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Factors Associated with the Incidence of Lyme Disease in North Carolina
By Katherine S. Adams, William J. Taylor, Mark Moore, and Peter Ahiawodzi
Introduction
Transmitted by Ixodes scapularis (blacklegged ticks), Lyme disease is a multisystem bacterial infection caused by the spiralshaped bacterium, Borrelia burgdorferi. First documented in 1975 as childhood arthritis, Lyme disease received its name from the small town of Lyme, Connecticut. According to the Centers for Disease Control and Prevention (CDC), Lyme disease is the most commonly reported vector-borne illness in the United States and the fifth most common Nationally Notifiable disease. (2) Recent inquiry suggests that Lyme disease is greater and more widespread across the nation than formerly assumed. According to a recent survey, blacklegged ticks are now discoverable in twice the number of counties as in 1998 (Appendix A). (3) More than 36,000 confirmed cases were reported nationally in 2013; however, the actual number of cases is predicted to be approximately 300,000 per year, costing the U.S. healthcare system between $712 million and $1.3 billion a year – or nearly $3,000 per patient on average. (3-5) In 2014, 96% of confirmed Lyme disease cases were reported from the following 14 states: Connecticut, Delaware, Maine, Maryland, Massachusetts, Minnesota, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont, Virginia and Wisconsin (Appendix B). (2) While most cases are located in the northeast and north central United States, in recent years, the geographical magnitude of disease has expanded raising the prospect that Lyme disease is becoming endemic in the southeast as well. (6)
Some states have not always considered Lyme disease to be a serious health concern; therefore, not educating their residents on the possible repercussions of infection and recommended measures to prevent the onset of disease. Consequently, the state of North Carolina, sharing a sizeable boundary with Virginia (1 of the 14 states mentioned previously), has experienced a concerning increase in Lyme disease cases. The conclusions of a geographical study conducted by medical providers associated with Duke University Hospital revealed, “the geographic distribution of Lyme disease cases significantly expanded in Virginia between 2000 and 2014, particularly southward in the Virginia mountain ranges. If these trends continue, North Carolina can expect autochthonous Lyme disease transmission in its mountain region in the coming years.” (6) North Carolina reported 173 (39 confirmed and 134 probable) cases in 2013 and 601 (134 confirmed and 467 probable) cases during the 5-year period from 2009 to 2013.1 Four North Carolina counties were classified as endemic for Lyme disease in 2013: Alleghany, Haywood, Guilford and Wake (Appendix C). (1) For a county to be classified as endemic there must be at least two confirmed cases of Lyme disease acquired within the county or an established population of infected blacklegged ticks documented in the county. According to the CDC, 24 of the 173 cases of Lyme disease in 2013 were recorded in Wake County. (2) The number cases in Wake County is quite disturbing given it is one of the fastest growing areas in the nation and secondmost populous county in North Carolina with approximately 1,025,000 residents. (7)
The geographic distribution of Lyme disease is sporadic in North Carolina, ranging from the eastern shore to the western mountains. Maps reveal several instances where counties considered endemic or having an elevated number of cases of Lyme disease have no reported cases in their bordering counties; thereby, causing suspicion on the accuracy of reporting as county borders do not create an impassable barrier for blacklegged ticks. By understanding the distribution of Lyme disease and the factors that contribute to its occurrence, public health officials may be successful in controlling and preventing the problem. The purpose of this study was to examine the geographic distribution of Lyme’s disease in North Carolina and to evaluate what factors influence the reporting of the disease.
Study Design
This was an ecological study of 100 counties in North Carolina. For the purposes of this study, individual county characteristics were collected. Data was gathered from the CDC, North Carolina Wildlife Resources Commission and the United States Census Bureau. Variables analyzed in the study included: population, gender, race, education, income, and the number of whitetail deer harvested in each county in North Carolina. Cumulative percent of population per square mile was used to identify fifty counties as less dense and fifty counties as more dense. Less dense counties had a population of less than 112,000 residents while more dense counties had a population of greater than or equal to 112,000 residents.
Statistical Analysis
Independent sample t-tests were used to demonstrate associations between study variables and county categorization. Simple linear regression modeled the relationship between Lyme disease and explanatory variables individually considered in the study. Additionally, multiple linear regression modeled Lyme disease as a function of multiple explanatory variables. Statistical Package for the Social Sciences (SPSS) software was used to analyze the secondary data. All analysis was considered significant with a p-value of 0.05.
Results
As seen in Table 1, the population was significantly different between the less dense and more dense counties (P <0.0001). The number of individuals living in more dense counties was significantly larger (M = 160,410.68, S.D. = 177,851.589) than the number living in less dense counties (M = 30,292.24, S.D = 18003.169). A notably higher percentage (P = 0.047) of women lived in the denser counties (M = 51.148, S.D. = 1.2082) than less dense counties (M = 50.560, S.D. = 1.8964). A significant difference was found in the racial demographics between less dense and more dense counties. The percent of white and African-American residents was higher in less dense than more dense counties (P=0.008 and P<0.0001, respectively). No significant difference was found between less dense and more dense counties in terms of the percentage of Hispanic or Latino citizens (P = 0.803). The percentage of high school graduates did not differ significantly between less dense and more dense counties (P= 0.237); however, the percentage of those who held a bachelor’s degree or higher in less dense counties (M = 12.4972, S.D. = 6.8197) was considerably lower (P= 0.003) compared to those residing in more dense counties (M= 22.890, S.D. = 9.8798). There was no significant difference in the percentage of individuals living in poverty between less dense and more dense counties (P= 0.177). The results revealed a significant variance between the number of whitetail deer harvested in less dense counties compared to more dense counties (P= 0.001). Finally, the results showed a significant difference in the number of Lyme disease cases for the 14-year period of 2000-2014 between less dense and more dense counties (P < 0.0001). Considerably fewer cases of Lyme disease were reported in less dense counties (M= 5.00, S.D. = 4.785) than more dense counties (M= 23.42, S.D = 33.444).
Population per square mile in North Carolina counties was found to be significantly as-
sociated with Lyme disease (P < 0.0001) in the unadjusted regression estimates displayed in Table 2. As the population of people per square mile increased, the number of cases increased by 64 per 1,000 persons (B = 0.064, S.E = 0.007); however, gender as well as race were not found to be significant in the regression analysis.
The results showed that the incidence of Lyme disease increased as educational background level increased (High School: B =0.765; S.E. = 0.270, P= 0.006; Bachelor’s: B =1.641; S.E. = 0.236 P= 0.000). In that same accord, those who were impoverished had a lowered reported incidence of Lyme disease (B = -1.802; S.E. = 0.511, P = 0.001). Lastly, whitetail deer harvest reports for individual North Carolina counties was not significantly associated with the prevalence of Lyme disease (P= 0.501). not significantly associated with Lyme disease in North Carolina. The percentage of high school graduates or higher was also not significant in the analysis; however, an association between the percentage of individuals who held bachelor’s degrees or higher and Lyme disease was found to be significant (B= 0.843, S.E.= 0.320, P= 0.010). Indicating as percentages of individuals who held bachelor’s degrees or higher increased, the number of Lyme disease cases also increased. The results also suggested that the percentage of persons living in poverty was not significantly associated with Lyme disease occurrence in North Carolina. Finally, whitetail deer harvest reports were found to be significantly associated with Lyme disease cases (P= 0.010). As the number of whitetail deer harvested increased the number of Lyme disease cases increased by 6 cases per 1,000 persons (B =0.006, S.E.= 0.002, P= 0.010).
As illustrated in Table 3, the multivariable adjusted model, population per square mile was found to be significantly associated with Lyme disease cases (P < 0.0001). As the population of people per square mile increased, the number of cases increased by 44 per 1,000 persons (B= 0.044, S.E. = 0.010, P < 0.0001). Also, the percentage of women in a county was found to be significantly inversely associated with Lyme disease (B = -2.385, S.E. = 1.1.84, P=0.047), suggesting that as the female population decreased, Lyme disease cases increased. Race was
Discussion
Once the counties were divided into less dense and more dense categories, the analysis revealed significant differences between the defined categories. The number of reported Lyme disease cases was found to be higher in more dense North Carolina counties. Furthermore, the adjusted regression analysis found population per square mile to be significantly associated with Lyme disease. As the population increased, the number of Lyme disease cases increased by 44 per 1,000 people. The t-test found that larger percent of women occupied more dense counties than less dense counties. Additionally, multiple regression analysis indicated a significant inverse relationship between percent of women and reported Lyme disease cases; suggesting that Lyme disease is more prevalent in men in North Carolina. Since men are more likely to engage in forestry, agriculture, or construction-related occupations, future research should analyze occupational data for each county to potentially determine the association between Lyme disease cases and occupation by county in North Carolina. White and African American individuals were found to occupy less dense counties than more dense counties; however, racial differences among the counties had no correlation to the number of Lyme disease cases reported. The number of residents who held bachelor’s degrees were noticeably higher in more dense counties. Counties with residents possessing a higher education level had higher rates of reporting likely attributable to heightened awareness and medical attention. Medical providers specializing in infectious disease are also more likely to practice in more dense counties which could potentially explain why more dense counties had higher reporting of Lyme disease. The percentage of persons living in poverty did not significantly differ between less dense and more dense counties. No association was found between percentage of persons in poverty and reported cases by county from the regression
analysis. The number of whitetail deer harvested in less dense counties was significantly higher than the number harvested in more dense counties. Counties occupied by fewer residents tend to have larger areas of preferred habitat for wildlife and hunting which explains why less dense counties have more whitetail deer harvested. Furthermore, the results suggested as the number of whitetail deer harvested increased in North Carolina the number of Lyme disease cases increased by 6 cases per 1,000 persons. This variable was selected because whitetail deer are known to host blacklegged ticks; therefore, possibly indicating areas with larger deer populations would have elevated numbers of Lyme disease cases. The study found that more dense counties in North Carolina experienced higher cases of Lyme disease, implying perhaps, hunters who reside in more dense counties traveled to less dense counties to harvest deer and got exposed and became infected with Borrelia burgdorferi.
This study is not without limitations. First, the data used was secondary, and, hence, we were limited to variables available in the reports we used for our analysis. Second, the analyses performed in this study were performed on county level rather than on an individual level. Although we found associations between various factors and Lyme disease, the associations may be applicable at the county level and not necessarily at the individual level. Third, the design and analysis of this study only allows us to talk about associations and not causation; however, the findings form an important step in hypothesis formulation for further testing.
Conclusion
On par with the vaccine-preventable diseases varicella and pertussis, Lyme disease ranks among the top 10 most reportable illnesses in the United States. Lyme disease is a serious and growing public health concern in the United States as well as the state of North Carolina. Governor Roy Cooper released a statement on May 11, 2018 written by the state public health veterinarian, Carl Williams, to all North Carolina medical providers. (1) The statement encourages providers to remain aware of patients who present with Lyme symptoms since the incidence has increased considerably in North Carolina. By obtaining a better understanding of the sporadic reporting of Lyme disease in North Carolina, public health officials have the potential to reduce the number of infections. Considering the economical and health repercussions of Lyme disease on North Carolina and its residents, future precautions should be taken.
Since Lyme disease was found to be more common in male North Carolina residents, occupations and hobbies traditionally dominated by men could be significant in determining prevalence of Lyme disease by county in North Carolina; therefore, additional research should be conducted to determine association of occupation and Lyme disease reporting by county in North Carolina. If an association is found, then extensive education of prevention methods for those commonly exposed to vectors or their host would be beneficial in reducing the occurrence Lyme disease. Education of appropriate prevention techniques would also be advantageous to whitetail deer hunters who spend time in deciduous forests since they have an elevated risk for Lyme disease. By understanding the geographic distribution of Lyme disease and contributing county characteristics, additional work can be done to develop effective prevention. Future research should be conducted to analyze additional county characteristics.
Katherine S. Adams is a Doctor of Pharmacy Candidate, MSPH Candidate, and MBA Candidate at the Campbell University College of Pharmacy & Health Sciences. William J. Taylor, PharmD, is an Associate Professor of Public Health at the Campbell University College of Pharmacy & Health Sciences. Mark Moore, PharmD, MS, MBA is an Associate Dean for Student Affairs & Admissions at the Campbell University College of Pharmacy & Health Sciences. Peter Ahiawodzi, PhD, MPH, CPH, is an Assistant Professor of Epidemiology at the Campbell University College of Pharmacy & Health Sciences. taylorw@ campbell.edu
Table 1
Descriptive Characteristics of the Less Dense and More Dense Counties in North Carolina
Variables Less Dense Counties (N=50) M (S.D) More Dense Counties (N = 50) (M (S.D) P-value
Total Population
30,292.24 (18003.169) 160,410.68 (177,851.589) < 0.0001
Population per square mile
63.536 (25.6109) 325.782 (318. 5523) < 0.0001
% Women
50.560 (1.8964) 51.148 (1.2082) 0.047
% White
71.940 (20.0822) 70.750 (15.2430) 0.008
% African American % Hispanic or Latino % High school diploma % Bachelor’s degree or higher % At poverty level # of whitetail deer harvested Lyme disease cases, 2000-2014
21. 614 (19.9817) 5.160 (4.0189) 79.430 (11.7983) 17.154 (6.8197) 20.750 (4.8121) 1579.84 (1116.719) 5.00 (4.785) 19.850 (12.4972) < 0.0001 7.888 (3.1901) 0.803 83.862 (4.6147) 0.273 22.890 (9.8798) 0.033 17.908 (4.2773) 0.177 1472.74 (707.216) 0.001
23.42 (33.444) < 0.0001
Table 2
Variables Population per square mile % Women % White % African American % Hispanic or Latino % High school diploma % Bachelor’s degree or higher % At poverty level # of whitetail deer harvested Estimate (B) Std. Error P-value
Variables Population per square mile Percent female Percent white Percent African American
Estimate (B) Std. Error P-value
0.044 0.010 <0.0001 -2.385 1.184 0.047 -0.453 0.375 0.230 -0.325 0.386 0.402 Percent Hispanic or Latino -0.063 0.571 0.912 Percent high school graduates or higher 0.102 0.219 0.644 Percent Bachelor’s degree or higher 0.843 0.320 0.010 Percent persons in poverty -0.810 0.544 0.140 Number white-tail deer harvested 0.006 0.002 0.010
References
1. North Carolina Department of Health and Human Services. Lyme Disease in North Carolina. Accessed on June 22, 2018. URL: http://epi.publichealth.nc.gov/cd/lyme/docs/lyme_fs.pdf. 2. Centers for Disease Control. Lyme disease. Accessed on June 22, 2018. URL: https://www. cdc.gov/lyme/index.html. 3. Foster H. Ticks that Transmit Lyme Disease Reported in 48.6% of U.S. Counties. Entomology Today. 2016. URL: https://entomologytoday.org/2016/01/18/ticks-that-transmit-lyme-diseasereported-in-fifty-percent-of-u-s-counties/. 4. Mead P. Epidemiology of Lyme Disease. Infectious Disease Clinics of North America. URL: https://www.id.theclinics.com/article/S0891-5520(15)00024-0/abstract. 5. Johns Hopkins Bloomberg School of Public Health. (2015). Lyme disease costs up to $1.3 billion per year to treat, study finds. Retrieved from http://www.jhsph.edu/news/news-releases/2015/lyme-disease-costs-more-than-one-billion-dollars-per-year-to-treat-study-finds.html 6. Lantos M, Pan K, Gaines N, et al. Geographic Expansion of Lyme Disease in the Southeastern United States, 2000-2014. Open Forum Infectious Diseases. 2015. 7. Walston, S. (2016). Learn about Wake County. Retrieved from http://www.wakegov.com/ about/facts/Pages/default.asp
The first map (A) reveals I. scapularis distribution in 1998 and the second map (B) reveals distribution in 2015. Counties reporting six or more ticks were classified as established (red and green) and counties reporting at least one tick were classified as reported (blue and yellow).
Appendix B (Reference 2) The map shows the distribution of probable and
confirmed cases of Lyme disease in the United States in 2014. Appendix C (Reference 1)
The lightest colored counties indicate zero reported cases of Lyme disease. Meanwhile, the
darkest colored counties indicate greater than
five reported cases of Lyme disease. The four counties colored in red have been classified as
endemic for the disease.
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