forecasting costs of medicare treatmentscoronary artery ... · coronary artery disease, diabetes...
TRANSCRIPT
. . 2556 16-18 2556
Forecasting Costs of Medicare TreatmentsCoronary Artery Disease,
Diabetes, and Hypertension in Thailand
*1 2
10800
3
10520 Email: [email protected]@gmail.com2 [email protected]
2 2556-2560
(Time Series Methods)
Linear Trend Analysis Yt = 43913+ 15413*t
Gompertz Curve Yt =107/(7.43749+66.6098*(0.841320t)) Yt =107/(1.41468+75.7866*(0.835007t))
Monte Carlo Simulation 2 (GDP)
GDP 2556 1.18% 1.74% 2560
2556 2560 0.38% 0.38% 1.34% 1.34%
:
. . 2556 16-18 2556
Abstract
Coronary artery disease, diabetes and high blood pressure are leading causes of illness and become major public health problems in Thailand. Previous research shows a clear-cut
relationship between coronary heart disease, diabetes and high blood pressure. In the past decade, thousands of Thai patients suffer from these diseases. The objectives of the current
study are to examine the overall cost of treatment for a variety of treatment modalities for
coronary heart disease, diabetes and high blood pressure, and estimate the number of patients and the treatment cost of these diseases during 2013-2017. Statistical methods for time series
are used in analyzing and forecasting the series data obtained from national health statistics reports of Thailand. Trend analysis suggests a linear increase in the number of coronary heart
disease patients as given in a regression equation, Yt = 43913 + 15413*t. A nonlinear trend is found in the number of diabetes and high blood pressure patients as given in equations, Yt = 107
/ (7.43749 +66.6098 * (0.841320t)), and Yt = 107 / (1.41468 +75.7866 * (0.835007t)), respectively.
Monte Carlo Simulation is used in sensitivity analysis of monetary inflation, which is a rise in the overall cost of medical treatment. Trend analysis suggests an increased cost of high pressure
treatment from 1.17% to 1.84% GDP during 2013-2017.
Keyword: Forecasting Costs of Medicare Treatments, Coronary Artery Disease, Diabetes,
Hypertension
1.
10 [1]
214 2544 366 2554 304 2544
866 2554 335 2544 1,321 2554
[2] 2552
624 / /
1,961 / / 1,103 / /
37,612 / / 51,473 / /
29,738 / /
2556-2560
. . 2556 16-18 2556
2.
2.1 2556-2560
2.2 2556-
2560
2.3
2.4
3.
3.1
(Time Series
Methods) (Minitab)
(double exponential
smoothing) (Linear Trend Analysis)
Gompertz curve
3.2
2556-2560
3.2.1
2544-2553 1
1
2544-2554
1 3
(Non parametric test) Run Test
Ho: Hi:
Runs test for P-value = 0.004
Runs test for
P-value = 0.004 Runs test for
P-value = 0.004 p-value
3 0.004 0.05 Ho
0.05 3.2.2
3
(Minitab)
. . 2556 16-18 2556
(Forecasting Model) 3.2.3
3.2.3.1 (Double
Exponential Smoothing)
23 4
2
2
Alpha ( ) 1.74813,
Gamma ( ) 0.17238
3
3
Alpha ( )
1.75985,
Gamma ( )
0.16309
4
4
Alpha ( )
1.51500,
Gamma ( ) 0.18393
3
2556-2560
3.2.3.2 (linear Trend Analysis)
5 6 7
5
. . 2556 16-18 2556
5
Minitab Yt = 43913+ 15413*t
6
6 Minitab Yt = 96789+
40250*t
7
7
Minitab Yt =
36015+ 63523*t 3.2.3.3
Gompertz curve 3
1
Gompertz Curve
Minitab 8 9 10
8
8
Minitab Yt =
106/(1.45352+14.9886*(0.863080t))
9
9
Minitab Yt = 107/(7.43749+66.6098*(0.841320t))
. . 2556 16-18 2556
10
10 Minitab Yt =
107/(1.41468+75.7866*(0.835007t)) 3.2.4
3
1 2 3
1
MAD MAPE MSE Double Expo. Smoothing 9,596 6 158,434,301
Liner Trend Analysis 7,449 5 102,199,783
Gompertz Curve 7,473 5 127,542,019
1
Linear Trend Analysis MAD 7,449
MSE 102,199,783
Yt = 43913+ 15413*t Yt = ( )
t = ( ) 1
2544
2556, 2557, 2558, 2559 2560 244,283 259,696 275,109 290,522
305,935
2
2 Gompertz Curve MAD 19,048
MAPE 5
Yt =107/(7.43749+66.6098*(0.841320t)) Yt = ( )
t = ( ) 1 2544 2556
2557, 2558, 2559 2560 690,371 748,129804,775859,529 911,716
3
3 Gompertz Curve MAD 28,686
MAPE 5
MAD MAPE MSE Double Expo. Smoothing 30,943 9 1,443,363,927
Liner Trend Analysis 21,712 6 750,218,822
Gompertz Curve 19,048 5 867,400,165
MAD MAPE MSE Double Expo. Smoothing 37,873 7 3,567,276,154
Liner Trend Analysis 44,652 12 2,485,458,130
Gompertz Curve 28,686 5 2,815,495,774
. . 2556 16-18 2556
Yt =107/(1.41468+75.7866*(0.835007t))
Yt = ( )
t = ( ) 1 2544
2556 2557, 2558, 2559 2560 1,151,392 1,335,905 1,542,280 1,770,690
2,020,558 3.2.5
3 2
4
4
2556-2560 ( / )
4
2560 277,387/28,548
10:1 706,066/205,650 2.5:1
997,143/1,023,415
1:1 3
2544
62,794 2560 305,935 387.2% 2544
151,115 2560 911,716
503.3% 2544 153,876 2560
2,020,558 1,213.1%
3
3.3
3.3.1
3
2 5 6
5
2552
( / )
400 1.57 624
352 5.60 1,961
241 4.61 1,103
:
5
5.60 / / 4.61 / /
2556 223,913 20,370 566,750 123,621 777,554 373,838
2557 237,281 22,355 601,579 146,550 832,451 503,454
2558 250,650 24,459 636,408 168,367 887,348 654,932
2559 264,018 26,504 671,237 188,292 942,246 828,444
2560 277,387 28,548 706,066 205,650 997,143 1,023,415
. . 2556 16-18 2556
1.57 / /
1,961 1,103 624 / /
6
2552
( / )
34,826 1.08 37,612
10,613 4.85 51,473
24,375 1.22 29,738
:
6
4.85/ / 1.22 / /
1.08 / / 51,473
29,738 37,612 / /
(Inflation Rate) 7
7 2554-2560 ( )
2552 -0.9 -0.9 -0.9
2553 3.3 3.3 3.3
2554 3.0 4.0 4.5
2555 1.5 2.5 3.0
2556 1.5 2.5 3.0
2557 1.5 2.5 3.0
2558 1.5 2.5 3.0
2559 1.5 2.5 3.0
2560 1.5 2.5 3.0
: (
2560)
7
2552 2554
2555-2560
GDP
3
2556-2560
8 2556-2560 ( )
2556 2,275,895 2,409,426 2,511,512
2557 2,394,894 2,594,827 2,742,774
2558 2,511,626 2,786,026 2,986,284
2559 2,630,544 2,987,736 3,247,536
2560 2,753,647 3,202,536 3,529,982
: (
2560)
3 (Sensitivity Analysis)
GDP
3.3.2
(Sensitivity Analysis)
3 ( / /
) 3 ( / / )
. . 2556 16-18 2556
9
( )
2556 9,245,498,762 9,520,113,051 9,659,436,135
2557 9,944,967,168 10,341,247,628 10,543,771,046
2558 10,663,465,721 11,197,621,523 11,472,608,476
2559 11,401,196,301 12,090,260,365 12,447,593,618
2560 12,158,702,551 13,020,578,719 13,470,800,196
10
( )
2556 32,242,944,582 33,200,640,163 33,685,518,382
2557 34,771,216,632 36,156,757,026 36,864,852,439
2558 37,365,619,638 39,237,343,433 40,200,919,270
2559 40,025,778,492 42,444,851,445 43,699,328,715
2560 42,751,478,360 45,781,939,890 47,364,973,412
11
( )
2556 25,798,136,657 26,564,405,427 26,953,164,980
2557 28,160,507,316 29,282,628,546 29,856,100,745
2558 30,615,175,735 32,148,755,359 32,938,252,332
2559 33,165,039,324 35,169,463,788 36,208,913,601
2560 35,812,012,946 38,350,566,738 39,676,640,576
3 2556-2560
2560 47,384,973,412
39,676,640,578 13,470,800,196
3
GDP
3
25602559255825572556
1.50
1.25
1.00
0.75
0.50
Year
CAD
Diabetes
HBP
Variable
Scatterplot of CAD, Diabetes, HBP vs Year
I 10
GDP ( )
10
GDP
25561.13% 2560 1.57%
2556 2560 1.42%
1.55% 0.41% 0.44%
25602559255825572556
1.75
1.50
1.25
1.00
0.75
0.50
Year
CAD
Diabetes
HBP
Variable
Scatterplot of CAD, Diabetes, HBP vs Year
11
GDP ( )
11
GDP
. . 2556 16-18 2556
25561.17% 2560
1.69% 2556 2560 1.38%
1.43% 0.40% 0.41% 2558
GDP
25602559255825572556
1.75
1.50
1.25
1.00
0.75
0.50
Year
CAD
Diabetes
HBP
Variable
Scatterplot of CAD, Diabetes, HBP vs Year
12
GDP ( )
12
GDP
25561.18% 2560 1.74%
2556 2560 1.34% 1.34% 0.38% 0.38%
2557
GDP
4.
(Time Series Methods)
Linear Trend Analysis
Gompertz Curve
3
3
Monte Carlo Simulation
3 2
GDP 3
GDP 2556 1.18%
1.74% 2560
2556 2560
0.38% 0.38% 1.34% 1.34%
3
( ,
. . 2556 16-18 2556
,
)
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