The Financial Implications of a Well-Hidden and Ignored Chronic Lyme Disease Pandemic
<p>The number of people that tested positive in both groups (z) when the number of infected people in the disease group (x) and the number of healthy people in control groups (y) are unknown.</p> "> Figure 2
<p>The % number of people that tested positive in both groups (zz) when the number of infected people in the disease group (x) and the number of healthy people in control groups (y) are unknown.</p> "> Figure 3
<p>The relationship between y, x and p.</p> "> Figure 4
<p>The number of people that tested positive in both groups (z) adjusted for the relationship between y and x (p).</p> "> Figure 5
<p>The % number of people that tested positive in both groups (zz) adjusted for the relationship between y and x (p).</p> "> Figure 6
<p>The number of infected people in the disease group (x) given total number of people (T) and the relationship (p) between x and y.</p> "> Figure 7
<p>The number of healthy people in the control group (y) given the total number of people (T) and the relationship (p) between x and y.</p> "> Figure 8
<p>The value of observed % positive cannot be equal to predicted % positive for the average scenario in the CDC model.</p> "> Figure 9
<p>Number of Lyme disease infections in the USA between the years 2008 to 2050.</p> "> Figure 10
<p>Annual incidence rate of Lyme disease in the USA between the years 2008 to 2050.</p> "> Figure 11
<p>Total number of Lyme disease infections in the USA between the years 2008 to 2050.</p> "> Figure 12
<p>% of the population in the USA that have been infected with Lyme disease between the years 2008 to 2050.</p> "> Figure 13
<p>Number of Lyme disease infections in Europe between the years 2008 to 2050.</p> "> Figure 14
<p>Annual incidence rate of Lyme disease in Europe between the years 2008 to 2050.</p> "> Figure 15
<p>Total number of Lyme disease infections in Europe between the years 2008 to 2050.</p> "> Figure 16
<p>% of the population in Europe that have been infected with Lyme disease between the years 2008 to 2050.</p> "> Figure 17
<p>Number of people with chronic Lyme disease in 2050 in the USA</p> "> Figure 18
<p>Number of people with chronic Lyme disease in 2050 in Europe.</p> "> Figure 19
<p>Today’s government costs and revenues in the USA over time.</p> "> Figure 19 Cont.
<p>Today’s government costs and revenues in the USA over time.</p> "> Figure 19 Cont.
<p>Today’s government costs and revenues in the USA over time.</p> "> Figure 20
<p>Future government costs and revenues in the USA over time.</p> "> Figure 20 Cont.
<p>Future government costs and revenues in the USA over time.</p> "> Figure 20 Cont.
<p>Future government costs and revenues in the USA over time.</p> "> Figure 21
<p>Today’s government costs and revenues in Europe over time.</p> "> Figure 21 Cont.
<p>Today’s government costs and revenues in Europe over time.</p> "> Figure 21 Cont.
<p>Today’s government costs and revenues in Europe over time.</p> "> Figure 22
<p>Future government costs and revenues in Europe over time.</p> "> Figure 22 Cont.
<p>Future government costs and revenues in Europe over time.</p> "> Figure 22 Cont.
<p>Future government costs and revenues in Europe over time.</p> "> Figure 22 Cont.
<p>Future government costs and revenues in Europe over time.</p> ">
Abstract
:1. Introduction
2. Methods
3. Results
4. Discussion
5. Conclusions
Acknowledgments
Conflicts of Interest
VBA Code
References
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Assumed values | |||
---|---|---|---|
Number of infected people in disease group (x) | 100 | ||
Number of healthy people in control group (y) | 100 | ||
Test sensitivity (se) | 0.44 | ||
Test specificity (sp) | 0.99 | ||
Formulas | |||
Number of infected people in the disease group that tested positive = true positive (a) | x * se | ||
Number of infected people in the disease group that tested negative = false negative (b) | x - a = x - x * se | ||
Number of healthy people in the control group that tested positive = false positive (c) | y * (1-sp) | ||
Number of healthy people in the control group that tested negative = true negative (d) | y - c = y - y * (1-sp) | ||
Confusion matrix | |||
Disease group | Control group | ||
Number of true positives (a) = | 44 | Number of false positives (c) = | 1 |
Number of false negatives (b) = | 56 | Number of true negatives (d) = | 99 |
Statistics | |||
The relationship between y and x (p) | y / x | 1 | |
Test sensitivity (se) | a / (a + b) | 0.44 | |
Test specificity (sp) | d / (c + d) | 0.99 | |
Number of infected people in the disease group (x) | a + b | 100 | |
Number of healthy people in the control group (y) | c + d | 100 | |
Total number of tested people | a + b + c + d | 200 | |
Number of people that tested positive in both groups (z) | a + c = x * se + y * (1-sp) | 45 | |
% of people that tested positive in both groups (zz) | (z / (x + y)) * 100 | 22.5 | |
Number of false test results in disease group | b | 56 | |
% number of false test results in disease group | (b / (a + b)) * 100 | 56 | |
Number of correct test results in disease group | a | 44 | |
% number of correct test results in disease group | (a / (a + b)) * 100 | 44 | |
Number of correct test results in control group | d | 99 | |
% number of correct test results in control group | (d / (c + d)) * 100 | 99 | |
Number of false test results in control group | c | 1 | |
% number of false test results in control group | (c / (c + d)) * 100 | 1 | |
Number of false test result in both groups | b + c | 57 | |
% number of false test results in both groups | (b + c) / (a + b +c +d) | 0.285 | |
% of people that tested positive in disease group | (a / (a +b)) * 100 | 44 |
The equation we need to solve is the following: | ||
Number of people that tested positive in both groups (z) = | ||
the number of true positives (a) + the number of false positives (c) | ||
where | ||
Number of people with an infection in disease group | x | |
Number of people without an infection in control group | y | |
Total number of people in the disease and control groups | x + y | |
Test sensitivity (se) | 0.44 | |
Test specificity (sp) | 0.99 | |
Number of true positives (a) = x * se | x * 0.44 | |
Number of false positives (c) = y * (1 - sp) | y * (1-0.99) | |
Number of people that tested positive in both groups (z) | 100 000 | |
which means: | ||
The equation we need to solve is | z = x * se + y * (1-sp) | |
with constraint | z = | 100 000 |
se = | 0.44 | |
sp = | 0.99 | |
y = | p*x | |
p = | 2.5 | |
We can solve such equation in excel by using matrix algebra | ||
The system of linear equations we should solve is | A * B = C | |
where | ||
A = | 0.44 | 0.01 |
−2.5 | 1 | |
B = | x | |
y | ||
C = | 100 000 | |
0 | ||
A^-1 = | 2.150538 | −0.021505 |
5.376344 | 0.946237 | |
B = A ^ (-1) * C | x = | 215 054 |
y = | 537 634 | |
The relationship between y and x (p) = y / x | 2.5 | |
Number of people that test positive in the disease group = true positive (a) = x * se | 94 624 | |
Number of people that test positive in the control group = false positive (c) = y * (1-sp) | 5 376 | |
Number of people that tested positive in both groups (z) = a + c = x * se + y *(1-sp) | 100 000 | |
% of people that tested positive in both groups (zz) = (z / ( x + y) )*100 | 13.29 | |
I have written a user defined function (udf) in VBA that does the above calculations automatically | ||
Lyme1(se ; sp ; p ; z ; output) where output is either "x", "y" or "zz" | ||
Lyme1(0.44;0.99;2.5;100000;"x") | x = | 215 054 |
Lyme1(0.44;0.99;2.5;100000;"y") | y = | 537 634 |
Lyme1(0.44;0.99;2.5;100000;"zz") | zz = | 13.29 |
We know that | We know that | |
z = x * se + y * (1-sp) | z = x * se + y * (1-sp) | |
y = p * x | x = y / p | |
which means that | which means that | |
z = x * se + p * x * (1 - sp) | z = (y / p) * se + y * (1 - sp) | |
we solve for x | we solve for y | |
x = z / (-sp * p + se + p) | y = p * z / (-sp * p + se + p) | |
For the previous example with z = 100 000 and p= 2.5 we get | ||
x = | 215 054 | |
y = | 537 634 | |
I have again written a udf in VBA | ||
Lyme2(se ; sp ; p ; z ; output) where output is either "x", "y" or "zz" | ||
Lyme2(0.44;0.99;2.5;100000;"x") | x = | 215 054 |
Lyme2(0.44;0.99;2.5;100000;"y") | y = | 537 634 |
Lyme2(0.44;0.99;2.5;100000;"zz") | zz = | 13.29 |
we know that | we know that | |
zz = (z / (x + y)) * 100 | zz = (z / (x + y)) * 100 | |
y = p * x | x = y / p | |
which means that | which means that | |
zz = (z / (x + p * x)) * 100 | zz = (z / ((y / p) + y)) * 100 | |
we solve for x | we solve for y | |
x = 100 * z / (p * zz + zz) | y = 100 * p * z / (p * zz + zz) | |
For the previous example with z=100 000, zz =13.29 and p= 2.5 we get | ||
x = | 215 054 | |
y = | 537 634 | |
I have again written a udf in VBA | ||
Lyme3(se ; sp ; p ; z ; zz ; output) where output is either "x", "y" | ||
Lyme3(0.44;0.99;2.5;100000;13.29;"x") | x = | 215 054 |
Lyme3(0.44;0.99;2.5;100000;13.29;"y") | y = | 537 634 |
Number of infected people in disease group | x |
Number of healthy people in control group | y |
Total number of people (T) | x + y |
T = x + y --> y = T - x | T = x + y --> x = T - y |
y = p * x | x = y / p |
This means that | This means that |
T - x = p * x | T - y = y / p |
We can solve for x | We can solve for y |
x = T / ( p + 1 ) | y = p * T / ( p + 1 ) |
Total number of infections (tni1)) at year y given an annual percentage growth rateof infection (g) is given by --> tni1(y) = tni1(y-1) * (1+g/100) | |||||||||||||||||||||||||||||
Total number of infections (tni2) at year y given a percentage transmission rate (t) and given that each infected person has (n) number of healthy sexual partners each year is given by | |||||||||||||||||||||||||||||
--> tni2(y) = tni2(y-1) * (1+(t/100) * n) | |||||||||||||||||||||||||||||
We can see that given that each infected person has sex with one healthy person each year then the transmission rate is | |||||||||||||||||||||||||||||
equal to the annual percentage growth rate of infection | |||||||||||||||||||||||||||||
g = | 100 | t = | 100 | t = | 100 | ||||||||||||||||||||||||
n = | 1 | n = | 2 | ||||||||||||||||||||||||||
Year (y) | tni1 | % change | Year (y) | tni2 | % change | Year (y) | tni2 | % change | |||||||||||||||||||||
1 | 1 | na | 1 | 1 | na | 1 | 1 | na | |||||||||||||||||||||
2 | 2 | 100 | 2 | 2 | 100 | 2 | 3 | 200 | |||||||||||||||||||||
3 | 4 | 100 | 3 | 4 | 100 | 3 | 9 | 200 | |||||||||||||||||||||
4 | 8 | 100 | 4 | 8 | 100 | 4 | 27 | 200 | |||||||||||||||||||||
5 | 16 | 100 | 5 | 16 | 100 | 5 | 81 | 200 | |||||||||||||||||||||
6 | 32 | 100 | 6 | 32 | 100 | 6 | 243 | 200 | |||||||||||||||||||||
7 | 64 | 100 | 7 | 64 | 100 | 7 | 729 | 200 | |||||||||||||||||||||
g = | 2 | t = | 2 | t = | 2 | ||||||||||||||||||||||||
n = | 1 | n = | 2 | ||||||||||||||||||||||||||
Year (y) | tni1 | % change | Year (y) | tni2 | % change | Year (y) | tni2 | % change | |||||||||||||||||||||
1 | 1 | na | 1 | 1.0000 | na | 1 | 1.0000 | na | |||||||||||||||||||||
2 | 1.02 | 2 | 2 | 1.0200 | 2 | 2 | 1.0400 | 4 | |||||||||||||||||||||
3 | 1.04 | 2 | 3 | 1.0404 | 2 | 3 | 1.0816 | 4 | |||||||||||||||||||||
4 | 1.06 | 2 | 4 | 1.0612 | 2 | 4 | 1.1249 | 4 | |||||||||||||||||||||
5 | 1.08 | 2 | 5 | 1.0824 | 2 | 5 | 1.1699 | 4 | |||||||||||||||||||||
6 | 1.10 | 2 | 6 | 1.1041 | 2 | 6 | 1.2167 | 4 | |||||||||||||||||||||
7 | 1.13 | 2 | 7 | 1.1262 | 2 | 7 | 1.2653 | 4 | |||||||||||||||||||||
tni2 can also be expressed as tni2(y+1) = number of old infections +number of new infections = tni2(y) + tni2(y) * (t/100) * n | |||||||||||||||||||||||||||||
t = | 100 | t = | 2 | ||||||||||||||||||||||||||
n = | 1 | n = | 1 | ||||||||||||||||||||||||||
Year (y) | Number of old infections | Number of new infections | tni2 | Year (y) | Number of old infections | Number of new infections | tni2 | ||||||||||||||||||||||
1 | 0 | 1 | 1 | 1 | 0.0000 | 1.0000 | 1.0000 | ||||||||||||||||||||||
2 | 1 | 1 | 2 | 2 | 1.0000 | 0.0200 | 1.0200 | ||||||||||||||||||||||
3 | 2 | 2 | 4 | 3 | 1.0200 | 0.0204 | 1.0404 | ||||||||||||||||||||||
4 | 4 | 4 | 8 | 4 | 1.0404 | 0.0208 | 1.0612 | ||||||||||||||||||||||
5 | 8 | 8 | 16 | 5 | 1.0612 | 0.0212 | 1.0824 | ||||||||||||||||||||||
6 | 16 | 16 | 32 | 6 | 1.0824 | 0.0216 | 1.1041 | ||||||||||||||||||||||
7 | 32 | 32 | 64 | 7 | 1.1041 | 0.0221 | 1.1262 |
CDC equations | |||
---|---|---|---|
Observed % Positive = % True Infection * Sensitivity Lyme test + (1 - % True Infection) * (1 – Specificity Lyme test) | |||
Observed positive = (% True Infection * Sensitivity + (1 - % True Infection) * (1 – Specificity)) | |||
* number of performed individual Lyme disease tests | |||
% True Infection = (Observed % Positive + Specificity – 1) / (Specificity + Sensitivity – 1) | |||
True Infection = ((Observed % Positive + Specificity – 1) / (Specificity + Sensitivity – 1)) | |||
* number of performed individual Lyme disease tests | |||
The performed number of individual Lyme disease test in 2008 in the USA (n) | 2 400 000 | ||
Lyme disease test sensitivity assumed by the CDC (CDC se) | 0.669 | ||
Lyme disease test specificity assumed by the CDC (CDC sp) | 0.961 | ||
CDC scenario | Low | Average | High |
% true infection also known as predicted % positive estimated by the CDC (CDC xx) | 0.1 | 0.1189 | 0.185 |
Observed % positive in the control and disease group according to or implied by the CDC (CDC zz) | 0.102 | 0.1189 | 0.15555 |
The number of people that tested positive in the control and disease group in the USA in 2008 according to the CDC (observed positive or CDC z) | 244 800 | 285 360 | 373 320 |
The number of infected people in the disease group estimated by the CDC (predicted positive also known as true infection or CDC x) | 240 000 | 285 360 | 444 000 |
CDC z = CDC x * CDC se + CDC y * (1 - CDC sp) | 244 800 = 240 000 * 0.669 + y * (1-0.961) | 285 360 = 285 360 * 0.669 + y * (1-0.961) | 373 320 = 444 000 * 0.669 + y * (1-0.961) |
The number of healthy people in the control group according the CDC (CDC y) | 2 160 000 | 2 421 901 | 1 965 000 |
The relationship between y and x = CDC p = CDC y / CDC x | 9.0000 | 8.4872 | 4.4257 |
Number of healthy people in the control group (y) : number of infected people in the disease group (x) | 9 : 1 | 8.4872 : 1 | 4.4257 : 1 |
My model | |||
We assume p = 1, se = 0.44, sp = 0.99 and z = CDC z in Lyme2(se ; sp ; p ; z ; Output = q) where q is either "x", "y" or "zz" ? | |||
x = | 544 000 | 634 133 | 829 600 |
y = | 544 000 | 634 133 | 829 600 |
zz = | 22.50 | 22.50 | 22.50 |
We assume p = 0.5, se = 0.44, sp = 0.99 and z = CDC z in Lyme2(se ; sp ; p ; z ; Output = q) where q is either "x", "y" or "zz" ? | |||
x = | 550 112 | 641 258 | 838 921 |
y = | 275 056 | 320 629 | 419 461 |
zz = | 29.67 | 29.67 | 29.67 |
CDC scenario | Low | Average | High |
---|---|---|---|
The number of infected people in the disease group estimated by the CDC (predicted positive also known as true infection or CDC x) | 240 000 | 285 360 | 444 000 |
Assumptions | CDC z = 244 800 | CDC z = 285 360 | CDC z = 373 320 |
Lyme2(se ; sp ; p ; z ; output) = Lyme2(0.669 ; 0.961 ; 1 ; CDC z ; "x") | 345 763 | 403 051 | 527 288 |
CDC assumptions | CDC p = 9 and CDC z = 244 800 | CDC p = 8.4872 and CDC z = 285 360 | CDC p = 4.4257 and CDC z = 373 320 |
(A) Lyme2(0.669 ; 0.961 ; CDC p ; CDC z ; "x") | 240 000 | 285 360 | 443 582 |
CDC x - A | 0 | 0 | 418 |
(B) Lyme2(0.44 ; 0.99 ; 1 ; CDC z ; "x") | 533 333 | 634 133 | 829 600 |
B - A | 293 333 | 348 773 | 386 018 |
% difference between A and B = (B - A)/B | 55 | 55 | 47 |
Number of Lyme disease infections in the USA between 2008 to 2050 given an assumed 2% annual growth rate of infection | 55 715 494 | |
USA's population in 2050 given an annual population growth of 1% | 461 712 128 | |
% of the population in the USA that has been infected with Lyme disease between 2008 and 2050 | 12.07 | |
People that are infected with Lyme disease that develop chronic Lyme disease | 0.63 | |
People that are infected with Lyme disease that develop an acute infection | 0.37 | |
Number of people that has had acute Lyme disease between 2008 and 2050 in the USA | 20 614 733 | |
% of population in 2050 that has had acute Lyme disease between 2008 and 2050 in the USA | 4.46 | |
Assumed cure rate | Number of people with chronic Lyme disease in 2050 in the USA | % of USA's population with chronic Lyme disease in 2050 |
0% cure rate | 35 100 762 | 8 |
25% cure rate | 26 325 571 | 6 |
50% cure rate | 17 550 381 | 4 |
75% cure rate | 8 775 190 | 2 |
100% cure rate | 0 | 0 |
Number of Lyme disease infections in Europe between 2008 to 2050 given an assumed 2% annual growth rate of infection | 134 890 144 | |
Europe's population in 2050 given an annual population growth of 0.2% | 800 427 717 | |
% of the population in Europe that has been infected with Lyme disease between 2008 and 2050 | 16.85 | |
People that are infected with Lyme disease that develop chronic Lyme disease | 0.63 | |
People that are infected with Lyme disease that develop an acute infection | 0.37 | |
Number of people that has had acute Lyme disease between 2008 and 2050 in Europe | 49 909 353 | |
% of population in 2050 that has had acute Lyme disease between 2008 and 2050 in Europe | 6.24 | |
Assumed cure rate | Number of people with chronic Lyme disease in 2050 in Europe | % of Europe’s population with chronic Lyme disease in 2050 |
0% cure rate | 84 980 791 | 11 |
25% cure rate | 63 735 593 | 8 |
50% cure rate | 42 490 396 | 5 |
75% cure rate | 21 245 198 | 3 |
100% cure rate | 0 | 0 |
A government's financial balance after tax for a disabled person not working without the lost tax revenue because a chronic Lyme patient is not working | |||
---|---|---|---|
Annual disability benefits after tax (ADAT) | 13 000 | ||
% tax rate on income from work | 0.40 | ||
Tax rate in % on disability benefits | 0.20 | 0.40 | |
Annual disability benefits before tax T1 = ADAT / (1 - T1) | 16 250 | 21 667 | |
Difference | |||
Government revenues | |||
Tax revenues from disability benefits (+) | 3 250 | 8 667 | 5 417 |
Government costs for a disabled person | |||
Disability benefits (-) | -16 250 | -21 667 | -5 417 |
A government's financial balance = tax revenue + government cost | -13 000 | -13 000 | 0 |
A government's financial balance for an enabled person working vs a disabled person not working before tax | |||
Annual disability benefits before tax (ADAT) | 13 000 | ||
Annual income from work before tax | 35 000 | ||
% tax rate on income from work | 0.4 | ||
An enabled person that is working | A disabled person that is not working | Difference | |
Government revenues | |||
Tax revenues from income from work (+) | 14 000 | 0 | -14 000 |
Government costs for a disabled person (-) | |||
Disability benefits (-) | 0 | -13 000 | -13 000 |
Lost tax revenues because a disabled person is not working (-) | 0 | -14 000 | -14 000 |
Total costs | 0 | -27 000 | -27 000 |
A government's financial balance = tax revenue + government cost | 14 000 | -27 000 | -41 000 |
Treatment costs for Lyme disease for the USA for 2018 | |||
Values | |||
Number of infections 2018 in the USA | 1 011 278 | ||
People infected with Lyme that develop chronic Lyme | 0.63 | % | |
People infected with Lyme that develop acute infection | 0.37 | % | |
The assumed annual cost of oral antibiotics is | 1 400 | USD | |
The assumed annual cost of IV antibiotics is | 15 000 | USD | |
The cost of treating acute Lyme disease with oral antibiotics for one month for the USA for 2018 (Oral USA 2018) | 43 653 490 | 44 | million USD |
The cost of treating chronic Lyme disease with IV antibiotics for 0.5 years for the USA for 2018 (IV0.5 USA 2018) | 4 778 287 467 | 4.8 | billion USD |
The cost of treating chronic Lyme disease with IV antibiotics for 1 year for the USA for 2018 (IV1 USA 2018) | 9 556 574 934 | 9.6 | billion USD |
Total cost of treating acute with oral antibiotics for one month and chronic with IV antibiotics 0.5 years for the USA for 2018 | 4 821 940 958 | 4.8 | billion USD |
Total cost of treating acute with oral antibiotics for one month and chronic with IV antibiotics 1 year for the USA for 2018 | 9 600 228 425 | 9.6 | billion USD |
Oral USA 2018 / ((IV0.5 USA 2018 + IV1 USA 2018)/2) | 0.6 | % |
Treatment costs for Lyme disease for Europe for 2018 | |||
Values | |||
Number of infections 2018 in Europe | 2 448 357 | ||
People infected with Lyme that develop chronic Lyme | 0.63 | % | |
People infected with Lyme that develop an acute infection | 0.37 | % | |
The assumed annual cost of oral antibiotics is | 1 200 | EUR | |
The assumed annual cost of IV antibiotics is | 13 000 | EUR | |
The cost of treating acute Lyme disease with oral antibiotics for one month for Europe in 2018 (Oral Europe 2018) | 90 589 198 | 91 | million EUR |
The cost of treating chronic Lyme disease with IV antibiotics for 0.5 years for Europe for 2018 (IV0.5 Europe 2018) | 10 026 020 721 | 10.0 | billion EUR |
The cost of treating chronic Lyme disease with IV antibiotics for 1 year for Europe for 2018 (IV1 Europe 2018) | 20 052 041 441 | 20.1 | billion EUR |
Total cost of treating acute with oral antibiotics for one month and chronic with IV antibiotics 0.5 years for Europe in 2018 | 10 116 609 919 | 10.1 | billion EUR |
Total cost of treating acute with oral antibiotics for one month and chronic with IV antibiotics 1 year for Europe in 2018 | 20 142 630 639 | 20.1 | billion EUR |
Oral Europe 2018 / ((IV0.5 Europe 2018 + IV1 Europe 2018)/2) | 0.6 | % |
Government costs for chronic Lyme disease for the USA for 2018 if the government does not finance IV treatment for chronic Lyme disease | |||
Values | |||
Number of Lyme disease infections 2018 in the USA | 1 011 278 | ||
People infected with Borrelia that develop chronic Lyme | 0.63 | % | |
Number of chronic infections in the USA for 2018 | 637 105 | ||
% of chronic Lyme patients that does not work | 0.42 | % | |
% of chronic Lyme patients that does work | 0.58 | ||
Average annual income from work before tax for the USA for 2016 | 60 154 | USD | |
Income tax rate for the USA for 2016 | 0.396 | % | |
Average annual disability benefits after tax for the USA for 2017 | 14 052 | USD | |
Lost personal income for chronic Lyme patients not working in the USA for 2018 | 16 096 253 841 | 16.1 | billion USD |
Lost tax revenues because chronic Lyme patients are not working in the USA in 2018 (LTR USA 2018) | 6 374 116 521 | 6.4 | billion USD |
Disability benefits for chronic Lyme patients for the USA for 2018 (DB USA 2018) | 3 760 091 747 | 3.8 | billion USD |
Government cost for chronic Lyme disease for the USA in 2018 (GCL USA 2018) = LTR USA 2018 + DB USA 2018 | 10 134 208 268 | 10.1 | billion USD |
GCL USA 2018 / IV0.5 USA 2018 | 2.1 | ||
GCL USA 2018 / IV1 USA 2018 | 1.1 |
Government costs for chronic Lyme disease for Europe for 2018 if the governments do not finance IV treatment for chronic Lyme disease | ||||
Values | ||||
Number of infections 2018 in Europe | 2 448 357 | |||
People infected with Borrelia that develop chronic Lyme | 0.63 | % | ||
Number of chronic infections in Europe for 2018 | 1 542 465 | |||
People that cannot work due to chronic Lyme | 0.42 | % | ||
Average annual income from work before tax for Germany for 2016 | 38 302 | EUR | ||
Income tax rate for Germany for 2016 | 0.45 | % | ||
The minimum annual disability benefits after tax in Sweden for 2016 is 11 000 Swedish kroner | 13 778 | EUR | ||
Lost personal income for chronic Lyme patients not working in Europe in 2018 | 24 813 383 257 | 24.8 | billion EUR | |
Lost tax revenues because chronic Lyme patients are not working in Europe for 2018 (LTR Europe 2018) | 11 166 022 466 | 11.2 | billion EUR | |
Disability benefits for chronic Lyme patients in Europe for 2018 (DB Europe 2018) | 8 925 976 833 | 8.9 | billion EUR | |
Government cost for chronic Lyme disease for Europe for 2018 (GCL Europe 2018) = LTR Europe 2018 + DB Europe 2018 | 20 091 999 298 | 20.1 | billion EUR | |
GCL Europe 2018 / IV0.5 Europe 2018 | 2.0 | |||
GCL Europe 2018 / IV1 Europe 2018 | 1.0 |
The number of Lyme disease infections per year | n | 100 000 | |
% of people that develops a chronic infection | %ch | 63 | |
The number of chronic infections (nn) | n * (%ch/100) | 63 000 | |
% of chronic Lyme patients that work | w | 58 | |
% of chronic Lyme patients that do not work | nw | 42 | |
% cure rate (cr) | Cured from treatment | Not cured from treatment | Sum |
Number of people | C = nn * (cr/100) | NC = nn - C | C + NC |
Number of people that are working | C * 1 | NC * (w / 100) | C * 1 + NC * (w / 100) |
Number of people that are not working | C * 0 | NC * (nw / 100) | C * 0 + NC * (nw / 100) |
Sum | C*1 + C*0 | NC * (w / 100) + NC * ( nw / 100 ) | C * 1 + NC * (w / 100) + C * 0 + NC * (nw / 100) |
0 | Cured from treatment | Not cured from treatment | Sum |
Number of people | 0 | 63 000 | 63 000 |
Number of people that are working | 0 | 36 540 | 36 540 |
Number of people that are not working | 0 | 26 460 | 26 460 |
Sum | 0 | 63 000 | 63 000 |
25 | Cured from treatment | Not cured from treatment | Sum |
Number of people | 15 750 | 47 250 | 63 000 |
Number of people that are working | 15 750 | 27 405 | 43 155 |
Number of people that are not working | 0 | 19 845 | 19 845 |
Sum | 15 750 | 47 250 | 63 000 |
50 | Cured from treatment | Not cured from treatment | Sum |
Number of people | 31 500 | 31 500 | 63 000 |
Number of people that are working | 31 500 | 18 270 | 49 770 |
Number of people that are not working | 0 | 13 230 | 13 230 |
Sum | 31 500 | 31 500 | 63 000 |
75 | Cured from treatment | Not cured from treatment | Sum |
Number of people | 47 250 | 15 750 | 63 000 |
Number of people that are working | 47 250 | 9 135 | 56 385 |
Number of people that are not working | 0 | 6 615 | 6 615 |
Sum | 47 250 | 15 750 | 63 000 |
100 | Cured from treatment | Not cured from treatment | Sum |
Number of people | 63 000 | 0 | 63 000 |
Number of people that are working | 63 000 | 0 | 63 000 |
Number of people that are not working | 0 | 0 | 0 |
Sum | 63 000 | 0 | 63 000 |
Number of people that are working and not cured from treatment can also be calculated as: nn * (w / 100) * (1 - cr / 100) | |||
Number of people that are not working and not cured from treatment can also be calculated as: nn * (nw / 100) * (1 - cr / 100) | |||
Cure rate in % | Number of people that are working and not cured from treatment | Number of people that are not working and not cured from treatment | |
0 | 36 540 | 26 460 | |
25 | 27 405 | 19 845 | |
50 | 18 270 | 13 230 | |
75 | 9 135 | 6 615 | |
100 | 0 | 0 |
A government’s Financial Balance | Today | Future | ||
---|---|---|---|---|
If the Government Finance IV Treatment | If the Government Does not Finance IV Treatment | If the Government Finance IV Treatment | If the Government Does not Finance IV Treatment | |
A government's chronic Lyme disease revenue (+) | x1 = tax revenues from chronic Lyme patients that are cured from treatment and are working | x5 = 0 | x11 = future tax revenues from chronic Lyme patients that are cured from treatment and are working | x55 = 0 |
A government's chronic Lyme disease revenue (+) | x2 = saved disability benefits for chronic Lyme patients that are cured from treatment and are working | x6 = 0 | x22 = saved future disability benefits for chronic Lyme patients that are cured from treatment and are working | x66 = 0 |
A government's chronic Lyme disease cost (-) | - X3 = lost tax revenues for chronic Lyme patients that are still sick after treatment and are not working | - x7 = lost tax revenues from chronic Lyme patients that are sick and have not received treatment and are not working | - x33 = lost future tax revenues for chronic Lyme patients that are still sick after treatment and are not working | - x77 = lost future tax revenues from chronic Lyme patients that are sick and have not received treatment and are not working |
A government's chronic Lyme disease cost (-) | - x4 = disability payments to chronic Lyme patients that are still sick after treatment and are not working | - x8 = disability payments to chronic Lyme patients that are sick and have not received treatment and are not working | - x44 = future disability payments to chronic Lyme patients that are still sick after treatment and are not working | - x88 = future disability payments to chronic Lyme patients that are sick and have not received treatment and are not working |
Treatment cost for 0.5 year of IV antibiotics (-) | - IV0.5 | |||
Treatment cost for 1 year of IV antibiotics (-) | - IV1 | |||
A government's revenues because of treatment | x1 + x2 | x11 + x22 | ||
A government's costs for chronic Lyme disease with IV 0.5 year | x3 + x4 + IV0.5 | x33 + x44 + IV0.5 | ||
A government's cost for chronic Lyme disease with IV 1 year | x3 + x4 + IV1 | X33 + X44 + IV1 | ||
A government's cost without treatment (-) | x7 + x8 | x77 + x88 | ||
A government's financial balance based on today's revenues and costs for 0.5 year of IV treatment (GBTiv0.5) | x1 + x2 + x3 + x4 + IV0.5 - x7 - x8 | A government's financial balance based on future revenues and costs for 0.5 year of IV treatment (GBFiv0.5) | x11 + x22 + x33 + x44 + IV0.5 - x77 - x88 | |
A government's financial balance based on today's revenues and costs for 1 year of IV treatment (GBTiv1) | x1 + x2 + x3 + x4 + IV1 - x7 - x8 | A government's financial balance based on future revenues and costs for 1 year of IV treatment (GBFiv1) | x11 + x22 + x33 + x44 + IV1 - x77 - x88 | |
To justify IV treatment for 0.5 years x11 + x22 + x33 + x44 + IV0.5 > x55 + x66 + x77 + x88 x11 + x22 + x33 + x44 + IV0.5 - (x55 + x66 + x77 + x88) > 0 GBFiv0.5 = x11 + x22 + x33 + x44 + IV0.5 - x77 - x88 > 0 To justify IV treatment for 1 year x11 + x22 + x33 + x44 + IV1 > x55 + x66 + x77 + x88 x11 + x22 + x33 + x44 + IV1 - (x55 + x66 + x77 + x88) > 0 GBFiv1 = x11 + x22 + x33 + x44 + IV1 - x77 - x88 > 0 |
1 = If the government finance IV treatment | ||||||
2 = If the government does not finance IV treatment | ||||||
3 = is GBFT > GBFnT ? | ||||||
4 = GBFT - GBFnT | ||||||
5 = is GFBBT - GFBnT > 0 ? | ||||||
6 = Should a government finance treatment with IV antibiotics? | ||||||
1 | 2 | 3 | 4 | 5 | 6 | |
A government's future chronic Lyme disease revenues | GFR | GFRnT | ||||
A government's future chronic Lyme disease costs | - GFC | - GFCnT | ||||
Treatment cost today for IV antibiotics | - TCT | na | ||||
A government's balance based on future government revenues and costs | GBFT = GFR + GFC + TCT | GBFnT = GFRnT + GFCnT | ||||
A government's future chronic Lyme disease revenues | 200 | 0 | ||||
A government's future chronic Lyme disease costs | -200 | -100 | ||||
Treatment cost today for IV antibiotics | -500 | na | ||||
A government's balance based on future government revenues and costs | -500 | -100 | no | -400 | no | no |
A government's future chronic Lyme disease revenues | 200 | 0 | ||||
A government's future chronic Lyme disease costs | -200 | -200 | ||||
Treatment cost today for IV antibiotics | -300 | na | ||||
A government's balance based on future government revenues and costs | -300 | -200 | no | -100 | no | no |
A government's future chronic Lyme disease revenues | 500 | 0 | ||||
A government's future chronic Lyme disease costs | -300 | -200 | ||||
Treatment cost today for IV antibiotics | -200 | na | ||||
A government's balance based on future government revenues and costs | 0 | -200 | yes | 200 | yes | yes |
A government's future chronic Lyme disease revenues | 600 | 0 | ||||
A government's future chronic Lyme disease costs | -400 | -500 | ||||
Treatment cost today for IV antibiotics | -100 | na | ||||
A government's balance based on future government revenues and costs | 100 | -500 | yes | 600 | yes | yes |
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Davidsson, M. The Financial Implications of a Well-Hidden and Ignored Chronic Lyme Disease Pandemic. Healthcare 2018, 6, 16. https://doi.org/10.3390/healthcare6010016
Davidsson M. The Financial Implications of a Well-Hidden and Ignored Chronic Lyme Disease Pandemic. Healthcare. 2018; 6(1):16. https://doi.org/10.3390/healthcare6010016
Chicago/Turabian StyleDavidsson, Marcus. 2018. "The Financial Implications of a Well-Hidden and Ignored Chronic Lyme Disease Pandemic" Healthcare 6, no. 1: 16. https://doi.org/10.3390/healthcare6010016