i. sejarah epidemiology
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I. SEJARAH EPIDEMIOLOGY. Circa 400 B.C . Hippocrates attempted to explain disease occurrence from a rational rather than a supernatural viewpoint. “ On Airs, Waters, and Places,” Hippocrates suggested that environmental and host factors such as behaviors might influence - PowerPoint PPT PresentationTRANSCRIPT
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EPIDEMIOLOGY 1
Oleh :NANIK SETIJOWATI
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I. SEJARAH EPIDEMIOLOGY
Circa 400 B.C. Hippocrates attempted to explain
disease occurrence from a rational rather than a supernatural viewpoint.
“On Airs, Waters, and Places,” Hippocrates suggested that environmental and host factors such as behaviors might influence
the development of disease.
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Con’t..SEJARAH EPIDEMIOLOGY TAHUN 1662 John Graunt, a London haberdasher and
councilman who published a landmark analysis of mortality data in 1662. This
publication was the first to quantify patterns of birth, death, and disease occurrence, noting
disparities between males and females, high infant mortality, urban/rural differences, and seasonal variations.
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Con’t..SEJARAH EPIDEMIOLOGY TAHUN 1800 William Farr built upon Graunt’s work by systematically collecting and analyzing
Britain’s mortality statistics. The father of modern vital statistics and surveillance, developed many of the
basic practices used today in vital statistics and disease classification. He concentrated his efforts on collecting
vital statistics, assembling and evaluating
those data, and reporting to responsible health authorities and the general public.
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Con’t..SEJARAH EPIDEMIOLOGY
TAHUN 1854 In the mid-1800s, an anesthesiologist named
John Snow was conducting a series of investigations in London that warrant his being considered the “father of field epidemiology.”
Twenty years before the development of the microscope, Snow conducted studies of cholera outbreaks both to discover the cause of disease and to prevent its recurrence.
Because his work illustrates the classic sequence from descriptive epidemiology to hypothesis
generation to hypothesis testing (analytic epidemiology)
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Con’t..SEJARAH EPIDEMIOLOGY
19 th and 20 th centuries In the mid- and late-1800s, epidemiological
methods began to be applied in the investigation of disease occurrence. Then Epidemiology has been
applied to the entire range of health-related outcomes, behaviors, and even knowledge and attitudes.
In the The studies by Doll and Hill linking lung cancer to smoking.
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Epidemiology :
is the study (scientific, systematic, data-driven) of the distribution (frequency, pattern) and determinants (causes, risk factors) of healthrelated states and events (not just diseases) in specified populations (patient is community, individuals viewed collectively), and the application of (since epidemiology is a discipline within public health) this study to the control of health problems.
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EPIDEMIOLOGY
Definisi : Ilmu yang mempelajari
frekuensi dan distribusi penyakit/
kejadian berdasarkan orang,
tempat dan waktu Ada 2 pembagian :
1.Deskriptif2.Analitik
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EPIDEMIOLOGY DESKRIPTIF
Mempelajari Karakteristik kejadian kesehatan berdasarkan :
1. Orang : - KARAKTERISTIK MENETAP (USIA, SEX, RAS, DLL).
- KARAKTERISTIK YANG DIDAPAT (KEKEBALAN, STATUS
PERNIKAHAN, DLL). - AKTIVITAS (PEKERJAAN,
AKTIVITAS WAKTU SENGGANG,
PENGGUNAAN OBAT/ROKOK/MINUMAN, DLL). - KONDISI DIMANA MEREKA HIDUP
(ST. SOSEK, PELAYANAN KESEHATAN,
DLL).
2. Tempat : - KEADAAN GEOGRAFIS -BATAS ADMINISTRATIF / POLITIK
3. Waktu : - SEASONALITY
- DAY OF WEEK & TIME OF DAY ** - EPIDEMIC PERIOD - SECULAR (LONG-TERM) TRENDS
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CONT’…EPIDEMIOLOGY DESKRIPTIF
Statistik yang biasa dipakai adalah : statistik deskriptif.
Statistik deskriptif menghitung: frekuensi, terdiri dari:
1. Presentile (kuartil, cut point), 2. Central tendency (mean, median , modus) 3. Dispersion (SD, Variance, range, minimum, maximum, SE mean) 4. Distribusi (Skewness, Kurtosis)
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EPIDEMIOLOGY ANALITIKUntuk mengetahui hubungan
antara exposure (cause) dan outcome (efek) serta untuk menguji hipotesa tentang hubungan kausal.
Biasanya ditandai dengan pertanyaan why atau how
Ada 2 yang dipelajari : 1. observasional 2. Experimental
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CONT’…EPIDEMIOLOGY ANALITIK
1. OBSERVASIONAL : a. Crossectional b. Case control c. Cohort
2. EKSPERIMENTAL : a. Kuasi ekperimental b. True eksperimental
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DASAR-DASAR STATISTIK
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HUBUNGAN ANTAR VARIABELBagaimana membuktikannya ?
KONSEP DASAR : Bila sebuah variabel X memiliki hubungan atau
ke-terkaitan dengan variabel Y, maka setiap perubahan nilai X akan segera diikuti oleh perubahan pada nilai Y.
Variabel X = Independent; Y= Dependent If X(X1,X2 .. Xn) Y (Y1, Y2 .. Yn), then:
X1 X2 Y1 Y2; Xn-1 Xn Yn-1 Yn Distribution of Y values of X1 X2 .. Xn
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CONTOH-1 Jenis Kelamin berpengaruh terhadap resiko
Incidence Osteoporosis pada lansia Bila benar, seharusnya :
Incidence Osteoporosis penduduk lansia laki-laki berbeda secara nyata dibanding incidence osteoporosis pada penduduk lansia perempuan.
osteoporsehat
OSTEOPNORMAL
LAKI-LAKI PEREMPUAN
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CONTOH-2 Tingkat pendidikan ibu berpengaruh terhadap
paritas anak ibu usia menopause. Bila benar, seharusnya :
Rata-rata jumlah anak ibu-ibu pendidikan SD Ibu berpendidikan SLTP Ibu berpendidikan SLTA+
00,5
11,5
22,5
33,5
44,5
RATA2 PARITAS
SDSLTPSLTA+
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CONTOH-3 Tingkat paritas ibu berpengaruh terhadap
tingkat kematian bayi (IMR) Bila benar, seharusnya :
Semakin tinggi rata-rata paritas ibu usia 45+ maka IMR juga semakin tinggi
0255075
100125150175200
0 2 4 6 8 10
PARITAS
IMR
GARISREGRESI
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CARA MEMBUKTIKAN HUBUNGANANTAR 2 VARIABEL (BIVARIAT)
Membandingkan distribusi variabel dependen (Y) pada setiap nilai variabel independen (X) Beda Proporsi (X & Y berskala nominal) Beda Rerata (Mean) (X nominal, Y Interval)
Menghitung Koefisien Korelasi dan regresi setelah memperhatikan pola hubungan pada Scatter Plot Diagram
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JENIS-JENIS KORELASI ?
010203040
0 2 4 6 8
X
Y
r + positip
010203040
0 2 4 6 8
X
Y
r - negatip
010203040
0 2 4 6 8
X
Y
r=O, no correlation
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BESARAN KOEFISIEN KORELASI
0
20
40
60
0 2 4 6 8 10
r = 0.7
0
20
40
60
0 2 4 6 8 10
r = - 1.0
0
20
40
60
0 2 4 6 8 10
r= -0.3
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MAKNAKOEFISIEN KORELASI
Hubungan nyaris SEMPURNA LINIER (r = +1.0 atau r = -1.0)
Hubungan nyaris TAK ADA (r = 0.0) Hubungan kuat ( 0.7 r 0.9) Hubungan sedang (0.4 r 0.6) Hubungan lemah (0.1 r 0.3)
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Apakah setiap ada hubungan/asosiasi merupakan
sebab-akibat ?
Antar dua variabel bisa : Tidak ada hubungan (asosiasi) Ada Hubungan (Asosiasi)
Hubungan Palsu (spurious association) Hubungan Kebetulan (accidental
association) Hubungan kausal (causal association)
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5 - SYARATHUBUNGAN KAUSAL
· Statistical Association· Temporal Association
· Scientific Plausibility·Repeatability (Consistency)
·Universality
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Bisakah variabel dependen dipengaruhi
lebih dari 1 var Independen ?
Kausa tunggal, efek tunggal Kena api, Kulit melepuh
Kausa tunggal, efek ganda (multiefek) Jatuh dari pohon, fraktur kaki, kulit lecet, comotio
cerebri Multi kausa, efek tunggal
Jenis Kelamin & Usia, osteoporosis Multi kausa, multi efek
Miskin, kumuh, tak sekolah, TBC paru, kurang gizi
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RINGKASAN Variabel kependudukan sangat terkait erat de
ngan variabel kesehatan masyarakat; Hubungan dua variabel dapat dibuktikan mela
lui : Beda distribusi atau Koefisien Korelasi, baik secara tabular atau grafis;
Hubungan 2 variabel : +, -, 0 Hubungan antar variabel tidak selalu hubungan
kausal; Ada 5 Syarat dikatakan sebagai Hubungan
Kausal
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WASSALAM