| Literature DB >> 26503011 |
Camilo E Khatchikian1, Robert B Nadelman2, John Nowakowski3, Ira Schwartz4, Michael Z Levy5, Dustin Brisson6, Gary P Wormser7.
Abstract
BACKGROUND: Lyme disease, caused by Borrelia burgdorferi, is the most common tick-borne infection in the United States. Although humans can be infected by at least 16 different strains of B. burgdorferi, the overwhelming majority of infections are due to only four strains. It was recently demonstrated that patients who are treated for early Lyme disease develop immunity to the specific strain of B. burgdorferi that caused their infection. The aim of this study is to estimate the reduction in cases of Lyme disease in the United States that may occur as a result of type specific immunity.Entities:
Mesh:
Year: 2015 PMID: 26503011 PMCID: PMC4621928 DOI: 10.1186/s12879-015-1190-7
Source DB: PubMed Journal: BMC Infect Dis ISSN: 1471-2334 Impact factor: 3.090
Frequency of the different OspC types that were cultured from the skin of 200 patients with erythema migrans [17, 18].
| OspC type | Number (%) |
|---|---|
| Total | 200 (100 %) |
|
| 73 (36.5 %) |
|
| 38 (19 %) |
|
| 28 (14 %) |
|
| 14 (7 %) |
| N | 13 (6.5 %) |
| E | 13 (6.5 %) |
| U | 9 (4.5 %) |
| H | 5 (2.5 %) |
| C | 2 (1 %) |
| D | 2 (1 %) |
| F | 1 (0.5 %) |
| G | 1 (0.5 %) |
| M | 1 (0.5 %) |
| J | 0 (0 %) |
| L | 0 (0 %) |
| T | 0 (0 %) |
Bold font indicates invasive OspC types, comprising 76.5 % of the total cases
Summary of the characteristics of the three models used to estimate the number of averted cases of Lyme disease due to type specific immunity
| Model | Key assumptionsa | Benefits | Limitations |
|---|---|---|---|
| Deterministic probability | Lyme disease patients’ probability of exposure to infectious bite similar to general population. | Extremely simple and flexible. Allows a separate analysis focusing on infections caused by invasive strains of | May over estimate the impact of immunity on averted cases. |
| Immunity is permanent. | |||
| Provides the upper limit of averted cases. | |||
| Equilibrium dynamic | Lyme disease patients’ probability of exposure to infectious bite similar to general population. | Simple. | May under estimate the impact of immunity on averted cases. |
| Provides the lower limit of averted cases. | |||
| Immunity lasts 5 to 30 years. | |||
| Lyme disease patients are at risk for tick bites for 30 years. | |||
| Individual-based stochastic | Lyme disease patients’ probability of exposure to infectious bite higher than in general population. | Most complex, allows manipulation of many parameters. | Simulations are time-demanding. |
| May provide the most realistic estimate of the number of averted cases. | |||
| Immunity lasts 5 to 30 years. | |||
| Patients are at risk for tick bites for 30 years. |
aall models share the key assumptions that immunity provides 100 % protection to a particular OspC type of B. burgdorferi, that there is no cross-immunity across different OspC types, and that in the absence of immunity the likelihood of developing infection with a particular OspC type follows the strain frequencies presented in Table 1
Estimation of the yearly number of averted Lyme diseases cases in the United States based on the number of Lyme disease cases reported (approximately 30,000) or estimated (around 300,000) using three different analyses. The number of averted cases was calculated using three different estimates of reinfection rates: 1 %, 3 %, and 5 %
| Lyme disease incidence | Immunity length | Estimated number of averted cases | ||
|---|---|---|---|---|
| Deterministic probability modela | ||||
| 1 % reinfection | 3 % reinfection | 5 % reinfection | ||
| 30,000 cases | life span | 77 | 232 | 387 |
| 300,000 cases | life span | 775 | 2,324 | 3,873 |
| Equilibrium dynamic model | ||||
| 1 % reinfection | 3 % reinfection | 5 % reinfection | ||
| 30,000 cases | 5 years | 11 | 32 | 53 |
| 30,000 cases | 30 years | 78 | 233 | 390 |
| 300,000 cases | 5 years | 106 | 319 | 532 |
| 300,000 cases | 30 years | 776 | 2,333 | 3,898 |
| Individual-based stochastic model | ||||
| 1 % reinfection | 3 % reinfection | 5 % reinfection | ||
| 30,000 cases | 5 years | 23 | 55 | 80 |
| 30,000 cases | 30 years | 90 | 238 | 410 |
| 300,000 cases | 5 years | 227 | 549 | 799 |
| 300,000 cases | 30 years | 899 | 2,378 | 4,100 |
aCalculations based on the values in Table 1