Chapter 3 Study Design and Data Source
3.2 Study design
3.2.1 Cross-sectional survey
For the part I of this study “Using a population-based cohort study to compare the two
detection methods for detecting Parkinson’s disease”, we used a one-stage method in a
cross-sectional survey to detect PD. A total of 58 PD cases were detected.
PD ascertainment in active detection method
In this Keelung neurological survey, each of 11,244 participants was evaluated for Parkinson’s disease by neurologists from National Taiwan University Hospital using a
standardized diagnostic protocol including neurological examination, motor function
examination, and a thorough standardized history. The Unified Parkinson’s Disease Rating
Scale (UPDRS)88was used to examine motor function.
The UPDRS is made up of five sections as follows:
x Part I: evaluation of mentation, behavior, and mood
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x Part II: self-evaluation of the activities of daily life (ADLs) including speech,
swallowing, handwriting, dressing, hygiene, falling, salivating, turning in bed,
walking, and cutting food
x Part III: clinician-scored monitored motor evaluation
x Part IV: Hoehn and Yahr staging of severity of Parkinson's disease
x Part V: Schwab and England ADL scale
The four cardinal signs for parkinsonism are resting tremor, rigidity, bradykinesia,
and impaired postural reflex. We defined those with parkinsonism as subjects in whom at
least two of four cardinal signs were present. PD was diagnosed by ruling out
parkinsonism caused by other reasons, such as vascular disease-related parkinsonism,
drug-induced parkinsonism, multiple system atrophy, and parkinsonism secondary to brain
insults. Except for subjects previously diagnosed with PD, every newly diagnosed PD case
was evaluated again by another neurologist. The diagnoses were reviewed and discussed
by a group of senior neurologists. The remaining 11,186 non-PD cases were followed by
linkage of these screenees with health insurance claims record to track potential diagnosis
of PD between 2001 and 2004.
PD ascertainment in passive detection method
For the passive method, 9,560 subjects filled out the screening questionnaires for Parkinson’s disease. The validation of the questionnaires has been described elsewhere.89,
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90The overall validity of this instrument was measured in a hospital sample of 30 patients
with Parkinson’s disease and the sensitivity was 100%. Specificity was investigated in 30
hospital visitors free of Parkinson’s disease and other diseases and found to be 95%.
Subjects who screened positive for Parkinson’s disease were informed by a trained nurse
to seek medical help. We examined screenee data for 2001 to 2004 to estimate the
incidence rate by year by the linkage of these screenees with the National Health
Insurance claims for PD, using the International Classification of Disease, Ninth Revision
(ICD-9) code 332.0 for Parkinson’s disease. We defined the PD cases if the code 332.0
appeared consecutively more than 3 times in the same individual. The KCIS project was
approved by local health committee, which was run by the health authority in Keelung.
3.2.2 Natural History of Parkinson’s Disease with Hoehn-Yahr Stage with Stochastic Process Based on Case-cohort Design
For the second part, since we did not have complete information on H-Y stage, we used
a non-standard case-cohort design as mentioned in the chapter 2 of literature review for
assessing the natural history of H-Y stage-based Parkinson’s disease. Because the average
age onset of PD is around 60 years old. We included participants age 60 and older for the
following analysis.
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records. Fifty-five new PD cases was detected by the screening program. The H-Y stage of
the 55 PD cases diagnosed in 2001 was confirmed by the neurologists. The remaining 9,774
non-PD cases were followed by linkage of these screenees with health insurance claims
record to track potential diagnosis of PD between 2001 and 2004. There were 208 PD cases
diagnosed by the linkage with health insurance claims records from 2001 to 2004. We
ascertained 62 of 208 PD cases to confirm their H-Y stage by chart review. The flow chart
of the participant age 60 and older was illustrated in figure 5-2-1.
The sampling fraction of screening detective and clinical detective cases was 1 (55/55)
and 62/208, respectively. These two sampling fractions in each state would be applied to get
transitional probability in the following models by using Bayesian inversion.
3.2.3 Data Collection
Information of anthropometric measurement, blood pressure measurement,
biochemical markers, personal medical history, food intake questionnaire, and life style
factors were collected and described as follows.
Anthropometric measurement
Body height, waist and hip circumferences were measured by a trained staff to the
nearest 0.1 cm. Body weight (BW) was measured to the nearest 0.1 kg. Waist
circumference was measured at the midway point between the inferior margin of the last
rib and iliac crest in a horizontal plane. Hip circumference was measured as the maximal m. ThThThThThe e HHHHHHHHH---YYYYYY YY Y stststststststststagagagagagagagaga e e ofofofofof
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circumference over the buttocks. Body mass index (BMI) was calculated as weight (kg)
divided by height squared (m2).
Blood pressure measurement
Blood pressure (BP) was measured with an automated sphygmomanometer twice
with five-minute headway. BP was then calculated according to an average of the two
measurements. High BP was defined as a systolic BPʁ140mmHg and/or diastolic BPʁ
90mmHg.
Biochemical markers
A venous blood sample was taken after 12 hours of fasting for measuring plasma
glucose, triglycerides, total cholesterol (TCHO), low-density lipoprotein (LDL)
cholesterol and high-density lipoprotein (HDL) cholesterol, serum uric acid(SUA),
glutamyl oxaloacetic trasaminase (GOT), glutamyl pyrubic transaminase (GPT), blood
urea nitrogen (BUN), creatinine…ect. Low uric acid level wad defined as SUA <5.5
mg/dl.
Questionnaire
Demographic data, personal medical history, family medical history, lifestyle factors,
and dietary intake habits were collected from a structured questionnaire administered by a
trained staff. Personal and family medical history included the chronic diseases, such as
hypertension, diabetes mellitus, gout, hyperlipidemia, cardiovascular disease (CAD), ed dasasasasaswwwwwwweieieieieieieieieighghghghghghghghghttttt t ttt(k(k(k(k(k(k(k(k(kg)g)gggggg
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cerebrovascular disease…etc, and cancers, such as breast cancer, cervical cancer, colon
cancer…ect. Lifestyle factors such as cigarette smoking, alcohol drinking and betel quid
chewing were classified as never, quit and current user. About the diet intake habits, the
food molds and standard dishes of each food were displayed to show the proportion of
each food that was consumed at one time. Every participant was asked about the diet
habits during past six months food categories include meat, vegetables, fruits, beans,
viscus, fish, seafood, milk and caffeine drinks. Frequency of every food category intake
was divided into five groups: more than two times per day, one time per day, 2-3 times per
week, 2-3 times per month, and never or seldom use. We defined frequencies less than 2-3
times per week as less intake of that food categories.
3.2.4 Homogeneous Markov model incorporated with covariates associated with the transition rates
We incorporated the covariate that would possible associated with the transition rates
with various stochastic processes (see below) according to the previous literature review.
Variables such as age, sex, smoking, coffee drinking and alcohol drinking (listed in table
5-2-5) were put into the model from normal to SD phase in three-state Markov model.
Variables include age and coffee drinking were incorporated to the model from SD phase
to CD phase.
3.2.5 Cost-effectiveness analysis for early detection of Parkinson’s disease
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We used a five-state Markov model to construct cost-effectiveness of screening based
on the simulated experiments on the randomized strategies. A controlled trial with
hypothetical cohort of general population aged 60 years and older were simulated for the
disease progression of PD with H-Y stage by different screening regimes (see Figure 3-1).
Each subject was followed up for 20 years or to death. The decision structure for the
control group was illustrated in Figure 3-2-1. The symbol in the end of each treatment
arm, , indicates a Markov chain for the stochastic process for advanced PD evolving
with time. We used 1 year as the length of each cycle in the Markov decision model. We
used both deterministic and probabilistic cost-effectiveness approach to perform CEA
analysis.
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