1. RSCH410 final

3. Observational / Descriptive Studies Characteristics Adv, disadv Uses Analyses Interpretation This lec Q’s will be MCQ not as case like in the other 3 studies

4. Characteristics Careful and detailed report by one or more clinicians of the profile of a single patient e.g. previously un-described disease e.g. unexpected link between diseases e.g. unexpected new therapeutic effect e.g. adverse events o exposures(i.e. a case report gave the clue that “oral contraceptives” use increases the risk of venous thromboembolism. o “Luck” in being the first to encounter an interesting case. o Rigor in diagnosis, testing and documentation of clinical findings Case Reports

5. Strengths over one million case reports indexed on Medline. uses language that is familiar to clinicians and easy to interpret. useful reminder about conditions, diagnoses etc. that are rarely seen in most practices. for researchers, case reports generate hypotheses that can be tested using other study designs.

6. No appropriate comparison group. Cannot be used to test for presence of a valid statistical association. Since based on the experience of one person: --- presence of any risk factor may be purely coincidental --- Not a true epidemiologic design. Tendency to publish reports of strange conditions that have little relevance to daily practice. Some authors erroneously try to imply causation, therapeutic benefits, etc. Limitations:

7. Characteristics Experience of a group of patients with a similar diagnosis. Cases may be identified from a single or multiple sources. Generally report on new/unique condition. May be only realistic design for rare disorders Case Series

8. Useful for hypothesis generation. Used as an early means to identify the beginning or presence of an epidemic. Can suggest the emergence of a new disease (i.e. AIDS). Informative for very rare disease with few established risk factors. Strengths:

9. Limitations: Lack of an appropriate comparison group Cannot be used to test for presence of a valid statistical association Not a true epidemiologic design.

10. Cross-sectional studies surveys exposures and disease status at a single point in time (a cross-section of the population) Characteristics Often used to study conditions that are relatively frequent with long duration of expression (nonfatal, chronic conditions). It measures prevalence, not incidence of disease. Not suitable for studying rare or highly fatal diseases or a disease with short duration of expression.

11. Strengths: Provides prevalence estimates of exposure and disease for a well-defined population. Easier to perform than studies that require follow-up (hence relatively inexpensive). Can evaluate multiple risk (and protective) factors and health outcomes at the same point in time. May identify groups of persons at high or low risk of disease Can be used to generate hypotheses about associations between predictive factors and disease outcomes

12. Limitations : Prevalent rather than incident (new) cases are used – the exposure could be associated with survival after disease occurrence, rather than development of the disease Temporal sequence between exposure and disease cannot be established i.e. Which came first, chicken or the egg? Usually don’t know when disease occurred. Rare events: a problem Quickly emerging diseases: a problem

13. Ecologic studies Characteristics Measures that represent characteristics of entire populations are used to describe disease and to postulate causal associations. Measure of interest is correlation between exposure rates and disease rates among different groups.

14. Strengths: Cheap, quick, and simple (generally make use of secondary data) Limitations: Cannot link exposure-disease relationship at the individual level Uses average exposure levels rather than actual levels of exposure Inability to control for confounding factors

15. Summary Observational studies are the starting point. Case Reports, Case Series and Cross-sectional studies are useful for generation of hypotheses. Cross-sectional studies: -provide prevalence estimates of exposure and disease -may identify groups of persons at high or low risk of disease

16. Case control, cohort, clinical trials Characteristics Adv, disadv Uses Analyses Interpretation MCQ’s on Case scenario like the examples in the lecs Analysis ( odd ratio or risk factors ) البسط والمقام Example Cases: case control Follow up : cohort Effect or drug : clinical trails Memorize the examples

17. Case - Control Studies Definition: It is a type of observational analytic epidemiologic investigation in which subjects are selected on the basis of whether they do or do not have the particular disease under study.

18. Importance: The most frequently undertaken analytical epidemiological studies The only practical approach for identifying risk factors for rare diseases They are best suited to the study of diseases for which medical care is sought, such as cancers or hip fracture

19. Design : At baseline: Selection of cases (disease) and controls (no disease) based on disease status Exposure status is unknown Retrospective design – lacks temporality!

20. Selecting Cases: Study cases should be representative of all cases The study need not include all cases in the population Incident cases are preferable to prevalent cases for reducing (a) recall bias and (b) over-representation of cases of long duration

21. Sources of cases and controls Spouses, siblings or associates of cases Any of the above methods Sample of patients in the same hospital who do not have the disease All cases diagnosed in a single hospital Sample of patients in all hospitals who do not have the disease All cases diagnosed in all hospitals Non-cases in a sample of the population All cases diagnosed in a sample of the population Sample of general population All cases diagnosed in the community CONTROLSCASES

22. Analysis: Odds Ratio (OR) A ratio that measures the odds of exposure for cases compared to controls Odds of exposure = number exposed  number unexposed OR Numerator: Odds of exposure for cases OR Denominator: Odds of exposure for controls

23. Analysis: Odds Ratio (OR) 400200Total 22488 Non- smoker 176112 Smoker Exposure Status No CHD (Controls) CHD cases (Cases) Disease Status Odds Ratio == ad bc 112 x 224 176 x 88 = 1.62 a b cd a c b d =

24. Analysis: Odds Ratio (OR) The odds of exposure for cases are 1.62 times the odds of exposure for controls. Those with CHD are 1.62 times more likely to be smokers than those without CHD Interpreting the Odds Ratio STUDY DESIGNS Research Methodology Observational / Analytical Studies Case - Control Studies

25. Exposure increases disease risk (Risk factor) Exposure is not a risk factor nor a protective factor Exposure reduces disease risk (Protective factor) Exposure as a risk factor for the disease? Odds of exposure for cases are greater than the odds of exposure for controls Odds of exposure are equal among cases and controls Odds of exposure for cases are less than the odds of exposure for controls Odds comparison between cases and controls OR>1 OR=1 OR<1 Interpreting the Odds Ratio

26. Analysis: Odds Ratio (OR) Possible Sources of Bias and Error Information on the potential risk factor (exposure) may not be available: - either from records - or the study subjects’ memories Cases may search for a cause for their disease and thereby be more likely to report an exposure than controls (recall bias) The investigator may be unable to determine with certainty whether the suspected agent caused the disease or whether the occurrence of the disease caused the person to be exposed to the agent

27. Advantages: Quick and easy to complete, cost effective Most efficient design for rare diseases Usually requires a smaller study population than a cohort study Several exposures can be studied.

28. Disadvantages: Uncertainty of exposure-disease time relationship Inability to provide a direct estimate of risk Not suitable for studying rare exposures Subject to biases (recall & selection bias)

29. Summary Case-control study: Observational, analytic study. Most frequently undertaken analytical studies Quick and easy to complete, cost effective Most efficient design for rare diseases Subjects are selected on the basis of presence or absence disease under study Odds Ratio (OR)

30. A cohort is a group of people who share a common characteristic or experiencecohort Cohort study: An observational, longitudinal, analytic epidemiologic study in which a particular outcome, such as death from a heart attack, is compared in groups of people who are alike in most ways but differ by a certain characteristic, such as smoking ( EXPOSURE ) Definition: Cohort study

31. Characteristics : A “cohort” is a group of people, referred to as “disease-free population” or “population at risk” A survey is first carried out to exclude prevalent cases from the cohort A period of "follow-up“ is specified, for possible new cases' occurrence We know the exposure status, looking for the disease status

32. Types: Two types are recognized: Prospective (longitudinal): forward in time follow-up study Retrospective (historical): backward in time study (depends on records: medical / employment). This is the type preferred under occupational settings.

33. Advantages: No / little temporal ambiguity (suggests cause-effect relationship) Calculation of incidence rates Suitable for rare exposures Factors associated with selection cannot influence disease status and hence the results. Several outcomes can be studied, after follow-up starts.

34. Disadvantages (of prospective): Expensive Time-consuming May be impractical Loss to follow-up may affect sample-size

35. INDICATIONS (uses): ?? When there is good evidence of exposure and disease. When exposure is rare but incidence of disease is higher among exposed When follow-up is easy, cohort is stable When ample funds are available

36. Problems during Follow-up : Follow-up of a large group. Limited resources. Time scarcity. Paucity of trained personnel Attrition Ethical concerns

37. Attrition Reduction : Obtaining an informed consent. Recording commitment to continue and cooperate in the study. Tracing LOST subjects. Considering Information of lost subjects at the time of analysis Keeping non-response at a low level to improve the validity.

38. Calculation of incidence rates among exposed and non exposed groups Estimation of risk Analysis

39. dc ba YesNo Disease Status Yes No Exposure Status a+b b+d a+c Total Study cohort Comparison cohort c+d N Total Incidence among exposed = a a+b Incidence rate Incidence among unexposed = c c+d ANALYSIS

40. Estimation of risk ANALYSIS Relative Risk (RR) incidence of disease among exposed Incidence of disease among non-exposed RR= _____________________________ a/a+b c/c+d _________ RR=

41. Estimation of risk ANALYSIS Incidence of disease among exposed Incidence of disease among non exposed a/a+b – c/c+d a/a+b AR = = Incidence of disease among exposed _______________________________ _______________ Attributable Risk (AR):

42. SmokingLung cancerTotal SmokingYESNOTotal YES7069307000 NO329973000 Total73992710000 Calculate RR and AR for above data

43. Incidence of lung cancer among smokers 70/7000 = 10 per 1000 Incidence of lung cancer among non-smokers 3/3000 = 1 per thousand RR = 10 / 1 = 10 (lung cancer is 10 times more common among smokers than non smokers) AR = 10 – 1 / 10 X 100 = 90 % (90% of the cases of lung cancer among smokers are attributed to their habit of smoking)

44. The Ideal Cohort : An ideal cohort should be: Stable. Cooperative. Committed Well-informed

45. Definition: A planned experiment on humans. The setting is usually a health institutions and it usually involves patients. Rationale: Before a new treatment method is made available to the public it must be studied and tested for safety and effectiveness. STUDY DESIGNS Research Methodology Clinical trials Experimental studies

46. STUDY DESIGNS Research Methodology Clinical trials Experimental studies Treatment Prevention Diagnostic Screening Quality of Life Types of Trials

47. STUDY DESIGNS Research Methodology Clinical trials Experimental studies  Analyze study data and interpret the results Type of analysis is dictated by goals and objectives Minimize bias and uncertainty incomplete data terminology attrition specified analyses statistical analytic plan Analysis follows study protocol

48. STUDY DESIGNS Research Methodology Clinical trials Experimental studies  Analyze study data and interpret the results Steps of clinical trial Relative Risk(RR): -------------------------------- RR= Total outcome Group NegativePositive a+bba Intervention c+ddc Control Total Experimental Event Rate (EER) Control Event Rate (CER )

49. STUDY DESIGNS Research Methodology Clinical trials Experimental studies  Analyze study data and interpret the results Steps of clinical trial Relative Risk(RR): Total outcome Group SurvivalDeath 644618 Ligation 653332 Sclerotherapy Total -------------------------------- RR= Experimental Event Rate (EER) Control Event Rate (CER )

50. STUDY DESIGNS Research Methodology Clinical trials Experimental studies  Analyze study data and interpret the results Steps of clinical trial Relative Risk(RR): ---------- RR= Experimental Event Rate (EER) Control Event Rate (CER ) Statistical power EER =a/(a+b) 18/64 CER = c/(c+d) 32/65 RR= = 0.57 -------------------------------- Total Outcome Group Surv ival Dea th 644618 Ligation 653332 Scleroth erapy =18/64 =32/65

51. STUDY DESIGNS Research Methodology Clinical trials Women’s Health Initiative RQ: Does calcium plus vitamin D reduce risk of fractures in postmenopausal women? Design: Randomized trial Subjects: 36,282 PM women enrolled in WHI Intervention: 1 gm calcium + 400 IU vitamin D Outcome: clinical fractures Adherence at end of trial 60% and about 60% of placebo group was taking calcium Experimental studies

52. STUDY DESIGNS Research Methodology Clinical trials Follow-up RQ: Does diet and exercise reduce risk of developing type 2 diabetes in persons with glucose intolerance? Design: Randomized trial Subjects: 2500 with glucose intolerance Intervention: low fat weight loss diet and moderate intensity aerobic exercise outcome = development of frank diabetes Experimental studies

53. STUDY DESIGNS Research Methodology Clinical trials Diet and Exercise to Prevent Diabetes in Persons with Glucose Intolerance DM No DM D&E No D& E 65560 125 500 625 625 1060190 1250 RR =.5; p =.001 Experimental studies

54. STUDY DESIGNS Research Methodology Clinical trials Conclusions & Take Home Message Clinical trials often yield important results that affect health and well being Must follow guidelines & protocol Must ensure well-being of participant Clinical trials are susceptible to human error either on part of investigator or patient Rank high in the ladder of causality Experimental studies

55. Know RF for DM, HTN, Breast,cervical cancer

56. Non-Communicable Disease 1. Obesity 2. Sedentary Lifestyle 3. Unhealthy Eating Habits 4. Family History and Genetics 5. Age 6. Hypertension and High Cholesterol 7. History of Gestational Diabetes Top 7 Risk Factors for Type 2 Diabetes Diabetes

57. Non-Communicable Disease Primary Hypertension Risk Factors  Age  Alcohol  Cigarette Smoking  Diabetes Mellitus  Elevated serum lipids  Excess Na+ in diet  Gender  Family History  Obesity  Sedentary Lifestyle  Stress Hypertension

58. Non-Communicable Disease  Human papilloma virus infection  Smoking  Immunosuppression  Chlamydia infection  Diet  Overweight  Oral contraceptives  Multiple full-term pregnancies  Young age at the first full-term pregnancy  Poverty  Family history of cervical cancer Risk Factors of Ca Cervix Ca Cervix

59. Non-Communicable Disease Early menarche. Late menopause. Being older at the birth of the first child. Nulliparous. Not breastfeeding. Long-term use of hormone-replacement therapy. Risk Factors of Ca Breast Ca Breast Age. Family history of breast cancerFamily history Being overweight (after menopause). Alcohol (more than one drink a day). Inactivity. Other Risk Factors Reproductive Risk Factors

60. CHP400: Community Health Program-lI SCREENING Know Validity ( sensitivity- specificity) Reliability Predictive values

61. SCREENING Suitable Screening Test : Validity: (Sensitivity, Specificity) Reliability: (repeatability/precision) Yield (performance): Predictive values of the test.

62. SCREENING Validity of Screening Test : How good is the screening test compared with the confirmatory diagnostic test (Gold Standard test)? The test will correctly classify a diseased person as likely to have the condition (“sensitivity”). The test will correctly classify a non- diseased person as unlikely to have the condition (“specificity”).

63. SCREENING Validity of Screening Test : Total Gold standard Screening test NegativePositive PS(FP)(TP) Positive NS(TN)(FN) Negative GTTHTD Total Screening test compared to gold standard a b c d

64. SCREENING Validity of Screening Test : a d c b True Disease Status + - Results of Screening Test + - Sensitivity: The probability of testing positive if the disease is truly present Sensitivity = a / (a + c)

65. SCREENING Validity of Screening Test : a d c b True Disease Status + - + - Specificity: The probability of screening negative if the disease is truly absent Specificity = d / (b + d) Results of Screening Test

66. SCREENING Validity of Screening Test : Disease Test DNo D 90595 1095105 100 200 Sensitivity: a / (a + c) = 90/100 =90% Specificity: d / (b + d) = 95/100 =95% Prevalence of disease =(a+c)/(a+b+c+d) =100/200 =50% a b c d

67. SCREENING Reliability of Screening Test : RELIABILITY (Reproducibility) Precision: The extent to which the screening test will produce the same (or very similar) results each time it is administered (repeated). --- A test must be reliable before it can be valid.

68. SCREENING Yield(Performance) of Screening Test: Yield is the amount of previously unrecognized disease that is diagnosed and brought to treatment as a result of screening. It is measured by: Predictive Value Positive (PV+) Predictive Value Positive (PV+) Predictive Value Negative (PV-) Predictive Value Negative (PV-)

69. SCREENING Yield(Performance) of Screening Test: a d c b True Disease Status + - Results of Screening Test + - Predictive value positive (PV+): The probability that a person actually has the disease given that he or she tests positive. i.e. The ability to predict the presence of disease from test results. PV+ = a / (a + b)

70. SCREENING Yield(Performance) of Screening Test: a d c b True Disease Status + - Results of Screening Test + - Predictive value negative (PV-): The probability that a person is truly disease free given that he or she tests negative. i.e. The ability to predict the absence of disease from test results. PV- = d / (c + d)

71. SCREENING Yield(Performance) of Screening Test: Disease Test DNo D 1999118 118811882 2019802000 a b c d Calculate: PV+ =19/118=16% PV-= 1881/1882=99.95%

72. SCREENING Yield(Performance) of Screening Test: Disease Test DNo D 57259 33841 6040100 a b c d Calculate: PV+ =57/59=96.6% PV-= 38/41=93%