Lyme Disease Awareness Month: New model identifies 162 US counties where tick-borne illness is likely to emerge

A new study where researchers have drawn on 17 years of data to develop a model identifies areas in which the tick-borne illness is likely to emerge


                            Lyme Disease Awareness Month: New model identifies 162 US counties where tick-borne illness is likely to emerge
(US Centers for Disease Control and Prevention)

An estimated 162 US counties may already have Lyme disease, but they have not yet been reported to the US Centers for Disease Control and Prevention (CDC). This is according to a new study where researchers have drawn on 17 years of data to develop a model that identifies areas in which the tick-borne illness is likely to emerge.

The CDC receives reports of about 30,000 cases of Lyme disease each year. The real number, according to the agency, is around 300,000. Underreporting affects the ability of public health authorities to assess risk, allocate resources and devise prevention strategies.

It also makes early detection very difficult, hampering efforts to treat the condition quickly and effectively, say experts. But the new data model developed by researchers from Columbia University and RTI International, a nonprofit research institute, can change this.

It helps identify areas of the US where Lyme disease cases may go unreported. Models have been created in the past to identify high-risk areas in a few states or regions in the US, but the latest one expands the geographic scope to all areas in the country where the disease is most likely to occur.

To develop their model, the researchers analyzed publicly available data, tracking the geographic spread of Lyme disease over nearly two decades. They studied an estimated 500,000 cases of the illness reported to CDC from different counties across the US between 2000 and 2017.

According to the study, between 2000 and 2017, a total of 497,569 Lyme disease cases were reported to the CDC in the study area. The study included 1,405 counties in the following regions: 27,963 in West North Central, 39,774 in East North Central, 130,992 in New England, 247,029 in the Middle Atlantic, and 51,811 in the South.

The publicly available data do not include sex, age or other demographic information.

Among the counties in the study, the probability of a county reporting its first case of Lyme disease (between 2000 and 2017) was associated with a county’s and the county’s neighbors’ forest coverage, elevation, percentage of the population living in the wildland-urban interface, tick presence, county’s population size, the proportion of neighbors reporting cases, and neighbors’ years since first reporting.

The model estimated that the first report of Lyme disease cases in a county occurred on average 5.5 years earlier than was reported to the CDC. The researchers were able to show that about 162 US counties may have Lyme disease, which have not yet been reported.

Further, 29% of counties not reporting a case between 2000 and 2017 had an 80% probability of reporting a case by 2018, say researchers. 

"The model that included these variables showed high predictive power. The model predicted the first reported Lyme disease case a mean of 5.5 years earlier than was reported to the CDC, with a mean spread velocity estimated at 27.4 km per year."

"Among 162 counties without reported cases, 47 (29.0%) had a high probability of reporting Lyme disease by 2018. The estimated mean time lag between the first reported case in a neighboring county and any county was 7 years," says the study published in JAMA Network. 

Underreporting affects the ability of public health authorities to assess risk, and devise prevention strategies. It also makes early detection very difficult, hampering efforts to treat the condition quickly, say experts (Getty Images)

The CDC collects Lyme disease data from state and local health departments, which base the number of cases on notifications from clinicians, hospitals and laboratories. During 2008-2015, a total of 275,589 cases of Lyme disease were reported to CDC (208,834 confirmed and 66,755 probable), according to the latest CDC summary.

The report says that while most cases continue to be reported from states with high incidence in the Northeast, mid-Atlantic and upper Midwest regions, case counts in most of these states have remained stable or decreased during the reporting period.

Lyme disease is difficult to diagnose, and accurate case assessment depends on many variables, the researchers said, from provider awareness and testing methods to reporting practices, state budgets and personnel.

The research team adds that even though Lyme disease has been diagnosed in almost every state, most cases reported to the CDC are in the Northeast and upper Midwest. Experts believe that their analysis can help predict the trajectory of where Lyme disease will spread.

"Our predictive model, particularly if it is updated annually and expanded geographically, can enable states and counties to develop more specific Lyme disease prevention and control strategies, including optimizing active surveillance of disease cases and ticks, to increase early detection," says the research team.

Identifying high-risk areas can lead to surveillance in counties and areas where infections are likely to emerge. It also allows authorities to alert physicians and the public, which can lead to early treatment, when it is most effective, say experts. 

"This model will facilitate more targeted communication to and sensitization of the general population and the medical community to the risk of tick exposure and Lyme disease. Our findings can help public health authorities focus surveillance efforts in counties identified as being at high risk of Lyme disease emergence but where cases have yet to be reported," say researchers.

Disclaimer : This article is for informational purposes only and is not a substitute for professional medical advice, diagnosis, or treatment. Always seek the advice of your physician or other qualified health provider with any questions you may have regarding a medical condition.