This map shows the location of reported Lyme disease cases and changes in these cases over time from 2000 to 2020. Each dot on the map represents one case of Lyme disease. Cases are marked in the case’s county of residence, not necessarily the county of exposure. The map does not include data where county of residence was not reported. People travel between counties and states, and the place of residence is sometimes different from the place where the patient became infected.The map also shows shaded states with high incidence of Lyme disease. Many high incidence states have modified surveillance practices. Contact your state health department for more information.Data used to make this map are reported through the National Notifiable Disease Surveillance System.Many high incidence states have modified surveillance practices that have led to notable decreases in case counts over time. Consequently, these data may not accurately represent disease trends in those areas. Reference MaterialsLyme Disease | Lyme Disease | CDCAnnual statistics from the National Notifiable Diseases Surveillance System (NNDSS). (cdc.gov)Contact InformationBZB_Public@cdc.gov

In 2023, the U.S. states with the highest rates of Lyme disease were Rhode Island, Vermont, and Maine. However, the states with the highest total number of Lyme disease cases were New York, Pennsylvania, and Massachusetts. That year, there were a total of 2,942 cases of Lyme disease in the state of Maine, with an incidence rate of 213 per 100,000 population. What is Lyme disease? Lyme disease is caused by bacteria, usually transmitted to humans through the bite of a tick. Lyme disease is the most common vector-borne disease in the United States; however, it is much more prevalent in some states than others, with the upper Midwest and the Northeastern states most at risk. Symptoms of Lyme disease can vary and usually come in stages but may include a rash, fever, headache, stiffness in the joints, tiredness, and muscle aches and pains. Lyme disease is usually treated with antibiotics. In 2023, funding for Lyme disease from the National Institutes of Health (NIH) totaled around 43 million U.S. dollars. Trends in Lyme disease Although the number of Lyme disease cases per year fluctuates, over the past couple decades, the number of Lyme disease cases in the United States has steadily increased. Between 1996 and 2023, the highest number of Lyme disease cases was in the year 2023 when almost 89,500 cases were reported. The lowest number reported during this period was in 1997, with around 12,800 cases. Cases of Lyme disease are much more common in the summer months of June and July, as this is when people are most likely to encounter ticks. The risk of Lyme disease is expected to increase in the future as climate change contributes to an expanded habitat for ticks.

Promoting health equity is a fundamental public health objective, yet health disparities remain largely overlooked in studies of vectorborne diseases, especially those transmitted by ticks. We sought to identify health disparities associated with Lyme disease and human monocytic ehrlichiosis, two of the most pervasive tickborne diseases within the United States. We used general linear mixed models to measure associations between county-level disease incidence and six variables representing racial/ethnic and socioeconomic characteristics of counties (percent white non-Hispanic; percent with a bachelors degree or higher; percent living below the poverty line; percent unemployed; percent of housing units vacant; per capita number of property crimes). Two ecological variables important to tick demography (percent forest cover; density of white-tailed deer) were included in secondary analyses to contextualize findings. Analyses included data from 2,695 counties in 37 states and the District of Columbia during 2007–2013. Each of the six variables was significantly associated with the incidence of one or both diseases, but the direction and magnitude of associations varied by disease. Results suggested that the incidence of Lyme disease was highest in counties with relatively higher proportions of white and more educated persons and lower poverty and crime rates; the incidence of human monocytic ehrlichiosis was highest in counties with relatively higher proportions of white and less educated persons, higher unemployment rates and lower crime rates. The percentage of housing units vacant was a strong positive predictor for both diseases with a magnitude of association comparable to those between incidence and the ecological variables. Our findings indicate that racial/ethnic and socioeconomic disparities in disease incidence appear to be epidemiologically important features of Lyme disease and human monocytic ehrlichiosis in the United States. Steps to mitigate encroachment of wild flora and fauna into areas with vacant housing might be warranted to reduce disease risk.

Tick-borne diseases are the most commonly reported vector-borne diseases in North America and recent estimates of Lyme disease prevalence in the United States alone are as high as 300,000 cases annually. While the vast majority of cases in the US are reported in the upper Midwest and northeast, there are cases reported from throughout California every year, yet research on ticks and the diseases they vector has been restricted primarily to a handful of northern California counties. Recent research has extended this work to Santa Barbara County and yielded results suggesting both that phenology of some tick species is remarkably constant over large geographic areas and that tick populations and communities can be highly variable even within relatively small geographic areas with similar habitats and climate. As a result, acaralogical risk is expected to be highly variable across the landscape. Recent large-scale studies and surveillance efforts have been undertaken in the eastern US to characterize this variability and identify areas of high expected acaralogical risk. This type of large-scale sampling effort on the ground is crucial to our understanding of the highly geographically variable nature of ticks and tick-borne diseases and what factors are driving their emergence. This project aims to use the UC Natural Reserve System to carry out a standardized sampling and surveillance effort applied at a larger geographical scale than has yet been undertaken in California. The goal of which is both to begin to characterize regional differences in tick communities, infection prevalence, and human risk as well as provide baseline information for the UC Reserves, and for the safety of staff and researchers working on them, that can be built upon in future studies. Specifically, data collected from the reserves will be used to ask the following questions: 1. How do tick community composition and relative abundance differ regionally in California? H1: I. pacificus will make up a larger proportion of the community at higher latitudes and in cooler, wetter climates; the proportion will decrease as latitude decreases and as climate becomes hotter and drier where it will be dominated by Dermacentor spp. H2: Overall tick abundance will decrease with latitude and as climate becomes more arid. 2. Are there differences in infection prevalence of I. pacificus with B. burgdorferi on a regional scale in California? H3: Infection prevalence will be highest in the north and decrease with latitude.

What is Lyme Disease? Lyme disease is an infectious, autoimmune disease caused by bacteria from the genus Borrelia that are transmitted by infected ticks. It's the most common vector-borne disease in North America and Europe.

Here are some of the symptoms of Lyme disease:

Fever Headache Fatigue A characteristic skin rash called erythema migrans (EM) - This rash appears as a red bull's-eye, but not everyone gets it. Muscle and joint pain Swelling of the lymph nodes Neurological problems, such as Bell's palsy (facial paralysis)

The dataset contains data on Lyme disease cases across various counties in the United States from 1992 to 2011. The dataset has 3194 entries and includes the following columns:

StateCode: Integer code representing the state. CountyCode: Integer code representing the county. StateName: Name of the state. CountyName: Name of the county. ConfirmedCount_1992_1996: Number of confirmed Lyme disease cases from 1992 to 1996 ConfirmedCount_1997_2001: Number of confirmed Lyme disease cases from 1997 to 2001. ConfirmedCount_2002_2006: Number of confirmed Lyme disease cases from 2002 to 2006 ConfirmedCount_2007_2011: Number of confirmed Lyme disease cases from 2007 to 2011

Column Description:

StateCode: Numeric code representing each state in the United States.

CountyCode: Numeric code representing each county within the states.

StateName: Full name of the state.

CountyName: Full name of the county.

ConfirmedCount_1992_1996: Number of confirmed Lyme disease cases in the county from 1992 to 1996.

ConfirmedCount_1997_2001: Number of confirmed Lyme disease cases in the county from 1997 to 2001.

ConfirmedCount_2002_2006: Number of confirmed Lyme disease cases in the county from 2002 to 2006.

ConfirmedCount_2007_2011: Number of confirmed Lyme disease cases in the county from 2007 to 2011.

License: Open Data Commons Open Database License (ODbL) Summary

https://catalog.data.gov/dataset/lymedisease-9211-county

https://opendatacommons.org/licenses/odbl/summary/

Open Data Commons Open Database License (ODbL) v1.0

Overview: Public health surveillance data are collected and reported voluntarily to CDC by U.S. states and territories through the National Notifiable Diseases Surveillance System (NNDSS) (https://www.cdc.gov/nndss/index.html). Data include demographic, clinical, and geographic information; data do not include direct identifiers. Two types of datasets of human Lyme disease case data collected through public health surveillance are available: one includes annual case count aggregated by county of residence according to specific demographic variables and one is line-listed with patient demographic factors, month of illness onset, and clinical presentation information but without corresponding geographic information. These privacy-protected datasets were implemented in accordance with methodology described in Lee et al. Protecting Privacy and Transforming COVID-19 Case Surveillance Datasets for Public Use. Public Health Rep. 2021 Sep-Oct;136(5):554-561. doi: 10.1177/00333549211026817.

Lyme disease became nationally notifiable in 1991. Different surveillance case definitions have been in effect over time; details are available here: https://ndc.services.cdc.gov/conditions/lyme-disease/. In 2008, a probable case definition was included in public health surveillance for the first time. In 2022, states with a high incidence of Lyme disease started reporting cases based on laboratory evidence alone without requirement for a clinical investigation, precluding comparison with historical data (for more information: https://www.cdc.gov/mmwr/volumes/73/wr/mm7306a1.htm?s_cid=mm7306a1_w). As such, Lyme disease surveillance data are grouped into separate datasets based on when these major changes occurred; data are provided for download separately for 1992–2007, 2008–2021, and 2022 to current. Data will be updated annually upon final verification of Lyme disease surveillance data by health departments.

Data Limitations: Surveillance data have significant limitations that must be considered in the analysis, interpretation, and reporting of results. 1. Under-reporting and misclassification are features common to all surveillance systems. Not every case of Lyme disease is reported to CDC, and some cases that are reported may be reflect illness due to another cause. 2. Please note that before the 2022 surveillance case definition went into effect, several states with high Lyme disease incidence had initiated alternative methods of surveillance and those data were not reportable to CDC. 3. Final case data are subject to each state’s abilities to capture and classify cases, which is dependent upon budget and personnel. This can vary not only between states, but also from year to year within a given state. Consequently, a sudden or marked change in reported cases does not necessarily represent a true change in disease incidence. Every effort should be made to construct analyses to limit overinterpretation of this variation (see the following reference for more context: Kugeler KJ, Eisen RJ. Challenges in Predicting Lyme Disease Risk. JAMA Netw Open. 2020 Mar 2;3(3):e200328. doi: 10.1001/jamanetworkopen.2020.0328.)

Monthly tick reports show seasonal trends in reported tick bites and tick-borne disease diagnoses in Massachusetts residents.

1. Infectious diseases are rapidly emerging and many are increasing in incidence across the globe. Processes of land-use change, notably habitat loss and fragmentation, have been widely implicated in emergence and spread of zoonoses such as Lyme disease, yet evidence remains equivocal.
2. Here we discuss and apply an innovative approach from the social sciences, instrumental variables, that seeks to tease out causality from observational data. Using this approach, we revisit the effect of forest fragmentation on Lyme disease incidence, focusing on human interaction with fragmented landscapes. Though human interaction with infected ticks is of clear and fundamental importance to human disease incidence, human activities that influence exposure have been nearly universally overlooked in the ecology literature. 3. Using county-level land-use and Lyme disease incidence data for ~800 counties from the northeastern United States over the span of a decade, we illustrate (1) human interaction with fragmented forest landscapes reliably predicts Lyme disease incidence, while ecological measures of forest fragmentation alone are unreliable predictors and (2) that identifying the effect of forest fragmentation on human disease requires addressing the feedback between Lyme disease risk and human decisions to avoid interaction with high-risk landscapes. 4. Synthesis and applications. The innovative approach and novel results help to clarify the equivocal literature on forest fragmentation and Lyme disease, and illustrate the key role that human behavior may be playing in the ecology of Lyme disease in North America. Accounting for human activity and behavior in the ecology of disease more broadly may result in improved understanding of both the ecological drivers of disease, as well as actionable intervention strategies to reduce disease burden in a changing world. For example, our model results have practical implications for land-use policy aimed at disease reduction. Our model suggests land use regulations that reduce parcel size would be an actionable approach for policy makers concerned about increasing Lyme disease incidence in the northeastern US.10-Oct-2018

Lyme disease, caused by Borrelia burgdorferi, continues to be the most commonly reported vector-borne disease in the United States (US) affecting the public health and the economy. Suffolk County, New York (NY) has one of the highest incidence in NY State affecting primarily the Hispanic/Latino population working ingardening, landscaping, and agriculture (field workers). However, there is a paucity of research among this population. Thus, the aim of this longitudinal study was to assess the current seroprevalence and seroconversion of the Borrelia burgdorferi infection and its risk factors such as sociodemographic, symptoms, tick encounter, and use of the Fatigue Severity Scale, associated with seropositivity in the Hispanic/Latino immigrant worker population of Eastern Suffolk County. Recruitment of participants were based on several towns of this County. Following signed informed consent, participants completed a questionnaire and had their blood drawn. Samples were tested using the conventional 2-tiered serological testing for Borreliosis.

Lyme disease is the leading tick-borne infection in the United States, caused by the pathogenic spirochete Borreliella burgdorferi, formerly known as Borrelia burgdorferi. Diderms, or bacteria with dual-membrane ultrastructure, such as B. burgdorferi, have multiple methods of transporting and integrating outer membrane proteins (OMPs). Most integral OMPs are transported through the β-barrel assembly machine (BAM) complex. This complex consists of the channel-forming OMP BamA and accessory lipoproteins that interact with the five periplasmic, polypeptide transport-associated (POTRA) domains of BamA. Another system, the translocation and assembly module (TAM) system, has also been implicated in OMP assembly and export. The TAM system consists of two proteins, the BamA paralog TamA which has three POTRA domains and the inner membrane protein TamB. TamB is characterized by a C-terminal DUF490 domain that interacts with the POTRA domains of TamA. Interestingly, while TamB is found in almost all diderms, including B. burgdorferi, TamA is found almost exclusively in Proteobacteria. This strongly suggests a TamA-independent role of TamB in most diderms. We previously demonstrated that BamA interacts with TamB in B. burgdorferi and hypothesized that this is facilitated by the BamA POTRA domains interacting with the TamB DUF490 domain. In this study, we utilized protein-protein co-purification assays to empirically demonstrate that the B. burgdorferi TamB DUF490 domain interacts with BamA POTRA2 and POTRA3. We also observed that the DUF490 domain of TamB interacts with the accessory lipoprotein BamB. To examine if the BamA-TamB interaction is more ubiquitous among diderms, we examined BamA-TamB interactions in Salmonella enterica serovar Typhimurium (St). Interestingly, even though St encodes a TamA protein that interacts with TamB, we observed that the TamB DUF490 of St interacts with BamA in this organism. Our combined findings strongly suggest that the TamB-BamA interaction occurs independent of the TamA component of the TAM protein export system.