) has increased in the United States over the last decade.MethodsIn this retrospective cohort study, validated ICD codes were used to identify all new cases of CVT (n = 5,567) in the State Inpatients Databases (SIDs) of New York and Florida (2006–2016). A new CVT case was defined as first hospitalization for CVT in the SID without prior CVT hospitalization. CVT counts were combined with annual Census data to compute incidence. Joinpoint regression was used to evaluate trends in incidence over time.ResultsFrom 2006 to 2016, annual age- and sex-standardized incidence of CVT in cases per 1 million population ranged from 13.9 to 20.2, but incidence varied significantly by sex (women 20.3–26.9, men 6.8–16.8) and by age/sex (women 18–44 years of age 24.0–32.6, men 18–44 years of age 5.3–12.8). Incidence also differed by race (Blacks: 18.6–27.2; Whites: 14.3–18.5; Asians: 5.1–13.8). On joinpoint regression, incidence increased across 2006 to 2016, but most of this increase was driven by an increase in all age groups of men (combined annualized percentage change [APC] 9.2%, p < 0.001), women 45 to 64 years of age (APC 7.8%, p < 0.001), and women ≥65 years of age (APC 7.4%, p < 0.001). Incidence in women 18 to 44 years of age remained unchanged over time.ConclusionCVT incidence is disproportionately higher in Blacks compared to other races. New CVT hospitalizations increased significantly over the last decade mainly in men and older women. Further studies are needed to determine whether this increase represents a true increase from changing risk factors or an artifactual increase from improved detection.

Pan America: Military Mobilization and Disease in the United States

War Epidemics ◽  
2004 ◽  
Author(s):  
Matthew Smallman-Raynor ◽  
Andrew Cliff
Keyword(s):  
United States ◽  
New York ◽  
Disease Risk ◽  
Disease Incidence ◽  
The United States ◽  
Spanish American War ◽  
Population Mixing ◽  
Us Army ◽  
Time Space ◽  
Military Mobilization

In the previous chapter, we outlined a number of methods employed by geographers to study time–space patterns of disease incidence and spread. In this and the next four chapters we use these methods to explore five linked themes in the epidemiological history of war since 1850. We begin here with Theme 1, military mobilization, taking the United States as our geographical reference point. Military mobilization at the outset of wars has always been a fertile breeding ground for epidemics. The rapid concentration of large—occasionally vast—numbers of unseasoned recruits, usually under conditions of great urgency, sometimes in the absence of adequate logisitic arrangements, and often without sufficient accommodation, supplies, equipage, and medical support, entails a disease risk that has been repeated down the years. The epidemiological dangers are multiplied by the crowding together of recruits from different disease environments (including rural rather than urban settings) while, even in relatively recent conflicts, pressures to meet draft quotas have sometimes demanded the enlistment of weak, physically unfit, and sometimes disease-prone applicants. The testimony of Major Samuel D. Hubbard, surgeon to the Ninth New York Volunteer Infantry, US Army, during the Spanish–American War (1898) is illustrative: . . . I examined all the recruits for this regiment . . . Practically all the men belonged to one class . . . They were whisky-soaked, homeless wanderers, the majority of whom gave Bowery lodging houses as their places of residence . . . Certainly the regiment was composed of a class of men likely to be susceptible to disease . . . The regiment was hastily recruited, and while the greatest care was used to get the best, the best had to be selected from the worst. (Hubbard, cited in Reed et al., 1904, i. 223) . . . But the problem of mobilization and disease is not restricted to new recruits. As part of the broader pattern of heightened population mixing, regular service personnel may also be swept into the disease milieu while, occasionally, infections may escape the confines of hastily established assembly and training camps to diffuse widely in civil populations.

scholarly journals 1386. Current Estimates of the Impact of Routine Childhood Immunizations in Reducing Vaccine-Preventable Diseases in the United States

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S703-S703
Author(s):  
Elizabeth M La ◽  
Justin Carrico ◽  
Sandra E Talbird ◽  
Ya-Ting Chen ◽  
Mawuli K Nyaku ◽  
...  
Keyword(s):  
United States ◽  
Research Study ◽  
Disease Incidence ◽  
Age Groups ◽  
Scientific Research ◽  
The United States ◽  
Annual Number ◽  
Study Investigator ◽  
Routine Childhood ◽  
The Impact

Abstract Background Routine immunizations for children aged 10 years and younger in the United States (US) currently cover 14 diseases. Updated estimates of public health impact are needed, given changes in disease epidemiology, evolving recommendations, and the dynamic nature of compliance with the immunization schedule. Methods Pre-vaccine disease incidence was estimated before each routine vaccine was recommended, with average values across multiple years obtained directly from published literature or calculated based on disease surveillance data or annual case estimates from the published literature. Pre-vaccine incidence then was compared to current, post-vaccine incidence, which was generally calculated as average values over the most recent 5 years of available incidence data. Overall incidence estimates and estimates by age group were calculated. Differences in pre- and post-vaccine disease incidence rates were used to calculate the annual number of cases averted, based on 2019 US population estimates. This analysis did not separately estimate the proportion of disease incidence reduction that may be attributed to adult vaccines or booster doses. Results Post-vaccine disease incidence decreased overall and for all age groups across all diseases evaluated (Table 1). Decreases ranged from 17.4% for influenza to 100.0% for polio (Figure 1). Over 90% reduction in incidence was achieved for 10 of the 14 diseases evaluated (including reduction in incidence of rotavirus hospitalizations). Overall post-vaccine disease incidence estimates were highest for influenza, rotavirus, and varicella. Estimated annual cases averted by vaccination in 2019 ranged from 1,269 for tetanus to more than 4.2 million for varicella. Table 1. Pre- and Post-Vaccine Disease Incidence Estimates, Annual Cases, and 2019 Cases Averted, by Disease Figure 1. Percentage Reduction in Disease Incidence Post-Vaccine, by Disease Conclusion Routine childhood immunization in the US continues to result in high, sustained reduction in disease across all vaccines and for all age groups evaluated. Disclosures Elizabeth M. La, PhD, RTI Health Solutions (Employee) Justin Carrico, BS, GlaxoSmithKline (Consultant) Sandra E. Talbird, MSPH, RTI Health Solutions (Employee) Ya-Ting Chen, PhD, Merck & Co., Inc. (Employee, Shareholder) Mawuli K. Nyaku, DrPh, Merck & Co. Inc. (Employee, Shareholder) Cristina Carias, PhD, Merck (Employee, Shareholder) Gary S. Marshall, MD, GlaxoSmithKline (Consultant, Scientific Research Study Investigator)Merck (Consultant, Scientific Research Study Investigator)Pfizer (Consultant, Scientific Research Study Investigator)Sanofi Pasteur (Consultant, Grant/Research Support, Scientific Research Study Investigator, Honorarium for conference lecture)Seqirus (Consultant, Scientific Research Study Investigator) Craig S. Roberts, PharmD, MPA, MBA, Merck & Co., Inc (Employee, Shareholder)

scholarly journals Toward Using Twitter for PrEP-Related Interventions: An Automated Natural Language Processing Pipeline for Identifying Gay or Bisexual Men in the United States

2021 ◽  
Author(s):  
Ari Z. Klein ◽  
Steven Meanley ◽  
Karen O’Connor ◽  
José A. Bauermeister ◽  
Graciela Gonzalez-Hernandez
Keyword(s):  
United States ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Large Scale ◽  
Age Groups ◽  
User Profile ◽  
The United States ◽  
Bisexual Men ◽  
Self Reports

AbstractBackgroundPre-exposure prophylaxis (PrEP) is highly effective at preventing the acquisition of Human Immunodeficiency Virus (HIV). There is a substantial gap, however, between the number of people in the United States who have indications for PrEP and the number of them who are prescribed PrEP. While Twitter content has been analyzed as a source of PrEP-related data (e.g., barriers), methods have not been developed to enable the use of Twitter as a platform for implementing PrEP-related interventions.ObjectiveMen who have sex with men (MSM) are the population most affected by HIV in the United States. Therefore, the objective of this study was to develop and assess an automated natural language processing (NLP) pipeline for identifying men in the United States who have reported on Twitter that they are gay, bisexual, or MSM.MethodsBetween September 2020 and January 2021, we used the Twitter Streaming Application Programming Interface (API) to collect more than 3 million tweets containing keywords that men may include in posts reporting that they are gay, bisexual, or MSM. We deployed handwritten, high-precision regular expressions on the tweets and their user profile metadata designed to filter out noise and identify actual self-reports. We identified 10,043 unique users geolocated in the United States, and drew upon a validated NLP tool to automatically identify their ages.ResultsBased on manually distinguishing true and false positive self-reports in the tweets or profiles of 1000 of the 10,043 users identified by our automated pipeline, our pipeline has a precision of 0.85. Among the 8756 users for which a United States state-level geolocation was detected, 5096 (58.2%) of them are in the 10 states with the highest numbers of new HIV diagnoses. Among the 6240 users for which a county-level geolocation was detected, 4252 (68.1%) of them are in counties or states considered priority jurisdictions by the Ending the HIV Epidemic (EHE) initiative. Furthermore, the majority of the users are in the same two age groups as the majority of MSM in the United States with new HIV diagnoses.ConclusionsOur automated NLP pipeline can be used to identify MSM in the United States who may be at risk for acquiring HIV, laying the groundwork for using Twitter on a large scale to target PrEP-related interventions directly at this population.

scholarly journals Severity of the COVID-19 pandemic assessed with all-cause mortality in the United States during 2020.

2021 ◽  
Author(s):  
Fredrick Dahlgren ◽  
Lauren Rossen ◽  
Alicia Fry ◽  
Carrie Reed
Keyword(s):  
United States ◽  
New York ◽  
Age Groups ◽  
The United States ◽  
Mortality Data ◽  
Demographic Groups ◽  
High Severity ◽  
All Cause Mortality ◽  
The Impact ◽  
Very High

Background. In the United States, infection with SARS-CoV-2 caused 380,000 reported deaths from March to December 2020. Methods. We adapted the Moving Epidemic Method to all-cause mortality data from the United States to assess the severity of the COVID-19 pandemic across age groups and all 50 states. By comparing all-cause mortality during the pandemic with intensity thresholds derived from recent, historical all-cause mortality, we categorized each week from March to December 2020 as either low severity, moderate severity, high severity, or very high severity. Results. Nationally for all ages combined, all-cause mortality was in the very high severity category for 9 weeks. Among people 18 to 49 years of age, there were 29 weeks of consecutive very high severity mortality. Forty-seven states, the District of Columbia, and New York City each experienced at least one week of very high severity mortality for all ages combined. Conclusions. These periods of very high severity of mortality during March through December 2020 are likely directly or indirectly attributable to the COVID-19 pandemic. This method for standardized comparison of severity over time across different geographies and demographic groups provides valuable information to understand the impact of the COVID-19 pandemic and to identify specific locations or subgroups for deeper investigations into differences in severity.

scholarly journals Initial economic damage from the COVID-19 pandemic in the United States is more widespread across ages and geographies than initial mortality impacts

2020 ◽  
Vol 117 (45) ◽  
pp. 27934-27939 ◽  
Author(s):  
Maria Polyakova ◽  
Geoffrey Kocks ◽  
Victoria Udalova ◽  
Amy Finkelstein
Keyword(s):  
United States ◽  
New York ◽  
Excess Mortality ◽  
Age Groups ◽  
The United States ◽  
Economic Damage ◽  
Monthly Data ◽  
Policy Responses ◽  
All Cause Mortality ◽  
Control And Prevention

The economic and mortality impacts of the COVID-19 pandemic have been widely discussed, but there is limited evidence on their relationship across demographic and geographic groups. We use publicly available monthly data from January 2011 through April 2020 on all-cause death counts from the Centers for Disease Control and Prevention and employment from the Current Population Survey to estimate excess all-cause mortality and employment displacement in April 2020 in the United States. We report results nationally and separately by state and by age group. Nationally, excess all-cause mortality was 2.4 per 10,000 individuals (about 30% higher than reported COVID deaths in April) and employment displacement was 9.9 per 100 individuals. Across age groups 25 y and older, excess mortality was negatively correlated with economic damage; excess mortality was largest among the oldest (individuals 85 y and over: 39.0 per 10,000), while employment displacement was largest among the youngest (individuals 25 to 44 y: 11.6 per 100 individuals). Across states, employment displacement was positively correlated with excess mortality (correlation = 0.29). However, mortality was highly concentrated geographically, with the top two states (New York and New Jersey) each experiencing over 10 excess deaths per 10,000 and accounting for about half of national excess mortality. By contrast, employment displacement was more geographically spread, with the states with the largest point estimates (Nevada and Michigan) each experiencing over 16 percentage points employment displacement but accounting for only 7% of the national displacement. These results suggest that policy responses may differentially affect generations and geographies.

scholarly journals All Models Are Useful: Bayesian Ensembling for Robust High Resolution COVID-19 Forecasting

2021 ◽  
Author(s):  
Aniruddha Adiga ◽  
Lijing Wang ◽  
Benjamin Hurt ◽  
Akhil Peddireddy ◽  
Przemyslaw Porebski ◽  
...  
Keyword(s):  
United States ◽  
New York ◽  
High Resolution ◽  
Disease Incidence ◽  
The United States ◽  
Statistical Machine Learning ◽  
Multiple Methods ◽  
Intervention Measures ◽  
Probabilistic Forecasts ◽  
The Individual

ABSTRACTTimely, high-resolution forecasts of infectious disease incidence are useful for policy makers in deciding intervention measures and estimating healthcare resource burden. In this paper, we consider the task of forecasting COVID-19 confirmed cases at the county level for the United States. Although multiple methods have been explored for this task, their performance has varied across space and time due to noisy data and the inherent dynamic nature of the pandemic. We present a forecasting pipeline which incorporates probabilistic forecasts from multiple statistical, machine learning and mechanistic methods through a Bayesian ensembling scheme, and has been operational for nearly 6 months serving local, state and federal policymakers in the United States. While showing that the Bayesian ensemble is at least as good as the individual methods, we also show that each individual method contributes significantly for different spatial regions and time points. We compare our model’s performance with other similar models being integrated into CDC-initiated COVID-19 Forecast Hub, and show better performance at longer forecast horizons. Finally, we also describe how such forecasts are used to increase lead time for training mechanistic scenario projections. Our work demonstrates that such a real-time high resolution forecasting pipeline can be developed by integrating multiple methods within a performance-based ensemble to support pandemic response.ACM Reference FormatAniruddha Adiga, Lijing Wang, Benjamin Hurt, Akhil Peddireddy, Przemys-law Porebski,, Srinivasan Venkatramanan, Bryan Lewis, Madhav Marathe. 2021. All Models Are Useful: Bayesian Ensembling for Robust High Resolution COVID-19 Forecasting. InProceedings of ACM Conference (Conference’17). ACM, New York, NY, USA, 9 pages.https://doi.org/10.1145/nnnnnnn.nnnnnnn

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