epi and fammed

1

วิ�ทยาการระบาดในงานเวิชศาสตร�ครอบคร�วิ

นพ. ส�ธี�ร� ร�ตนะมงคลก�ลภาควิ�ชาเวิชศาสตร�ป้�องก�นและส�งคม

มหาวิ�ทยาล�ยศร�นคร�นทรวิ�โรฒ

2

ห�วิข้%อบรรยาย• ควิามหมายข้องระบาดวิ�ทยา• การกระจายข้องโรค• ค'าจ'าก�ดควิามข้องโรค• Causal models• Screening• Prognostic factor• Epidemiological methods• Systematic reviews• Standardization• Measures in epidemiology

• Chance/ Random error• Bias• Confounder

3

Definition of EpidemiologyDefinition of Epidemiology

“The study of the distribution and determinants of disease frequency in human populations and the application of this study to control health problems”

Key Words in DefinitionKey Words in Definition • Disease frequency ‑ count cases, rate, records • Disease distribution ‑ who, when, where

– Frequency, distribution, other factors generate hypotheses about determinants

• A determinant is a characteristic that influences whether or not disease

occurs

4

การกระจายของโรค( Distribution )

• เวลาเวลา ( TIME ) ( TIME ) เมื่��อไร นานแค�ไหน เมื่��อไร นานแค�ไหน

• สถานที่�� สถานที่�� (PLACE) (PLACE) ที่��ไหนที่��ไหน

• บุ�คคล บุ�คคล (PERSON(PERSON )) อาย� เพศ อาชี�พ เชี�!อชีาติ# อาย� เพศ อาชี�พ เชี�!อชีาติ#

5

สถานการณ์%ไข&เล�อดออก(DHF+DF+DSS) เขติ 6 เปร�ยบุเที่�ยบุค�ามื่)ธยฐาน - 253943 2544( ), , มื่ค - . พย.2545(ส)ปดาหที่��48)

229 114 15

69616

6114

70

71

1026

1327

613

1251

2243

1240

598428

196

4641055

10811363

95241

26 23

613

285326368

166 195543

987

2061

3062

02505007501000125015001750200022502500275030003250

มื่ค กพ มื่�ค เมื่ย พค มื่#ย กค สค กย ติค พย ธค

MED(39-43)

2544

2545

จ-านวน(ราย)

11719/26

Time

6

Stomach cancer mortality rates: Migration study

58.4

29.9

11.7

8

0 10 20 30 40 50 60 70

Japanese in Japan

JapaneseImmigrants to

California

Sons of JapaneseImmigrants

Native Californians(Caucasians)

Mortality rates (per 100,000)

7

Population Pyramid

8

ระบาดวิ�ทยาสถานท�)ท�)เส�ยช�วิ�ตข้องผู้+%ป้,วิยมะเร-งท�)

ร�ฐโนวิาสโกเท�ย แคนนาดา

Conclusion: “Over time, more patients with cancer, especially women, elderly people and people with longer survival after diagnosis, died outside of hospital in Nova Scotia.”

11

Proportion of deaths out of hospital and in hospital among adults in Nova Scotia who died of cancer, 1992–1997.

CMAJ. 2003 February 4; 168(3): 265–270.

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC140467/

12

Home visit ก�บการลดการเส�ยช�วิ�ตในโรงพยาบาล

14

Natural Progression in Natural Progression in Epidemiologic ReasoningEpidemiologic Reasoning

1st – Suspicion that a factor influences disease occurrence. Arises from clinical practice, lab research, examining disease patterns by person, place and time, prior epidemiologic studies

2nd – Formulation of a specific hypothesis

3rd – Conduct epidemiologic study to determine the relationship between the exposure and the disease. Need to consider chance, bias, confounding when interpreting the study results.

4th – Judge whether association may be causal. Need to consider other research, strength of association, time directionality

15

ค'าจ'าก�ดควิามข้องโรค(Case Definition)

16

Case definition

Criteria

• Epidemiologic criteria

• Clinical criteria

• Diagnostic criteria

• Exclusion criteria

19

Measuring Occurrence of Diseases

20

• อ)ติรา (Rate) = ติ)วติ)!ง ติ)วหาร

– ความื่ชี�ก (Prevalence Rate) อ)ติราป.วยรายเก�า+

รายใหมื่�

– อ�บุ)ติ#การ (Incidence Rate) อ)ติราป.วยรายรายใหมื่�เที่�าน)!น

– อ)ติราติาย (Mortality Rate) อ)ติราติายในประชีากร

– อ)ติราติายจากโรค (Fatality Rate ) อ)ติราติายในคนที่��ป.วย

การว)ดการเก#ดของโรค

21

Incidence densityCumulative incidence

Suthee Rattanamongkolgul

22

JAN1995

JAN1996

APR1995

JUL1995

OCT1995

DeathCuredDisease

1234

5

6

78910

Normal

1212

129

3

6

6

23

มื่� 2 แนวที่างในการว)ด

1) อ�บุ)ติ#การสะสมื่ Cumulative incidence

= จ-านวนผู้1&ป.วยรายใหมื่�ในชี�วงเวลาที่��ก-าหนด

ประชีากรที่��เส��ยงในชี�วงเวลาน)!น= 40 = 1.25 /1,000

32,000

X 10(n)

การว)ด อ�บุ)ติ#การของการเก#ดโรค

24

2) อ)ติราอ�บุ)ติ#การ (Incidence density or Incidence rate )

• “ ” การเพ#�มื่ มื่#ติ#ของเวลา ลงไปในติ)ว “หาร Person-time”

• Person-month, Person-year• 1 Person-year = Following 1 person for 1 year period

• 10 Person-year = Following 1 person for 10 year period

= Following 10 persons for 1 year period


25

•2) อ)ติราอ�บุ)ติ#การ Incidence density or Incidence rate = จ-านวนผู้1&ป.วยรายใหมื่�ในชี�วง

เวลาที่��ก-าหนด จ-านวน Person-years of ของการติ#ดติามื่ในชี�วงไมื่�เก#ดโรค

• หากติ#ดติามื่คน 100 คนในเวลา 1 ป2และพบุ ว�า 20 คนเก#ดโรค อ)ติราอ�บุ)ติ#การค�อ

• 20 cases/100 person-years การติ#ดติามื่

• 20 = 20 / 100 person-years

100 person-years

X 10X 10(n)(n)


26

Smoking No. of stroke Person-

years Incidence rate

of observation

/100,000 person-years

Never 70 395,594

17.7

Ex-smoker 65 232,712

27.9

Smoker 139 280,141

49.6

Total 274 908,477

30.2

ความื่ส)มื่พ)นธ%ระหว�างการส1บุบุ�หร��ก)บุอ�บุ)ติ#การ

การเก#ดอ)มื่พาติ ของประชีากร 118,539 คนในเวลา 8 ป2

27

Epidemiological methods

28

InducInductive tive apprapproachoach

HypothesisHypothesis

ObservationObservation

DeduDeductive ctive approapproachach

การสร&างสมื่มื่�ติ#ฐาน

การที่ดสอบุสมื่มื่�ติ#ฐาน

การก#นหมื่ากอาจเป3นสาเหติ�ของมื่ะเร4งชี�องปาก

มื่ะเร4งที่��ชี�องปากส�วนใหญ่�มื่)กก#นหมื่าก

การศ6กษาเชี#งพรรณ์นา (Descriptive epidemiology)

การศ6กษาเชี#งว#เคราะห% (Analytic epidemiology)

29

Observational

Intervention

Descriptive

Analytical

Clinical Trials

Case reportsCase series

Ecological Studies

Cross Sectional Survey

Cohort Study

Case-Control Study

Study Designs Clinical Research

Population

Individuals

Field Trials

Community Trials•Equivalent trial•Non-inferior trial•Superior trial

•Time trend•Migration study•Geographical correlation

•Case-control•Nested-case control

•Cohort•Historical cohort

•Prevalence study•Analytical cross-sectional

Generating hypothesis

Testing hypothesis

Experimental study

Observational study

Longitudinal

30

Intervention studyExperimental study

Clinical trial

31

RAMDOMIZATION

Eligibility criteria

TARGET POPULATION

EXPERIMENTAL POPULATION

Unsuitable subjects

Informed consentRefusals

Intervention group

STUDY POPULATION

Control group

Follow-up

OutcomeOutcome

Follow-up

Experimental Studies

32

Types of Intervention studies

Types Purposes Subjects

Clinical Trials Treatment Individuals

Field Trials Prevention Individuals

Community Trials Prevention Community

33

Concepts• Phase I, II, III, and IV• Comparison:

– historical control, before and after, parallel • Improvements unrelated to treatment

– Contamination, co-intervention, regression to the means• Randomization

– Sequence generation + sequence concealment• Admissibility criteria

– Uniform characteristics, comparability via homogeneity• Outcome ascertainment

– Blinded • Ethics

– Informed consent, ethic guideline

34

Strengths• Randomisation ensures that

on average known and unknown confounders are equally distributed between intervention and control groups

• Experimental, hence control over intervention, circumstances and subjects

• Can study multiple effects from single exposures

• Clear temporal relationship can be established

Weaknesses

• May be expensive, time-consuming and logistically difficult (especially if prolonged follow-up)

• Generalisability from trial setting to everyday practice may be problematic

• Less useful for rare outcomes

• Loss to follow may affect validity of results

• Lack of blinding may introduce bias

• Ethical problems

35

Cohort studyHistorical/Retrospective cohort study

36

Cohort study

SampleNon-participants

Non-exposedExposed

Lost to follow-up

Measure outcome Measure outcome

Self Selection

No outcome at the beginning of study

37

Smokers Non-smokers

Incidence rate of

lung cancer

Incidence rate of

lung cancer

Cohort study

Folllow up Folllow up

38

a

c+d

a+b

a+c

c

Nb+d

Y N

Y

N

Lung cancer

Smoking

Incidence exposed = a/(a+b)Incidence not exposed = c/(c+d)Relative Risk = [a / (a+b)] / [c / (c+d)]

b

d

40

Cohort study and Relative Risk

100

50,000

10,000

150

50

N58,950

Y N

Y

N

Lung cancer

Smoking

Incidence exposed (Ie) = a/a+b = 100/10,000 = 0.01 = 10/1,000Incidence not exposed (Io)= c/c+d = 50/50,000 = 0.001= 1/1,000 Relative Risk = Ie/Io = 0.01 / 0.001 = 10

9,900

49,050

a

c+d

a+b

a+c

c

Nb+d

Y N

Y

N

b

d

41

Strengths and weaknesses of prospective cohort studies

Strengths

• Prospective studies minimise bias in exposure measurement and may allow multiple measurements, increasing precision

• Can study multiple effects from single exposures

• Clear temporal relationship can be established

• Good for rare exposures

Weaknesses

• Expensive, time-consuming and logistically difficult (especially prospective studies in diseases with long latency)

• Less useful for rare diseases

• Loss to follow may affect validity of results

42

Historical CohortRetrospective Cohort

• Cohort data can be collected prospectively (as events occur over time) or retrospectively (historical records)

• Dyestuff workers and bladder cancer– Employment records and vital statistic used to

capture events from the past– On-the-job exposure to aniline dyes was

associated with a large increase in bladder cancer

43

Case-control studyNested case-control study

44

People with liver cancer

[Case]

People with no liver cancer[Control]

History of hepatitis B carrier

History of hepatitis B carrier

Case-control study

Ask Ask

45

Case Control Studies

Strengths

• Relatively quick and inexpensive

• Can be used for rare diseases

• Can study multiple aetiological factors of single diseases

Weaknesses

• Less useful for rare exposures

• Particularly prone to bias (especially selection and recall bias)

• Cannot compute incidence rates (unless population based)

46

Nested Case-Control StudiesFigure 3

Study PopulationTIME 1

YEARS

TIME 2DevelopDisease

Do NotDevelopDisease

CASES CONTROLS

CASE-CONTROL STUDY

Obtain interviews, bloods, urines, etc.

47

A. Advantages of Nested Case-Control Studies

1. Possibility of recall bias is eliminated, since data on exposure are obtained before disease develops.

2. Exposure data are more likely to represent the pre-illness state since they are obtained years before clinical illness is diagnosed.

3. Costs are reduced compared to those of a prospective study, since laboratory tests need to be done only on specimens from subjects who are later chosen as cases or as controls.

48

Predictors of Follow-up of Atypical and ASCUS Papanicolaou Test Results in a High-Risk Population

M. Diane McKee, MD, MS; Clyde Schechter, MA, MD; William Burton, PhD; Michael Mulvihill, DrPH

July 2001 · Vol. 50, No. 7 JFP

Online

49

Prevalence, incidence, morbidity and treatment patterns in a cohort of patients diagnosed with

anxiety in UK primary care.• Fam Pract. 2010 Feb;27(1):9-16. Epub 2009 Nov 1. Martín-Merino E, Ruigómez A,

Wallander MA, Johansson S, García-Rodríguez LA.

METHODS: • The Health Improvement Network was used to identify all patients aged 10-79 years

with • a new diagnosis of anxiety in 2002-04 (n = 40 873) and • age-, sex- and calendar-year-matched controls (n = 50 000). • A nested case-control analysis was used to quantify potential risk factors for anxiety

by multivariate logistic regression.RESULTS: • The prevalence of anxiety was 7.2% and • the incidence was 9.7 per 1000 person-years. • Incidence and prevalence were highest in women and young adults (20-29 years). • Anxiety was associated with heavy alcohol use, smoking and addiction problems as

well as stress, sleep and depression disorders. • Anxiety patients used health care services more frequently than controls. • Among patients diagnosed with anxiety, 63% were treated pharmacologically.

Antidepressants accounted for almost 80% of prescriptions.

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Mart%C3%ADn-Merino%20E%22%5BAuthor%5D

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Mart%C3%ADn-Merino%20E%22%5BAuthor%5D

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Ruig%C3%B3mez%20A%22%5BAuthor%5D

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Ruig%C3%B3mez%20A%22%5BAuthor%5D

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Wallander%20MA%22%5BAuthor%5D

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Wallander%20MA%22%5BAuthor%5D

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Johansson%20S%22%5BAuthor%5D

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Garc%C3%ADa-Rodr%C3%ADguez%20LA%22%5BAuthor%5D

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Garc%C3%ADa-Rodr%C3%ADguez%20LA%22%5BAuthor%5D

50

Case-control study study

Liver cancer

No liver cancer

HBsAg+ 400a.

100b.

HBsAg- 200c.

600d.

Odd ratio = = = 400/100

200/600

400 x 600

100 x 200

Odd in smokers

Odd in non-smokers

Odd ratio (OR) = 12 =a/b

c/d

51

Odd ratio (OR) and relative risk (RR)

• Relative risk and odd ratio have the same interpretation

• OR and RR are ususally presented with 95% confience intervals (CI) to indicate possible value of OR or RR within possibility of 95%

e.g. OR = 2.1(95%CI:1.2-3.6)

OR = 1.5(95%CI: 0.9-2.3)

OR = 0.5 (95%CI:0.2-0.8)

52

X X ค0อ ค1าเฉล�)ยข้องต�วิอย1าง ค0อ ค1าเฉล�)ยข้องต�วิอย1าง ควิามแป้รป้รวิน ควิามแป้รป้รวิน , Variance , Variance (S(S2))

A

Probability

53

Ecological study

54

Types of ecological study

• Time trends

• Ecological Correlation or Geographical studies)

• Migrant studies

55

Time trend

Incidence rate of breast cancer in US

0

20

40

60

80

100

120

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Year

Inci

den

ce r

ate

Time trends• Trends over time can give clues to aetiology• Can sometimes be linked to trends in exposure• Need to consider changing definitions over time

60

Ecological study

Female Literacy %

100806040200

120

100

80

60

40

20

0

US

ROM

PR

PER

PAN

NIC

NEP

MON

MAL

KEN

J

A

IND

GUA

GAM

K

CHI

USA

BOL

BAN

T

Infant Mortality

Infant mortality rates

61

Strengths and Weaknesses

Strengths

• Simple and cheap

• Often use routinely available information

• Good first step - hypothesis generating

• Assess effect of exposures on outcomes at the population rather than individual level

Weaknesses

• Ecological fallacy - averaging of exposure and outcome information at population level -> cannot link exposure and disease in individuals

• Lack of information on confounders

• Limitations of routine data sources – possible inaccuracy and biased comparisons

• May miss sub-groups effects

62

Cross-sectional study

63

Cross-sectional studies

• also called survey or prevalence study • Survey of group to identify prevalence of disease or

exposure• Exposure and disease status assessed at the same time• Individual is unit of observation and analysis• Typically descriptive in nature to quantify magnitude of

the problem• used to generate hypotheses

64

Does smoking cause depression? Or does depression cause smoking?

School Sample

Identify SmokersIdentify mood

disturbed students

Findings: strong association between mooddisturbance, especially depression and smoking

Crossectional study

65

1120180

68020

เป3น ไมื่�เป3น

โรคห)วใจ

ส1บุ

ไมื่�ส1บุการส1บุบุ�หร��700

-

2000

รวมื่

1300

200 1800

ส)ดส�วนของการส1บุบุ�หร�� ในคนที่��คน เป3นโรคห)วใจ =

ส)ดส�วนของการส1บุบุ�หร��ในคนที่�ไมื่�เป3น โรคห)วใจ =

รวมื่

680/1800 = 38%

20/200 = 10%

66

Strengths• Magnitude of

problems

• May generate new etiologic hypotheses

Weaknesses• No cause-effect

• No temporality

• Prevalent cases are survivors

Cross sectional studies

67

Case-seriesCase report

71

INVASIVE DEVICES USE in Patients with MRSA (16 cases)

Procedure Frequency

IV cath.NG tubeET tubeVentilatorTracheostomyCut downFoley’s Cath.Others

1611887761

Sensitivity Frequency

VancomycinChloramphenicolFosfomycin

161410

Drug sensitivity (16 cases)

MRSA infection in HRH Maha Chakri Sirindhorn Medical Center during the last 2 years By Brian Lee, Chayanant Siwarungson

Case-series studyCase-series study

72

Case report/study Case report: CHD as a potential cause of chest pain in adult patients.

Tsuang W, Ahmed K, Eckert D, Helmy T, Grunenwald P.

Am Fam Physician. 2008 Oct 15;78(8):911, 914. :

Case report: amelanotic melanoma located on the lower extremity.

Valdes A, Kulekowskis AM, Curtis L.

Am Fam Physician. 2007 Dec 1;76(11):1614, 1618.

Case report: Patient adherence to drug regimens vital to treatment.

Simmons BB, Dubreuil AL.

Am Fam Physician. 2007 Sep 15;76(6):769-70. :

Case report: differentiating artifact from true ventricular tachycardia.

Mascitelli L, Pezzetta F.

Am Fam Physician. 2006 Sep 15;74(6):921. :

Sleep disturbances in the disabled child--a case report and literature review.

Adlington K, Liu AJ, Nanan R.

Aust Fam Physician. 2006 Sep;35(9):711-5.

http://www.ncbi.nlm.nih.gov/pubmed/18953966

73

Herpes encephalitis presenting as mild aphasia: case report.Khan OA, Ramsay A.BMC Fam Pract. 2006 Mar 24;7:22.Case report: expanding the differential diagnosis of

intractable cough.Shah RK, Ditkoff M, Karmody CS.Am Fam Physician. 2005 Nov 15;72(10):1975-6. : Case report: use of prazosin for treatment of

posttraumatic stress disorder.Griffith LJ.Am Fam Physician. 2005 Sep 1;72(5):758, 761. : The role for vitamin B-6 as treatment for

depression: a systematic review.Williams AL, Cotter A, Sabina A, Girard C, Goodman

J, Katz DL.Fam Pract. 2005 Oct;22(5):532-7. Epub 2005 Jun

17. : Case report: Urticaria following intentional

ingestion of cicadas.Piatt JD.Am Fam Physician. 2005 Jun 1;71(11):2048, 2050. Case report: insect bite reveals botfly myiasis in an

older woman.Sajjad N, Biederman G.Am Fam Physician. 2005 Apr 1;71(7):1262-3. : Case report: Late-onset eclampsia presents as

bilateral cortical blindness.Gold KJ, Barnes C, Lalley J, Schwenk TL.Am Fam Physician. 2005 Mar 1;71(5):856, 858, 861. A case report: ethics of a proposed qualitative

study of hospital closure in an Australian rural community.

Fraser J.Fam Pract. 2004 Feb;21(1):87-91.Hypermagnesemia. Elderly over-the-counter drug

users at risk.Fung MC, Weintraub M, Bowen DL.Arch Fam Med. 1995 Aug;4(8):718-23. : 7620603

[PubMed - Detailed description of a successful outpatient

taper of phenobarbital therapy.Geurian K, Burns I.Arch Fam Med. 1994 May;3(5):458-60.PMID: 8032508

[PubMed - indexed for Hyponatremia due to enalapril in an elderly patient.

A case report.Gonzalez-Martinez H, Gaspard JJ, Espino DV.Arch Fam Med. 1993 Jul;2(7):791-3.PMID: 8111505

[PubMed - indexed for



75

Case Report: Subarachnoid Hemorrhage in Woman with Migraines

• Am Fam Physician. 2009 Mar 1;79(5):374-376.� � �• TO THE EDITOR: • A 45-year-old woman with a history of migraines and a recent

diagnosis of hypertension presented with a two-day history of increasing headache.

• She had taken over-the-counter medications with some relief. On the second day, the patient awoke with terrible pain located in the occiput and radiating to the forehead, associated with photophobia, nausea, and vomiting.

• The headache was unlike any she had ever experienced. Physical examination revealed a dehydrated, distressed woman with a blood pressure of 180/120 mm Hg, pulse of 110 beats per minute, and body temperature of 97 F (36.1 C). � �

• There were no neurologic deficits. A computed tomography (CT) scan of the head showed a subarachnoid hemorrhage

76

• Computed tomography scan of the brain post aneurysm repair surgery, status post left frontoparietal craniotomy, showing boneflap (yellow arrow) and suprasellar aneurysm clips (green arrow). There is high-attenuation blood in the interhemispheric fissure showing subarachnoid hemorrhage (white arrow), and progressive generalized cerebral edema with diffuse attenuation of cerebral sulci (red arrow). Acute ischemia involving the medial frontal and parietal lobes (purple arrow) is likely the result of compromise of the anterior cerebral artery bilaterally, perhaps related to vasospasm.

77

Case report and case series

ข&อด�• ค&นพบุโรคใหมื่� ๆ• เป3นพ�!นฐานในการสร&างสมื่มื่�ติ#ฐานเก��ยวก)บุป9จจ)ย

เส��ยง

ข&อด&อย• Case report มื่าจาก ผู้1&ป.วยรายเด�ยวการน-า

ผู้ลไปใชี&ก)บุผู้1&ป.วยรายอ��นอาจใชี&ไมื่�ได& เชี�น ผู้ลของการร)กษาด&วยสมื่�นไพร

• Case series แมื่&มื่าจากผู้1&ป.วยหลายรายแติ�แติ�ย)งไมื่�ได&พ#ส1จน%สมื่มื่�ติ#ฐานของสาเหติ�

78

Systematic reviewsMeta-analysis

79

Systematic review and meta-analysis

• Systematic review – aims to identify, evaluate and interpret all the relevant available research evidence for a particular question.

• Meta-analysis – process of using statistical methods to combine quantitatively the results of different studies

85

Elements of a study protocol for a systematic review and meta-analysis

ObjectivesBackgroundInformation retrievalData collectionData analysis1. Create summary data2. Examine heterogeneity3. Examine publication bias4. Consider conducting subgroup analysis

(according to a priori hypotheses)

86

Funnel plot to detect publication bias

Egger et al. Systematic reviews in health care. London: BMJ books, 2001.

87

Hierarchy of study design

• Systematic reviews and meta-analysis• Randomised controlled trial• Non-randomised trial

– Controlled trial– natural experiment– before and after study etc

• Cohort study• Case control study• Cross sectional study• Ecological study• Case series• Case study/report

Experimental

Observational

Analytical

Descriptive

88

In practice, choice of study design depends on:

• State of knowledge

• Frequency of exposure and disease

• Time, cost and other feasibility considerations

• Each study design has unique and complementary advantages and disadvantages

89

General model of causation

Sufficient causeComponent causeNecessary cause

90

GENERAL MODEL OF CAUSATION (CAUSAL PIES)

This illustration shows a disease that has 3 sufficient causal complexes, each having 5 component causes. A is a necessary cause since it appears as a member of each sufficient cause. B, C, and F are not necessary causes since they fail to appear in all 3 sufficient causes.

91

GENERAL MODEL OF CAUSATION (CAUSAL PIES) BY KJ ROTHMAN

Sufficient cause: • A set of conditions without any one of

which the disease would not have occurred. (This is one whole pie.)

92


Component cause:

• Any one of the set of conditions which are necessary for the completion of a sufficient cause. (This is a piece of the pie.)

93


Necessary cause: • A component cause that is a member

of every sufficient cause.

94

Attributes of the causal pie

1. Completion of a sufficient cause is synonymous with occurrence (although not necessarily diagnosis) of disease.

2. Component causes can act far apart in time. 3. A component cause can involve the

presence of a causative exposure or the lack of a preventive exposure.

4. Blocking the action of any component cause prevents the completion of the sufficient cause and therefore prevents the disease by that pathway

95

Causal "guidelines" suggested by Sir AB Hill (1965)

Purpose: Guidelines to help determine if associations are causal.Should not be used as rigid criteria to be followed slavishly. Hill even stated that he did not intend for these "viewpoints" to be used as “hard and fast rules.”

96

Causal "guidelines" suggested by Sir AB Hill (1965)

1. Strength of the association2. Consistency3. Specificity4. Temporality5. Biological gradient6. Plausibility7. Coherence8. Experiment9. Analogy

97

เกณ์ฑ์%การติ)ดส#นการเป3นเหติ�และผู้ล• Magnitude of effect ข้นาดข้องควิามส�มพ�นธี�• Temporal relationship การเก�ดสาเหต�ก1อนการเก�ด

โรค• C onsistency ควิามสอดคล%องข้องการศ3กษาใน

ป้ระชากรหลาย ๆ กล�1ม• - Dose response relationship การเพ�)มข้37นหร0อลด

ลงข้องสาเหต�ม� ผู้ลกระทบตอ1การเก�ดโรค• Plausibility การอธี�บายได%ด%วิยหล�กวิ�ทยาศาสตร�• Reversibility การลดป้9จจ�ยสาเหต� แล%วิท'าให%การเก�ด

โรคลดลง

98

การวิ�ดผู้ลกระทบข้องป้9จจ�ยเส�)ยงข้องการเก�ดโรค

(Measures of Impact)

Attributable risk

Population attributable risk

Lyle Petersen 1999, Thomas Grein 2000-2004, Marta Valenciano 2005

99

• Cohort study to examine causes for automobile-related deaths

ถ&าที่�านเป3น รมื่ติ.กระที่รวงสาธารณ์ส�ขซึ่6�งติ&องการลดการติายจากอ�บุ)ติ#เหติ�ที่างถนนด&วยงบุประมื่าณ์ที่��จ-าก)ดที่��จะรณ์รงค%ในเร��องในเพ��อให&เก#ดผู้ลกระที่บุที่��ส1งส�ดระหว�างการเมื่าแล&วข)บุหร�อการข)บุรถเร4วในประชีากรแห�งหน6�ง

100

RR = 5.0

Incidence exposed

Incidence unexposed

Risk difference

Relative risk

0.0005 0.0001 0.0004 5.0

0.005 0.001 0.004 5.0

0.05 0.01 0.04 5.0

0.5 0.1 0.4 5.0

Relative risk ไม1สามารถแสดงผู้ลกระทบข้องexposure ท�)ม�ในป้ระชากรได%

101

Measures of Impact

• Measures of association providing information about absolute effects of exposure

• Reflect apparent contribution of an exposure to the frequency of disease

• Two concepts– Attributable risk among exposed– Population attributable risk

102

1. Impact measures among exposed

2. Impact measures in the population

103

Attributable Risk (AR)

• Quantifies disease burden in exposed group attributable to exposure

• Provides answers to– what is the risk attributed to the exposure?– what is the excess risk due to the exposure?

• Calculated as risk difference (RD)

104

Attributable Risk

Incidence

Exposed Unexposed

Iexposed – Iunexposed

I = Incidence

105

Dead Not dead Risk RD

Fast 100 1900 2000 0.05

0.04

Slow 80 7920 8000 0.01

180 9820 10000

A cohort study of accidental death due to fast driving

106


Fast 100 1900 2000 0.05

0.04

Slow 80 7920 8000 0.01

180 9820 10000


107

Drunk 45 255 300 0.150

0.136

Not d. 135 9565 9700 0.014

180 9820 10000

A cohort study of accidental death due to drunk driving


108

Drunk 45 255 300 0.150

0.136

Not d. 135 9565 9700 0.014

180 9820 10000



109

Attributable Risk Percent

Incidence

Exposed Unexposed

%exposed

unexposedexposed

I

I - I100 x

RR

1 -RR x100

110

Dead Not dead Risk Attributable Risk %

Fast 100 1900 2000 0.0500.050- 0.010 0.050

= 80%Slow 80 7920 8000 0.010

180 9820 10000


AR% = [Ie – Io] x100 Ie

RR% = Ie = 0.050

Io 0.010

111

Dead Not dead Risk Attributable Risk %

Drunk 45 255 300 0.150

Not d. 135 9565 9700 0.014

180 9820 10000


0.150- 0.014 0.150

= 91%

AR% = [Ie – Io] x100 Ie

RR% = Ie = 0.150

Io 0.014

112

Prevented Fraction (PF)• For exposures associated

with decreased riskAttributable risk (AR) is called Prevented fraction

(PF)

• If relative risk <1– proportion of potential cases which would have

occurred if the exposure had been absent– proportion of potential cases prevented by the

exposure– E.g. in the case of vaccination for disease

prevention

113

Prevented fraction

RR -1

I

I -I PF

unexposed

exposed unexposed

114

Prevented Fraction Percent (PF%)

Incidence

Exposed Unexposed

%unexposed

exposedunexposed

I

I - I x100RR)-(1x100

Unexposed Exposed

115

PF: Vaccine efficacy

Pop. Cases Cases/1000 RR

Vaccinated 301,545 150 0.49 0.28

Unvaccinated 298,655 515 1.72 Ref.

Total 600,200 665 1.11

0.72 0.28 - 1

0.72 1.72

0.49 - 1.72 PF

116

1. Impact measures among exposed

2. Impact measures in the population

117

Population Attributable Risk

Risk

Population Unexposed

unexposed population I -I

118

Population Attributable Risk Percent (PAR%)

100 x 1 1)-(RR P

1) -(RR P PAR%

100 x I

I - I PAR%

population

unexposedpopulation

where P = % population exposed

119

Dead Not dead Risk

Fast 100 1900 2000 0.050

Slow 80 7920 8000 0.010

180 9820 10000 0.018

PAR%: Fast driving

44% 100 x 0.018

0.010 - 0.018 PAR%

0.008 0.010 - 0.018 PAR

120

Dead Not dead Risk

Drunk 45 255 300 0.150

Not d. 135 9565 9700 0.014

180 9820 10000 0.018

PAR%: Drunk driving

22% 100 x 0.018

0.014 - 0.018 PAR%

0.004 0.014 - 0.018 PAR

121

Conclude

• Driving related deaths in population

– 44% presumably due to fast driving

– 22% presumably due to drunk driving

122

PAR% in USA and Italy for cervical cancer,

various risk factors

Source, Parazzini et al. 1990

123

Direct standardizationIndirect standardization

การเป้ร�ยบเท�ยบ incidence rate หร0อ mortality rate ระหวิ1าง

ป้ระชากร หร0อระหวิ1างกล�1มป้ระชากรท�)สนใจก�บป้ระชากรอ%างอ�ง

124

อ�ตราการตายท�)ส+งข้องร�ฐฟอร�ดา

128

กล�1มอาย�

ส)ดส�วนประชีากรจ)งหว)ด

ส)ดส�วนประชีากร

อ-าเภอ

อ)ติราการเป3น อ)มื่พาติติ�อ

1000

จ)งหว)ด

อ)ติราการเป3น อ)มื่พาติติ�อ

1000

อ-าเภอ15-34 30% 24% 3 3

35-44 28% 22% 8 6

45-59 22% 27% 14 12

60+ 20% 27% 23 21

รวมื่ 100% 100% 11 11

ส)ดส�วนประชีากรและอ)ติราการเป3นโรคห)วใจติ�อ 1000 ในระด)บุจ)งหว)ดและอ-าเภอ

129

กล�1มอาย� ส)ดส�วนประชีากรจ)งหว)ด

(A)

อ)ติราการเป3น อ)มื่พาติติ�อ 1000

ในอ-าเภอ(B)

A*B

15-34 0.30 3 0.9/1000

35-44 0.28 6 1.68/1000

45-59 0.22 12 2.64/1000

60+ 0.20 21 4.2/1000

รวมื่ 1.00 11 9.42/1000

DIRECT STANDARDIZATION

130

เม0)อเป้ร�ยบเท�ยบแล%วิพบวิ1าage-standardized MI mortality rate

ของอ-าเภอเที่�าก)บุ 9.42/1,000ซึ่6�งติ-�ากว�าอ)ติรา

ของจ)งหว)ดซึ่6�งมื่�ค�าเที่�าก)บุ 11.0/1,000

การที่-า age-standardized rate ใชี&ได&ก)บุที่)!งmortality และ incidence

มื่)กใชี&เปร�ยบุเที่�ยบุค�า incidence หร�อmortality ระหว�างประชีากร เชี�น incidence rate ของ โรคมื่ะเร4ง โดยเร�ยกว�า adjusted rate โดยใชี&ประชีากรโลกเป3นประชีากรอ&างอ#ง

131

กล�1มอาย� ประชีากรอ-าเภอ(C)

อ)ติราติายของ จ)งหว)ดติ�อ 1000

(D)

C*D

15-34 240 3 0.72

35-44 220 8 1.76

45-59 270 14 3.79

60+ 270 23 6.21

รวมื่ 1,000 11 12.47

Standardized mortality ratio (SMR)= actual deaths/expected

deaths= 11/12.47

= 0.88 หร�อ ค#ดเป3น 88%

INDIRECT STANDARDIZATION

132

Standardized mortality ratio (SMR)= actual deaths/expected deaths= 11/12.47= 0.88 หร�อ ค#ดเป3น 88%SMR = 1 , การติายในอ-าเภอเที่�าก)บุจ)งหว)ดSMR < 1 , การติายในอ-าเภอน&อยกว�าจ)งหว)ดSMR > 1 , การติายในอ-าเภอมื่ากกว�าจ)งหว)ด

การที่-า indirect standardisation ใชี&ได&ก)บุที่)!งincidence และ mortality

133

Number Needed to TreatNumber Needed to Harm

134

การแสดงผู้ลกระทบข้องการได%ร�บ exposure ต1อการเก�ดโรคเป้;นค1าควิามเส�)ยงม�ควิามยากต1อการ

ป้ระเม�นหร0อเห-นภาพต1อผู้ลกระทบ จ3งม�การแสดง ผู้ลกระทบข้องการได%ร�บ exposure ซึ่3)งอย+1ในร+ป้

ข้อง intervention เป้;น จ'านวินผู้+%ป้,วิยท�7งหมดท�)จะผู้1านการร�กษา ซึ่3)งจะได%ร�บ

ผู้ลข้องการป้�องก�นโรค 1 คน เร�ยกวิ1า number needed to treat (NNT)

จ'านวินผู้+%ป้,วิยท�7งหมดท�)จะผู้1านการร�กษา ซึ่3)งจะเก�ด ภาวิะแทรกซึ่%อนจากการร�กษา 1 คน เร�ยกวิ1า

number needed to harm (NNH)

135

โรคไข้%หวิ�ด ไม1เป้;นโรคไข้%หวิ�ด

Totals

ฉ�ดวิ�คซึ่�น(experim

ent)

1 29 30

ไม1ฉ�ดวิ�คซึ่�น(control)

9 21 30

Totals 10 50 60

30.0219

9

dc

cCER

033.0291

1

ba

aEER

ควิามเส�)ยงเป้;ฯโรคไข้%หวิ�ด

ในกล�1มได%วิ�คซึ่�น

ควิามเส�)ยงเป้;ฯโรคไข้%หวิ�ดในกล�1มไม1ได%วิ�คซึ่�น

136

• เพราะฉะน�7น Absolute risk reduction (ARR)

• = |EER-CER| = |0.033-0.30| = 0.267

• NNT =

745.3267.0

1

ARR

1

137

• การเป้ร�ยบเท�ยบแอสไพร�นก�บยาหลอกเพ0)อ ป้�องก�นการเก�ด myocardial infarction

• ม�ค1า ARR เท1าก�บ 0.04

• ด�งน�7นค1า NNT จะเท1าก�บ 25

• น�)นค0อ ถ%าให%แอสไพร�นแก1ผู้+%ป้,วิยจ'านวิน 25 ราย จะ สามารถป้�องก�นการเก�ด myocardial infarction

ได% 1 ราย

138

• ในท'านองเด�ยวิก�น การให%ยาก�บผู้+%ป้,วิยอาจท'าให%เก�ดผู้ลอ0)น ท�)ไม1ต%องการและอาจเป้;นอ�นตรายต1อผู้+%ป้,วิยได%

• ค1า NNT ท�)ค'านวิณได%อาจเร�ยกวิ1า number needed to harm (NNH)

• ต�วิอย1างเช1น การใช%แอสไพร�นท�)ท'าให%ผู้+%ป้,วิยม�ควิามเส�)ยง ต1อการม�เล0อดออก ถ%า ARI ม�ค1าเท1าก�บ 020. จะได%

NNH เท1าก�บ 5 (1/0.2 ) น�)นค0อ ในผู้+%ป้,วิยท�)ใช%ยา แอสไพร�นท�กๆ 5 รายจะม�ผู้+%ป้,วิย 1 รายท�)ม�เล0อดออก2.0

1

139

• ค0อ จ'านวินผู้+%ป้,วิยท�7งหมดท�)ได%ร�บการร�กษาแล%วิเห-น ป้ระส�ทธี�ผู้ล 1 คน ซึ่3)ง NNT สามารถช1วิยให%

แพทย�สามารถป้ระเม�น Relative Risk และBenefit ข้องการร�กษาได%

NNT = ARR

1

• NNT = ; ARR = EER-CER

CEREER

1

140

• จ'านวินผู้+%ป้,วิยท�7งหมดท�)ได%ร�บการร�กษาแล%วิได%ผู้ลท�) ไม1พ3งป้ระสงค�ตามมา 1 คน

NNH = ARI

1

• NNH = ; ARI = CER - EER EERCER

1

141

ผู้ลแที่รกซึ่&อน ผู้ลการร)กษา การ

ร)กษา A การ

ร)กษาB

RRR =(CER-EER)/CER

ARR =CER-EER

NNT =1/ARR

ผู้ลที่��เก#ดจร#ง

8%

4%

(8-4)/8 =50%

8-4 =4%

1/4= 25

เมื่��อผู้ลด�มื่าก

80%

40%

(80-40)/80 =50%

80-40 =40%

1/40=2.5

=3 เมื่��อผู้ลไมื่ด�

0.08%

0.04%

(0.08-0.04)/0.08

=50%

0.08-0.04 =0.04%

1/0.04 =2500

CER = Control Event Rate, EER = Experiment Event Rate

142

Random error/Chance,

BiasConfounder

Suthee Rattanamomgkolgul

143

เราอาจ สร�ปการศ6กษาผู้#ด ได&

สร�ปการศ6กษาความื่ส)มื่พ)นธ%ผู้#ด ได&จาก 3 สาเหติ�

•I. ความื่บุ)งเอ#ญ่CHANCE/Random error

•II. อคติ# BIAS

•III. ป9จจ)ยที่�� 3 หร�อ ติ)วกวน-

3rd VARIABLES :

Confounder

144

•การว)ดติ�างๆในการว#จ)ยเก#ดจากการส��มื่ติ)วอย�าง

• จ-านวนติ)วอย�าง มื่�ความื่ส-าค)ญ่ติ�อความื่ ถ1กติ&อง ของการประมื่าณ์ค�าของค�าที่��

แที่&จร#งในธรรมื่ชีาติ# ก)บุค�าที่��ได&จากการว#จ)ย

•Number of sample == Validity

I. ความื่บุ)งเอ#ญ่ Chance

145

ค1าเฉล�)ยข้องน'7าหน�กน�ส�ตเม0)อใช%จ'านวินท�)น'า มาเฉล�)ยต1าง ๆ ก�น

146

N=100

N=25

N=11

147

ผู้ลการร�กษาเบาหวิานด%วิย ยาลดน'7าตาลในเล0อด

เม0)อทดลองด%วิยข้นาดต�วิอย1างท�)แตกต1างก�น

148

Precision of the effect size

Size of the problems

• Prevalence

• Incidence

• Mean

Precision of the size• Prevalence of HT in this

population = 30% (95% CI = 25 - 36%)

• Incidence of skin cancer =100 ติ�อแสน (95% CI= 89-

112 ติ�อแสน)• Mean fasting blood glucose

of the population = 126 mg/mL (95% CI = 80 -

160 %)

149

Confidence interval of the estimates• Prevalence of HT in this

population = 30% (95% CI = 25 - 36%

• ได&มื่าจากการศ6กษาความื่ด)นจากการส��มื่คน1000 คน มื่าจากชี�มื่ชีนแห�งหน6�ง

• หากที่-าการส��มื่ใหมื่� เล�อกคน 1000 คนมื่าว)ด ความื่ด)นอ�กเป3นจ-านวน 100 คร)!ง ค�า

Prevalence ที่��ได&จะมื่� 95 คร)!งที่��ติกอย1�ในชี�วง 25 - 36 %

• มื่�ความื่เชี��อมื่)�นถ6ง 95% ว�า Prevalence of HT ในประชีากรน�!จะอย1�ระหว�าง 25 - 36%

150

II. อคติ# (bias)- มื่� 2 ชีน#ด1. การสร�ปผู้#ดที่��เก#ดจาก การค)ดเล�อกกล��มื่ที่��มื่า

เปร�ยบุเที่�ยบุที่��ไมื่�ย�ติ#ธรรมื่ (selection bias)

2. หร�อการว)ดค�าของ exposure หร�อoutcome ที่��ไมื่�ย�ติ#ธรรมื่ (information bias หร�อ measurement bias)

ที่-าให&เก#ดค�าของความื่ส)มื่พ)นธ% Relative Risk, Odds Ratio, Prevalence Rate Ratio ที่��ผู้#ดไปจากความื่เป3นจร#งในธรรมื่ชีาติ#

151

ควิามแตกต1างระหวิ1าง chance และ bias

Error

Study size

Source: Rothman, 2002

Systematic error (bias)

Random error (chance)

152

Selection bias•ความื่ผู้#ดพลาดที่��เก#ดจากข)!นติอนการค)ดเล�อก

ประชีากรที่��มื่าศ6กษาที่��ไมื่�เหมื่าะสมื่

•มื่�การค)ดเล�อกประชีากรที่��เข&ามื่าในการศ6กษาที่��ไมื่� เที่�าเที่�ยมื่ก)นของกล��มื่ที่��น-ามื่าเปร�ยบุเที่�ยบุที่)!งสอง

ซึ่6�งการเล�อกน)!นไปเก��ยวข&องก)บุ

– สถานะของการเป3น case การเป3น control

– สถานะของการมื่� หร�อไมื่�มื่� ป9จจ)ยที่��ติ&องการศ6กษา

153

Healthy worker effect

154

Healthy worker effect

155

Information Bias

หร�อ measurement bias ความื่ผู้#ดพลาดที่��เก#ดจากข)!นติอนการว)ดค�าของexposure หร�อ outcome ที่��ไมื่�ย�ติ#ธรรมื่

สาเหติ�อาจเก#ดจากผู้1&ว#จ)ย อาสาสมื่)คร หร�อเคร��องมื่�อว)ด

• Subject bias– Recall bias– Public awareness

• Observer bias– Interviewer bias– Follow-up bias

• Diagnostic bias

156

ที่ารกพ#การแติ�ก-าเน#ด VS การได&ร)บุร)งส� X-ray ขณ์ะติ)!ง

ครรภ%• ส)มื่ภาษณ์%ประว)ติ#ของการได&ร)บุร)งส� X-ray

ขณ์ะติ)!งครรภ%•มื่ารดาที่��มื่�บุ�ติรพ#การแติ�ก-าเน#ดจะพยายามื่

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157Unrealistic Odds Ratio (Recall bias)

CaseControl

ExposedNot

Exposed

A B

C D

Hx of radiation

No Hx of radiation

Malformation+No Malformation

OR=AxD BxC

158

Effect of bias

159

Non-differential Misclassification

Case Control

Exposed 200 100

Non-exposed 100 200

Sensitivity 100% , Specificity 60%

Case Control

Exposed 240 60

Non-exposed 180 120

True OR = 4.0 Apparent OR = 2.7- 40 false+

+ 40 false+

- 80 false+

+ 80 false+

Reduction!

160

Differential Misclassification

Case Control

Exposed 200 100

Non-exposed 100 200

Case Control

Exposed 240 60

Non-exposed 100 200

True OR = 4.0 Apparent OR = 8.0

Sensitivity Specificity

Exposed 100% 60%

Non-exposed 100% 100%

161

Misclassification

Measurement error leads to assigning wrong exposure or outcome category

Non-differential Due to random error

Unrelated to exposure or outcome status

Not a bias

Weakens measure of association

Differential Due to systematic error

Related to exposure or outcome status

Bias

Measure of association distorted in any direction

162

Confounder

163

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สามารถทางคณ�ตศาสตร� (Maths Score)

164

When the data were subdivided by age they looked like this

i.e. within each age group there was no association. In this case age was a confounding variable.

165

ConfoundingConfounding

• Not the investigator’s fault, just a fact of life

• Confounding is a mixing of effects between the association of the disease and third factor (the confounder)

• What confounding factors could explain the finding?

166

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2 ประการ ; - Confounder และ Effect

modifier or Interaction

167

Alcohol VS Lung Cancer

CA Lung

+

CA Lung-

Alcohol+

800 200

Alcohol-

200 800

1000 1000OR = 800x800 = 4 200x200

OR=AxD BxC

168

Alcohol VS CA Lung

•Odds Ratio = 4

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169

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ๆ แล&วเก#ดจากป9จจ)ยอ��น

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ระหว�าง Alcohol ก)บุ มื่ะเร4งปอด

170

CA Lung

+

CA Lung

-Alcohol+ 800 200

Alcohol- 200 800

1000 1000

OR = 800x800 = 4 200x200

CA Lung

+

CA Lung

-Alcohol+ 200 25

Alcohol- 600 75

800 100

OR = 1

SMOKE NON – SMOKE

OR = 1

CA Lung

+

CA Lung

-Alcohol+ 50 225

Alcohol- 150 675

200 900

171

Confounder

SMOKING

CA LungAlcohol

SMOKING

CA LungAlcohol

(1) (2)

172

การจ)ดการก)บุConfounder

• Restriction inclusion criteria

• Matching (in case-control study) by Smoking status or by Gender

• Stratified analysis Mantel Haenszel Method

• Multivariate analysis • ในการศ6กษาแบุบุ RCT ใชี&ว#ธ#การ

randomisation

173

Confounder

CD+4 Level

Time to AIDS DeathHIV-1 RNA

•Not be an intermediate of the association between the exposure and the outcome

(3)

174

Confounding is

• An incidental factor can be a confounder if it satisfies the following conditions:(1) Must be associated with exposure(2) Must be an independent risk factor for the outcome(3) Must not be an intermediate step in the causal chain

between exposure and outcome(4) Must be present to a greater or lesser degree in the

study group vs. the comparison group• If confounder is unequally distributed between study and

comparison groups → Confounding• If confounder is equally distributed between study and

comparison groups → NO Confounding (even if the 1st three conditions are met)

175

Interpreting Associations Between Exposure and Disease

2. Is it due to bias?

3. Is it due to confounding?

Is it causal? According to causal criteria

1. Is it the result of chance?

Intro

176

External Validity

• Generalizability• Degree to which study results are relevant for

populations other than the target population

• Must use judgment as to whether the findings would make sense in other populations– No simple formula for determining external validity

177

Screening test

เคร0)องม0อตรวิจค�ดกรอง

178

Clinical Diagnosis

Disease Onset

No Disease Asymptomatic Disease

Clinical Course

Primary

Remove Risk Factors

Secondary

Early Detection and Treatment

Tertiary

Reduce Complications

Levels of Prevention

Fletcher RF, Fletcher SW, Wagner EH. Clinical Epidemiology: The Essentials, 3 rd ed. Williams and Wilkins, Baltimore, 1996.

179

Sojourn time and Lead time

No detectable disease

t0 t2 t1

Clinically detectable (symptomatic) disease

Period in which cancer is screen detectable

Sojourn time

Lead time

Time

Screen time

180

Ideal Screening Program:

Features of the diseaseSignificant impact on public health

Asymptomatic phase during which detection is possible

Outcomes improved by treatment during asymptomatic phase

Goroll AH, May LA, Mulley AG. Primary Care Medicine. 3d ed. Philadelphia: Lippincott, 1995: 13-6.

181


Features of the test

Sufficiently sensitive to detect disease during asymptomatic phase

Sufficiently specific to minimize false positive results

Acceptable to patients


182


Features of the screened populationSufficiently high prevalence of the disease to

justify screening

Relevant medical care is accessible

Patients willing to comply with further work-up and treatment


183

Disease Present Disease Absent

RE SULT

+

True PositiveWill benefit from test, (have disease that will be revealed).(A)

False PositiveRequire add’l tests, procedures to prove no ds.(B)

Positive predictive value:% with + test who have ds.A/(A+B)

_

False NegativeFalsely reassured they do not have ds.(C)

True NegativeFound to have no ds. but may have had anxiety about test (D)

Negative predictive value:% with – test with no ds. D/(C+D)

Sensitivity:% with ds. with + testA/(A+C)

Specificity:% without ds. with - test D/(B+D)

Operating Characteristics

184

Bias in the Evaluation of Screening Tests

“Early diagnosis will always appear to improve survival, even when the therapy is worthless.”

Sackett DL, Haynes RB, et al. Early Diagnosis. In: Clinical Epidemiology: a basic science for clinical medicine. 2d ed. Boston: Little, Brown, 1991: 153-170.

185

Compliance/Volunteer Bias

Type of selection bias

Volunteers are healthier, with lower mortality from all causes

Observed benefit may be due to self-selection of volunteers

186

Overdiagnosis Bias

Detection of disease that would never have become clinically evident during

the time of the trial and follow-up

187

Clinical illness

2 years

Lead-Time Bias

4 yearsDeath

Diagnosis at Clinical

Presentation

3 years

Natural History

Unscreened Population

Screened Population

Clinical symptoms

Diagnosis at Clinical

Presentation

Death

3 years

Gates TJ, Screening for Cancer: Evaluating the Evidence. Am Fam Physician. 2001;63:513-22

Death3 years

Screening Diagnosis5 years

Death

Asymptomatic periodOnset

188

Length-time Bias

Aggressive Disease

Onset Clinical

Presentation

Death

Clinical

PresentationDeathOnset

1 yr sx

Screening interval

1 year

6 mo. asymptomatic period

2 year asymptomatic period

4 yr sx

Less Aggressive Disease

Gates TJ, Screening for Cancer: Evaluating the Evidence. Am Fam Physician. 2001;63:513-22.

190

1. Screen for Risk factors การค�ดกรองป้9จจ�ยเส�)ยง - เพ0)อค%นหากล�1มคนท�)ม�ป้9จจ�ยเส�)ยงต1อการเก�ดโรคน

ช�มชน เช1น การค�ดกรองผู้+%ท�)ม�ระด�บไข้ม�นในเล0อดส+ง การใช%ป้ระวิ�ต�ในการค�ดกรองหญี่�งท�)ม�ป้9จจ�ยเส�)ยง

ต1อควิามด�นโลห�ตส+ง เช1น ส+บบ�หร�)จ�ด ม� ควิามเคร�ยดส+ง ม�ป้ระวิ�ต�คนในครอบคร�วิ

2. Screen for Diseases การค�ดกรองโรค - เพ0)อค%นหาโรคในกล�1มป้ระชากรและหากสงส�ยวิ1าจะม�

โรคจะท'าการตรวิจวิ�น�จฉ�ยต1อไป้ เช1น การใช% pap smear ค�ดกรองโรคมะเร-งป้าก

มดล+ก

Objectives of screening in community

191

IN EVERY 100 PEOPLE, 4 WILL HAVE THE DISEASE

Disease +

4

Disease -

96

Population

100

If these 100 people are representative of the population at risk, the assessed rate of those with the

disease (4%) represents the PREVALENCE of the disease – it can also be considered the PRE-TEST

PROBABILITY of having the disease

192

OF THE 4 PEOPLE WITH THE DISEASE, THE TEST WILL DETECT 3

Disease +

4

Disease -

96

Test +

3

Test -

1

Population

100

In other words, the sensitivity is

75%

193

AMONG THE 96 PEOPLE WITHOUT THE DISEASE, 7 WILL TEST POSITIVE

Disease +

4

Disease -

96

Test +

7

Test -

89

Test +

3

Test -

1

Population

100

In other words, the specificity is

93%

194

POSITIVEPREDICTIVE

VALUE = 30%

AMONG THOSE WHO TEST POSITIVE, 3 IN 10 WILL ACTUALLY HAVE THE DISEASE

Disease +

4

Disease -

96

Test +

7

Test -

89

Test +

3

Test -

1

Population

100

This is also the POST-TEST PROB- ABILITY of having

the disease

195

NEGATIVEPREDICTIVEVALUE = 99%

AMONG THOSE WHO TEST NEGATIVE, 89 OF 90 WILL NOT HAVE THE DISEASE

Disease +

4

Disease -

96

Test +

7

Test -

89

Test +

3

Test -

1

Population

100

196

CONVERSELY, IF SOMEONE TESTS NEGATIVE, THE CHANCE OF HAVING THE

DISEASE IS ONLY 1 IN 90

Disease +

4

Disease -

96

Test +

7

Test -

89

Test +

3

Test -

1

Population

100

197

PREDICTIVE VALUES AND CHANGING PREVALENCE

Disease +

4

Disease -

996

Population

1000

Prevalence reduced by an order of magnitude from 4%

to 0.4%

198

PREDICTIVE VALUE AND CHANGING PREVALENCE

Disease +

4

Disease -

996

Test +

70

Test -

926

Test +

3

Test -

1

Population

1000Sensitivity

and Specificity unchanged

199

POSITIVEPREDICTIVEVALUE = 4%

POSITIVE PREDICTIVE VALUE AT LOW PREVALENCE

Disease +

4

Disease -

996

Test +

70

Test -

926

Test +

3

Test -

1

Population

1000Previously,

PPV was 30%

200

NEGATIVEPREDICTIVEVALUE >99%

NEGATIVE PREDICTIVE VALUE AT LOW PREVALENCE

Disease +

4

Disease -

996

Test +

70

Test -

926

Test +

3

Test -

1

Population

1000

Previously, NPV was 99%

201

PREDICTION OF LOW PREVALENCE EVENTS

• Even highly specific tests, when applied to low prevalence events, yield a high number of false positive results

• Because of this, under such circumstances, the Positive Predictive Value of a test is low

• However, this has much less influence on the Negative Predictive Value

202

RELATIONSHIP BETWEEN PREVALENCE AND PREDICTIVE VALUE

0

0.2

0.4

0.6

0.8

1

0.05 0.2 0.4 0.6 0.8 0.95

Pre-test Probability (Prevalence)

Pre

dic

tive V

alu

e

PPVNPV

Based on a test with 90% sensitivity and 82% specificity

Difference between PPV and

NPV relatively small

Difference between PPV and

NPV relatively large

203

Diagnostic tests:Likelihood ratios

Group Test

Not-normal

Normal

x > +

a b a+b

x -

c d c+d

a+c b+d n

• Positive likelihood ratio [a/(a+c)] / [b/(b+d)]

• Negative likelihood ratio [c/(a+c)] / [d/(b+d)]

They do not depend as much on the prevalence !!

Shrikant I. Bangdiwala, Ph.D. UNC

204

Positive Likelihood Ratio (LR+)

• Reflects the degree of confidence that a person who scores in the positive does have the disease– sensitivity / (1-specificity)

• The higher the LR+, the more informative is the test for identifying people who have the disorder

Ref: Sackett 2000 Shrikant I. Bangdiwala, Ph.D. UNC

205

Interpreting LR+ values (Sackett et al., 1991)

• LR+ > 20 Very high– Virtually certain that a person with this score has

the disorder

• LR+ = 10 High– Disorder very likely in a person with this score

• LR+ = 4 Intermediate– Test is suggestive of disorder but insufficient to

diagnose

• LR+ = 1 Equivocal– A person who scores in the disordered range on the

measure may or may not have the disorder; the measure provides no new informationRef: Sackett 2000


206

Negative Likelihood Ratio (LR-)

• Reflects the degree of confidence that a person scoring in the negative (normal) range on the diagnostic indicator truly does not have the disorder – (1-sensitivity) / specificity

• The lower the LR-, the more informative the test is for ruling out the presence of disorder

Ref: Sackett 2000


207

Interpreting LR- values (Sackett et al., 1991)

• LR- < 0.10 Very low– virtually certain that a person scoring in this range

does not have the disorder

• LR- = 0.20 Low– disorder very unlikely

• LR- = 0.40 Intermediate– test is suggestive but insufficient to rule out the

disorder

• LR- = 1.0 Equivocal– A person scoring in the normal range on this measure

may or may not be normalRef: Sackett 2000


208

80 a

30 b

c 20

d 70

Disorder Status (re: Gold Standard)

+ Disorder (LI) - Disorder (LN)

New Test Result

+ Disorder (LI)

-Disorder (LN)

100100 100100Sens = .80Spec = .70LR+ = sens/(1-spec) = .80/.30 = 2.67LR- = (1-sens)/spec = .20/.770 = 0.29


209

METHOD 4: A TEST WITH NORMALLY DISTRIBUTED VALUES

Negative Positive

Degree of ‘positivity’ on test

% o

f G

rou

p

DISEASED

NON-DESEASED

Test cut-off

Assessing the performance of the test assumes that these two

distributions remain constant. However, each

of them will vary (particularly through spectrum or selection

bias)

210

CASESNON-CASES

PERFORMANCE OF A DIAGNOSTIC TEST

Negative Positive


% o

f G

rou

p

DISEASED

NON-DESEASED

Test cut-offFALSE

NEGATIVESFALSE

POSITIVES

211

MINIMISING FALSE NEGATIVES: A SENSITIVE TEST

Negative Positive


% o

f G

rou

p

DISEASED

NON-DESEASED

Test cut-off

Cut-off shifted to minimise false negatives ie to optimise sensitivity

CONSEQUENCES:

- Specificity reduced

- A Negative result from a seNsitive test rules out the diagnosis - snNout

CASESNON-CASES

212

MINIMISING FALSE POSITIVES: A SPECIFIC TEST

Negative Positive


% o

f G

rou

p

DISEASED

NON-DESEASED

Test cut-off

Cut-off shifted to minimise false positives ie to optimise specificity

CONSEQUENCES:

- Sensitivity reduced

- A Positive result from a sPecific test rules in the diagnosis - spPin

213

Trade- Off between Sensitivity and Specificity when Diagnosing Diabetes

Blood Sugar Level 2 hr after Eating

(mg/100mL)

Sensitivity (%)

Specificity (%)

70

98.6

8.8

80

97.1

25.5

90

94.3

47.6

100

88.6

69.8

110

85.7

84.1

120

71.4

92.5

130

64.3

96.9

140

57.1

99.4

150

50.0

99.6

160

47.1

99.8

170

42.9

100.0

180

38.6

100.0

190

34.3

100.0

200

27.1

100.0

214

RECEIVER OPERATING CHARACTERISTIC CURVE

Overall shape is predicted by the reciprocal relationship between sensitivity and specificity

The closer the curve gets to Sensitivity=1 and Specificity=1, the better the overall performance of the test

The diagonal line (representing Sensitivity=0.5 and Specificity=0.5) represents performance no better than chance

Hence the area under the curve gives a measure of the test’s performance

FALSE POSITIVE RATE (1-Specificity)

TR

UE P

OS

ITIV

E R

ATE

(Sen

sit

ivit

y)

215

0

100

1-Specificity

Sensi

tivi

tyAREA UNDER ROC CURVES

0

100

1-Specificity

Sensi

tivi

ty Sensitivity and specificity both 100% - TEST PERFECT

Sensitivity and specificity both 50% - TEST USELESS

AREA=1.0

AREA=0.5The area under a ROC curve will be between 0.5 and 1.0

216

0

100

1-Specificity

Sensi

tivi

tyAREA UNDER ROC CURVES

Area = 0.7 (between 0.5 and

1.0)

•Consider (hypothetically) two patients drawn randomly from the DISEASE+ and DISEASE- groups respectively

• If the test is used to guess which patient is from the DISEASE+ group, it will be right 70% of the time

217

RECEIVER OPERATING CHARACTERISTIC (ROC) CURVE

0102030405060708090

100

0 20 40 601-Specificity

Sensi

tivit

y

ACAT

MC

This study compared the performance of a dementia screening test in a community sample (ACAT) and a memory clinic sample (MC)

Flicker L, Loguidice D, Carlin JB, Ames D. The predictive value of dementia screening instruments in clinical populations. International Journal of Geriatric Psychiatry 1997 ; 12 : 203-209

218

Improving Predictive Value

• It is possible to increase the pre-test likelihood (and thereby improve the predictive value) by targeting testing to those at highest risk, or by selectively screening specific populations.

221

Types of diagnostic tests

• Qualitative ordinal categorical data– E.g. clinical examination and determination of

categories of severity of illness– E.g. psychological assessment of mental state

• Quantitative continuous data– E.g. laboratory measurement of blood

chemistry


222

Combining tests

• Goal: To increase the confidence in a given decision

• Methods:– Parallel tests– Tests in series– Screening/

confirmatory tests– Re-testing negatives


223

Parallel testing

• Decision: positive if any of the tests is positive

• Preferred if starting treatment quickly is valuable

• Effects:– Increases sensitivity and

negative predictive value less false negatives

– Decreases specificity and positive predictive value more false positives


224

Tests in series

• Decision: positive if all of the tests are positive

• Preferred if treatment may have side effects

• Effects:– Increases specificity

and positive predictive value less false positives

– Decreases sensitivity and negative predictive value more false negatives


225

Screening / confirmatory tests

Special case of several tests with following characteristics:

• Test 1: – High sensitivity– Reasonable specificity– Low cost

• Test 2:– Highly specific


226

Re-testing negativesSpecial case of several

tests with following characteristics:– Same test is applied

repeatedly at different time points

– Those already positive are not re-tested

• Effects: – Increases sensitivity at

the aggregate level

– Specificity becomes more important with decreasing prevalence

TIME 1 TIME 2 TIME 3


227

Studying prognostic factor

228

Stage of Pre-pathologic

onset

Pre-symptomatic stage

Clinically manifest disease

Progress to a fatal termination

Remission and relapses

Regress spontaneously, leading to recovery

Risk Factors Effect of Treatment Prognostic factor

Natural history of disease

Screening

229

Survival Curves

1 year survival 95%Median survival unknown

1 year survival 20%Median survival 3 months



230

Survival curves

• A plot of outcome over time for each group

• At predetermined intervals, measure how many people have the outcome compared to how many were at risk at the beginning of the period

• Can compare the curves for two different groups and see if they are different

231

The differential blood pressure sign in general practice: prevalence and prognostic value.

Clark CE, Powell RJ. Fam Pract. 2002 Oct;19(5):439-41.

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Clark%20CE%22%5BAuthor%5D

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Powell%20RJ%22%5BAuthor%5D

235

Kaplan Meier Method

Time ti

(month)

No. at risk No. events Survival rate

0 20 0 1.00

5 20 2 [1-(2/20)]*1.00=0.90

6 18 0 [1-(0/18)]*0.90=0.90

10 15 1 [1-(1/15)]*0.90=0.84

13 14 2 (1-(2/14)]*0.84=0.72

Follow up 20 patients with lung cancer for 13 months

236

Kaplan Meier Curve

0.60.7

0.80.9

1.00 5 10 15 20

Survival Time

0.6

0.7

0.8

0.9

1.0

Pro

port

ion

Sur

vivi

ng (

95%

Con

fiden

ce)

237

Kaplan Meier

• One way to estimate survival

• Nice, simple, can compute by hand

• Can add stratification factors

• Cannot evaluate covariates like Cox model

• No sensible interpretation for competing risks

239

Hazard function (h(t))

• Defn: the probability of an event at a specific moment in time (t), given the patient has already survived to that point in time.

– closely linked to the survival function. – indicates the probability of the patient "failing"

during the next time period. – a direct measure of prognosis.

240

Prognostic factors and clinical outcome in acute lower respiratory tract infections: a prospective study in general practice.

• BACKGROUND: Unrealistic expectations about illness duration are likely to result in reconsultations and associated unnecessary antibiotic prescriptions. An evidence-based account of clinical outcomes in patients with lower respiratory tract infection (LRTI) may help avoid unnecessary antibiotic prescriptions and reconsultations.

• OBJECTIVES: We aimed to identify clinical factors that may predict a prolonged clinical course or poor outcome for patients with LRTI and to provide an evidence-based account of duration of an LRTI and the impact of the illness on daily activities in patients consulting in general practice.

• METHODS: A prospective cohort study of 247 adult patients with a clinical diagnosis of LRTI presenting to 25 GPs in The Netherlands was carried out. Multivariable Cox regression analysis was used to identify baseline clinical and infection parameters that predicted the time taken for symptoms to resolve. A Kaplan-Meier curve was used to analyse time-to-symptom resolution. Clinical cure was recorded by the GPs at 28 days after the initial consultation and by the patients at 27 days.

Fam Pract. 2006 Oct;23(5):512-9. Epub 2006 Jun 20.Hopstaken RM, Coenen S, Butler CC, Nelemans P, Muris JW, Rinkens PE, Kester AD, Dinant GJ.Institution of Health Centres Eindhoven Eindhoven, GC Meerhoven, Eindhoven, The Netherlands. [email protected]

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Hopstaken%20RM%22%5BAuthor%5D


http://www.ncbi.nlm.nih.gov/pubmed?term=%22Coenen%20S%22%5BAuthor%5D


http://www.ncbi.nlm.nih.gov/pubmed?term=%22Butler%20CC%22%5BAuthor%5D

http://www.ncbi.nlm.nih.gov/pubmed?term=%22Nelemans%20P%22%5BAuthor%5D


http://www.ncbi.nlm.nih.gov/pubmed?term=%22Muris%20JW%22%5BAuthor%5D


http://www.ncbi.nlm.nih.gov/pubmed?term=%22Rinkens%20PE%22%5BAuthor%5D


http://www.ncbi.nlm.nih.gov/pubmed?term=%22Kester%20AD%22%5BAuthor%5D


http://www.ncbi.nlm.nih.gov/pubmed?term=%22Dinant%20GJ%22%5BAuthor%5D


242

RESULTS: • Co-morbidity of asthma was a statistically significant predictor

of delayed symptom resolution, whereas the presence of fever, perspiring and the prescription of an antibiotic weakly predicted enhanced symptom resolution.

• The GPs considered 89% of the patients clinically cured at 28 days, but 43% of these nevertheless reported ongoing symptoms.

• Patient-reported cure was much lower (51%), and usual daily activities were limited in 73% of the patients at baseline, and 19% at final follow-up.

CONCLUSIONS: • The course of LRTI was generally uncomplicated, but the

morbidity of this illness was considerable with a longer duration than generally reported, especially for patients with co-existent asthma.

• These results underline once again the importance of providing GPs with an evidence-based account of outcomes to share with patients in order to set realistic expectations and of enhancing their communication skills within the consultation.

244

Hazard function (h(t))

• Defn: the probability of an event at a specific moment in time (t), given the patient has already survived to that point in time.

– closely linked to the survival function. – indicates the probability of the patient "failing"

during the next time period. – a direct measure of prognosis.

245

Prognostic factors and clinical outcome in acute lower respiratory tract infections: a prospective study in general practice.

• BACKGROUND: Unrealistic expectations about illness duration are likely to result in reconsultations and associated unnecessary antibiotic prescriptions. An evidence-based account of clinical outcomes in patients with lower respiratory tract infection (LRTI) may help avoid unnecessary antibiotic prescriptions and reconsultations.

• OBJECTIVES: We aimed to identify clinical factors that may predict a prolonged clinical course or poor outcome for patients with LRTI and to provide an evidence-based account of duration of an LRTI and the impact of the illness on daily activities in patients consulting in general practice.

• METHODS: A prospective cohort study of 247 adult patients with a clinical diagnosis of LRTI presenting to 25 GPs in The Netherlands was carried out. Multivariable Cox regression analysis was used to identify baseline clinical and infection parameters that predicted the time taken for symptoms to resolve. A Kaplan-Meier curve was used to analyse time-to-symptom resolution. Clinical cure was recorded by the GPs at 28 days after the initial consultation and by the patients at 27 days.

Fam Pract. 2006 Oct;23(5):512-9. Epub 2006 Jun 20.Hopstaken RM, Coenen S, Butler CC, Nelemans P, Muris JW, Rinkens PE, Kester AD, Dinant GJ.Institution of Health Centres Eindhoven Eindhoven, GC Meerhoven, Eindhoven, The Netherlands. [email protected]





http://www.ncbi.nlm.nih.gov/pubmed?term=%22Butler%20CC%22%5BAuthor%5D











246

สร�ป้การวิ�ดทางระบาดวิ�ทยา•ควิามเส�)ยงข้องการเก�ดโรค Incidence rate

•ข้นาดข้องป้9ญี่หา Prevalence rate

•ควิามส�มพ�นธี�ข้องโรคก�บป้9จจ�ยเส�)ยง

Relative risk (RR)Incidence rate ratioOdds ratio (OR)

•ผู้ลกระทบข้องป้9จจ�ยเส�)ยงต1อการเก�ดโรคในป้ระชากร

Population attributable risk (PAR)Population prevented fraction

•ผู้ลกระทบข้องการร�กษา Number needed to treat (NNT)Number needed to harm (NNH)

• ต%องการเป้ร�ยบเท�ยบincidence/mortality ระหวิ1างป้ระชากร

Direct and indirect standardization Standardized mortality ratio (SMR)

247

Thank you for your kind attentions

248

วิ�ทยาการระบาดในงานเวิชศาสตร�ครอบคร�วิ

นพ. ส�ธี�ร� ร�ตนะมงคลก�ลภาควิ�ชาเวิชศาสตร�ป้�องก�นและส�งคม

มหาวิ�ทยาล�ยศร�นคร�นทรวิ�โรฒ

epi and fammed

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