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I. SEJARAH EPIDEMIOLOGY

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

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I. SEJARAH EPIDEMIOLOGY

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  1. 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.

  2. 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.

  3. Con’t..SEJARAH EPIDEMIOLOGY • TAHUN 1800 William Farr built upon Graunt’s work by systematically collecting and analyzing Britain’s mortality statistics. The fatherof 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.

  4. 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)

  5. Con’t..SEJARAH EPIDEMIOLOGY • 19 thand 20 thcenturies 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.

  6. Epidemiology : is the study (scientific, systematic, data-driven) of the distribution (frequency, pattern) and determinants (causes, risk factors) of health related 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.

  7. EPIDEMIOLOGY • Definisi : Ilmu yang mempelajari frekuensi dan distribusi penyakit/ kejadian berdasarkan orang, tempat dan waktu • Ada 2 pembagian : • Deskriptif • Analitik

  8. EPIDEMIOLOGY DESKRIPTIF • MempelajariKarakteristikkejadiankesehatanberdasarkan : • 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

  9. CONT’…EPIDEMIOLOGY DESKRIPTIF • Statistikyang biasadipakaiadalah : statistikdeskriptif. • Statistikdeskriptifmenghitung: frekuensi, terdiridari: 1. Presentile (kuartil, cut point), 2. Central tendency (mean, median , modus) 3. Dispersion (SD, Variance, range, minimum, maximum, SE mean) 4. Distribusi (Skewness, Kurtosis)

  10. EPIDEMIOLOGY ANALITIK • Untuk 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

  11. CONT’…EPIDEMIOLOGY ANALITIK • OBSERVASIONAL : a. Crossectional b. Case control c. Cohort • EKSPERIMENTAL : a. Kuasiekperimental b. True eksperimental

  12. DASAR-DASAR STATISTIK

  13. 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

  14. 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. PEREMPUAN LAKI-LAKI

  15. 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+

  16. 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 GARIS REGRESI

  17. 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

  18. r + positip r - negatip r=O, no correlation JENIS-JENIS KORELASI ?

  19. r = 0.7 r = - 1.0 r= -0.3 BESARAN KOEFISIEN KORELASI

  20. 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)

  21. 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)

  22. 5 - SYARATHUBUNGAN KAUSAL • Statistical Association • Temporal Association • Scientific Plausibility • Repeatability (Consistency) • Universality

  23. 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

  24. 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

  25. WASSALAM

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