cac thiet ke trong dich te hoc

Dịch tễ học là gì?Môn học khảo sát nghiên cứu sự phân bố bệnh tật trong quần thể Môn học tìm nguyên nhân, lý giải tại sao có sự phân bố đó trong quần thể - Môn học ứng dụng can thiệp , k

ĐẠI CƯƠNG VỀ DỊCH TỄ HỌC

PGS, TS LÊ HOÀNG NINH

VIỆN V.S-YT CÔNG CỘNG

Mục tiêu học tập

• Hiểu được dịch tễ học là gì

• Mục tiêu dịch tễ học?

•Vai trò của dịch tễ học: trong phòng ngừa

trong điều trị, trong y tế công cộng, trong xã hội học

Dịch tễ học là gì?

Môn học khảo sát nghiên cứu sự phân bố bệnh tật trong quần thể

Môn học tìm nguyên nhân, lý giải tại

sao có sự phân bố đó trong quần thể

- Môn học ứng dụng can thiệp , khống chế

kiểm soát nguyên nhân nhằm bảo vệ ,

nâng cao sức khỏe của quần thể

Phương cách tiếp cận của dịch tễ học

• 1 Mô tả: sự phân bố bệnh tật:

-Who?

What? When? -Where? -

-Phương cách tiếp cận dịch

tễ học

Phương cách tiếp cận dịch

tễ học

 3 Làm cách nào ? ứng dụng can thiệp

 HOW ? - Can thiệp

 Hiệu quả can thiệp?

Dịch tễ mô tả và phân

tích

• mô tả : trả lời các câu hỏi: ai (Who), cái gì (What),khi nào( When), và ở đâu (Where)

• phân tích: trả lời 2 câu hỏi: tại sao?

(Why) và làm cách nào ( How)

• giúp nhận ra, xác định dân số, nhóm dân số

có nguy cơ cao về một vấn đề sức khỏe nào đó

• giúp có thông tin cần cho phân bố nguồn lựcs

• hình thành một giả thuyết có thể

kiểm định được

Thí dụ : mô tả cái gì ?

• thí dụ có bao nhiêu ca nhiễm salmonella?

- Giúp nhận ra/xác định gánh nặng bệnh tật Không có so sánh với nhóm dân số khác

Race # of Salmonella cases Pop size Black 119 1,450,675 White 497 5,342,532

http://www.vdh.virginia.gov/epi/Data/race03t.pdf

Dịch tễ phân tích

 Giúp kiễm định giả thuyết về căn

nguyên, yếu tố nguy cơ

 Kết luận về yếu tố nguy cơ, nguyên nhân của sự phân bố bệnh tật

 Nguyên tắc phân tích¸ là có sự so

sánh giửa 2 nhóm:

VAI TRÒ CỦA DỊCH TỄ

HỌC

 Y học dự phòng và y tế công cộng?

cường sức khỏe, kéo dài tuổi thọ

 3 cấp độ dự phòng:

bệnh: chủng ngừa, không tiếp xúc yếu tố nguy cơ

sớm giảm trầm trọng, tử vong và các biến chứng thí dụ sàng lọc bệnh ung thư tử cung…

 Cấp III: tertiary prevention: giảm tác động, ảnh hưởng của bệnh Thí dụ phục hồi chức năng sau đột qụi…

VAI TRÒ CỦA DỊCH TỄ

HỌC

 Mô tả dịch tễ Mô tả lâm sàng

 Giả thuyết dịch tễ Giả thuyết lâm sàng ( chẩn đoán sơ bộ)

 Phân tích dịch tễ Phân tích lâm sàng

 Kiểm định giả thuyết Kiểm định giả thuyết dịch tễ lâm sàng

( nguyên nhân/ yếu tố nguy cơ) ( chẩn đoán xác định)

 Can thiệp nguyên nhân Can thiệp điều trị (cộng đồng khỏe mạnh) (bệnh nhân hồi phục)

sàng

VAI TRÒ CỦA DỊCH TỄ

HỌC

 Chính sách y tế và dịch vụ y tế:

 Bao nhiêu bệnh viện? Bệnh viện đa

khoa, chuyên khoa?

VAI TRÒ CỦA DỊCH TỄ

HỌC

 Xã hội học?

# of people in the population

Prevalence Example

In 1999, Virginia reported an estimated 253,040

residents over 20 years of age with diabetes The

US Census Bureau estimated that the 1999

Virginia population over 20 was 5,008,863

253,040 Prevalence= = 0.051

Prevalence

• Useful for assessing the burden of disease within a population

• Valuable for planning

• Not useful for determining what caused

disease

# of new cases of disease over

a specific period of time Incidence = -

# of persons at risk of disease over that specific period of time

Incidence Example

• A study in 2002 examined depression among persons

with dementia The study recruited 201 adults with

dementia admitted to a long-term care facility Of the

201, 91 had a prior diagnosis of depression Over the

first year, 7 adults developed depression

7 Incidence = = 0.0636

Incidence

• High incidence represents diseases with

high occurrence; low incidence represents diseases with low occurrence

• Can be used to help determine the causes

of disease

• Can be used to determine the likelihood of developing disease

Prevalence and Incidence

• Prevalence is a function of the incidence

of disease and the duration of disease

Prevalence and Incidence

Prevalence

= prevalent cases

Prevalence and Incidence

New prevalence

Incidence Old (baseline)

prevalence

No cases die

or recover = prevalent cases = incident cases

Prevalence and Incidence

= prevalent cases = incident cases = deaths or recoveries

Time for you to try it!!!

Dịch tễ mô tả

Con người, nơi chốn, thời gian

Mô tả: con người, khi nào, ở đâu?

Liên hệ tới Person, Place, and Time

• con người (Person)

- Có thể mô tả theo các đặc trưng như:

- Có thể là lúc khởi phát bệnh, khi tiếp

xúc với yếu tố nguy cơ

Data Characterized by Person

http://www.vahealth.org/civp/Injury%20in%20Virginia_Report_2004.pdf

Data Characterized by Person

http://www.vdh.virginia.gov/std/AnnualReport2003.pdf

Data Characterized by Person

http://www.vdh.virginia.gov/epi/cancer/Report99.pdf

Data Characterized by Person

http://www.vahealth.org/chronic/Data_Report_Part_3.pdf

Dữ liệu thời gian

• thường mô tả dạng biểu đồ graph

- Số ca trục tung (y) axis – thời gian trục hoành (x) axis

• thời khoảng tùy theo cái gì được mô tả

• cho thấy khuynh hướng, mùa, tuần, ngày, thời khoảng dịch

Data Characterized by Time

Epi Curve for E.Coli outbreak

Data Characterized by Time

http://www.vdh.virginia.gov/std/HIVSTDTrends.pdf

Data Characterized by Time

http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5153a1.htm

Data Characterized by Time

http://www.health.qld.gov.au/phs/Documents/cdu/12776.pdf

shadings/colors to indicate the count / rate of cases in an area

Data Characterized by Place

http://www.vdh.virginia.gov/epi/Data/region03t.pdf

Data Characterized by Place

http://www.vdh.virginia.gov/epi/Data/Maps2002.pdf

Data Characterized by Place

http://www.vahealth.org/civp/preventsuicideva/epiplan%202004.pdf

Data Characterized by Place

http://www.vahealth.org/civp/preventsuicideva/epiplan%202004.pdf

Data Characterized by Place

Source: Olsen, S.J et al N Engl J Med 2003 Dec 18; 349(25):2381-2

5 Minute Break

Dịch tễ phân tích

Hypotheses and Study Designs

Descriptive vs Analytic

Epidemiology

• Descriptive Epidemiology deals with the questions: Who, What, When, and Where

• Analytic Epidemiology deals with the

remaining questions: Why and How

Analytic Epidemiology

• Used to help identify the cause of disease

• Typically involves designing a study to test hypotheses developed using descriptive epidemiology

Borgman, J (1997) The Cincinnati Enquirer King Features Syndicate

Exposure and Outcome

A study considers two main factors:

exposure and outcome

• Exposure refers to factors that might influence one’s risk of disease

• Outcome refers to case definitions

Case Definition

• A set of standard diagnostic criteria that

must be fulfilled in order to identify a

person as a case of a particular disease

• Ensures that all persons who are counted

as cases actually have the same disease

• Typically includes clinical criteria (lab

results, symptoms, signs) and sometimes restrictions on time, place, and person

Developing Hypotheses

• A hypothesis is an educated guess about

an association that is testable in a

Example

were more likely to become ill

example, ill persons are those who have diarrhea and fever

church picnic were more likely to have laboratory- confirmed Salmonella

Experimental Studies

• Can involve individuals or communities

• Assignment of exposure status can be

Experimental Study Examples

• Randomized clinical trial to determine if

giving magnesium sulfate to pregnant

women in preterm labor decreases the risk

of their babies developing cerebral palsy

• Randomized community trial to determine if fluoridation of the public water supply

decreases dental cavities

Cross-Sectional Studies

• Exposure and outcome status are

determined at the same time •

Examples include:

- Behavioral Risk Factor Surveillance System (BRFSS) - http://www.cdc.gov/brfss/ - National Health and Nutrition Surveys (NHANES) -

http://www.cdc.gov/nchs/nhanes.htm

• Also include most opinion and political polls

Prospective Assessed at Followed into the

beginning of study future for outcome Retrospective Assessed at some Outcome has

point in the past already occurred

Cohort Studies

Study Population

Exposure is self selected

Non-exposed Exposed

Follow through

time

Cohort Study Examples

• Study to determine if smokers have a

higher risk of lung cancer

• Study to determine if children who receive influenza vaccination miss fewer days of

school

• Study to determine if the coleslaw was the cause of a foodborne illness outbreak

Case-Control Studies

Study Population

Controls Cases

Case-Control Study Examples

• Study to determine an association between autism and vaccination

• Study to determine an association between lung cancer and radon exposure

• Study to determine an association between salmonella infection and eating at a fast food restaurant

Cohort versus Case-Control Study

Classification of Study Designs

Source: Grimes DA, Schulz KF Lancet 2002; 359: 58

Time for you to try it!!!

5 Minute Break

Analytic Epidemiology

Measures of Association

and Statistical Tests

Measures of Association

• Assess the strength of an association

between an exposure and the outcome

of interest

• Indicate how more or less likely one is to

develop disease as compared to another

• Two widely used measures:

2 x 2 Tables

Used to summarize counts of disease and exposure in

order to do calculations of association

Outcome Exposure Yes No Total

Total a + c b + d a + b + c + d

2 x 2 Tables

a = number who are exposed and have the outcome b = number who are exposed and do not have the outcome c = number who are not exposed and have the outcome d = number who are not exposed and do not have the outcome

**

a + b = total number who are exposed c + d = total

number who are not exposed a + c = total number

who have the outcome b + d = total number who do

not have the outcome a + b + c + d = total study

population

Relative Risk

• The relative risk is the risk of disease in the

exposed group divided by the risk of disease in the non-exposed group

• RR is the measure used with cohort studies

Relative Risk Example

Odds Ratio

• In a case-control study, the risk of disease cannot be directly calculated because the population at risk is not known

• OR is the measure used with case-control studies

ax d

OR =

bx c

Odds Ratio Example

Interpretation

Both the RR and OR are interpreted as follows:

= 1 - indicates no association > 1 - indicates a positive association < 1 - indicates a negative association

Interpretation

• If the RR = 5

- People who were exposed are 5 times more likely to have the outcome when compared with persons who were not exposed

• If the RR = 0.5

- People who were exposed are half as likely to have the outcome when compared with persons who were not exposed

Tests of Significance

• Indication of reliability of the association that

was observed

• Answers the question “How likely is it that the

observed association may be due to chance?”

• Two main tests:

1 95% Confidence Intervals (CI)

2 p-values

95% Confidence Interval (CI)

• The 95% CI is the range of values of the measure of association (RR or OR) that has a 95% chance of containing the true

RR or OR

• One is 95% “confident” that the true

measure of association falls within this

interval

95% CI Example

Gonorrhea 2.4 1.3 - 4.4 Trichomonas 1.9 1.3 - 2.8 Yeast 1.3 1.0 - 1.7 Other vaginitis 1.7 1.0 - 2.7 Herpes 0.9 0.5 - 1.8 Genital warts 0.4 0.2 - 1.0

Grodstein F, Goldman MB, Cramer DW Relation of tubal infertility to history of sexually transmitted diseases Am J Epidemiol 1993 Mar 1;137(5):577-84

Interpreting 95% Confidence Intervals

• To have a significant association between

exposure and outcome, the 95% CI

should not include 1.0

• A 95% CI range below 1 suggests less risk

of the outcome in the exposed population

• A 95% CI range above 1 suggests a higher

risk of the outcome in the exposed

population

p-values

• The p-value is a measure of how likely the

observed association would be to occur by

chance alone, in the absence of a true

association

• A very small p-value means that you are very

unlikely to observe such a RR or OR if there was

no true association

• A p-value of 0.05 indicates only a 5% chance

that the RR or OR was observed by chance

alone

p-value Example

Gonorrhea 2.4 1.3 - 4.4 0.004 Trichomonas 1.9 1.3 - 2.8 0.001 Yeast 1.3 1.0 - 1.7 0.04 Other vaginitis 1.7 1.0 - 2.7 0.04 Herpes 0.9 0.5 - 1.8 0.80 Genital warts 0.4 0.2 - 1.0 0.05

Grodstein F, Goldman MB, Cramer DW Relation of tubal infertility to history of sexually transmitted diseases Am J Epidemiol 1993 Mar 1;137(5):577-84

Time for you to try it!!!

Questions???

Epidemiology Pocket Guide: Quick Review Any Time!

• Measures of Disease Frequency •

Classification of Study Designs •2 x

2 Tables

• Measures of Association •

Tests of Significance

http://www.vdh.virginia.gov/EPR/Training.asp

References and Resources

• Centers for Disease Control and Prevention (1992) Principles of Epidemiology: 2 nd Edition Public Health Practice Program Office: Atlanta, GA

• Gordis, L (2000) Epidemiology: 2 nd Edition W.B Saunders Company: Philadelphia, PA

• Gregg, M.B (2002) Field Epidemiology: 2 nd Edition Oxford University Press: New York

• Hennekens, C.H and Buring, J.E (1987) Epidemiology in

Medicine Little, Brown and Company: Boston/Toronto

References and Resources

• Last, J.M (2001) A Dictionary of Epidemiology: 4 th Edition Oxford University Press: New York

• McNeill, A (January 2002) Measuring the Occurrence of Disease:

Prevalence and Incidence Epid 160 lecture series, UNC Chapel Hill

School of Public Health, Department of Epidemiology • Morton, R.F,

Hebel, J.R., McCarter, R.J (2001) A Study Guide to Epidemiology and Biostatistics: 5 th Edition Aspen Publishers, Inc.: Gaithersburg, MD

• University of North Carolina at Chapel Hill School of Public Health,

Department of Epidemiology, and the Epidemiologic Research &

Information Center (June 1999) ERIC Notebook Issue 2

http://www.sph.unc.edu/courses/eric/eric_notebooks.htm

References and Resources

• University of North Carolina at Chapel Hill School of Public Health, Department of Epidemiology, and the Epidemiologic Research & Information Center (July 1999) ERIC Notebook Issue 3

http://www.sph.unc.edu/courses/eric/eric_notebooks.htm

• University of North Carolina at Chapel Hill School of Public Health, Department of Epidemiology, and the Epidemiologic Research & Information Center (September 1999) ERIC Notebook Issue 5

http://www.sph.unc.edu/courses/eric/eric_notebooks.htm

• University of North Carolina at Chapel Hill School of Public Health, Department of Epidemiology (August 2000) Laboratory Instructor’s Guide: Analytic Study Designs Epid 168 lecture series

http://www.epidemiolog.net/epid168/labs/AnalyticStudExerInstGuid2 000.pdf