Quantifying the Public Health Impact of Lyme Disease in Minnesota: A Simulation Analysis of Reported and Unreported Cases

<b>Background:</b> Lyme disease, the most common vector-borne disease in Minnesota, is estimated to be underreported by a factor of 10. Delayed diagnosis and misdiagnosis may lead to health complications and increased personal and societal costs. Environmental factors can help to predict high disease years, allowing for early intervention to decrease disease burden. <b>Objective:</b> To estimate the health and cost burdens of Lyme disease and the extent to which they could be diminished by public health intervention when high-incidence Lyme disease years are forecasted. <b>Methods:</b> We used 5 two-dimensional Monte Carlo simulations to estimate (1) average annual expected burden of Lyme disease, (2 and 3) average burden in low- and high-incidence years, and (4 and 5) the expected burden saved with public health educational interventions preceding high-incidence years. We employed cases reported to the Minnesota Department of Health adjusted for estimates of underreporting found in the literature. <b>Results:</b> Among an average of 8436 Lyme disease cases annually, 6074 of them were unidentified. High-incidence years saw over 3700 more cases than low-incidence years, with incremental costs to patients and society exceeding <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>3</mml:mn> <mml:mi>m</mml:mi> <mml:mi>i</mml:mi> <mml:mi>l</mml:mi> <mml:mi>l</mml:mi> <mml:mi>i</mml:mi> <mml:mi>o</mml:mi> <mml:mi>n</mml:mi> <mml:mo>.</mml:mo> <mml:mi>W</mml:mi> <mml:mi>e</mml:mi> <mml:mi>e</mml:mi> <mml:mi>s</mml:mi> <mml:mi>t</mml:mi> <mml:mi>i</mml:mi> <mml:mi>m</mml:mi> <mml:mi>a</mml:mi> <mml:mi>t</mml:mi> <mml:mi>e</mml:mi> <mml:mi>d</mml:mi> <mml:mi>t</mml:mi> <mml:mi>h</mml:mi> <mml:mi>a</mml:mi> <mml:mi>t</mml:mi> <mml:mi>p</mml:mi> <mml:mi>u</mml:mi> <mml:mi>b</mml:mi> <mml:mi>l</mml:mi> <mml:mi>i</mml:mi> <mml:mi>c</mml:mi> <mml:mi>h</mml:mi> <mml:mi>e</mml:mi> <mml:mi>a</mml:mi> <mml:mi>l</mml:mi> <mml:mi>t</mml:mi> <mml:mi>h</mml:mi> <mml:mi>e</mml:mi> <mml:mi>d</mml:mi> <mml:mi>u</mml:mi> <mml:mi>c</mml:mi> <mml:mi>a</mml:mi> <mml:mi>t</mml:mi> <mml:mi>i</mml:mi> <mml:mi>o</mml:mi> <mml:mi>n</mml:mi> <mml:mi>b</mml:mi> <mml:mi>e</mml:mi> <mml:mi>f</mml:mi> <mml:mi>o</mml:mi> <mml:mi>r</mml:mi> <mml:mi>e</mml:mi> <mml:mi>h</mml:mi> <mml:mi>i</mml:mi> <mml:mi>g</mml:mi> <mml:mi>h</mml:mi> <mml:mo>-</mml:mo> <mml:mi>i</mml:mi> <mml:mi>n</mml:mi> <mml:mi>c</mml:mi> <mml:mi>i</mml:mi> <mml:mi>d</mml:mi> <mml:mi>e</mml:mi> <mml:mi>n</mml:mi> <mml:mi>c</mml:mi> <mml:mi>e</mml:mi> <mml:mi>y</mml:mi> <mml:mi>e</mml:mi> <mml:mi>a</mml:mi> <mml:mi>r</mml:mi> <mml:mi>s</mml:mi> <mml:mi>c</mml:mi> <mml:mi>o</mml:mi> <mml:mi>u</mml:mi> <mml:mi>l</mml:mi> <mml:mi>d</mml:mi> <mml:mi>r</mml:mi> <mml:mi>e</mml:mi> <mml:mi>d</mml:mi> <mml:mi>u</mml:mi> <mml:mi>c</mml:mi> <mml:mi>e</mml:mi> <mml:mi>L</mml:mi> <mml:mi>y</mml:mi> <mml:mi>m</mml:mi> <mml:mi>e</mml:mi> <mml:mi>d</mml:mi> <mml:mi>i</mml:mi> <mml:mi>s</mml:mi> <mml:mi>e</mml:mi> <mml:mi>a</mml:mi> <mml:mi>s</mml:mi> <mml:mi>e</mml:mi> <mml:mi>c</mml:mi> <mml:mi>a</mml:mi> <mml:mi>s</mml:mi> <mml:mi>e</mml:mi> <mml:mi>s</mml:mi> <mml:mi>b</mml:mi> <mml:mi>y</mml:mi> <mml:mn>390</mml:mn> <mml:mi>t</mml:mi> <mml:mi>o</mml:mi> <mml:mn>787</mml:mn> <mml:mi>a</mml:mi> <mml:mi>n</mml:mi> <mml:mi>n</mml:mi> <mml:mi>u</mml:mi> <mml:mi>a</mml:mi> <mml:mi>l</mml:mi> <mml:mi>l</mml:mi> <mml:mi>y</mml:mi> <mml:mo>,</mml:mo> <mml:mi>s</mml:mi> <mml:mi>a</mml:mi> <mml:mi>v</mml:mi> <mml:mi>i</mml:mi> <mml:mi>n</mml:mi> <mml:mi>g</mml:mi> <mml:mi>u</mml:mi> <mml:mi>p</mml:mi> <mml:mi>t</mml:mi> <mml:mi>o</mml:mi></mml:math> 1.9 million in societal costs. <b>Discussion:</b> The simulations presented revealed substantial health and cost burden from Lyme disease, including hidden impacts from undiagnosed and unreported cases. Burden varied widely between high- and low-incidence years, highlighting the need to prioritize prevention when peak years are predicted. While we estimated the effects of individual prevention measures, real-world interventions often combine strategies, potentially producing a greater, multiplicative impact, suggesting our estimates may be conservative. <b>Conclusions:</b> Simulation modeling demonstrates Lyme disease's significant impact on individuals and society. Annual forecasting-triggered public health interventions could reduce cost and disease burden, and these findings may help justify the cost of prevention efforts in policy decision-making.