Bài giảng Đại cương về dịch tễ học - Lê Hoàng Ninh

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 học2. Phân tích : Tìm nguyên nhân, lý giải tại sao có sự phân bố bệnh tật đóWHY ? ---------------- căn nguyên: Cause?Không so sánh ----- Giả thuyết căn nguyên? Căn nguyên?So Sánh ------------- căn nguyên Phương cách tiếp cận dịch tễ học3. Làm cách nào ? ứng dụng can thiệpHOW ? ----------- Can thiệpHiệu quả can thiệp?---> so sánh ? 2 cách tiếp cận: Mô tả và phân tích ( kể cả can thiệp)3 cách tiếp cận: Mô tả, phân tích, can thiệpDịch 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) Dịch tễ mô tả • phương pháp có hệ thống để biết, xác định vấn đề sức khỏe, đảm bảo hiểu được xu thế/ khuynh hướng các vấn đề sức khỏe• 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 Dịch tễ phân tíchGiúp kiễm định giả thuyết về căn nguyên, yếu tố nguy cơKết luận về yếu tố nguy cơ, nguyên nhân của sự phân bố bệnh tậtNguyên tắc phân tích¸ là có sự so sánh giửa 2 nhóm: Nhóm bệnh vs nhóm không bệnhNhóm tiếp xúc vs nhóm không tiếp xúcNhóm can thiệp vs nhóm không can thiệpMỤC TIÊU DỊCH TỄ HỌCXác định tầm vóc/gánh nặng bệnh tậtXác định nguyên nhân, bệnh căn, yếu tố nguy cơĐánh giá hiệu quả của một biện pháp can thiệpNghiên cứu tiến trình tự nhiên và tiên lượng bệnhCơ sở, nền tảng cho các chính sách y tế, sức khỏeVAI TRÒ CỦA DỊCH TỄ HỌCY học dự phòng và y tế công cộng?Y tế công cộng: phòng ngừa bệnh tật, tăng cường sức khỏe, kéo dài tuổi thọY học phòng ngừa? 3 cấp độ dự phòng:Cấp I : nguyên phát : ngăn ngừa sự khởi phát bệnh: chủng ngừa, không tiếp xúc yếu tố nguy cơCấp II: secondary prevention: phát hiện bệnh sớm giảm trầm trọng, tử vong và các biến chứng thí dụ sàng lọc bệnh ung thư tử cungCấp III: tertiary prevention: giảm tác động, ảnh hưởng của bệnh. Thí dụ phục hồi chức năng sau đột qụiVAI TRÒ CỦA DỊCH TỄ HỌCY học lâm sàng?Mô tả dịch tễ Mô tả lâm sàngGiả 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)Dịch tễ học Dịch tễ học lâm sàngVAI TRÒ CỦA DỊCH TỄ HỌCChính sách y tế và dịch vụ y tế:Bao nhiêu bệnh viện? Bệnh viện đa khoa, chuyên khoa?Dịch vụ y tế gì? Chương trình y tế gì?Phân bố nguồn lực, hoạch định nguồn lực thế nào?Phát triển sức khỏe cộng đồng? Dân số?Đào tạo?Quá tải? VAI TRÒ CỦA DỊCH TỄ HỌCXã hội học?Prevalence • The number of affected persons present in the population divided by the number of people in the population # of cases Prevalence = ----------------------------------------- # 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 5,008,863 • In 1999, the prevalence of diabetes in Virginia was 5.1% - Can also be expressed as 51 cases per 1,000 residents over 20 years of age Prevalence • Useful for assessing the burden of disease within a population • Valuable for planning • Not useful for determining what caused disease Incidence • The number of new cases of a disease that occur during a specified period of time divided by the number of persons at risk of developing the disease during that period of time # 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 110 • The one year incidence of depression among adults with dementia is 6.36% - Can also be expressed as 63.6 (64) cases per 1,000 persons with dementia 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 gianMô 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: tuổi, chủng tộc, phái, học vấn, nghề nghiệp, những đặc trưng khác • nơi chốn (Place) - Có thể là địa phương, nhà, nơi làm việc, trường học • thời gian(Time) - Có thể là lúc khởi phát bệnh, khi tiếp xúc với yếu tố nguy cơ Data Characterized by Person Data Characterized by Person Data Characterized by Person Data Characterized by Person 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 n=108 12 10 8 6 4 2 0 Date of ons e t Data Characterized by Time Data Characterized by Time Data Characterized by Time Dữ liệu nơi chốn • có thể trình bày trong bảng, tốt nhất trình bày trên bản đồ • có 2 dạng trình bày: choropleth and spot - Choropleth maps use different shadings/colors to indicate the count / rate of cases in an area - Spot maps show location of individual cases Data Characterized by Place Data Characterized by Place Data Characterized by Place Data Characterized by Place 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 scientific investigation • Descriptive data provide information to develop hypotheses • Hypotheses tend to be broad initially and are then refined to have a narrower focus Example • Hypothesis: People who ate at the church picnic were more likely to become ill - Exposure is eating at the church picnic - Outcome is illness - this would need to be defined, for example, ill persons are those who have diarrhea and fever • Hypothesis: People who ate the egg salad at the church picnic were more likely to have laboratory- confirmed Salmonella - Exposure is eating egg salad at the church picnic - Outcome is laboratory confirmation of Salmonella Types of Studies Two main categories: 1. Experimental 2. Observational 1. Experimental studies - exposure status is assigned 2. Observational studies - exposure status is not assigned Experimental Studies • Can involve individuals or communities • Assignment of exposure status can be random or non-random • The non-exposed group can be untreated (placebo) or given a standard treatment • Most common is a randomized clinical trial 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 Observational Studies Three main types: 1. Cross-sectional study 2. Cohort study 3. Case-control study Cross-Sectional Studies • Exposure and outcome status are determined at the same time • Examples include: - Behavioral Risk Factor Surveillance System (BRFSS) - - National Health and Nutrition Surveys (NHANES) - • Also include most opinion and political polls Cohort Studies • Study population is grouped by exposure status • Groups are then followed to determine if they develop the outcome Exposure Outcome 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 Disease No Disease Disease No Disease 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 is grouped by outcome • Cases are persons who have the outcome • Controls are persons who do not have the outcome • Past exposure status is then determined Case-Control Studies Study Population Controls Cases Had Exposure No Exposure Had Exposure No Exposure 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: 1. Relative risk (a.k.a. risk ratio, RR) 2. Odds ratio (a.k.a. OR) 2 x 2 Tables Used to summarize counts of disease and exposure in order to do calculations of association Outcome Exposure Yes No Total Yes a b a + b No c d c + d 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 a a + b RR = c c + d Relative Risk Example Escherichia coli? Pink Total hamburger Yes No Yes 23 10 33 No 7 60 67 Total 30 70 100 a / (a + c) 23 / 33 RR = = = 6.67 c / (c+ d) 7 / 67 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 Autism MMR Total Vaccine? Yes No Yes 130 115 245 No 120 135 255 Total 250 250 500 a x d 130 x 135 OR = = = 1.27 b x c 115 x 120 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 • If the RR = 1 - People who were exposed are no more or less likely to have the outcome when compared to 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 Disease Odds Ratio 95% CI 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 Disease Odds Ratio 95% CI p-value 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 References and Resources • Centers for Disease Control and Prevention (1992). Principles of Epidemiology: 2nd Edition. Public Health Practice Program Office: Atlanta, GA. • Gordis, L. (2000). Epidemiology: 2nd Edition. W.B. Saunders Company: Philadelphia, PA. • Gregg, M.B. (2002). Field Epidemiology: 2nd 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: 4th 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: 5th 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. 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. • 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. • 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. 000.pdf