scholarly journals The Effects of Lifestyle on the Risk of Lyme Disease in the United States: Evaluation of Market Segmentation Systems in Prevention and Control Strategies

2021 ◽  
Vol 18 (24) ◽  
pp. 12883
Author(s):  
Esra Ozdenerol ◽  
Rebecca Michelle Bingham-Byrne ◽  
Jacob Daniel Seboly
Keyword(s):  
At Risk ◽  
Lyme Disease ◽  
Disease Risk ◽  
Disease Incidence ◽  
Life Stage ◽  
National Level ◽  
The United States ◽  
Incidence Rates ◽  
Statistical Testing ◽  
Solid Base

The aim of this study was to investigate lifestyles at risk of Lyme disease, and to geographically identify target populations/households at risk based on their lifestyle preferences. When coupled with geographically identified patient health information (e.g., incidence, diagnostics), lifestyle data provide a more solid base of information for directing public health objectives in minimizing the risk of Lyme disease and targeting populations with Lyme-disease-associated lifestyles. We used an ESRI Tapestry segmentation system that classifies U.S. neighborhoods into 67 unique segments based on their demographic and socioeconomic characteristics. These 67 segments are grouped within 14 larger “LifeModes” that have commonalities based on lifestyle and life stage. Our dataset contains variables denoting the dominant Tapestry segments within each U.S. county, along with annual Lyme disease incidence rates from 2000 through 2017, and the average incidence over these 18 years. K-means clustering was used to cluster counties based on yearly incidence rates for the years 2000–2017. We used analysis of variance (ANOVA) statistical testing to determine the association between Lyme disease incidence and LifeModes. We further determined that the LifeModes Affluent Estates, Upscale Avenues, GenXurban, and Cozy Country Living were associated with higher Lyme disease risk based on the results of analysis of means (ANOM) and Tukey’s post hoc test, indicating that one of these LifeModes is the LifeMode with the greatest Lyme disease incidence rate. We further conducted trait analysis of the high-risk LifeModes to see which traits were related to higher Lyme disease incidence. Due to the extreme regional nature of Lyme disease incidence, we carried out our national-level analysis at the regional level. Significant differences were detected in incidence rates and LifeModes in individual regions. We mapped Lyme disease incidence with associated LifeModes in the Northeast, Southeast, Midcontinent, Rocky Mountain, and Southwest regions to reflect the location-dependent nature of the relationship between lifestyle and Lyme disease.

Lyme Disease Risk of Exposure to Blacklegged Ticks (Acari: Ixodidae) Infected with Borrelia burgdorferi (Spirochaetales: Spirochaetaceae) in Pittsburgh Regional Parks

2019 ◽  
Vol 57 (1) ◽  
pp. 273-280 ◽  
Author(s):  
Alison E Simmons ◽  
Anna B Manges ◽  
Tashi Bharathan ◽  
Shannon L Tepe ◽  
Sara E McBride ◽  
...  
Keyword(s):  
Lyme Disease ◽  
Borrelia Burgdorferi ◽  
Disease Risk ◽  
Disease Incidence ◽  
The United States ◽  
Sensu Stricto ◽  
Urban Landscapes ◽  
Infection Prevalence ◽  
Risk Of Exposure ◽  
Lyme Disease Risk

Abstract Lyme disease is the most commonly reported vector-borne illness and sixth most commonly reported notifiable infectious disease in the United States. The majority of cases occur in the Northeast and upper-Midwest, and the number and geographic distribution of cases is steadily increasing. The blacklegged tick (Ixodes scapularis Say) is the principal vector of the Lyme disease spirochete (Borrelia burgdorferi sensu stricto) in eastern North America. Although Lyme disease risk has been studied in residential and recreational settings across rural to urban landscapes including metropolitan areas, risk within U.S. cities has not been adequately evaluated despite the presence of natural and undeveloped public parkland where visitors could be exposed to B. burgdorferi-infected I. scapularis. We studied the occurrence of I. scapularis and infection prevalence of B. burgdorferi in four insular regional parks within the city of Pittsburgh to assess Lyme disease risk of exposure to infected adults and nymphs. We found that the density of I. scapularis adults (1.16 ± 0.21 ticks/100 m2) and nymphs (3.42 ± 0.45 ticks/100 m2), infection prevalence of B. burgdorferi in adults (51.9%) and nymphs (19.3%), and density of infected adults (0.60 ticks/100 m2) and nymphs (0.66 ticks/100 m2) are as high in these city parks as nonurban residential and recreational areas in the highly endemic coastal Northeast. These findings emphasize the need to reconsider, assess, and manage Lyme disease risk in greenspaces within cities, especially in high Lyme disease incidence states.

Changing Trends in Age and Sex Distributions of Lyme Disease—United States, 1992-2016

2021 ◽  
pp. 003335492110267
Author(s):  
Kiersten J. Kugeler ◽  
Paul S. Mead ◽  
Amy M. Schwartz ◽  
Alison F. Hinckley
Keyword(s):  
United States ◽  
Lyme Disease ◽  
Disease Incidence ◽  
Age Distribution ◽  
Relative Size ◽  
The United States ◽  
Male Predominance ◽  
Age Related ◽  
Over Time ◽  
Age And Sex

Lyme disease is the most common vector-borne disease in the United States and is characterized by a bimodal age distribution and male predominance. We examined trends in reported cases during a 25-year period to describe changes in the populations most affected by Lyme disease in the United States. We examined demographic characteristics of people with confirmed cases of Lyme disease reported to the Centers for Disease Control and Prevention during 1992-2016 through the National Notifiable Diseases Surveillance System. We grouped cases into 5-year periods (1992-1996, 1997-2001, 2002-2006, 2007-2011, 2012-2016). We calculated the average annual incidence by age and sex and used incidence rate ratios (IRRs) to describe changes in Lyme disease incidence by age and sex over time. We converted patient age at time of illness into patient birth year to ascertain disease patterns according to birth cohorts. The incidence of Lyme disease in the United States doubled from 1992-1996 to 2012-2016 (IRR = 1.74; 95% CI, 1.70-1.78) and increased disproportionately among males; IRRs were 39%-89% higher among males than among females for most age groups. During the study period, children aged 5-9 years were most frequently and consistently affected. In contrast, the average age of adults with Lyme disease increased over time; of all adults, people born during 1950-1964 were the most affected by Lyme disease. Our findings suggest that age-related behaviors and susceptibilities may drive infections among children, and the shifting peak among adults likely reflects a probability proportional to the relative size of the baby boom population. These findings can inform targeted and efficient public health education and intervention efforts.

scholarly journals Preventing chronic disease risk factors: Rationale and feasibility

Medicina ◽  
2008 ◽  
Vol 44 (10) ◽  
pp. 745 ◽  
Author(s):  
Courtney Jordan ◽  
Megan Slater ◽  
Thomas Kottke
Keyword(s):  
Health Care ◽  
Risk Factor ◽  
Chronic Disease ◽  
Disease Risk ◽  
Disease Incidence ◽  
Developed Countries ◽  
The United States ◽  
Hazardous Drinking ◽  
Chronic Disease Risk ◽  
Risk Factor Prevention

Objective. The majority of the mortality, morbidity, and disability in the United States and other developed countries is due to chronic diseases. These diseases could be prevented to a great extent with the elimination of four root causes: physical inactivity, poor nutrition, smoking, and hazardous drinking. The objective of this analysis was to determine whether efficacious risk factor prevention interventions exist and to examine the evidence that populationwide program implementation is justified. Materials and methods. We conducted a literature search for meta-analyses and systematic reviews of trials that tested interventions to increase physical activity, improve nutrition, reduce smoking and exposure to environmental tobacco smoke, and reduce hazardous drinking. Results. We found that appropriately designed interventions can produce behavioral change for the four behaviors. Effective interventions included tailored fact-to-face counseling, phone counseling, and computerized tailored feedback. Computer-based health behavior assessment with feedback and education was documented to be an effective method of determining behavior, assessing participant interest in behavior change and delivering interventions. Some programs have documented reduced health care costs associated with intervention. Conclusions. Positive results to date suggest that further investments to improve the effectiveness and efficiency of chronic disease risk factor prevention programs are warranted. Widespread implementation of these programs could have a significant impact on chronic disease incidence rates and costs of health care.

First Report of National Estimates of the Incidence of Myelodysplastic Syndromes and Chronic Myeloproliferative Disorders from the U.S. SEER Program.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 247-247 ◽  
Author(s):  
Dana E. Rollison ◽  
Matthew Hayat ◽  
Martyn Smith ◽  
Sara S. Strom ◽  
William D. Merritt ◽  
...  
Keyword(s):  
Myelodysplastic Syndromes ◽  
Disease Incidence ◽  
Myeloproliferative Disorders ◽  
The United States ◽  
Incidence Rates ◽  
Age At Diagnosis ◽  
Seer Program ◽  
Refractory Anemia ◽  
Chronic Myeloproliferative Disorders ◽  
The U.S

Abstract BACKGROUND: Incidence rates for myelodysplastic syndromes (MDS) and chronic myeloproliferative disorders (CMD) in the United States were unavailable prior to the addition of these stem cell malignancies to the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) Program and other central cancer registries in 2001. Description of national incidence rates for 2001–2003 will provide an important baseline for future studies of secular trends and allow for the examination of rates by selected demographic factors to define risk profiles of these malignancies in the American population. METHODS: Incidence rates of MDS and CMD were calculated for 18 SEER areas between 2001–2003. These rates were stratified by disease subtype using the FAB classification (including chronic myelomonocytic leukemia [CMML]) with the addition of the WHO deletion 5q category, sex, age at diagnosis and race. Based on the observed SEER incidence rates, counts were estimated for the entire U.S. population. RESULTS: In 2003, 2,538 cases of MDS and 1,421 cases of CMD were observed for all 18 SEER areas combined. Similar numbers of cases were observed in 2001 and 2002. Age-adjusted incidence rates for 2001–2003 were significantly higher among males than females for MDS (4.5 per 100,000 in males vs. 2.7 per 100,000 in females, p <0.0001) and CMD (2.4 per 100,000 in males vs. 1.7 per 100,000 in females, p<0.0001). This gender rate difference was observed consistently across all disease subtypes, including refractory anemia (2.0 per 100,000 in males vs. 1.2 per 100,000 in females (p<0.0001). Incidence rates were significantly associated with age at diagnosis for both MDS (p=0.01) and CMD (p=0.001), and were highest among White, non-Hispanics (2.4 per 100,000 for CMD; 4.2 per 100,000 for MDS). An estimated national total of 14,648 cases of MDS (including CMML) and CMD were diagnosed in 2003, with overall incidence rates for MDS and CMD of 3.1 and 1.9 per 100,000, respectively. The MDS incidence rate for the U.S. is remarkably similar to those previously reported from European countries including England and Wales (3.6 per 100,000), Germany (4.1 per 100,000), Sweden (3.6 per 100,000) and France (3.2 per 100,000). Estimated incidence rates in the U.S. were greater among men than women for all diseases, including CMML (0.40 per 100,000 in males versus 0.3 per 100,000 in females, p< 0.0001). Disease incidence increased with age for MDS, CMD, and CMML, although the increase was greatest for MDS, with an approximate five-fold difference in estimated rates for those diagnosed at ages 60–69 years vs. 80 years and older (7.4 per 100,000 vs. 36.3 per 100,000). The increase in MDS incidence with age was greater for males than females, whereas the age-related increase in CMD and CMML incidence was similar across sexes. Rates of CMD, MDS and CMML were all estimated to be highest among White, non-Hispanics. CONCLUSION: Male sex and advanced age are important risk factors for the development of CMD and MDS. Diagnostic recording differences may underestimate the total annual U.S. MDS and CMD case burden. Future prevention intervention and disease causality studies of MDS and CMD should target high-risk groups.

scholarly journals Seasonality of Acute Lyme Disease in Children

2021 ◽  
Vol 6 (4) ◽  
pp. 196
Author(s):  
Kathryn M. Sundheim ◽  
Michael N. Levas ◽  
Fran Balamuth ◽  
Amy D. Thompson ◽  
Desiree N. Neville ◽  
...  
Keyword(s):  
Lyme Disease ◽  
Regression Models ◽  
Disease Incidence ◽  
Erythema Migrans ◽  
The United States ◽  
Lyme Arthritis ◽  
Cranial Neuropathy ◽  
Acute Arthritis ◽  
Presenting Symptoms ◽  
Fourier Regression

Due to the life cycle of its vector, Lyme disease has known seasonal variation. However, investigations focused on children have been limited. Our objective was to evaluate the seasonality of pediatric Lyme disease in three endemic regions in the United States. We enrolled children presenting to one of eight Pedi Lyme Net participating emergency departments. Cases were classified based on presenting symptoms: early (single erythema migrans (EM) lesion), early-disseminated (multiple EM lesions, headache, cranial neuropathy, or carditis), or late (arthritis). We defined a case of Lyme disease by the presence of an EM lesion or a positive two-tier Lyme disease serology. To measure seasonal variability, we estimated Fourier regression models to capture cyclical patterns in Lyme disease incidence. While most children with early or early-disseminated Lyme disease presented during the summer months, children with Lyme arthritis presented throughout the year. Clinicians should consider Lyme disease when evaluating children with acute arthritis throughout the year.

scholarly journals Artificial Neural Network Modeling of Novel Coronavirus (COVID-19) Incidence Rates across the Continental United States

2020 ◽  
Vol 17 (12) ◽  
pp. 4204 ◽  
Author(s):  
Abolfazl Mollalo ◽  
Kiara M. Rivera ◽  
Behzad Vahedi
Keyword(s):  
Neural Network ◽  
United States ◽  
Disease Incidence ◽  
Ground Truth ◽  
The United States ◽  
Incidence Rates ◽  
Machine Learning Algorithms ◽  
Neural Network Modeling ◽  
Public Health Decision ◽  
Explanatory Variables

Prediction of the COVID-19 incidence rate is a matter of global importance, particularly in the United States. As of 4 June 2020, more than 1.8 million confirmed cases and over 108 thousand deaths have been reported in this country. Few studies have examined nationwide modeling of COVID-19 incidence in the United States particularly using machine-learning algorithms. Thus, we collected and prepared a database of 57 candidate explanatory variables to examine the performance of multilayer perceptron (MLP) neural network in predicting the cumulative COVID-19 incidence rates across the continental United States. Our results indicated that a single-hidden-layer MLP could explain almost 65% of the correlation with ground truth for the holdout samples. Sensitivity analysis conducted on this model showed that the age-adjusted mortality rates of ischemic heart disease, pancreatic cancer, and leukemia, together with two socioeconomic and environmental factors (median household income and total precipitation), are among the most substantial factors for predicting COVID-19 incidence rates. Moreover, results of the logistic regression model indicated that these variables could explain the presence/absence of the hotspots of disease incidence that were identified by Getis-Ord Gi* (p < 0.05) in a geographic information system environment. The findings may provide useful insights for public health decision makers regarding the influence of potential risk factors associated with the COVID-19 incidence at the county level.

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 Lifestyle Effects on the Risk of Transmission of COVID-19 in the United States: Evaluation of Market Segmentation Systems

2021 ◽  
Vol 18 (9) ◽  
pp. 4826
Author(s):  
Esra Ozdenerol ◽  
Jacob Seboly
Keyword(s):  
United States ◽  
The United States ◽  
Mortality Rates ◽  
Incidence Rates ◽  
Statistical Testing ◽  
Average Risk ◽  
County Level ◽  
Household Level ◽  
The Impact ◽  
The U.S

The aim of this study was to associate lifestyle characteristics with COVID-19 infection and mortality rates at the U.S. county level and sequentially map the impact of COVID-19 on different lifestyle segments. We used analysis of variance (ANOVA) statistical testing to determine whether there is any correlation between COVID-19 infection and mortality rates and lifestyles. We used ESRI Tapestry LifeModes data that are collected at the U.S. household level through geodemographic segmentation typically used for marketing purposes to identify consumers’ lifestyles and preferences. According to the ANOVA analysis, a significant association between COVID-19 deaths and LifeModes emerged on 1 April 2020 and was sustained until 30 June 2020. Analysis of means (ANOM) was also performed to determine which LifeModes have incidence rates that are significantly above/below the overall mean incidence rate. We sequentially mapped and graphically illustrated when and where each LifeMode had above/below average risk for COVID-19 infection/death on specific dates. A strong northwest-to-south and northeast-to-south gradient of COVID-19 incidence was identified, facilitating an empirical classification of the United States into several epidemic subregions based on household lifestyle characteristics. Our approach correlating lifestyle characteristics to COVID-19 infection and mortality rate at the U.S. county level provided unique insights into where and when COVID-19 impacted different households. The results suggest that prevention and control policies can be implemented to those specific households exhibiting spatial and temporal pattern of high risk.

scholarly journals A 24-year exploratory spatial data analysis of Lyme disease incidence rate in Connecticut, USA

2017 ◽  
Author(s):  
Abolfazl Mollalo ◽  
Jason K. Blackburn ◽  
Lillian R. Morris ◽  
Gregory E. Glass
Keyword(s):  
Lyme Disease ◽  
Spatial Data ◽  
Empirical Bayes ◽  
Disease Incidence ◽  
Incidence Rates ◽  
Bayes Estimation ◽  
Scan Statistics ◽  
Spatial Data Analysis ◽  
Detection Methods ◽  
Targeted Interventions

Despite efforts to control Lyme disease in Connecticut, USA, it remains endemic in many towns, posing a heavy burden. We examined changes in the spatial distribution of significant spatial clusters of Lyme disease incidence rates at the town level from 1991 to 2014 as an approach for targeted interventions. Lyme disease data were grouped into four discrete time periods and incidence rates were smoothed with Empirical Bayes estimation in GeoDa. Local clustering was measured using a local indicator of spatial autocorrelation (LISA). Elliptic spatial scan statistics (SSS) in different shapes and directions were also performed in SaTScan. The accuracy of these two cluster detection methods was assessed and compared for sensitivity, specificity, and overall accuracy. There was significant clustering during each period and significant clusters persisted predominantly in western and eastern parts of the state. Generally, the SSS method was more sensitive, while LISA was more specific with higher overall accuracy in identifying clusters. Even though the location of clusters changed over time, some towns were persistently (across all four periods) identified as clusters in LISA and their neighbouring towns (three of four periods) in SSS suggesting these regions should be prioritized for targeted interventions.

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