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.

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, New York had ****** cases of Lyme disease. That year, there were a total of ****** cases of Lyme disease in the United States. Lyme disease is a bacterial infection spread by ticks. Lyme disease is the most commonly reported vector-borne illness in the United States. This statistic displays the number of confirmed Lyme disease cases in each state in the United States in 2023.

In 2023, there were 89,468 confirmed cases of Lyme disease in the United States. Lyme disease is a bacterial infection transmitted by ticks to humans. It is the most commonly reported vector-borne illness in the United States. This statistic displays the number of confirmed Lyme disease cases in the United States from 1996 to 2023.

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.)

Data for "By-degree Health and Economic Impacts of Lyme Disease, Eastern and Midwestern United States", published March 2024. The datasets show cases by state, fips code, case status, sex, age (by 5-year increments), and frequency. Citation information for this dataset can be found in Data.gov's References section.

Cartographic boundaries and fill indicated US States where Lyme disease cases are low in incidence. Filtered and styled from publicly available USA State Cartographic Boundary feature layers on ArcGIS Online.Reference MaterialsLyme Disease WebsiteLyme Disease Maps Annual statistics from the National Notifiable Diseases Surveillance System (NNDSS). (cdc.gov) Contact Informationbdbepigroup@cdc.gov

This map shows the Lyme Disease incidence rate per 100,000 by county. Counties are shaded based on quartile distribution. The lighter shaded counties have lower incidence rates of Lyme Disease. The darker shaded counties have higher incidence rates of Lyme Disease. New York State Community Health Indicator Reports (CHIRS) were developed in 2012, and are updated annually to consolidate and improve data linkages for the health indicators included in the County Health Assessment Indicators (CHAI) for all communities in New York. The CHIRS present data for more than 300 health indicators that are organized by 15 different health topics. Data if provided for all 62 New York State counties, 11 regions (including New York City), the State excluding New York City, and New York State. For more information, check out: http://www.health.ny.gov/statistics/chac/indicators/. The "About" tab contains additional details concerning this dataset.

This submission includes publicly available data extracted in its original form. Please reference the Related Publication listed here for source and citation information If you have questions about the underlying data stored here, please contact the Centers for Disease Control at https://www.cdc.gov/cdc-info/about-us/index.html. If you have questions or recommendations related to this metadata entry and extracted data, please contact the CAFE Data Management team at: climatecafe@bu.edu. "Lyme disease has been a nationally notifiable condition in the United States since 1991. Reports of Lyme disease are routinely collected and verified by state and local health departments in accordance with their legal mandate and surveillance practices. After removal of personal identifiers, selected information on cases is shared with CDC through the National Notifiable Diseases Surveillance System (NNDSS). Policies regarding case definitions, reporting, confidentiality, and data release are determined by states and territories under the auspices of the Council of State and Territorial Epidemiologists (CSTE). Surveillance data have a number of limitations that need to be considered in the analysis, interpretation, and reporting of results. Limitations of surveillance data: 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 due to another cause. 2.Surveillance data are captured by county of residence, not county of exposure. 3.States may close their annual surveillance dataset at a different time than CDC. Thus, the final case counts published by CDC may not exactly match numbers published by each state agency for a given year. 4.Following its implementation in 1991, the national surveillance case definition for Lyme disease was modified in 1996, 2008, 2011, 2017, and again in 2022. Some of these changes impacted surveillance data and must be considered when attempting to interpret trends. Case definitions for each period are available." [Quote from https://www.cdc.gov/lyme/data-research/facts-stats/index.html]

From 2010 to 2023, there were around 73,950 cases of Lyme disease with an onset in July. Lyme disease is a bacterial infection transmitted by ticks to humans. It is the most commonly reported vector-borne illness in the United States. This statistic displays the number of cases of Lyme disease in the U.S. from 2010 to 2023, by month.

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

From the period 2008 to 2022, the incidence rate of Lyme disease among males in the United States was highest among those aged 65 to 69 years. This statistic shows the incidence rate of Lyme disease in the United States in the period 2008 to 2022, by age and gender.

This indicator shows how reported Lyme disease incidence has changed by state since 1991, based on the number of new cases per 100,000 people. The total change has been estimated from the average annual rate of change in each state. This map is limited to the 15 states where Lyme disease is most common, where annual rates are consistently above 10 cases per 100,000. Connecticut, Massachusetts, New York, and Rhode Island had too much year-to-year variation in reporting practices to allow trend calculation. For more information: https://www.epa.gov/climate-change

Statistics of cases by region, age group, and gender since 2003 (Disease name: Lyme disease, Date type: Onset date, Case type: Confirmed case, Source of infection: Domestic, Imported)

NNDSS - Table II. Lyme disease to Meningococcal - 2015.In this Table, provisional cases of selected notifiable diseases (≥1,000 cases reported during the preceding year), and selected low frequency diseases are displayed.The Table includes total number of cases reported in the United States, by region and by states, in accordance with the current method of displaying MMWR data. Data on United States exclude counts from US territories. Note:These are provisional cases of selected national notifiable diseases, from the National Notifiable Diseases Surveillance System (NNDSS). NNDSS data reported by the 50 states, New York City, the District of Columbia, and the U.S. territories are collated and published weekly as numbered tables printed in the back of the Morbidity and Mortality Weekly Report (MMWR). Cases reported by state health departments to CDC for weekly publication are provisional because of ongoing revision of information and delayed reporting. Case counts in this table are presented as they were published in the MMWR issues. Therefore, numbers listed in later MMWR weeks may reflect changes made to these counts as additional information becomes available. Footnotes:C.N.M.I.: Commonwealth of Northern Mariana Islands. U: Unavailable. -: No reported cases. N: Not reportable. NN: Not Nationally Notifiable. NP: Nationally notifiable but not published. Cum: Cumulative year-to-date counts. Med: Median. Max: Maximum. * Three low incidence conditions, rubella, rubella congenital, and tetanus, have been moved to Table 2 to facilitate case count verification with reporting jurisdictions. ��� Case counts for reporting year 2015 are provisional and subject to change. For further information on interpretation of these data, see http://wwwn.cdc.gov/nndss/document/ProvisionalNationaNotifiableDiseasesSurveillanceData20100927.pdf. Data for TB are displayed in Table IV, which appears quarterly. �� Data for meningococcal disease, invasive caused by serogroups ACWY; serogroup B; other serogroup; and unknown serogroup are available in Table I.

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretability. We also formatted the data into a standard data format.

Each Project Tycho dataset contains case counts for a specific condition (e.g. measles) and for a specific country (e.g. The United States). Case counts are reported per time interval. In addition to case counts, datasets include information about these counts (attributes), such as the location, age group, subpopulation, diagnostic certainty, place of acquisition, and the source from which we extracted case counts. One dataset can include many series of case count time intervals, such as "US measles cases as reported by CDC", or "US measles cases reported by WHO", or "US measles cases that originated abroad", etc.

Depending on the intended use of a dataset, we recommend a few data processing steps before analysis: - Analyze missing data: Project Tycho datasets do not include time intervals for which no case count was reported (for many datasets, time series of case counts are incomplete, due to incompleteness of source documents) and users will need to add time intervals for which no count value is available. Project Tycho datasets do include time intervals for which a case count value of zero was reported. - Separate cumulative from non-cumulative time interval series. Case count time series in Project Tycho datasets can be "cumulative" or "fixed-intervals". Cumulative case count time series consist of overlapping case count intervals starting on the same date, but ending on different dates. For example, each interval in a cumulative count time series can start on January 1st, but end on January 7th, 14th, 21st, etc. It is common practice among public health agencies to report cases for cumulative time intervals. Case count series with fixed time intervals consist of mutually exclusive time intervals that all start and end on different dates and all have identical length (day, week, month, year). Given the different nature of these two types of case count data, we indicated this with an attribute for each count value, named "PartOfCumulativeCountSeries".

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.

This feature layer was created using the Join Features tool on ArcGIS Online. It contains county by county case counts of Lyme disease from 2000 to 2021. It is primarily used for dot density visualizations. It also contains estimated US county populations for 2020. United states data only.Reference MaterialsLyme Disease WebsiteLyme Disease Maps Contact Informationbdbepigroup@cdc.gov

Statistical table of cases by region, age group, and gender since 2003 (Disease name: Lyme disease, Date type: Judgment day, Case type: Confirmed case, Source of infection: Domestic, Imported).

Lyme Disease Therapeutics Market Outlook

According to our latest research, the global Lyme Disease Therapeutics market size was valued at USD 1.28 billion in 2024, reflecting the growing incidence and awareness of Lyme disease worldwide. The market is expected to expand at a healthy CAGR of 7.1% during the forecast period, reaching USD 2.36 billion by 2033. This robust growth is primarily driven by increasing tick-borne infections, rising public health initiatives, and continuous advancements in diagnostic and therapeutic modalities. The market’s strong trajectory is further supported by heightened investments in research and development, as well as the introduction of novel treatment approaches targeting both acute and chronic manifestations of Lyme disease.

One of the most significant growth factors propelling the Lyme Disease Therapeutics market is the escalating prevalence of Lyme disease, especially in temperate regions where tick populations are thriving due to climate change and expanding habitats. The Centers for Disease Control and Prevention (CDC) reports a consistent rise in Lyme disease cases annually, particularly in the United States and parts of Europe. This surge in incidence is prompting governments and private organizations to invest heavily in awareness campaigns, early diagnosis, and effective treatment regimens. Additionally, the growing recognition of persistent or chronic Lyme disease, often characterized by lingering symptoms even after initial antibiotic therapy, is driving the demand for more comprehensive and long-term therapeutic strategies. These factors collectively underscore the urgent need for innovative solutions and are catalyzing market growth.

Another key driver for the expansion of the Lyme Disease Therapeutics market is the advancement in pharmaceutical research and technology. Companies are increasingly focusing on developing next-generation antibiotics, immune modulators, and adjunctive therapies to address the diverse clinical presentations of Lyme disease. The emergence of combination therapies and personalized medicine approaches is further enhancing treatment efficacy and patient outcomes. Furthermore, the integration of digital health tools and telemedicine platforms is improving access to care, especially in remote and underserved regions, thereby broadening the patient base for Lyme disease therapeutics. The market is also benefiting from collaborations between academic institutions, biotech firms, and governmental agencies aimed at accelerating the discovery of novel drug candidates and optimizing existing treatment protocols.

The Lyme Disease Therapeutics market is also being shaped by the increasing adoption of preventive measures and early intervention strategies. Public health authorities are emphasizing the importance of tick-bite prevention, early recognition of symptoms, and prompt initiation of therapy to mitigate the risk of severe complications. Educational campaigns, improved diagnostic tools, and streamlined treatment pathways are collectively contributing to better disease management and higher demand for therapeutic products. Additionally, the growing availability of over-the-counter medications and online pharmacy platforms is making it easier for patients to access essential treatments, further supporting market expansion. As the understanding of Lyme disease pathophysiology deepens, the market is poised to witness the introduction of targeted therapies and vaccines, which hold the promise of transforming the treatment landscape in the coming years.

From a regional perspective, North America continues to dominate the Lyme Disease Therapeutics market, accounting for the largest share in 2024 due to its high disease burden, advanced healthcare infrastructure, and active research ecosystem. Europe follows closely, with countries like Germany, Sweden, and France reporting significant case numbers and robust government response. The Asia Pacific region, while currently representing a smaller market share, is expected to witness the fastest growth during the forecast period, driven by rising awareness, improving healthcare access, and increasing tick-borne disease surveillance. Latin America and the Middle East & Africa are also gradually emerging as important markets, supported by enhanced diagnostic capabilities and growing public health investments. The regional dynamics are further influenced by variations in disease prevalence, healthcare policies, and the availability of therapeutic options.